Assessments of Forest Reserve Deforestation in the Federal Capital Territory (FCT), Nigeria

TABLE OF CONTENT

TITLE PAGE

TABLE OF CONTENT

EXECUTIVE SUMMARY

 

1.0. INTRODUCTION

1.1. Background

1.2. History of Forest Reserves in Nigeria

1.3. Forest Reserve Law in Nigeria

2.0. PROJECT OBJECTIVES

3.0. PROBLEM STATEMENT

4.0. PROECT APPROACH

4.1. Study Location

4.1. Vegetation

4.2 Climate and Weather

4.3. Geology

 

5.0. METHODOLOGY

5.1. Data Acquisition and Source

5.2. Software used

5.3. Data Pre-Processing

5.4. Data Post Processing

5.4.1. Image Compositing

5.4.2. Image classification

5.4.2.1. Unsupervised Classification

5.4.2.2. Field Work/Data Collection

5.4.2.3. Supervised Classification

5.4.2.4. Accuracy Assessment

5.4.2.5. Change Detection and Statistical Analysis of Change

5.4.3. Prediction Analysis

5.4.4. Estimations of soil organic carbon and Carbon stock analysis

5.4.5. Image Differencing

 

6.0. PROJECT RESULTS

6.0.1. Gwagwalada Forest: Maje Abuchi

6.0.2 Chihuma forest reserve 1990 to 2021 LU/LC report in Bwari Area Council

6.0.3. Chikwei forest reserve in Bwari Area council LU/LC report 1990 to 2021

6.0.4. Kusoru forest reserve in Bwari Area council LU/LC report 1990 to 2021

6.0.5. Shaba forest reserve in Bwari Area council LU/LC report 1990 to 2021

6.0.6. Buga Hill forest reserve in Kuje Area council LU/LC report 1990 to 2021

6.0.7. NG_61 forest reserve in Kuje Area council LU/LC report 1990 to 2021

6.0.8. Odu forest reserve in Kuje Area council LU/LC report 1990 to 2021

6.0.9. Tukoki forest reserve in Kuje Area council LU/LC report 1990 to 2021

6.1. Change Detection and Prediction of land use land cover change

6.2. Prediction of possible land use/land cover by year 2030

6.3. Carbon stock Analysis

6.4. Forest carbon Mapping

7.0. PROJECT CONCLUSION

8.0.0. RECOMMENDATION

REFERENCES

EXECUTIVE SUMMARY

Forest and its resources play an essential role in the sustenance of millions of people around the world; they depend on forests for their livelihood and shelter. Inspite of the importance of this earth’s veritable resource, Man’s exploitation and use of it has led to its depletion but not without some consequence. The rapid depletion of forest and forest resources through deforestation has also resulted to loss of farm land, reduced income that negatively has affected the socio-economic wellbeing of the people. Massive awareness campaigns aimed at making the people know the importance of forest is surely a way of slowing down the rate of deforestation.

This work evaluated deforestation in Federal Capital Territory Forest reserve with land use/land cover change for a period of 31 years. Land Use Land Cover (LULC) change detection based on remote sensing data is an important source of information for various decision support systems. Information derived from land use and land cover change detection is important to land conservation, sustainable development, and management of forest reserve. The purpose of this study is therefore concerned with identifying the change in land use and land cover detection of the Federal Capital Territory forest reserve.

 ERDAS Imagine, IDRISI Selva and ArcGIS softwares were used to classify and identify the changes. The classification was done using five land cover classes (forest, cropland, water bodies, bare surface and settlement). Preprocessing and classification of the images were analyzed carefully and accuracy assessment was tested separately using the kappa coefficient. The results showed that overall accuracy of Federal Capital Territory for the years 1990, 2001, 2013 and 2021 are not less than 82%. This study indicated that in the last 31 years, some forest reserves had reduced significantly due to development while some forest reserve increased significantly from 2013 to 2021 due to insecurity in the area. Prediction analysis showed that if measures are not put in place to some of the forest reserves they will disappear and if this happens there would be serious climate change as more carbon are releasing to the atmosphere.

This study showed that remote sensing and GIS for forest quantification analysis of multiple forests areas of this present investigation is feasible with satellite remote sensing as opposed to time-consuming and expensive ground surveys as alternative.

Forests have declined, while cultivated land and artificial surfaces have increased in the area, and deforestation appears to be more pronounced in the Tukoki and Maje-abuchi forest reserve. Severe deforestation in Tukoki forest reserve appears to be strongly linked to increased soil erosion as a result of land use and land cover change. Notable drivers for LUCC include rapid population growth and macroeconomic activities occurring in Federal Capital Territory especially in the part of Kuje Area Council, and poor national policies that have failed to effectively enforce ban of uncontrolled harvesting of forest resources. It shows that remote sensing and GIS for forest quantification analysis of multiple forests areas of this present investigation is feasible with satellite remote sensing as opposed to time-consuming and expensive ground surveys as alternative.

In summary, agriculture  and clearing of forested land for food and wood products have been the main drivers of deforestation for millennia. This does not mean, however, that agriculture and land always cause deforestation; sustainable management is possible but not always practiced.

INTRODUCTION

1.1 BACKGROUND

Deforestation is one of the biggest challenges for people’s livelihoods, the environment all around the world. Despite being mentioned in the Sustainable Development Goals under goal 15 ‘Life on Land’, (UNGA, 2015), the United Nation’s Conference on Sustainable Development (“Rio + 20”) has requested to set up the goal of forest Degradation Neutrality, calling for a compensation of degrading forest through land use and land cover improvement (UNCCD, 2014). Furthermore, the sustainable development goals (SDGs) of the 2020 Agenda for Sustainable Development adopted by world leaders in September 2015 documents, the need to address the problem of deforestation. Similarly, sparse consideration has been given to how forest degradation on agricultural land could affect the ways in which forest is being managed and the implications it has for livelihoods and human development (Stringer et al., 2009). There is also an Economics of Deforestation (ELD) initiative, which is aimed at establishing a comprehensive framework for the evaluation of the economic losses due to forest degradation in order to assist the decision-making process (Nkonya et al., 2016).

The effective implementation of these international frameworks as well as well-informed planning and policy decisions, which are related to the sustainable forest management and the “zero net forest degradation” target requires credible and spatially explicit information on degraded forest (Stavi & Lal, 2015). A need to develop a standardized methodology for forest degradation assessment is also due to the necessity to support the sustainable development goal SDG 15.2. The SDG 15.2 aims “to protect, restore and promote sustainable use of forested land, to sustainably manage forests, as well as to halt and reverse land degradation” (UN, 2015). Deforestation of forest is caused by various factors, including climatic variations and human-induced activities. Human-induced deforestation occurs mainly due to overexploitation of forest resources for cropping and livestock farming, including irrigation practices, overgrazing of rangelands and fuel-wood exploitation (Adeel et al., 2005).

 Decision-making in ecosystem development and planning is becoming increasingly complex because of the interrelationship with various phenomenon including local people, stakeholders, local culture and natural environment. Cost-benefit plays an important role to developing ecosystem in a sustainable manner. GIS can be considered as a tool that provides techniques and technologies to achieve sustainable ecosystem development (Bahaire & White, 1999). Remote sensing and GIS is considered as a set of powerful tools to process spatially referenced data and this spatial data can be used to identify conflict, analyses impacts over time and find a suitable solution for a specific problem. Ecosystem activities generally can create various negative effects on surroundings. Impact assessment and simulation are increasingly important in ecosystem development and GIS can play a role in auditing environmental conditions, examining the suitability of locations for proposed developments site, identifying conflicting interests and modeling relationships.

The purpose of this work is to use space technology based assessment to quantify forest degradation in the study area to ameliorate the unsustainable management of forest, so as to protect and restore ecosystem services of Federal Capital Territory forest reserves.

1.2 History of Forest Reserves in Nigeria

Forest development in Nigeria began with reservation of forest lands in order to manage, maintain forest reserves and provide a supply of timber. Toward the end of the 1800s, the colonial government began establishing forest reserves. By 1900 more than 970 square kilometers had been set aside for this purpose. By 1930, the reserve had grown to almost 30,000 square kilometers, and by 1970, reserve lands had increased to over 93,420 km², mostly in the savanna regions (Oyebo, 2006; Okali and Eyog-Matig, 2004). These forest reserves are owned by the State Governments and managed the State Forestry Departments (SFDS) who have professional and technical staff including uniform guards for performing their various responsibilities. The reservation of land for forestry purposes was at its peak during colonial times. Efforts to increase the size of the reserves since then have not been too successful. Hence, only about 10% of the land area of the country is currently under forest reserves.

1.3 Forest Reserve Law in Nigeria

Forest law in Nigeria is passed by the Nigerian legal system. In Nigeria, forest law is essentially statute law; this is a law passed by a legislative body. The subject of agriculture falls under concurrent legislative list in the Nigerian constitution. Consequently, both the Federal and the state government are empowered to make laws on agriculture. In actual practice the bulk of the laws on forest is made by each state of the federation, taking into cognizance their peculiar geographical features.

  PROJECT OBJECTIVES

1.4 The aim of the study is to monitor spatiotemporal forest reserve deforestation in the Federal Capital Territory with the following objectives:

 

1. Assessment of land use and land cover change in Federal Capital Territory forest reserves.
2. Examination of land use and land cover change detection/prediction of Federal Capital Territory forest reserves.

III. Examination of forest cover loss in Federal Capital Territory forest reserves.

1. Assessment of carbon stock of Federal Capital Territory forest reserves.
2. Identification of Federal Capital Territory forest reserves deforestation hotspot area.

1.5 PROBLEM STATEMENT

The rate at which vegetation are removed in Federal Capital Territory forests to meet up our need is causing harm to mankind, damaging for both local and global climate, threatening the existence of other species and also limiting the ecological functions and services of the forest reserves. There is urgent need to measures to avoid, reduce and reverse deforestation to ensure sustainability of the environment and prevent biodiversity loss in the FCT forest. A lot has been done for the quantification of deforestaion like using tape rule to measure length and branches of trees to determine forest canopy and to select certain areas to generalize the whole study area. Before it could be done the work is already outdated. But by employing space-based technology the history of degradation can be trace, the total study area can be quantified at once; and furthermore, the factor responsible for soil degradation in order to reduce the rate of forest degradation.

 SUSTANABLE DEVELOPMENT GOAL ADDRESS DEFORESTATION

(Goal 15: Life on Land) 15.2: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss.

 PROJECT APPROACH

3.1. Study Location:

Federal Capital Territory is located within latitude 8.867⁰ N and 8.503⁰ N of the Equator and longitudes 7.233⁰ E and 7.239⁰ E and total Elevation of 536m (Figure 3) with a total population of 1,402,201 (2006 population census) the (FCT) has a land area of 923,768 square kilometres, this is about two and halftimes more than Lagos state land area, the former capital of Nigeria. The Federal Capital Territory is bounded on the north by Kaduna State, on the west by Niger State, on the east and south-east by Plateau State, and on the south-west by Kogi State. A scene that cannot be missed about Federal Capital Territory is coming together of the savannah grassland of the north and middle belt with the richness of the tropical rain forests of the south. This marriage of the nature has ensured that Nigeria′s capital is endowed with fertile land for agriculture.

Figure 1 Study Area Map

For the clarity, all the forests in figure 1 are presented as follow.

Figure 2 Tufa study forest reserve

Figure 3 Maje-Abuchi forest reserve

Figure 4 Chihuma forest reserve

Figure 5 Chikwei forest reserve

Figure 6 Kusoru forest reserve

Figure 7 Shaba forest reserve

Figure 8 Buga forest reserve

Figure 9 NG_61 forest reserve

Figure 10 Odu forest reserve

Figure 10 Tukoki forest reserve

4.1. VEGETATION

The Federal Capital Territory falls within the Guinean forest-savanna mosaic zone of the West African sub-region. Patches of rain forest occur in the Gwagwa plains, especially in the rugged terrain to the southeastern parts of the territory, where a landscape of gullies and rough terrain is found. This area in the Federal Capital Territory (FCT) form one of the few surviving occurrences of the mature forest vegetation in FCT of Nigeria. Urban forests are made up of the trees, shrubs, and other vegetative covers that play important role in human life. Urban forests serve important roles such as tree species diversity conservation and protection of fragile ecosystem; development of parks and event centres for relaxation and social engagements; provision of vegetable and fruits/seeds for foods and medicines; and purification of air, wind break, and beautification of the environment (Nowak and Dwyer 2007).

4.2. CLIMATE AND WEATHER

It experiences two weather conditions interlude of harmattan occasioned by the annually (Kuje Area Council, 2010) North East Trade Wind. According to these are warm, humid rainy season (Balogun, 2001), the maximum a dry season, which experiences a brief temperature during the dry season ranges between 30.4 and 35.1oC while the temperature ranges between 25.8 and 30.2oC in the raining season. The diurnal temperatures can be as high as 17 ⁰C during the dry season and rarely exceed 7oC at the peak (July, August) of the rainy season (Adekayi, 2000). The mean annual rainfall total ranges from 1145mm to 1631.7mm (Ishaya and Mashi, 2008). The prevailing wind in the FCT originates from the south and moisture laden and therefore brings a lot of rain (Adekayi, 2000).

