Assessment of Vegetation Cover Change of Kebbe Forest Reserve

African Journal of Sustainable Agricultural Development | ISSN: 2714-4402 Vol. 2, Number 2 (April-June, 2021) | www.ijaar.org Journal DOI: www.doi.org/10.46654/2714 Article DOI: www.doi.org/10.46654/2714.2258

ASSESSMENT OF VEGETATION COVER CHANGE OF KEBBE FOREST RESERVE, KEBBE LOCAL GOVERNMENT AREA OF SOKOTO STATE, NIGERIA USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM (GIS) TECHNIQUES

Ambursa, A.S1., A. Muhammad1, A.G. Bello2, H. Alhassan2 and A. Abdulrahman1&3 1. Department of Forestry and Fisheries Kebbi State University of Science and Technology, Aliero, Nigeria 2. Department of Forestry and Environment, Usmanu Danfodiyo University, Sokoto 3. Department of Agricultural Technology, College of agriculture and Animal Science, Bakura

Corresponding author: [email protected]

ABSTRACT This study examined the change in vegetation cover (land use cover) of Kebbe forest reserve, Kebbe local government area of Sokoto state. The research was divided into phases comprising satellite image sourcing; corrections of the images and classification of the land cover types based on the result from the ground truthing of the Kebbe Forest reserve. Two research hypotheses and research objectives were formulated for the study. Satellite images of 5 thematic mapper (TM) of 2003 and 8 operational land imager thermal infrared sensor (OLI-TIRS) of 2018 were selected for this study. The data were haze corrected using pancroma software with the Dark Object Subtraction (DOS) algorithm. The highest increase in area among the LUC between the dates is the shrub land with about 20.42% increase, which is from 138.42 to 881ha and the least being the sparse forest with 1.35% increase, also from 1309.59 to 1359ha of the reserve. On the other hand, the highest decrease was experienced in the deep forest, with 24.89% of the decrease being from 1649.25 to 744ha, and the least being the bare land with 8.73% decrease, which is from 406.71to 89ha of the total reserve area. The results from this study indicate that the reserve of the Kebbe forest is not properly managed, and has also faced high degradation rate that affected the land use and land cover of the reserve. Forest conservation measures should be put in place to salvage the remaining forest land area. Further research into the causes of the forest degradation should be conducted.

Key words: Vegetation cover, remote sensing, NDVI differencing, Forest reserve

Introduction In an effort to appreciate the need for and importance of (integrated) Forest Managementit’s necessary to understand what is forest and its relationship with other components in an environment. Forest reserve has been described as an important source of energy, water and biological diversity. Furthermore, it functions as a source of fuel from timber harvest, food from fruiting trees, shelter for wildlife (microclimate) and other Non-Timber Forest Products(NTPs).

Assessing and monitoring the state of the vegetation is a key requirement for global change research (NRC, 1999; Jung et al., 2006; Xie, 2008). Classifying and mapping vegetation is an important technical task for managing natural resources as vegetation provides a base for all living beings and plays an essential role in mitigating global climate change (Xiao et al., 2004; Xie, 2008). Remote sensing imagery device is used to extract vegetation information by interpreting satellite images (Xie, 2008).

People and livestock are the integral components interacting with forest reserve and their activities affect the productive and protective status of forest and its resources and vice versa. Biodiversity is nowadays considered to be one of the principal criteria for the sustainability of forest production and for ensuring its functions (Buriánek et al., 2013). From a planning standpoint, forest is considered the most ideal unit for analysis and management of natural resources. Integrated forest development approach is still viewed by many to be the most ideal as it helps in optimal use of environmental resources in a region and in maintaining the ecological basis of resources utilization (Stave et al., 2001).

Forest management deals with optimizing the use of land, water, and its resources to prevent soil erosion, improve water availability, improve animal health, provide energy (timber production) on a sustainable basis (Bretherton,1997). Additionally, conservation of forest enhances the protection of many habitats that are essential to other life support systems of the planet.

The image differencing technique is based on a cell-by-cell subtraction between different images in a time-series. This technique applies differences between remotely sensed images of vegetation characteristics, and indexes derived from image radiance or reflectance differencing, i.e., Normalized Difference Vegetation Index (NDVI) or change vector differencing (Schowengerdt, 1997).