4.3. GEOLOGY

Landform in Kuje Area Council consists of two major types; the hills and the plains (Hassan, 2008). The study area is predominantly underlain by the Precambrian basement complex rocks. The basement complex of Nigeria has been classified in several ways. The most recent and widely accepted is the classification of Oyawoye (1964) which is basically classified into three main geological units namely; migmatite-gneiss complex, older granite suite sometimes called the intrusive suite and the schist belt.

 5.0. METHODOLOGY

5.1. DATA ACQUISITION AND SOURCE

Remote Sensing Image: The Landsat data was acquired from the global land-cover website at the University of Maryland, USA (URL; http://glcfapp.umiacs.umd.edu:8080/esdi/index.jsp). The acquire images will be thematic mapper (TM) image of 1990, Enhance Thematic Mapper plus (ETM⁺) image of 2001 and 2013 and the Operational land Imager (OLI) of  2021  respectively as shown in Table 1. All the satellite images were obtained on 8^th of November to follow weather consistence and climatic suitability of the work. The satellite data have 30m spatial resolutions and the TM and ETM Plus images have spectral range of 0.45-2.35 micro meter with bands 1,2,3,4,5,6,7 and 8 while the Operational Land Imager (OLI) extends to band 12.

Table 1 data use for the project

S/N	Data Type	Year	Spatial Resolution
1	Landsat Thematic mapper (TM)	1990	30 meters
2	Landsat Enhanced Thematic mapper (ETM+)	2001	30 meters
3	Landsat Enhanced Thematic mapper (ETM+)	2013	30 meters
4	Landsat Operational Land Imager  (OLI)	2021	30 meters
5	FCT Forests Shape file

Table 2 Federal Capital Territory Existing Forests Reserve

S/N	Forest	Council Area
1	Chihurma,	Bwari
2	Chikwei	Bwari
3	Kusoru	Bwari
4	Shaba	Bwari
5	Buga Hill	Kuje
6	Gaida NG N0 61	Kuje
7	Odu	Kuje
8	Tukoki	Kuje
9	Maji Abuchi	Gwagwalada
10	Tufa	Abaji

 5.2. SOFTWARE USED

The software used is as listed below:

ArcGIS 10.4:

The statistical analyst extensions of the ArcGIS 10.4 version will be used to perform and create database, simple statistical analysis, and map.

Quantum GIS 2.8:

This version of QGIS will be used to carry out carbon stock analysis.

Idrisi Selva:

Supervised classification will be performed using Idrisi Selva and the land change modeler of the Idrisi will be used to analyze the change detection between the years of observation.

Several systematic techniques will be developed to perform vegetation dynamics analysis and change detection using as an input satellite images (time series or multitemporal images). The analysis of the spatiotemporal dynamics over the given observation period will be processed (Nagai, S., Nasahara, K.N., Inoue, T., Saitoh, T.M., &Suzuki, R. 2016).

 5.3. DATA PRE-PROCESSING

The satellite imageries was preprocessed in order to correct the error during scanning, transmission and recording of the data. The pre-processing steps used were:

Radiometric correction to compensate the effects of atmosphere; this was carried out using Semi-Automatic classification plugin in Quatun geographic information system (QGIS). In this interface, there are several satellite data set, which include; ASTER, GOES, Landsat etc, here, click on Landsat and navigate to the folder, check on Apply DOS 1 atmosphere correction, create a path to save the work and then click on run.

Geometric correction i.e. registration of the image to make it usable with other maps or images of the applied reference system; this was done by georeferencing the image in Arc Map to make it spatial reference and noise removal to remove any type of unwanted noise due to the limitation of transmission and recording processes. This process was carried out in QGIS as explained in radiometric correction.

5.4. DATA POST PROCESSING

5.4.1. Image Compositing

A false Colour Composite operation will be  performed using the Idrisi software and the Landsat bands were combined in the order of band 4,band 3 and band 2  for Landsat TM and ETM+ while Landsat OLI will be composited in the order of band 4,band4 and band 3 due to change in sensor.

 5.4.2. Image classification

Image Processing Phase (Classification)

During the image processing component of this study, image pixels were grouped into land use types. The specific land use categories of interest to the study, and the respective definitions were identified as

1. Settlement Area: Land covered by buildings and other human made features.
2. Bare surface: Rock outcrop, sand dunes, alluvial, gullies, mining areas
3. Cropland: Grazing fields, Derived Savannah Agricultural Land: Farmlands including plantations.
4. Forest: Areas dominated by woody vegetation
5. Water Bodies: Natural water bodies including lakes, rivers, canals, and reservoir.

The specific processes followed to achieve the image classification into the identified land use classes are highlighted in this sub-section.

5.4.2.1 Unsupervised Classification: Unsupervised classification using the ISODATA clustering criterion was be used for the initial clustering of the pixels in the images. This method helped in the examination of the large number of unknown pixels and divides them into a number of classes based on the spectral characteristics present in the image values. This classification does not require analyst-specified training data. The number of desired classes at this stage was 15. These classes aided in developing the image training sites during the supervised classification process.

5.4.2.2 Field Work/Data Collection: One field trip was conducted to each forest in November 9^th to12^th, 2021. During the field trips, the coordinates of land use samples were collected. Some of these samples were used as training sites for the supervised classification and also used to interpret the clusters derived during the unsupervised classification. The second set of samples was used for conducting accuracy assessment (User’s and Producer’s accuracies) to test the consistency and reliability of the supervised classification. In addition to the collection of information on the location of land use classes, the field trip provided an avenue to collect additional ancillary data on the deforestation sites. Some of the information collected on the field was used to estimate the volumes of forest loss at specific deforested sites. Details of the field exercise can be found in the report of the field exercise.

5.4.2.3. Supervised Classification: Supervised classification was used to cluster pixels in the satellite images into the identified six land use classes corresponding to user-defined training. The Maximum Likelihood classification algorithm was adopted because the approach is based on probability. In order to assign a pixel to a class, the probability of the pixel belonging to each of a predefined set of classes is calculated and the pixel is then assigned to the class for which the probability is highest. This approach is consistent and stable. Moreover, unless a probability threshold is selected, all pixels in the image of interest are classified.

5.4.2.4. Accuracy Assessment: The accuracy assessment of the results from the supervised classification were conducted based on simple random sampling, and the results from the assessment were presented using error matrix that reports both user’s and producer’s accuracies in the result chapter of this report.

5.4.2.5. Change Detection and Statistical Analysis of Change: A post-classification comparison change detection algorithm was used to determine changes in land use for the various epochs used in this study. The results are presented using transition matrix that indicates the transitions of the various land use during the various epochs.

The results of the land use change assessment are presented in the result chapter of this report.

5.4.3 Prediction Analysis

 The transition probability matrix and transition area matrix from 199 to 2021 was calculated in Markov chain analysis. The area of each land class to be converted to another LULC classes was estimated based on the transition probabilities.

 5.4.4. Estimations of soil organic carbon and Carbon stock analysis

 The IPCC 2000 embedded in plugin QGIS will be carefully adopted for the analysis of carbon stock and soil organic carbon.

Sustainable Development Goal 15.3 intends to combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world by 2030. In order to assess the progress to this goal, the agreed-upon indicator for SDG 15.3 (proportion of land area degraded) is a combination of three sub-indicators: change in land productivity, change in land cover and change in soil organic carbon. All these indicators are performed in QGIS.

Select the Land degradation indicator (SDG indicator 15.3.1) to open the window for this analysis.

There are several options for calculating the SDG 15.3.1 Indicator. Trends. Earth supports calculating the indicator using the same process as was used by the UNCCD for the default data provided to countries for the 2018 reporting process. The tool also supports customizing this data, or even replacing individual datasets with national-level or other global datasets.

To calculate all three SDG 15.3.1 indicators in one step, using default settings for most of the indicators, click “Calculate all three indicators in one step”.

To calculate one of the three SDG 15.3.1 indicators, using customized settings, or national-level data, click “Productivity”, “Land cover”, or “Soil organic carbon”.

To calculate a summary table showing statistics on each of the three indicators, click “Calculate final SDG 15.3.1 indicator and summary table”. Note that you must first compute the indicators using one of the above options.

To calculate a summary table showing statistics on each of the three indicators for multiple sub-divisions, click “Calculate area summaries of a raster on sub-units”. Note that you must first compute the indicators using one of the above options.

There are three different indicators that are combined to create the SDG 15.3.1 indicator

Productivity: measures the trajectory, performance and state of primary productivity

Land cover: calculates land cover change relative to a baseline period, enter a transition matrix indicating which transitions indicate degradation, stability or improvement.

Soil carbon: compute changes in soil organic carbon as a consequence of changes in land cover.

Way of carrying out statistical analysis

 5.4.5. Image Differencing

Image differencing is a method of subtracting the DN (Digital Number) value of one data with the other one of the same pixel for the same band which results in new image. Pixels with change in radiance are distributed in the tails of the distribution whereas pixels with no change are distributed around the mean. As changes can happen on both directions, the analyst has to decide the order of the image to be subtracted.

Figure 11 Methodology flow chat

6.0. PROJECT RESULTS

The terms Land Use and Land Cover (LULC) is often used interchangeably, but each term has its own unique meaning. Land cover refers to the surface cover on the ground like vegetation, urban infrastructure, water, bare soil etc. Identification of land cover establishes the baseline information for activities like thematic mapping and change detection analysis. Land use refers to the purpose the land serves, for example, recreation, wildlife habitat, or agriculture.

The growth of a society totally depends on its social and economic development. This is the basic reason this work is carried out. LULC maps play a significant and prime role in planning, management and monitoring programme at local, regional and national levels. This type of information, on one hand, provides a better understanding of land utilization aspects and on the other hand, it plays an important role in the formation of policies and programme required for development planning. For ensuring sustainable development, it is necessary to monitor the ongoing process on land use/land cover, Normalized difference vegetation index and carbon stock of the Federal Capital Territory forest pattern over a period of time. In order to achieve sustainable development in area of life on land, the accurate estimate of a forest over an area is required. This requires the present and past land use/land cover information of the area. LULC maps also help us to study the changes that are happening in our ecosystem and environment.

Figure 12 to 21 showed results of image differencing of hotspot areas of deforestation in federal capital territory forest reserve. The grey white colour indicates the spot areas that are being seriously deforested (deforestation hotspot).

Figure 12 Tufa forest reserve hotspot area

Figure 13 Maje-Abuchi hotspot area

Figure 14 Chikwei forest reserve hotspot area

Figure 15 Chihuma forest reserve hotspot area

Figure 16 kusoru forest reserve hotspot area

Figure 17 Shaba forest reserve hotspot area

Figure 18 Buga Hill forest reserve hotspot area

Figure 19 NG_ 61 forest reserve hotspot area

Figure 20 Odu forest reserve hotspot area

Figure 21 Tukoki forest reserve hotspot area

Below are the land use/land cover maps of existing forest reserves in the FCT, namely; Tufa in Abaji, Chihuma, Chikwei, Kusoru and Shaba in Bwari, Maje Abuchi in Gwagwalada, then, Buga Hill, NG61, Odu and Tukoki in Kuje Area Council.

Figures 22 showed land use/land cover Maps, and Table percentage land cover by each class showed area cover in hectares for each class from 1990 to 2021.

The results of classified image in Figure 22 a showed that the total land area of Tufa forest was 254.598982 hectares (ha). Individual class area and statistics for Tufa forest in 1990 are summarized in Table 3 and 4. The percentage area of each class as represented in Table are as follow; forest 37%, cropland 46%, water bodies 8%, bare surface 7% and settlement 2%. The result also showed that cropland area had the largest share of land mass of (692.284099 ha) and settlement had least of land mass of (38.748951 ha) of the total land use and land cover categories assigned.

The resulting land use/land cover maps of the Tufa 2001 shown in Figure 22. A had an overall map accuracy of 87.00 % for the image by using error matrix/accuracy tools. This is the commonly employed approach for evaluating per-pixel classification. Kappa statistics/index was also computed for each classified map to measure the accuracy of the results. The resulting classification of land use/cover maps of the two periods had a Kappa statistics was 0.8625. This was reasonably good overall accuracy and accepted for the subsequent analysis and change detection.

The results from the remote sensing exercise conducted for Tufa is presented in table 3 and 5. Forest covers an area of 516.22ha (35%), cropland covers an area of 417.74ha (28%), Water body covers an area of 50.88ha (3%), while Bare surface and Settlement cover an area of 363.94ha (24%) and 143.94ha (10%) respectively as shown in table 1. The overall classification accuracy is 89.09% and the confusion matrix is presented in table 2.

In Tufa 2013, cropland constitutes the larger area at 48% while water body constitutes the least area at 6%.

Tufa forest of 2021 has transitioned into an Agriculture hub over the years with 48.94 % of its coverage been croplands, Bare surfaces been the next dominant class at 24.79%; this could be as a result of deforestation and urbanization within and surrounding the forest. 14.35% from the total coverage are settlements, 2% water bodies and a low coverage of 9.64% is the only forest class remaining.

TUFA FOREST RESERVE FROM 1990 TO 2021

Figure 22 Tufa forest reserve LU/LC 1990 to 2021

Table 3 showed area in hectares and percentage of Tufa forest reserve from 1990 to 2o21

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	548.055915	37 %
2	CROPLAND	692.284099	46 %
3	WATER BODY	114.560906	8 %
4	BARE SURFACE	99.134525	7 %
5	SETTLEMENT	38.748951	2 %
6	TOTAL	254.598982	100%

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	516.22	35 %
2	CROPLAND	417.74	28 %
3	WATER BODY	50.88	3%
4	BARE SURFACE	363.94	24 %
5	SETTLEMENT	143.94	10 %
6	TOTAL	1492.72	100%

 

S/N	CLASSES	AREA (Hectare)	Area (%)
1	FOREST	276.2022	19%
2	CROPLAND	720.0283	48%
3	WATER BODY	94.00133	6%
4	BARE SURFACE	220.1121	15%
5	SETTLEMENT	182.0827	12%
6	TOTAL	1492.427	100.00%

S/N	CLASSES	AREA IN HECTARES	AREA %
1	FOREST	723.573128	49.00%
2	CROPLAND	366.525462	25.00%
3	WATERBODIES	33.672707	2.00%
4	BARESURFACES	212.149614	14.00%
5	SETTLEMENT	142.593532	10.00%
	TOTAL	1478.514443	100.00%

 

Table 4 Shows Accuracy assessment of the Tufa forest reserve of 1990 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	18	20	18	100.00%	90.00%
Cropland	19	20	17	89.47%	85.00%
Water bodies	21	20	17	80.95%	85.00%
Bare surface	21	20	18	85.71%	90.00%
Settlement	21	20	19	90.48%	95.00%
Totals	100	100	89	Over all total accuracy = 87.00%

Table 5 Shows Accuracy assessment of the Tufa forest reserve of 2021 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	19	20	17	89.57%	85.00%
Cropland	20	20	18	85.71%	90.00%
Water bodies	22	20	18	84.51%	87.00%
Bare surface	20	20	17	90.00%	90.00%
Settlement	19	20	19	94.74%	90.00%
Totals	100	100	89	Over all total accuracy = 88.76%

Table 6 Shows Accuracy assessment of the Tufa forest reserve of 2013 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	29	33	28	96.55%	84.85%
Cropland	44	33	28	63.64%	84.85%
Water bodies	28	33	23	82.14%	69.70%
Bare surface	35	33	30	85.71%	90.91%
Settlement	29	33	28	96.55%	84.85%
Totals	165	165	137	Overall total accuracy = 83.03%

Table 7 Shows Accuracy assessment of the Tufa forest reserve of 2021 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	8	7	6	75.00%	85.71%
Cropland	37	41	36	97.30%	87.80%
Water bodies	3	3	3	100.00%	100.00%
Bare surface	23	20	20	86.96%	100.00%
Settlement	8	8	8	100.00%	100.00%
Totals	79	79	73	Over all total accuracy = 92.41%

6.0.1. Gwagwalada Forest: Maje Abuchi

Findings from remote sensing exercise conducted for Maje Abuchi presented in table 1. Forest covers an area of 7297.40Ha (57%) cropland covers an area of 677.07Ha (5%) waterbody covers an area of 46.03Ha (1%) Bare surface covers an area of 147.08Ha (1%) and settlement covers an area of 4584.45Ha (36%) respectively as shown in Table 1.  The overall classification accuracy is 88.65% and confusion matrix is presented in Table 2.

In 2013 we have five classes for the Maje-Abuchi Forest Reserve as shown in the following table with their coverage showing in hectares and percentage.

MAJEABUCHI FOREST RESERVE 1990 TO 2021

Figure 23 Maje-Abuchi forest reserve LU/LC from 1990 to 2021

Table 8 showed area in hectares and percentage of Maje-Abichi forest reserve from 1990 to 2o21

S/N	CLASSES	AREA (ha)	AREA%
1	FOREST	7297.408	57%
2	CROPLAND	677.0764	5%
3	WATERBODY	46.03092	1%
4	BARESURFACE	147.0855	1%
5	SETTLEMENT	4584.45	36%
	TOTAL	12752.05	100%

S/N	CLASSES	AREA (ha)	AREA (%)
1	FOREST	3811.179	30%
2	CROPLAND	1053.316	8%
3	WATER BODY	480.0481	4 %
4	BARE SURFACE	6629.111	52%
5	SETTLEMENT	778.4717	6%
6	TOTAL	12752.13	100%

 

S/N	CLASSES			AREA (Hectare)	AREA (%)
1	FOREST			1833.39	14 %
2	CROPLAND			6277.25	49 %
3	WATER BODY			976.95	8 %
4	BARE SURFACE			2873.52	23 %
5	SETTLEMENT			796.23	6 %
6	TOTAL			12757.14	100%
S/N		CLASSES	AREA (Hectare)			AREA (%)
1		FOREST	3740.57293			29 %
2		CROPLAND	1554.74622			12 %
3		WATER BODY	2212.097969			17 %
4		BARE SURFACE	2743.540521			22 %
5		SETTLEMENT	2501.620386			20 %
6		TOTAL	12752.57803			100%

Table 9 Shows Accuracy assessment of the Maje-Abuchi forest reserve of 1990 LU/LC

CLASS NAME	REFERENCE TOTAL	CLASSIFIED TOTAL	NUMBER CORRECT	PRODUCERS ACCURATE	USERS ACCURACY
FOREST	155	129	129	83.23%	100.00%
CROPLAND	11	11	11	100.00%	100.00%
WATERBODY	0	0	0	75.25%	64.87%
BARESURFACE	2	2	2	100.00%	100.00%
SETTLEMENT	61	87	61	100.00%	70.00%
TOTAL	229	229	203	OVERALL  ACCURACY =88.65%

Table 10 Shows Accuracy assessment of the Maje-Abuchi forest reserve of 2001 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	39	39	39	100.00%	100.00%
Cropland	24	2	2	80.33%	100.00%
Water bodies	10	6	6	60.00%	100.00%
Bare surface	38	64	38	100.00%	59.38%
Settlement	1	2	2	52.03%	92.17%
Totals	112	113	87	Over all total accuracy = 89.00%

Table 11 Shows Accuracy assessment of the Maje-Abuchi forest reserve of 2013 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	12	13	12	100.00%	92.31%
Cropland	61	62	60	98.36%	96.77%
Water bodies	8	6	6	75.00%	100.00%
Bare surface	27	28	27	100.00%	96.43%
Settlement	5	4	4	80.00%	100.00%
Totals	113	113	109	Over all total accuracy = 96.46%

Table 12 Shows Accuracy assessment of the Maje-Abuchi forest reserve of 2021 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	19	20	17	89.57%	85.00%
Cropland	21	20	18	85.71%	90.00%
Water bodies	21	20	18	85.71%	90.00%
Bare surface	20	20	18	90.00%	90.00%
Settlement	19	20	18	94.74%	90.00%
Totals	100	100	89	Over all total accuracy = 89.00%

6.0.2 Chihuma forest reserve  1990 to 2021 LU/LC report in Bwari Area Council

The land use land cover results for Chihuma constitutes forest, cropland, waterbody, baresurface and others. The areas cover an area of 62%, 9%, 19%, 4% and 6% respectively. The largest area covered is the forest with 87.5 hectares. Chihuma 2001

The result from the remote sensing exercise conducted for Chihuma Hills is presented above in Table 3. Forest covers an area of 68.59902ha (49%), cropland covers an area of 14.32459ha (10%), water body covers an area of 8.412119ha (6%) , while bare surface and settlement covers an area of 6.220723ha (4%) and 44.20451ha (31%) respectively as shown above in Table3. The overall classification accuracy is 76.33%and the confusion matrix is presented in Table 4. Chihuma 2013, Table 4 Figure 2 Which is Chihuma Forest 2013 shows the area coverage by percentage for all classes studied. Forest covers an area of 58.47812ha (41%), Cropland covers an area of 27.92061ha (20%), Water body covers an area of 9.903998ha (7%), bare surface covers an area of 25.98497ha (18%) while settlement covers an area of 19.49555ha (14%). The overall classification accuracy is 82.50%. Chinuma 2021. The forest class still dominates Chihuma forest with 55%, however a large percentage of the class, cropland was 29% due to agricultural activities carried out within the forest. settlement was 16% could be attributed to deforestation for domestic drvelopment purposes, bare surface and water bodies settled at 0%.

Figure 24 Chihuma forest reserve LU/LC from 1990 to 2021

Table 13 showed area in hectares and percentage of Chihuma forest reserve from 1990 to 2o21

S/N	CLASSES	AREA (HECTARE)	AREA (%)
1	Forest	87.51449	62%
2	Cropland	12.66942	9%
3	Waterbody	27.65531	19%
4	Bare Surface	5.872475	4%
5	Settlement	8.168758	6%
6	TOTAL	141.8805	100.00%

CHIHUMA 2001
CLASS	AREA(HECTARES)	AREA%
FOREST	68.59902	49%
CROPLAND	14.32459	10%
WATERBODIES	8.412119	6%
BARESURFACE	6.220723	4%
SETTLEMENT	44.20451	31%
Total	141.761	100%

 

S/N	Classes	Area(Hectare)	Area
1	Forest	58.47812	41%
2	Cropland	27.92061	20%
3	Waterbody	9.903998	7%
4	Baresurface	25.98497	18%
5	Settlement	19.49555	14%
6	Total	141.7832	100%

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	78.241276	55 %
2	CROPLAND	41.061682	29 %
3	WATER BODY	0.405942	0 %
4	BARE SURFACE	0.151619	0 %
5	SETTLEMENT	21.818518	16 %
6	TOTAL	141.679037	100%

Table 14 Shows Accuracy assessment of the Chihuma forest reserve of 1990 LU/LC

Class Names	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	43	42	39	90.70%	92.86%
Cropland	21	23	18	85.71%	78.26%
Waterbody	39	42	39	100.00%	92.86%
Bare surface	14	4	2	14.29%	50.00%
SETTLEMENT	5	11	4	80.00%	36.36%
Totals	122	122	102	Overall total accuracy = 83.61%

 Table 15 Shows Accuracy assessment of the Chihuma forest reserve of 2001 LU/LC

CLASS NAME	REFERENCE TOTAL	CLASSIFIED TOTALS	NUMBERS CORREC T	PRODUCERS ACCURACY	USERS ACCURACY
FOREST	109	107	103	94.50%	96.26%
CROPLAND	29	15	14	48.28%	93.33
WATERBODY	20	13	12	60.00%	92.31%
BARESURFACE	25	6	5	20.00%	83.33%
SETTLEMENT	24	66	24	100.00%	36.36%
TOTAL	207	207	158	OVERALL  ACCURACY = 76.33%

Table 16 Shows Accuracy assessment of the Chihuma forest reserve of 2013 LU/LC

Class Name	Reference Total	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	70	80	64	91%	80.00%
Cropland	83	80	66	79%	82.50%
Waterbody	79	80	64	81%	80.00%
Baresurface	87	80	67	77%	83.75
Settlement	81	80	69	85%	86.25%
Totals	400	400	330	Overall Accuracy = 82.50%

Table 17 Shows Accuracy assessment of the Chihuma forest reserve of 2021 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	48	53	45	93.75%	84.71%
Cropland	24	22	19	89.47%	85.00%
Water bodies	2	7	3	80.95%	85.00%
Bare surface	6	4	4	85.71%	90.00%
Settlement	16	13	11	68.75%	95.00%
Totals	96	102	82	Over all total accuracy = 85.23%

Overall Kappa Statistics = 0.7437

6.0.3. Chikwei forest reserve in Bwari Area council LU/LC report 1990 to 2021

The land use land cover results for Chikwei constitutes forest, cropland, waterbody, baresurface and settlement. The areas cover an area of 82%, 6%, 7%, 4% and 1% respectively. The largest area covered is the forest with 117.7 hectares. Chikwei 2001. The result from the remote sensing exercise conducted for Chikwei is presented above in Table 5. Forest covers an area of 41.31955ha (29%), cropland covers an area of 3.936009ha (3%), water body covers an area of 39.84229ha (28%), while bare surface and settlement covers an area of 40.61603ha (28%) and 17.68812ha (12%) respectively as shown above in Table 5. The overall classification accuracy is 57.94%and the confusion matrix is presented in Table 6. The results of the image classification in Figure A showed that the total land area of Chikwei forest was 143.589745 hectares (ha). Individual class area and statistics for Chikwei forest in 2013 are summarized in Table A. The percentage area of each class as represented in Figure b are as follow; forest 60%, cropland 34%, water bodies 2%, bare surface 3% and settlement 1%. The result also showed that forest area had the largest share of land mass of (86.14095 ha) of the total LULC categories assigned. The resulting land use/land cover maps of the Chiwei 2013 shown in Figs. A had an overall map accuracy of 85.00 % for the image by using error matrix/accuracy tools. This is the commonly employed approach for evaluating per-pixel classification. Kappa statistics/index was also computed for each classified map to measure the accuracy of the results. The resulting classification of land use/cover maps of the two periods had a Kappa statistics was 0.8125. Chikwei 2021. Forest class still dominates all the other classes in Chikwei forest at 52.00%. Cropland is next with 36.00%, water bodies cover 3.00% of the area, bare surface 1.00% and the percentage coverage for others/settlements stand at 8.00%.