The NDVI differencing method usesestimated NDVI as the normalized difference between Near Infrared Red (NIR) and visible red bands, which discriminate vegetation from other surfaces based on green vegetation chlorophyll absorption of red light for photosynthesis, and reflection of NIR wavelengths.

The NDVI differencing technique was widely applied for both human-induced and natural forest cover change detection as land cover conversion, forest harvesting, afforestation that includes natural forest expansion and human-induced landscape restoration (Lyonet al., 1998; Wilson and Sader 2002; Sader et al., 2003; Lunetta et al., 2002, 2006).

In addition to the techniques for pre-processing and differencing images, the most important step for vegetation change detection analysis is discrimination between real changes and seasonal or inter-annual variability, represented by a threshold between these factors, which is generally determined by applying the standard deviation (SD) from the NDVI differencing image (Hayes and Sader, 2001; Coppin et al., 2004; Sepehry and Liu, 2006; Pu et al., 2008).

The present study used a vegetation change detection analysis based on the NDVI differencing technique to assess forest cover changes related to anthropogenic in Kebbe Forest in Sokoto (Northern Nigeria) from 2003 through 2018.

The objectives were as follows:

i. To source images of the forest reserve from 2003 and 2018 using 5 Thematic Mapper (TM) and 8 Operational Land Imager Thermal Infrared Sensor (OLI-TIRS) respectively. ii. To detect the vegetation from the maps of the two dates. iii. To determine the proportional differences of the cover between the two dates.

Ground truthing was subsequently carried out to ascertain varied conditions of change guided by hand-held Global Positioning System (GPS). A stratified random design was used where by sample plots is been randomly generated using GIS software. This helps in finding the exact Land Use Change (LUC) of a particular Land area.

Objectives of the Study

The primary objective of the research is to evaluate a change in vegetation of Kebbe Forest reserve using remote sensing and GIS techniques.

Specific objectives are:

i. to source images of the forest reserve from 2003 and 2018 using 5 Thematic Mapper (TM) and 8 Operational Land Imager Thermal Infrared Sensor (OLI-TIRS) respectively. ii. to detect the vegetation from the maps of the two dates. iii. to determine the proportional differences of the cover between the two dates.

Area of the Study

Kebbe Local Government area is in Sokoto State, Nigeria. The local government shares border with Zamfara State to the east and Kebbi State to the west, with the coordinate longitude 4°50'' to 5°06''E and latitude 11°91'' to 14°94''N. It is about 150km away from the state capital. Sokoto is however surrounded to the east by the Precambrian basement complex (Ekpoh et al., 2011). The mean annual temperature is 34.5°C, although dry season temperatures in the region often exceed 40°C (Schafer, 1998 cited by Mohammed).

Methodology

Satellite images of 5 Thematic Mapper (TM) of 2003 (06-01-2003) and 8 Operational Land Imager Thermal Infrared Sensor (OLI-TIRS) of 2018 (14-01-2018) were selected for this study. The multi-spectral data with a spatial resolution of 30m correspond to path/row 191/052 in the active archive centre of the United States Geological Survey (USGS), downloadable via glovis.usgs.gov. Also, the images selected are expectedly cloud free but likely to be affected by haze as the images were captured in January when the atmosphere is dusty in the region. The data are in Level 1T (precision and terrain corrected) format.

The data were haze corrected using the PANCROMA software with the dark object subtraction (DOS) algorithm. As already georeferenced data sets from source, both imageswere co-registered andoverlaid using nine (9) Ground Control Points (GCPs) which were derived from road intersections.

Normalized Difference Vegetation Index (NDVI) was computed for each of the years using (NIR- RED)/NIR+RED mathematical formula, where NIR is the near infrared band and RED is the red band of the data. The NDVI is an index of choice for vegetation mapping and change detection. The computed NDVI was also subjected to image classification first using unsupervised classification method. The method partitioned the image pixels into classes as the computerwas instructed to do so; thus, it was unsupervised.