Figure 25 Chikwei forest reserve LU/LC 1990 to 2021

Table 18 showed area in hectares and percentage of Chikwei forest reserve from 1990 to 2o21

S/N	CLASSES	AREA (HECTARE)	AREA (%)
1	Forest	117.6897	82.00%
2	Cropland	8.610333	6.00%
3	Waterbody	9.744188	7.00%
4	Bare Surface	5.398312	4.00%
5	Settlement	2.13025	1.00%
6	TOTAL	143.5728	100 %

 

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	240.760329	46 %
2	CROPLAND	215.776662	42 %
3	WATER BODY	3.550646	1 %
4	BARE SURFACE	5.067551	1 %
5	SETTLEMENT	51.865906	10 %
6	TOTAL	517.021094	100%

S/N	Classes	AREA (Hectare)	AREA (%)	S/N	CLASS	AREA IN HECTARES	AREA IN %
1	Forest	86.14095	60 %	1	FOREST	71.374106	52.00%
2	Cropland	48.641038	34 %	2	CROPLAND	48.390701	36.00%
3	Water Body	2.212949	2 %	3	WATERBODIES	4.671816	3.00%
4	Bare Surface	4.517656	3 %	4	BARESURFACE	1.76581	1.00%
5	Settlement	2.077152	1 %	5	SETTLEMENT	10.285356	8%
6	Total	143.589745	100%		TOTAL	136.487789	100%

Table 19 Shows Accuracy assessment of the Chikwei forest reserve of 1990 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	25	23	20	80.00%	86.96%
Cropland	27	23	18	66.67%	78.26%
Waterbody	18	23	16	88.89%	69.57%
Bare surface	32	23	18	56.25%	78.26%
SETTLEMENT	13	23	13	100.00%	56.52%
Totals	115	115	85	Overall total accuracy =73.91%

Table 20 Shows Accuracy assessment of the Chikwei forest reserve of 2001 LU/LC

CLASS NAME	REFERENCE TOTAL	CLASSIFIED TOTALS	NUMBERS CORREC T	PRODUCERS ACCURACY	USERS ACCURACY
FOREST	83	63	60	72.29%	95.24%
CROPLAND	69	2	2	2.90%	100.00%
WATERBODY	28	63	28	100.00%	44.44%
BARESURFACE	31	70	31	100.00%	44.29%
SETTLEMENT	3	16	3	100.00%	18.75%
TOTAL	214	214	124	OVERALLACCURACY=57.94%

Table 21 Shows Accuracy assessment of the Chikwei forest reserve of 2013 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	21	20	17	80.95%	85.00%
Cropland	20	20	17	85.00%	85.00%
Water bodies	22	20	18	81.82%	90.00%
Bare surface	19	20	17	89.47%	85.00%
Settlement	18	20	16	98.89%	80.00%
Totals	100	100	85	Over all total accuracy = 85.00%

Kappa Statistics = 0.8125

Table 22 Shows Accuracy assessment of the Chikwei forest reserve of 2021 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	101	108	101	100.00%	93.52%
Cropland	65	64	58	100.00%	89.23%
Water bodies	2	2	2	66.67%	100.00%
Bare surface	12	5	5	100.00%	100.00%
Settlement	36	39	36	100.00%	100.00%
Totals	87	87	80	Over all total accuracy = 93.23

Overall Kappa Statistics = 0.8842

6.0.4. Kusoru forest reserve in Bwari Area council LU/LC report 1990 to 2021

Kusoru 1990 findings from remote sensing exercise conducted for kusoru presented in table 1. Forest covers an area of 32.72Ha (33%) cropland covers an area of 4.53 Ha (5%) water body covers an area of 4.81Ha (5%) Bare surface covers an area of 22.53Ha (23%) and settlement covers an area of 32.73Ha (34%) respectively as shown in Table 1.  The overall classification accuracy is 78.52% and confusion matrix is presented in Table 2. Kusoru 2001 Forest Reserve Lu/Lc Dynamics and Classification Accuracy. The result from the remote sensing exercise conducted for Kusoru 2001 is presented in Table 1. The Area covers (Hectares) and Area % is presented below.  Forest covers an area of 18.15615Ha (19%), Cropland 11.07516Ha (11%), Waterbodies 5.536933Ha (6%), Bare surface 52.0456Ha (53%), and Settlement 10.61367Ha (11%) respectively. The overall classification accuracy is 83.50% and  matrix  classification is presented in Table 2. Kusoru 2013. The results of classified image in Figure b showed that the total land area of Kusoru forest was 97.499095 hectares (ha). Individual class area and statistics for Tufa forest in 2013 are summarized in Table b. The percentage area of each class as represented in Figure b are as follow; forest 48%, cropland 18%, water bodies 14%, bare surface 13% and settlement 7%. The result also showed that cropland area had the largest share of land mass of (12.203876 ha) and settlement had least of land mass of (6.745713 ha) of the total land use and land cover categories assigned.

The resulting land use/land cover maps of the Kusoru 2013 shown in Figs. A had an overall map accuracy of 82.00 % for the image by using error matrix/accuracy tools. This is the commonly employed approach for evaluating per-pixel classification. Kappa statistics/index was also computed for each classified map to measure the accuracy of the results. The resulting classification of land use/cover maps of the two periods had a Kappa statistics was 0.7750. This was reasonably good overall accuracy and accepted for the subsequent analysis and change detection. Kusoru 2021. Forest is the dominant class at 43.00%, next is Cropland at 38.00%, followed by others/settlement at 8.00%, Bare surface at 8.00% and water body at 3.00% coverage.

Figure 26 Kusoru forest reserve LU/LC from 1990 to 2021

Table 23 showed area in hectares and percentage of Kusoru forest reserve from 1990 to 2o21

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	32.7204	33.00%
2	CROPLAND	4.53548	5.00%
3	WATER BODIES	4.811935	5.00%
4	BARE SURFACE	22.531846	23.00%
5	SETTLEMENT	32.739479	34.00%
6	TOTAL	97.33914	100%

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	18.15615	19%
2	CROPLAND	11.07516	11%
3	WATER BODIES	5.536933	6%
4	BARE SURFACE	52.0456	53%
5	SETTLEMENT	10.61367	11%
6	TOTAL	97.42751	100%

 

S/N	CLASSES	AREA (H)	AREA
1	FOREST	47.251448	48 %
2	CROPLAND	17.892732	18%
3	WATER BODY	13.405326	14 %
4	BARE SURFACE	12.203876	13 %
5	SETTLEMENT	6.745713	7 %
6	TOTAL	97.499095	100%

S/N	CLASS	AREA IN HECTARES	AREA IN %
1	FOREST	41.144595	43%
2	CROPLAND	36.481811	38%
3	WATERBODIES	3.608698	3%
4	BARESURFACE	7.986322	8%
5	SETTLEMENT	8.100645	8%
	TOTAL	97.322071	100.00%

Table 24 Shows Accuracy assessment of the Kusoru forest reserve of 1990 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	134	75	79	58.96%	100.00%
Cropland	5	6	5	100.00%	83.33%
Water bodies	7	7	7	100.00%	100.00%
Bare surface	55	163	55	100.00%	87.73%
Settlement	16	62	16	100.00%	25.81%
Totals	217	217	162	Over all total accuracy = 78..52%

Table 25 Shows Accuracy assessment of the Kusoru forest reserve of 2001 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	57	35	35	61.40%	100.00%
Cropland	21	15	14	66.67%	93.33%
Water bodies	12	8	7	58.33%	87.50%
Bare surface	98	126	98	100.00%	77.78%
Settlement	18	22	18	100.00%	81.82%
Totals	206	206	172	Over all total accuracy = 83.50%

Table 26 Shows Accuracy assessment of the Kusoru forest reserve of 2013 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	22	20	17	77.27%	85.00%
Cropland	21	20	17	80.95%	85.00%
Water bodies	22	20	17	80.95%	85.00%
Bare surface	16	20	15	93.75%	75.00%
Settlement	19	20	16	84.21%	80.00%
Totals	100	100	82	Over all total accuracy = 82.00%

Table 27 Shows Accuracy assessment of the Kusoru forest reserve of 2021 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	97	108	97	100.00%	89.81%
Cropland	88	86	76	86.36%	88.37%
Water bodies	3	3	3	100.00%	100.00%
Bare surface	14	5	5	58.00%	80.00%
Settlement	5	5	5	100.00%	100.00%
Totals	207	207	185	Over all total accuracy = 89.37

Overall Kappa Statistics = 0.8156

6.0.5. Shaba forest reserve in Bwari Area council LU/LC report 1990 to 2021

Shaba 1990. The results of the image classification in Figure A showed that the total land area of Shaba forest was 254.598982 hectares (ha). Individual class area and statistics for Shaba forest in 1990 are summarized in Table A. The percentage area of each class as represented in Figure b are as follow; forest 60%, cropland 30%, water bodies 7%, bare surface 2% and settlement 1%. The result also showed that forest area had the largest share of land mass of (153.680939 ha) of the total LULC categories assigned.

The resulting land use/land cover maps of the Shaba 1990 shown in Figs. A had an overall map accuracy of 92.00 % for the image by using error matrix/accuracy tools. This is the commonly employed approach for evaluating per-pixel classification. Kappa statistics/index was also computed for each classified map to measure the accuracy of the results. The resulting classification of land use/cover maps of the two periods had a Kappa statistics was 0.9000.

Shaba 2001. The result from the remote sensing exercise conducted for Shaba Hills is presented above in Table9. Forest covers an area of 175.34.23ha (69%), cropland covers an area of 23.25093ha (9%), water body covers an area of 6.579971ha (3%), while bare surface and settlement covers an area of 18.8959ha (7%) and 30.62167ha (12%) respectively as shown above in Table9. The overall classification accuracy is 88.15%and the confusion matrix is presented in Table 10. Shaba 2021. A large percentage of the forest class has been lost due to afforestation. Wood from the forest is used for firewood and some part of the forest class was lost to cropland. This is evident in the coverage percentage of each class found in the forest with forest been the dominant class with 72.00%, cropland at 19.00%, waterbodies 6.00%, bare surface 1.00% and settlement 2.00%.

Figure 27 Shaba forest reserve LU/LC from 1990 to 2021

Table 28 showed area in hectares and percentage of Shaba forest reserve from 1990 to 2o21

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	153.680939	60 %
2	CROPLAND	76.144185	30 %
3	WATER BODY	18.049247	7 %
4	BARE SURFACE	5.376327	2 %
5	SETTLEMENT	1.348284	1 %
6	TOTAL	254.598982	100%

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	175.3423	69 %
2	CROPLAND	23.25093	9 %
3	WATER BODY	6.579971	3 %
4	BARE SURFACE	18.8959	7 %
5	SETTLEMENT	30.62167	12 %
6	TOTAL	254.6987	100%

 

S/N	CLASSES	AREA (ha)	AREA (%)
1	FOREST	73.44	29 %
2	CROPLAND	53.01	21 %
3	WATER BODY	4.05	1 %
4	BARE SURFACE	20.88	8 %
5	SETTLEMENT	103.68	41 %
6	TOTAL	255.06	100%

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	182.200316	72 %
2	CROPLAND	48.438375	19 %
3	WATER BODY	16.536238	6 %
4	BARE SURFACE	2.941874	1 %
5	SETTLEMENT	4.528887	2 %
6	TOTAL	254.64569	100%

Table 29 Shows Accuracy assessment of the Shsba forest reserve of 1990 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	19	20	19	94.74%	90.00%
Cropland	21	20	18	85.71%	90.00%
Water bodies	21	20	18	90.48%	95.00%
Bare surface	19	20	19	94.74%	90.00%
Settlement	20	20	18	95.00%	95.00%
Totals	100	100	92	Over all total accuracy = 92.00%

Kappa Statistics = 0.9000

Table 30 Shows Accuracy assessment of the Shaba forest reserve of 2001 LU/LC

CLASS NAME	REFERENCE TOTAL	CLASSIFIED TOTALS	NUMBERS CORREC T	PRODUCERS ACCURACY	USERS ACCURACY
FOREST	156	188	156	100.00%	86.67%
CROPLAND	11	9	9	81.82%	100.00%
WATERBODY	23	1	1	91.82%	78.52%
BARESURFACE	9	1	9	100.00%	100.00%
SETTLEMENT	12	12	11	100.00%	92.31%
TOTAL	211	211	186	OVERALL ACCURACY = 88.15%

Table 31 Table 30 Shows Accuracy assessment of the Shaba forest reserve of 2013 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	30	33	30	100.00%	90.91%
Cropland	43	33	33	76.74%	100.00%
Water bodies	25	29	22	88.00%	75.86%
Bare surface	33	33	30	90.91%	90.91%
Settlement	30	33	30	90.91%	90.91%
Totals	161	161	145	Over all total accuracy = 90.06%

Table 32 Table 30 Shows Accuracy assessment of the Shaba forest reserve of 2021 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	59	63	57	96.61%	90.48%
Cropland	11	11	9	81.82%	81.82%
Water bodies	6	4	3	50.00%	75.00%
Bare surface	2	1	1	50.00%	100.00%
Settlement	2	1	1	50.00%	100.00%
Totals	80	80	71	Over all total accuracy = 88.75%

Overall Kappa Statistics = 0.7159

6.0.6. Buga Hill forest reserve in Kuje Area council LU/LC report 1990 to 2021

Buga 1990. The land use land cover results for Buga constitutes forest, cropland, water body, bare surface and others. The areas cover an area of 75%, 19%, 2%, 2% and 2% respectively. The largest area covered is the forest with 407.8 hectares. Buga 2001. The result from the remote sensing exercise conducted for Buga Hills is presented above in Table 1. Forest covers an area of 397.8825ha (73%), cropland covers an area of 15.60325ha (3%) ,water body covers an area of 15.60325ha (0%) , while bare surface and settlement covers an area of 111.7942ha (21%) and 14.56828ha (3%) respectively as shown above in Table 1. The overall classification accuracy is 89.19% and the confusion matrix is presented in Table 2. Buga 2013. Figure 1 Table 2 which is Buga Hill 2013 shows the area coverage by percentage for all classes studied, Forest, Cropland, Waterbody, Baresurface, and Settlement. Forest covers an area of 194.438ha (37%), Cropland covers an area of 241.5412ha (45%), Waterbody covers an area of 15ha (3%),Baresurface covers an area of 52.55332ha(10%) while settlement covers an area of 27ha(5%). The overall Classification accuracy is 87.00%. Buga 2021. Buga forest is one of the forests less affected by anthropogenic activities and hasn’t lost a lot of the forest class to other classes. With 77.41% its percentage coverage, cropland with 16.21 percentage and Bare ground 6.3%. The only visible driver to the loss of class is agriculture.

Figure 28 Buga Hill forest reserve LU/LC from 1990 to 2021

Table 33 showed area in hectares and percentage of Buga Hill forest reserve from 1990 to 2021

S/N	CLASSES	AREA (ha)	AREA (%)
1	Forest	407.8403	75.00%
2	Cropland	100.8992	19.00%
3	Waterbody	10.37359	2.00%
4	Bare Surface	10.48304	2.00%
5	Settlement	11.3445	2.00%
6	TOTAL	540.9406	100.00%

S/N	CLASSES	AREA (ha)	AREA (%)
1	Forest	397.8825	73.00%
2	Cropland	15.60325	3.00%
3	Water body	0.68762	0.00%
4	Bare Surface	111.7942	21.00%
5	Settlement	14.56828	3.00%
6	TOTAL	540.5361	100.00%

S/N	Classes	Area(Hectare)	Area (%)
1	Forest	196.438	36%
2	Cropland	241.5412	45%
3	Water bodies	19.01321	3%
4	Bare surface	53.55332	10%
5	Settlement	30.001	6%
6	Total	540.54673	100%

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	352.962694	65 %
2	CROPLAND	153.514737	29 %
3	WATER BODY	10.849337	2 %
4	BARE SURFACE	0.147635	0 %
5	SETTLEMENT	23.12359	4 %
6	TOTAL	540.597993	100%

Table 34 Shows Accuracy assessment of the Buga Hill forest reserve of 1990 LU/LC

Class Names	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	47	42	39	82.98%	92.86%
Cropland	38	42	34	89.47%	80.95%
Waterbody	2	3	2	100.00%	66.67%
Bare surface	1	1	1	100.00%	100.00%
Settlement	1	1	1	100.00%	100.00%
Totals	89	89	77	Overall total accuracy = 86.52%

Overall Classification Accuracy =     86.52%

Table 35 Shows Accuracy assessment of the Buga Hill forest reserve of 2001 LU/LC

CLASS NAME	REFERENCE TOTAL	CLASSIFIED TOTALS	NUMBERS CORREC T	PRODUCERS ACCURACY	USERS ACCURACY
FOREST	155	172	153	98.71%	88.95%
CROPLAND	9	4	2	22.22%	50.00%
WATERBODY	11	0	0	42.37%	33.33%
BARESURFACE	26	28	23	88.46%	82.14%
SETTLEMENT	9	6	3	33.33%	50.00%
TOTAL	210	210	181	Overall total accuracy = 89.19%

Table 36 Shows Accuracy assessment of the Buga Hill forest reserve of 2013 LU/LC

Class Name	Reference Totals	Classified Total	Number Correct	Producers Accuracy	Users Accuracy
Forest	18	20	17	94.44%	85.00%
Cropland	19	20	16	84.21%	80.00%
Waterbody	20	20	17	85.00%	85.00%
Baresurface	21	20	18	90.00%	90.00%
Settlement	22	20	19	95.00%	95.00%
Total	100	100	87	Overall total accuracy=87.00%

Table 37 Shows Accuracy assessment of the Buga Hill forest reserve of 2021 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	28	20	27	96.43%	90.00%
Cropland	12	13	11	91.67%	84.62%
Water bodies	4	1	1	25.00%	100.00%
Bare surface	1	1	1	36.82%	46.23%
Settlement	2	2	2	100.00%	100.00%
Totals	47	47	42	Over all total accuracy = 89.13%

Ovarall Kappa Statistics = 0.7932

6.0.7. NG_61 forest reserve in Kuje Area council LU/LC report 1990 to 2021

Findings from the remote sensing exercise conducted for NG_61 is presented in Table 2 below with Area cover and Area% respectively.  Forest covers an area of 233.507457Ha (45.15%), Cropland 90.270029Ha (17.46%), Water body 23.939407Ha (4.63%), Bare surface 69.148566Ha (13.37%), and Settlement 100.275946Ha (19.39%) respectively. The overall classification accuracy is 89.09% with the classification matrix shown in Table. NG 61 2001. The result from the remote sensing exercise conducted for NG_61 is presented in Table 1. The Area covers (Hectares) and Area% is presented below.  Forest covers an area of 254.4255Ha (49%), Cropland 85.946Ha (16%), Waterbodies 55.73807Ha (11%), Bare surface 91.69773Ha (18%), and Settlement 29.20862Ha (11%) respectively. The overall classification accuracy is 88.68% and matrix classification is presented in Table 2. NG 61 2013. The land use and land cover identified and classified in this study includes forest, cropland, water body, bare surface and others.  The class others constitutes the largest area with 29.77% in NG_61 and forest having the least area at 10.43%. NG 61 2021. Forest class still remains the dominant class of NG61 at 46.00% coverage, cropland 42.00% waterbodies 1.00%, Bare surface 1.00% and settlement 10.00%.

Figure 29 NG-61 forest reserve LU/LC from 1990 to 2021

Table 38 showed area in hectares and percentage of NG_61 forest reserve from 1990 to 2o21

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	233.507457	45.15 %
2	CROPLAND	90.270029	17.46%
3	WATER BODY	23.939407	4.63%
4	BARE SURFACE	69.148566	13.37%
5	SETTLEMENT	100.275946	19.39%
6	TOTAL	517.141405	100%

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	254.4255	49 %
2	CROPLAND	85.94698	16 %
3	WATER BODY	55.73807	11 %
4	BARE SURFACE	91.69773	18 %
5	SETTLEMENT	29.20862	6 %
6	TOTAL	1035.741338	100%

 

S/N	CLASSES	AREA (ha)	Area (%)
1	FOREST	53.8876	10.43%
2	CROPLAND	104.333	20.19%
3	WATER BODY	100.819	19.51%
4	BARESURFACE	103.966	20.12%
5	SETTLEMENT	153.847	29.77%
6	TOTAL	516.8532	100.00%

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	240.760329	46 %
2	CROPLAND	215.776662	42 %
3	WATER BODY	3.550646	1 %
4	BARE SURFACE	5.067551	1 %
5	SETTLEMENT	51.865906	10 %
6	TOTAL	517.021094	100%

 Table 39 Shows Accuracy assessment of the NG_61 forest reserve of 1990 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	31	33	31	100.00%	93.94%
Cropland	34	33	30	88.24%	90.91%
Water bodies	37	33	28	75.68%	84.85%
Bare surface	33	33	28	84.85%	84.85%
Settlement	30	33	30	100.00%	90.91%
Totals	165	165	147	Over all total accuracy = 89.09%

Overall Classification Accuracy =     89.09%

Table 40 Shows Accuracy assessment of the NG_61 forest reserve of 2001 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	221	249	221	100.00%	88.76%
Cropland	90	68	65	72.22%	95.59%
Water bodies	52	31	30	57.69%	96.77%
Bare surface	46	54	45	97.83%	83.33%
Settlement	15	22	15	100.00%	68.18%
Totals	424	424	376	Over all total accuracy = 88.68%

Table 41 Table 40 Shows Accuracy assessment of the NG_61 forest reserve of 2013 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	31	33	29	93.55%	87.88%
Cropland	34	33	25	73.53%	75.76%
Water bodies	39	33	26	66.67%	78.79%
Bare surface	34	33	27	79.41%	81.82%
Settlement	27	33	27	100.00%	81.82%
Totals				Overall total accuracy = 81.21%

Overall Kappa Statistics = 0.7652

Table 42 Table 40 Shows Accuracy assessment of the NG_61 forest reserve of 2021 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	35	39	34	97.14%	87.18%
Cropland	38	35	33	86.84%	94.29%
Water bodies	1	1	1	80.95%	85.00%
Bare surface	2	1	1	50.00%	100.00%
Settlement	9	9	8	88.89%	88.89%
Totals	85	85	77	Over all total accuracy = 90.48%

Overall Kappa Statistics = 0.8429

6.0.8. Odu forest reserve in Kuje Area council LU/LC report 1990 to 2021

Odu 1990. Findings from the remote sensing exercise conducted for ODU forest is presented in Table 2 below with Area cover and Area% respectively.  Forest covers an area of 784.59549Ha (74.27%), Cropland 163.175886Ha (15.45%), Water body 19.962951Ha (1.89%), Bare surface 33.433577Ha (3.17%), and Settlement 55.177716Ha (5.22%) respectively. The overall classification accuracy is 86.06% with the classification matrix shown in Table 1. Odu 2001. The result from the remote sensing exercise conducted for Odu Hills is presented above in Table7. Forest covers an area of 697.5501ha (66%), cropland covers an area of 33.48854ha (3%), water body covers an area of 22.66372ha (2%), while bare surface and settlement covers an area of 261.9573ha (25%) and 40.52539ha (4%) respectively as shown above in Table7. The overall classification accuracy is 81.22% and the confusion matrix is presented in Table 8. Odu 2021. Forest are the dominant class at Odu forest with its percentage coverage at 46.00%, Agricultural activities within the forest saw the cropland take over 43.00% of the forest. The settlement class still covers 11.00% of Odu forest, bare surface and water bodies cover 0.00% each. Overall Kappa Statistics was 0.8875

Figure 30 Odu forest reserve LU/LC from 1990 to 2021

Table 43 showed area in hectares and percentage of Odu forest reserve from 1990 to 2021

S/N	CLASSES	AREA (ha)	AREA (%)
1	FOREST	784.59549	74.27%
2	CROPLAND	163.175886	15.45%
3	WATERBODY	19.962951	1.89%
4	BARE SURFACE	33.433577	3.17%
5	SETTLEMENT	55.177716	5.22%
6	TOTAL	1056.34562	100%

CLASS		AREA (ha)		%
FOREST		697.5501		66%
CROPLAND		33.48854		3%
WATERBODY		22.66372		2%
BARESURFAC		261.9573		25%
SETTLEMENT		40.52539		4%
TOTAL		1056.185		100%

 

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	143.37	13 %
2	CROPLAND	275.49	26 %
3	WATER BODY	16.65	2 %
4	BARE SURFACE	138.6	13 %
5	SETTLEMENT	483.12	46 %
6	TOTAL	1057.23	100%

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	482.145784	46 %
2	CROPLAND	454.543478	43 %
3	WATER BODY	2.472684	0 %
4	BARE SURFACE	0.610456	0 %
5	SETTLEMENT	116.505656	11 %
6	TOTAL	1056.278058	100%

Table 44 Shows Accuracy assessment of the Odu forest reserve of 1990 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	28	33	28	100.00%	84.85%
Cropland	33	33	27	81.82%	81.82%
Water bodies	39	33	29	74.36%	87.88%
Bare surface	37	33	30	81.08%	90.91%
Settlement	28	33	28	100.00%	84.85%
Totals	165	165	142	Over all total accuracy = 86.06%