The second classification was supervised and was preceded with ground truthing, where representative plots were selected from the unsupervised classified map based on stratified random sampling. The ground truthing is meant to describe and classify the vegetation at each plot for onward supervised classification. For this purpose, 88 GCPs were collected, although 30×n satisfies the sample size to be collected (n = number of bands) (Janssen and Gorte, 2004). As 30 GCPs are the minimum required to classify each NDVI image, additional number can help improve the classification.

The accuracy of classification was assessed using part of the collected GCPs and positions where they belongst to in supervised classification image and were varified. The percentage of true cases was computed and the overall accuracy of the classification was determined.

Two methods were adopted in this study. The first differenced the latter NDVI (2018) from the previous one (2003). The residual image has pixels the values of which are normally distributed. The descriptive statistics of the residual image was computed then mean and standard deviation were extracted. Change is detected by using one-standard-deviation-to-the-mean algorithm. The second change detection method was the post-classification comparison where the acreage of each vegetation class in the classified NDVI was calculated and compared within and between the years.

RESULTS

Land use/Land Cover Classification

The results of the Land Use (LU) classification performed on both dates are presented inFigures 1 and 2. Different Land-Uses and the resultant Land Covers (LU/LC)generated were being separated from each other. Thus, and given the advantage of the medium resolution of the data, the LU/LC classification was carried out with respect to land use practices and conditions of land cover. At first, 30 LU/LC classes were recognized. However, the pixel numbers of some of the classes did not meet up with the minimum requirement for signature extraction. Thus, classes were aggregated based on LU/LC similarity as recommended by Anderson et al., (1976). The broad categories of land use/land cover identified were: dense forest, sparse forest, Shrub-land, grassland and bare land. Based on error matrix table 1, the classification of image was performed with an overall accuracy of 91%.

Fig 1: 2003 Map of the Study Area Fig 2: 2018 Map of the Study Area Land use Land use cover. cover.

Table 1: Error Matrix: Ground Truth (Columns) against Land Use Land Cover 2018 (rows). 1 2 3 4 5 Total EC UA 1 19 1 0 0 0 20 5 95 2 1 14 1 0 0 16 13 88 3 1 0 15 0 1 17 12 88 4 0 0 1 16 1 18 11 89 5 0 0 0 1 16 17 6 94 Total 21 15 17 17 18 88 EO (%) 10 7 12 6 11 PA (%) 90 93 88 94 89 Overall Accuracy = (19+14+15+16+16)*100/88)) = 91. Columns: Ground truth; Rows: 2013 maps; EC = Error of Commission; UA = User's Accuracy; EO = Error of Omission; PA = Producer's Accuracy; Source: Generated from the GCPs and 2018 LUC map.

Land Use/land Cover Description and Detection A major class was the sparse forest land. This is mainly composed of somewhat scattered short trees, shrubs and grasses. The dominant tree species found were the; Combretum nigericans (Tsiriri), Guiera senegalensis (Sabara), Piliostigma reticulatum (Kalgo), Acacia seyal (Farar Kaya) and Azadirachta indica (Neem tree). It covers about 37.37% of the total land area of the reserve. Generally, these areas were subjected to continuous tree felling, farming and bush burning. Shrub/Grass land covers about 24.22% of the total land area of the study area. The grasses dominating are Pennisetum sp, Mitracarpus scaber and Striger sp. Various agricultural practices were observed, including; agricultural land expansion through tree felling, grazing, collection of medicinal herbs and bush burning.

Table 2 belowpresents changes in LU/LC types between 2003 and 2018. Figure 3 revealed the pathway of the LU/ LC changes and illustrates the observed change in a particular LU/LC type. The LU/LC change statistics revealed that the forest class lost 24.89% of its area in the reserve. This suggests that the area was subjected to more of wood extraction and agricultural farming than grazing activities over the study period.

Larger portion of trees and shrubs were exploited year round from the reserve, making a large proportion of the area being exposed to the direct sun radiation. . From 2003 to 2018, in 1649.25ha of the dense forest 24.89% was realized to be been cleared, and 8.73% of bare land had been realized from the reduced area been cleared. Increases in the sparse forest was up to 1.35%, shrubs increases up to 20.42% and grass land increases up to 11.85%.