Overall Kappa Statistics = 0.8258

Table 45 Shows Accuracy assessment of the Odu forest reserve of 2001 LU/LC

CLASS NAME	REFERENCE TOTAL	CLASSIFIED TOTALS	NUMBERS CORREC T	PRODUCERS ACCURACY	USERS ACCURACY
FOREST	106	132	106	100.00%	80.30%
CROPLAND	17	1	1	59.88%	100.00%
WATERBODY	15	0	0	78.47%	84.58%
BARESURFACE	36	5	43	100.00%	83.72%
SETTLEMENT	7	43	4	57.14%	80.00%
TOTAL	181	181	147	Over all accuracy = 81.22%

Table 46 Shows Accuracy assessment of the Odu forest reserve of 2013 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	24	26	24	100.00%	92.31%
Cropland	38	26	24	94.74%	100.00%
Water bodies	2	2	2	100.00%	100.00%
Bare surface	31	31	30	96.77%	96.77%
Settlement	77	77	76	98.70%	98.70%
Totals	172	172	168	Over all total accuracy = 76.70%

Table 47 Shows Accuracy assessment of the Odu forest reserve of 2021 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	40	38	34	85.00	89.47%
Cropland	37	42	34	91.89%	80.95%
Water bodies	1	1	1	80.15%	82.01%
Bare surface	2	1	1	81.51%	70.21%
Settlement	12	9	8	66.67%	88.89%
Totals	92	91	78	Over all total accuracy = 85.39%

Overall Kappa Statistics = 0.7559

6.0.9. Tukoki forest reserve in Kuje Area council LU/LC report 1990 to 2021

Tukoki 1990: The results of the image classification in Figure A showed that the total land area of Tukoki forest was 1035.741338 hectares (ha). Individual class area and statistics for Tukoki forest in 1990 are summarized in Table A. The percentage area of each class as represented in Figure b are as follow; forest 65%, cropland 28%, water bodies 1%, bare surface 0% and settlement 6%. The result also showed that forest area had the largest share of land mass of (675.649078 ha) of the total LULC categories assigned.

The resulting land use/land cover maps of the Tukoki 1990 showed in Figs. A had an overall map accuracy of 90.00 % for the image by using error matrix/accuracy tools. This is the commonly employed approach for evaluating per-pixel classification. Kappa statistics/index was also computed for each classified map to measure the accuracy of the results. The resulting classification of land use/cover maps of the two periods had a Kappa statistics was 0.8750. This was reasonably good overall accuracy and accepted for the subsequent analysis and change detection. Tukoki 2001. The results from the remote sensing image analysis are presented in table 3. Forest covers an area of 292.47ha (28.23%), cropland covers an area of 330.09ha (31.87%), water body covers an area of 44.84ha (4.33%), while bare surface and settlement cover an area of 276.53ha (26.70%), 91.92ha (8.87%) respectively as shown in table 1. The overall classification accuracy is 80.00% and the confusion matrix is presented in table 4. For Tukoki 2013, forest constitutes the largest area at 45.87% and water body with the least area at 7.51%. Tukoki 2021. The forest class has largely been lost in Tukoki forest with percentage coverage of only 7.52%. This can be attributed to an upsurge in agricultural activities and settlement springing up with the coverage percentage of cropland currently at 41.78% and others at 37.10%.

Figure 31 Tukoki forest reserve LU/LC from 1990 to 2021

Table 48 showed area in hectares and percentage of Tukoki forest reserve from 1990 to 2o21

S/N	CLASSES	AREA (Hectare)	AREA (%)
1	FOREST	675.649078	65 %
2	CROPLAND	283.279299	28 %
3	WATER BODY	13.127213	1 %
4	BARE SURFACE	2.051461	0 %
5	SETTLEMENT	61.634287	6 %
6	TOTAL	1035.741338	100%

S/N	CLASSES	AREA (Hectare)	Area (%)
1	FOREST	292.47	28%
2	CROPLAND	330.09	32%
3	WATER BODY	44.84	4%
4	BARE SURFACE	276.53	27%
5	SETTLEMENT	91.92	9%
6	TOTAL	1035.85	100.00%

 

S/N	CLASSES	AREA (ha)	AREA (%)
1	FOREST	81.873387	8.00%
2	CROPLAND	426.698952	41.00%
3	WATERBODIES	19.930948	2.00%
4	BARE SURFACES	128.389633	12.00%
5	SETTLEMENT	379.415908	37.00%
	TOTAL	1036.308828	100.00%

S/N	CLASSES	AREA (Ha)	Area (%)
1	FOREST	475.1985	45.87%
2	CROPLAND	233.0228	22.49%
3	WATER BODY	77.7927	7.51%
4	BARE SURFACE	140.1578	13.53%
5	SETTLEMENT	109.835	10.60%
6	TOTAL	1036.007	100.00%

Table 49 Shows Accuracy assessment of the Tukoki forest reserve of 1990 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	19	20	17	89.47%	85.00%
Cropland	21	20	18	85.71%	90.00%
Water bodies	20	20	18	90.00%	90.00%
Bare surface	21	20	19	90.48%	90.00%
Settlement	19	20	18	94.74%	90.00%
Totals	100	100	92	Over total accuracy = 90.00%

 

Overall Kappa Statistics = 0.8750

 

Table 50 Table 49 Shows Accuracy assessment of the Tukoki forest reserve of 2001 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	17	16	13	76.47%	81.25%
Cropland	18	16	13	72.22%	81.25%
Water bodies	13	15	13	100.00%	81.25%
Bare surface	13	16	12	92.31%	75.00%
Settlement	19	16	13	68.42%	81.25%
Totals	80	80	54	Over all total accuracy = 80.00%

Table 51 Table 50 Table 49 Shows Accuracy assessment of the Tukoki forest reserve of 2013 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	33	33	28	84.85%	84.85%
Cropland	33	33	28	84.85%	84.85%
Water bodies	38	33	32	84.21%	96.97%
Bare surface	31	33	29	93.55%	87.88%
Settlement	30	33	30	100.00%	90.91%
Totals	165	165	147	Overall total accuracy = 89.09%

Overall Kappa Statistics = 0.8636

Table 52 Table 50 Table 49 Shows Accuracy assessment of the Tukoki forest reserve of 2021 LU/LC

Class Name	Reference Totals	Classified Totals	Number Correct	Producers Accuracy	Users Accuracy
Forest	4	4	4	100%	100%
Cropland	35	39	35	100%	89.74%
Water bodies	2	2	2	100%	100%
Bare surface	12	5	5	51.67%	90.00%
Others	36	39	36	100.00%	92.31%
Totals	87	87	80	Over all total accuracy = 91.95%

Overall Kappa Statistics = 0.8711

6.1. Change Detection and Prediction of land use land cover change

LU/LC Change Detection for 1990 to 2001, 2001 to 2013 and 2013 to 2021

Based on the LU/LC analyses of Landsat data for the years 1990, 2001, 2013, and 2021, it was found that the LU/LC change trends varied significantly during the periods mentioned above. The results showed that in the period 1990–2013, most LULC were converted to cropland except Tukoki forest reserve and Maje-Abuchi forest reserve that reduced to settlement from 1990 to 2021. This indicates that the expansion of cropland and settlement was as a result of population growth, in the Federal Capital Territory, while the primary socioeconomic activity remains agriculture and also infrastructural development in Gwagwalada and Kuje Area Council. In the same vein, between 2013 and 2021, most of the forest reserve like Buga Hill, Chihuma, Chikwei, Kusoru, NG_61, Odu, Shaba and Tufa forest reserve, the forest class gained more area in this period due to insecurity around these forest reserve.

According to Abate S. (2011), an important aspect of change detection is to determine what is actually changing to what category of LULC type (i.e., which LULC type is changed to the other type of LULC class). LULC changes matrix depicts the direction of change and the LULC type that remains as it is at the end of the period. Thus, to clearly understand the source and destination of major LULC changes, change matrix for each period was analyzed. The details of LULCC from modeler between 1990 and 2001, 2001 and 2013, 2013 and 2021 are shown in figure 32 to 61.

Figure 32 to 34 Change detection of gain and loss in Abaji forest (Tufa) between 1990 and 2021

Between 1990 and 2001 bare surface was the highest gainer of the class, it gained about 390 hectares and the least gainer of the class was settlement, which was about 10 hectares. In the same vein, cropland class was the higest losses of area to another classes, it lost close to 500 hectare and bare surface was the least class loss to other classes, it lost about 30 ha.

Between 2001 and 2013 cropland was the highest gainer of the class, it gained about 390 hectares and the least gainer of the class was water bodies, which was about 70 hectares. In the same vein, bare surface class was the higest losses of area to another classes, it lost 300 hectare and settlement was the least class loss to other classes, it lost about 20 hectares.

Between 2013 and 2021 forest was the highest gainer of the class, it gained more than 1000 hectares and the least gainer of the class was settlement, which was about 5 hectares. In the same vein, cropland class was the higest losses of area to another classes, it lost more than 600 hectare and forest was the least class loss to other classes, it lost about 20 ha.

Figure 32 Gains and losses between 1990 and 2001 of Tufa forest reserve LU/LC

Figure 33 Gains and losses between 2001 and 2013 of Tufa forest reserve LU/LC

Figure 34 Gains and losses between 2013 and 2021 of Tufa forest reserve LU/LC

Figure 35 to 37 Change detection of gain and loss in Gwagwalada (Maje-Abuchi) forest between 1990 and 2021

Between 1990 and 2001 bare surface was the highest gainer of the class, it gained more than 6000 hectares and the least gainer of the class was forest, which was about 40 hectares. In the same vein, forest class was the higest losses of area to another classes, it lost close to 8000 hectare and water bodies was the least class loss to other classes, it lost about 0 ha.

Between 2001 and 2013 forest was the highest gainer of the class, it gained more than 4000 hectares and the least gainer of the class was settlement, which was about 600 hectares. In the same vein, bare surface class was the higest losses of area to another classes, it lost close to 6000 hectare and water bodies was the least class loss to other classes, it lost about 2000 hectares.

Between 2013 and 2021settlement was the highest gainer of the class, it gained more than 2000 hectares and the least gainer of the class was cropland, which was about 1000 hectares. In the same vein, forest class was the higest losses of area to another classes, it lost more than 4000 hectare and water bodies was the least class loss to other classes, it lost about 300 hectares.

Figure 35 Gains and losses between 1990 and 2001 of Maje-Abuchi forest reserve LU/LC

Figure 36 Gains and losses between 2001 and 2013 of Maje-Abuchi forest reserve LU/LC

Figure 37 Gains and losses between 2013 and 2021 of Maje-Abuchi forest reserve LU/LC

Figure 38 to 40 Change detection of gain and loss in Buwari forests  (Chihuma) between 1990 and 2021

Between 1990 and 2001 settlement was the highest gainer of the class, it gained more than 40 hectares and the least gainer of the class was bare surface, which was about 8 hectares. In the same vein, forest class was the higest losses of area to another classes, it lost more than 40 hectare and bare surface was the least class loss to other classes, it lost about 9 hectares.

Between 2001 and 2013 forest was the highest gainer of the class, it gained up to 30 hectares and the least gainer of the class was water bodies, which was about 10 hectares. In the same vein, forest class was the higest losses of area to another classes, it lost more than 40 hectare and bare surface was the least class loss to other classes, it lost about  8 hectares.

Between 2013 and 2021 forest was the highest gainer of the class, it gained more 40 hectares and the least gainer of the class was bare surface, which was about 1 hectares. In the same vein, bare surface class was the highest losses of area to another classes, it lost more than 40 hectare and water bodies was the least class loss to other classes, it lost about 10 hectares.

Figure 38 Gains and losses between 1990 and 2001 of Chihuma forest reserve LU/LC

Figure 39 Gains and losses between 2001 and 2013 of Chihuma forest reserve LU/LC

Figure 40 Gains and losses between 2013 and 2021 of Chihuma forest reserve LU/LC

Figure 41 to 43 Change detection of gain and loss in Buwari forests  (Chikwei) between 1990 and 2021

Between 1990 and 2001 bare surface was the highest gainer of the class, it gained about 40 hectares and the least gainer of the class was cropland, which was about 5 hectares. In the same vein, forest class was the highest losses of area to another classes, it lost about 80 hectare and settlement was the least class loss to other classes, it lost about 4 hectares.

Between 2001 and 2013 forest was the highest gainer of the class, it gained more than 60 hectares and the least gainer of the class was water bodies, which was about 3 hectares. In the same vein, bare surface class was the higest losses of area to another classes, it lost about 40 hectare  and cropland was the least class loss to other classes, it lost about 4 hectares.

Between 2001 and 2013 forest was the highest gainer of the class, it gained more than 50 hectares and the least gainer of the class bare surfca, which was about 2 hectares. In the same vein, cropland class was the higest losses of area to another classes, it lost about 40 hectare  and water bodies was the least class loss to other classes, it lost about 3 hectares.

Figure 41 Gains and losses betwee 1990 and 2001 of Chikwei forest reserve LU/LC

Figure 42 Gains and losses betwee 2001 and 2013 of Chikwei forest reserve LU/LC

Figure 43 Gains and losses betwee 2013 and 2021 of Chikwei forest reserve LU/LC

Figure 44 to 46 Change detection of gain and loss in Buwari forests  (Kusoru) between 1990 and 2021

Between 1990 and 2001 bare surface was the highest gainer of the class, it gained about 39 hectares and the least gainer of the class was water bodies, which was about 6 hectares. In the same vein, settlement class was the higest losses of area to another classes, it lost about 30 hectare and cropland was the least class loss to other classes, it lost about 3 hectares.

Between 2001 and 2013 forest was the highest gainer of the class, it gained about 35 hectares and the least gainer of the class was settlemet, which was about 5 hectares. In the same vein, bare surface class was the higest losses of area to another classes, it lost more than 40 hectare and water bodies was the least class loss to other classes, it lost about 7 hectares.

Between 2001 and 2013 forest was the highest gainer of the class, it gained about 33 hectares and the least gainer of the class bare surface, which was about 0.8 hectares. In the same vein, water bodies class was the higest losses of area to another classes, it lost more than 14 hectare and settlement was the least class loss to other classes, it lost about 8 hectares.

Figure 44 Figure 43 Gains and losses between 1900 and 2001 of Kusoru forest reserve LU/LC

Figure 45 Gains and losses between 2001 and 2013 of Kusoru forest reserve LU/LC

Figure 46 Gains and losses betwee 2001 and 2021 of Kusoru forest reserve LU/LC

Figure 47 to 49 Change detection of gain and loss in Buwari forests  (Shaba) between 1990 and 2021

Between 1990 and 2001 cropland was the highest gainer of the class, it gained more than 600 hectares and the least gainer of the class was bare surface, which was about 13 hectares. In the same vein, frorest class was the higest losses of area to another classes, it lost about 800 hectare and settlement was the least class loss to other classes, it lost about 16 hectares.

Between 2001 and 2013 forest was the highest gainer of the class, it gained more than 800 hectares and the least gainer of the class was bare surface, which was about 15 hectares. In the same vein, cropland class was the higest losses of area to another classes, it lost more than 800 hectare and bare surface was the least class loss to other classes, it lost about 11 hectares.

Between 2003 and 2021 forest was the highest gainer of the class, it gained more than 400 hectares and the least gainer of the class was bare surface and settlement, which was about 30 hectares. In the same vein, forest class was the higest losses of area to another classes, it lost more than 400 hectare and bare surface was the least class loss to other classes, it lost about 12 hectares.

Figure 47 Gains and losses betwee 1990 and 2001 of Shaba forest reserve LU/LC

Figure 48 Gains and losses betwee 2001 and 2013 of Shaba forest reserve LU/LC

Figure 49 Gains and losses betwee 2013 and 2021 of Shaba forest reserve LU/LC

Figure 50 to 52 Change detection of gain and loss in Kuje forests  (Buga Hill) between 1990 and 2021

Between 1990 and 2001 bare surface was the highest gainer of the class, it gained about 120 hectares and the least gainer of the class was water bodies, which was about 2 hectares. In the same vein, forest class was the higest losses of area to another classes, it lost exactly 120 hectare and bare surface was the least class loss to other classes, it lost about 19 hectares.

Between 2001 and 2013 cropland was the highest gainer of the class, it gained about 90 hectares and the least gainer of the class was settlemet, which was about 9 hectares. In the same vein, forest class was the higest losses of area to another classes, it lost exactly 140 hectare and water bodies was the least class loss to other classes, it lost about 0.1 hectares

Between 2001 and 2013 cropland was the highest gainer of the class, it gained about 120 hectares and the least gainer of the class was bare surface, which was about 0.3 hectares. In the same vein, forest class was the higest losses of area to another classes, it lost exactly 110 hectare and settlementwas the least class loss to other classes, it lost about 0.1 hectares

Figure 50 Gains and losses betwee 1990 and 2001 of Buga Hill forest reserve LU/LC

Figure 51 Gains and losses betwee 2001 and 2013 of Buga Hill forest reserve LU/LC

Figure 52 Gains and losses betwee 2013 and 2021 of Buga Hill forest reserve LU/LC

Figure 53 to 55 Change detection of gain and loss in Kuje forests  (NG_61) between 1990 and 2021

Between 1990 and 2001 bare surface was the highest gainer of the class, it gained about 150 hectares and the least gainer of the class was settlement, which was about 30 hectares. In the same vein, forest class was the higest losses of area to another classes, it lost exactly 200 hectare and water bodies was the least class loss to other classes, it lost about 0.3 hectares.

Between 2001 and 2013 settlement was the highest gainer of the class, it gained about 150 hectares and the least gainer of the class was forest, which was about 30 hectares. In the same vein, forest class was the higest losses of area to another classes, it lost more than 230 hectare and settlement was the least class loss to other classes, it lost about 30 hectares.

Between 2013 and 2021 forest was the highest gainer of the class, it gained about 220 hectares and the least gainer of the class was bare surface and wate bodies, which was about 10 hectares each. In the same vein, forest class was the higest losses of area to another classes, it lost more than 230 hectare and settlement was the least class loss to other classes, it lost about 30 hectares.

Figure 53 Gains and losses betwee 1990 and 2001 of NG_61 forest reserve LU/LC

Figure 54 Gains and losses betwee 2001 and 2013 of NG_61 forest reserve LU/LC

Figure 55 Gains and losses betwee 2013 and 2021 of NG_61 forest reserve LU/LC

Figure 56 to 58 Change detection of gain and loss in Kuje forests  (Odu) between 1990 and 2021

Between 1990 and 2001 bare surface was the highest gainer of the class, it gained more than 200 hectares and the least gainer of the class was cropland , which was about 35 hectares. In the same vein, forest class was the higest losses of area to another classes, it lost exactly 300 hectare and settlement was the least class loss to other classes, it lost about 0.8 hectares.

Between 2001 and 2013 forest was the highest gainer of the class, it gained more than 230 hectares and the least gainer of the class was bare surface , which was about 38 hectares. In the same vein, bare surface class was the higest losses of area to another classes, it lost exactly 280 hectare and water bodies was the least class loss to other classes, it lost about 40 hectares.

Between 2001 and 2013 cropland was the highest gainer of the class, it gained more than 300 hectares and the least gainer of the class was bare surface , which was about 0.2 hectares. In the same vein, forest class was the higest losses area of land to another classes, it losses more than  300 hectare and bare surface was the least class loss to other classes, it lost about 30 hectares.

Figure 56 Gains and losses betwee 1990 and 2001 of Odu forest reserve LU/LC

Figure 57 Gains and losses betwee 2001 and 2013 of Odu forest reserve LU/LC

Figure 58 Gains and losses betwee 2013 and 2021 of Odu forest reserve LU/LC

Figure 59 to 61 Change detection of gain and loss in Kuje forests  (Tukoki) between 1990 and 2021

Between 1990 and 2001 cropland was the highest gainer of the class, it gained about 300 hectares and the least gainer of the class was settlement , which was about 20 hectares. In the same vein, forest class was the higest losses of area to another classes, it lost more than 400 hectare and bare surface was the least class loss to other classes, it lost about 0.1 hectares.

Between 2001 and 2013 forest was the highest gainer of the class, it gained more than 300 hectares and the least gainer of the class was water bodies , which was about 70 hectares. In the same vein, cropland class was the higest losses of area to another classes, it lost more than 400 hectare and settlement was the least class loss to other classes, it lost about 10 hectares.

Between 2012 and 2021 cropland was the highest gainer of the class, it gained more than 300 hectares and the least gainer of the class was water bodies , which was about 20 hectares. In the same vein, forest class was the higest losses of area to another classes, it lost more than 200 hectare and water was the least class loss to other classes, it lost about 80 hectares.

Figure 59 Gains and losses betwee 1990 and 2001 of Tukoki forest reserve LU/LC

Figure 60 Gains and losses betwee 2001 and 2013 of Tukoki forest reserve LU/LC

Figure 61 Gains and losses betwee 2013 and 2021 of Tukoki forest reserve LU/LC

6.2. Prediction of possible land use/land cover by year 2030

Results of LULC prediction using CA-Markov analysis are shown in Table 53 to 72. Deforestation predictions in this study were conducted for 2021 and 2030 based on transition potentials maps and Markov chain transition probability. Prediction for 2021 conducted for the purpose of model validation while a prediction of 2030 was conducted for purpose of analyzing a future scenario. Markov chain transition probability is used to generate probability that helps to predict future scenario based on LULC maps.

Table 53 LU/LC change prediction of Buga Hill forest reserve by 2030

Category	Hectares	Legend
0	160.9200000	Unclassified
1	347.3100000	FOREST
2	157.8600000	CROPLAND
3	11.9700000	WATERBODIES
4	0.1800000	BARE SURFACE
5	23.7600000	SETTLEMENT

 

Table 54 Probability of change of the Buga Hill forest reserve from 2021 to 2030

	CL.1	CL.2	CL.3	CL.4	CL.5
Class1 Forest	0.4844	0.4128	0.0259	0.0000	0.0761
Class2 Cropland	1.0000	0.0000	0.0000	0.0000	0.0000
Class3 Waterbodies	0.5485	0.1800	0.2715	0.0000	0.0000
Class4 Baresurface	0.5331	0.3402	0.0491	0.0000	0.0777
Class5 Settlement	0.7699	0.1425	0.0876	0.0000	0.0000

Table 55 LU/LC change prediction of Chihuma forest reserve by 2030

Category	Hectares	Legend
0	38.7000000	Unclassified
1	70.8300000	FOREST
2	14.4900000	CROPLAND
3	8.6400000	WATERBODIES
4	7.3800000	BARE SURFACE
5	40.7700000	SETTLEMENT

Table 56 Probability of change of the Chihuma forest reserve from 2021 to 2030

	CL.1	CL.2	CL.3	CL.4	CL.5
Class1 Forest	0.5234	0.2547	0.0051	0.0000	0.2168
Class2 Cropland	0.8364	0.1502	0.0000	0.0135	0.0000
Class3 Waterbodies	0.3235	0.4047	0.0188	0.0000	0.2530
Class4 Baresurface	0.6539	0.1570	0.0000	0.0291	0.1599
Class5 Settlement	0.2681	0.6380	0.0000	0.0000	0.0939

Table 57 LU/LC change prediction of Chikwei forest reserve by 2030

Category	Hectares	Legend
0	34.4700000	Unclassified
1	92.2500000	FOREST
2	25.3800000	CROPLAND
3	3.5100000	WATERBODIES
4	3.9600000	BARE SURFACE
5	18.6300000	SETTLEMENT

Table 58 Probability of change of the Chikwei forest reserve from 2021 to 2030

	CL.1	CL.2	CL.3	CL.4	CL.5
Class1 Forest	0.6310	0.1875	0.0377	0.0000	0.1438
Class2 Cropland	0.4883	0.1996	0.0000	0.1482	0.1638
Class3 Waterbodies	0.9059	0.0000	0.0000	0.0028	0.0912
Class4 Baresurface	0.2115	0.6886	0.0972	0.0028	0.0000
Class5 Settlement	0.8681	0.0766	0.0000	0.0000	0.0553

Table 59 LU/LC change prediction of Kusoru forest reserve by 2030

Category	Hectares	Legend
0	21.1500000	Unclassified
1	57.3300000	FOREST
2	37.3500000	CROPLAND
3	1.8000000	WATERBODIES
4	0.0900000	BARE SURFACE
5	0.1800000	SETTLEMENT

Table 60 Probability of change of the Kusoru forest reserve from 2021 to 2030

	CL.1	CL.2	CL.3	CL.4	CL.5
Class1 Forest	0.4503	0.5497	0.0000	0.0000	0.0000
Class2 Cropland	0.8896	0.0000	0.0706	0.0000	0.0398
Class3 Waterbodies	0.0000	1.0000	0.0000	0.0000	0.0000
Class4 Baresurface	0.9813	0.0000	0.0000	0.0093	0.0094
Class5 Settlement	0.7146	0.2854	0.0000	0.0000	0.0000

Table 61 LU/LC change prediction of Maje-Abuchi forest reserve by 2030

Category	Hectares	Legend
0	16040.3400000	Unclassified
1	3409.2900000	FOREST
2	1838.7000000	CROPLAND
3	2556.9900000	WATERBODIES
4	2106.5400000	BARE SURFACE
5	2845.6200000	SETTLEMENT

Table 62 Probability of change of the Maje-Abuchi forest reserve from 2021 to 2030

	CL.1	CL.2	CL.3	CL.4	CL.5
Class1 Forest	0.3433	0.0686	0.1522	0.3472	0.0887
Class2 Cropland	0.3559	0.0350	0.1361	0.0000	0.4259
Class3 Waterbodies	0.0000	0.0381	0.8058	0.0000	0.1562
Class4 Baresurface	0.3624	0.2558	0.0000	0.0517	0.3302
Class5 Settlement	0.2317	0.2931	0.0000	0.2387	0.2366

Table 63 LU/LC change prediction of NG_61 forest reserve by 2030

Category	Hectares	Legend
0	145.9800000	Unclassified
1	262.0800000	FOREST
2	195.8400000	CROPLAND
3	4.0500000	WATERBODIES
4	5.3100000	BARE SURFACE
5	50.2200000	SETTLEMENT

Table 64 Probability of change of the NG_61 forest reserve from 2021 to 2030

	CL.1	CL.2	CL.3	CL.4	CL.5
Class1 Forest	0.2649	0.5722	0.0111	0.0145	0.1373
Class2 Cropland	0.6816	0.2706	0.0013	0.0075	0.0390
Class3 Waterbodies	0.6014	0.1131	0.2856	0.0000	0.0000
Class4 Baresurface	1.0000	0.0000	0.0000	0.0000	0.0000
Class5 Settlement	0.8245	0.0000	0.0000	0.0000	0.1755

Table 65 LU/LC change prediction of Odu forest reserve by 2030

Category	Hectares	Legend
0	481.7700000	Unclassified
1	465.7500000	FOREST
2	460.5300000	CROPLAND
3	3.0600000	WATERBODIES
4	0.9000000	BARE SURFACE
5	126.9900000	SETTLEMENT

Table 66 Probability of change of the Odu forest reserve from 2021 to 2030

	CL.1	CL.2	CL.3	CL.4	CL.5
Class1 Forest	0.4929	0.4043	0.0034	0.0001	0.0992
Class2 Cropland	0.3214	0.5079	0.0026	0.0027	0.1654
Class3 Waterbodies	0.4114	0.4812	0.0011	0.0027	0.1035
Class4 Baresurface	0.5980	0.3860	0.0159	0.0000	0.0000
Class5 Settlement	0.6918	0.2812	0.0000	0.0000	0.0270

Table 67 LU/LC change prediction of Shaba forest reserve by 2030

Category	Hectares	Legend
0	73.2600000	Unclassified
1	181.3500000	FOREST
2	44.2800000	CROPLAND
3	19.6200000	WATERBODIES
4	3.9600000	BARE SURFACE
5	5.8500000	SETTLEMENT

Table 68 Probability of change of the Shaba forest reserve from 2021 to 2030

	CL.1	CL.2	CL.3	CL.4	CL.5
Class1 Forest	0.7952	0.1104	0.0436	0.0201	0.0306
Class2 Cropland	0.4534	0.4545	0.0921	0.0000	0.0000
Class3 Waterbodies	0.5420	0.1081	0.3499	0.0000	0.0000
Class4 Baresurface	0.7520	0.1175	0.0000	0.1014	0.0291
Class5 Settlement	0.7799	0.1212	0.0707	0.0000	0.0282

Table 69 LU/LC change prediction of Tufa forest reserve by 2030

Category	Hectares	Legend
0	1366.3800000	Unclassified
1	1307.9700000	FOREST
2	101.4300000	CROPLAND
3	27.6300000	WATERBODIES
4	41.8500000	BARE SURFACE
5	14.9400000	SETTLEMENT

Table 70 Probability of change of the Tufa forest reserve from 2021 to 2030

	CL.1	CL.2	CL.3	CL.4	CL.5
Class1 Forest	0.8918	0.0691	0.0104	0.0247	0.0041
Class2 Cropland	0.8499	0.0870	0.0114	0.0232	0.0286
Class3 Waterbodies	0.5855	0.0600	0.3527	0.0019	0.0000
Class4 Baresurface	0.8517	0.0000	0.0000	0.0920	0.0563
Class5 Settlement	0.6039	0.0404	0.0000	0.1752	0.1806

Table 71 LU/LC change prediction of Tukoki forest reserve by 2030

Category	Hectares	Legend
0	866.7900000	Unclassified
1	444.6000000	FOREST
2	452.5200000	CROPLAND
3	39.1500000	WATERBODIES
4	45.6300000	BARE SURFACE
5	55.3500000	SETTLEMENT

Table 72 Probability of change of the Tufa forest reserve from 2021 to 2030

	CL.1	CL.2	CL.3	CL.4	CL.5
Class1 Forest	0.4418	0.4744	0.0479	0.0359	0.0000
Class2 Cropland	0.4368	0.4150	0.0000	0.0404	0.1078
Class3 Waterbodies	0.3272	0.5274	0.0414	0.0101	0.0938
Class4 Baresurface	0.4436	0.0959	0.3430	0.1176	0.0000
Class5 Settlement	0.2806	0.5915	0.0000	0.1279	0.0000

6.3. Carbon stock Analysis

The assessment of carbon stock and sequestration in the case studies of this report was carried out with data available in plugin of quantum geographic information system (QGIS).

The purpose of carbon stock quantification is to reduce the effects of climate change, of which many countries are implementing policies to achieve net-zero greenhouse gas (GHG) emissions by 2050 (Smith et al., 2012; Van Soest et al., 2021). A key policy measure in those countries is to incentivise land managers to sequester more soil carbon as some sectors of the economy will remain a source of GHG even in 2050 (Chambers et al., 2016). Because increasing soil carbon storage can be a very effective climate change mitigation strategy (Amin et al., 2020), many national governments are planning to pay farmers to increase the amount of carbon stored on their land (e.g. UK Government, 2021a).

The terrestrial carbon pools that are most often included in available maps are above-ground biomass (AGB), below-ground biomass (BGB) and soil organic carbon (SOC). Although SOC can be a substantial pool, which can be affected by land use change, there is more limited spatial data available than for vegetation carbon. For biomass carbon, a number of globally consistent AGB maps are now available, either for the world as a whole or for the tropics (Kindermann et al., 2008; Ruesch & Gibbs 2008). The quality of AGB data has progressed markedly in recent years, however, the existing products do not provide a consensus on the total amount of biomass carbon or its spatial distribution pattern, and in some cases show strong disagreement. Within the scientific community, no single method is considered definitive; some approaches may have advantages or disadvantages in particular areas or ecosystems, and a number of issues influence data quality. In this study many Federal Capital territory forest reserve had no require data to check the net forest loss and carbon emission except Maje-Abuchi and Odu forest we can have sufficient data to observe the net balance between forest loss and Carbon emission.

6.4. Forest carbon Mapping

Forest carbon loss based on Trends.Earth the MODIS NDVI dataset (MOD13Q1) was chosen to estimate the total carbon (above and below ground emissions from deforestation in the forest reserves within the Federal Capital Territory from 2001 to 2020, which was implemented in the Trends. Earth plugin of QGIS software. It was obtained by integrating the following:

• Hansen et al. global forest change product method using the 30m resolution
• Aboveground biomass dataset using the GEOCARBON (1 KM resolution global)
• The Intergovernmental Panel on Climate Change of 2000 (IPCC) method for calculation of root-to-root ratio

The tables and charts present the loss of organic carbon by year, total biomass at the end of year and carbon emissions during the year in all the forest reserve in FCT. In this study, the various forest reserves were computed using 2001 to 2020 as the baseline period.

Some of the forest reserves had some limitations with zero data, while some of the forest had no limitation of data for the analysis. In some forest we can observe the net forest loss and net emission of carbon. The purpose of this is to reduce the effects of climate change; many countries are implementing policies to achieve net-zero greenhouse gas (GHG) emissions by 2050.

Table 73 Tufa Forest reserve carbon loss due to deforestation

Table 73 shows the summary table of organic carbon loss in Tufa forest reserve. In the table we can find out there were missing data in the analysis, therefore to quantify the net change between organic carbon loss and forest loss cannot be realistic.

Table 74 Maje-Abuchi carbob loss due to deforestation

Table 74 shows a summary table of organic carbon loss in Maje-Abuchi forest reserve. In the table we can find out there were no missing data in the analysis. Therefore, we can observe the net change between the organic carbon loss and forest loss due to deforestation. The least forest lost during the year was 1 ha in 2012 and the highest forest loss during the year was 231 ha in 2017. The highest carbon emision 51,913 tonne of C02e in 2011 and the least was 141 tonne of C02e in 2012.

Table 75 Chihuma forest reserve carbon loss due to deforestation

Table 75 shows the summary table of organic carbon loss in Chihuma forest reserve. In the table we can find out there were missing data in the analysis, therefore to quantify the net change between organic carbon loss and forest loss cannot be realistic with the result here.

Table 76 Chikwei forest reserve carbon loss due to deforestation

Table 76 shows the summary table of organic carbon loss in Chikwei forest reserve. In the table we can find out there were missing data in the analysis, therefore to quantify the net change between organic carbon loss and forest loss cannot be realistic.

Table 77 Kusoru forest reserve carbon loss due to deforestation

Table 77 shows the summary table of organic carbon loss in Kusoru forest reserve. In the table we can find out there were missing data in the analysis, therefore to quantify the net change between organic carbon loss and forest loss cannot be realistic.

Table 78 Shaba forest reserve carbon loss due to deforestation

Table 78 shows the summary table of organic carbon loss in Shaba forest reserve. In the table we can find out there were missing data in the analysis, therefore to quantify the net change between organic carbon loss and forest loss cannot be realistic.

Table 79 Buga Hill forest reserve carbon loss due to deforestation

Table 79 shows the summary table of organic carbon loss in Buga Hill forest reserve. In the table we can find out there were missing data in the analysis, therefore to quantify the net change between organic carbon loss and forest loss cannot be realistic.

Table 80 NG_61 forest reserve carbon loss due to deforestation

Table 80 shows the summary table of organic carbon loss in NG_61 forest reserve. In the table we can find out there were missing data in the analysis, therefore to quantify the net change between organic carbon loss and forest loss cannot be realistic.

Table 81 Odu forest reserve carbon loss due to deforestation

Table 81 shows a summary table of organic carbon loss in Odu forest reserve. In the table we can find out there were no missing data in the analysis. Therefore, we can observe the net change between the organic carbon loss and forest loss due to deforestation. The least forest lost during the year was 1 ha in 2010 and 2012 respectively. The highest forest loss during the year was 18 ha in 2013. The highest carbon emision 1,995 tonne of C02e in 2018 and the least were 362 tonne of C02e in 2010.

Table 82 Tukoki forest reserve carbon loss due to deforestation

Table 82 shows the summary table of organic carbon loss in Tukoki forest reserve. In the table we can find out there were missing data in the analysis, therefore to quantify the net change between organic carbon loss and forest loss cannot be realistic.

7.0. PROJECT CONCLUSION

 Findings in this study show significant land use and land cover changes that have occurred in the Federal Capital Territory over the past 31 years which have also affected the forest reserve. Based on the LU/LC analyses of Landsat data for the years 1990, 2001, 2013, and 2021, it was found that the LU/LC change trends varied significantly during the periods mentioned above. The results showed that in the period 1990–2013, most LULC were converted to cropland except Tukoki forest reserve and Maje-Abuchi forest reserve that reduced to settlement from 1990 to 2021. This indicates that the expansion of cropland and settlement was as a result of population growth, in the Federal Capital Territory, while the primary socioeconomic activity remains agriculture and also infrastructural development in Gwagwalada and Kuje Area Council. In the same vein, between 2013 and 2021, most of the forest reserve like Buga Hill, Chihuma, Chikwei, Kusoru, NG_61, Odu, Shaba and Tufa forest reserve, the forest class gained more area in this period due to insecurity around these forest reserve.

Forests have declined, while cultivated land and artificial surfaces have increased in the area, and deforestation appears to be more pronounced in the Tukoki and Maje-abuchi forest reserve. Severe deforestation in Tukoki forest reserve appears to be strongly linked to increased soil erosion as a result of land use and land cover change. Notable drivers for LUCC include rapid population growth and macroeconomic activities occurring in Federal Capital Territory especially in the part of Kuje Area Council, and poor national policies that have failed to effectively enforce ban of uncontrolled harvesting of forest resources. It shows that remote sensing and GIS for forest quantification analysis of multiple forests areas of this present investigation is feasible with satellite remote sensing as opposed to time-consuming and expensive ground surveys as alternative.

8.0. RECOMMENDATION

The following recommendations are therefore suggested;

1. Adequate planning should be put in place by the government or its agencies for a planned and organized revegetation in these areas.
2. Urban planners in implementing various urban development projects should consider projective planning mechanisms. That is, consideration for a long-term effect of any developmental project on both human and the natural environment.
3. Both urban planners and environmental managers should work together in the implementation of developmental policies and execution of projects.
4. Use of remote sensing and geographic information system should be encouraged by urban planners and environmental managers in planning, management, and in making various developmental decisions.

THE MINISTRIES, DEPARTMENTS AND AGENCY FOR IMPLEMENTING THIS PROJECT

OFFICE OF MR PRESIDENT

OFFICE OF MR VICE PRESIDENT

MINISTRY OF FINANCE

MINISTRY OF ENVIRONMENT

DEPARTMENT OF FORESTRY

 

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