The highest increase in area among the LUC between the dates is the shrub land with about 20.42% increase, which is from 138.42to 881ha and the least being the sparse forest with 1.35% increase, also from 1309.59 to 1359ha of the reserve. On the other hand, the highest decrease was experienced in the deep forest, with 24.89% of the decrease being from 1649.25 to 744ha, and the

least being the bare land with 8.73% decrease, which is from 406.71 to 89ha of the total reserve area.

Table 2: Area estimates of the various LU/LC in the Study area and % of their decrease or increase over 2003-2018. LUC/LU Pattern Area in 2003 Area in 2018 % in % in % of (ha) (ha) 2003 2018 Change change

Deep Forest 1649.25 744 45.35 20.45 24.89 V

Sparse short trees 1309.59 1359 36.01 37.36 -1.35 ^

Shrub land 138.42 881 3.81 24.22 -20.42 ^

Grass land 133.02 564 3.66 15.51 -11.85 ^

Bare land 406.71 89 11.18 2.46 8.73 v

Total 3637 3637 100 100

Fig 3: Land use/Land Cover Post Classification Comparison.

Conclusion

Change events as observedhave led to significant effect on the forest vegetation cover/ land use diversification, including crop productivity in the nearby farm lands also been affected. Thus, livelihood, fertile lands for farming and fuel source could become jeopardized; hence they are the key factors of change in the reserve.

Recommendation

For optimum utilization of the reserve toward sustainable development, government should aid in enlightening farmers on importance of biodiversity, and also assist farmers by financing management of the forest reserve and Afforestation programmes so as to regenerate the changed cover of the forest. Forest conservation practices should also be emphasized, so as to avoid LU/LC change of the reserve.

References Anderson, J. R., Hardy, E. T., Roach, J. T., & Witmer, R. E. (1976). A land use and land cover classification system for use with remote sensor data. U.S. Geology Survey prof Paper 964. U.S.Government Printing Office, Washington, DC.

Bretherton, F., (1997). Earth System Science: A Closer View.Report of the Earth Systems Sciences, Committee to the NASA Advisory Committee, National Aeronautics and Space Administration, NASA, Washington, D.C.

Buriánek, V., R. Novotný, K. Hellebrandová, V. Šrámek (2013). Ground vegetation as an important factor in the biodiversity of forest ecosystems and its evaluation in regard to nitrogen deposition, Journal of forest science, 59, (6): 238–252.

Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E. (2004). Digital change detection methods in ecosystem monitoring; a review. International Journal of Remote Sensing 25: 1565- 1596. - doi: 10.1080/0143116031000101675.

Ekpoh, I. J. and Ekpenyong, N. (2011). The Effects of Recent Climatic Variations on Water Yield in the Sokoto Region of Northern Nigeria, International Journal of Business and Social Scienc, Vol. 2, 7.

Hayes, D. J, Sader, S. A. (2001). Comparison of change-detection techniques for monitoring tropical forest clearing and vegetation regrowth in a time series.Photogrammetric Engineering and Remote Sensing 67: 1067-1075. URL:http://www.asprs.org/a/publications/pers/2001-journal/september/2001_sep_1067- 1075.pdf

Janssen, L. L. F., and Gorte, B. G. H. (2004).Digital image classification.In N. Kerle, L. L. F. Janssen, & G. C. Huurneman (Eds.) Principles of remote sensing: An introductory textbook. TheInternational Institute for Geo-Information Science and EarthObservation (ITC), Hengelosestraat 99, P.O. Box 6, 7500 AA Enschede, The Netherlands.

Jung, M., Churkina, G., Henkel, K. (2006): Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sensing of Environment, n.101, 2006, p.534-553.

Lambin, E. F., Turner, B. L., Helmut, J. (2000). The causes of land-use and land-cover change: Moving beyond the myths. Global Environmental Change, n.11, 2001, p.261- 269.

Lunetta, R. S, Ediriwickrema, J., Johnson, D. M, Lyon, J. G, McKerrow, A. (2002). Impacts of vegetation dynamics on the identification of land-cover change in a biologically complex community in North Carolina, USA. Remote Sensing of Environment 82: 258-270. - doi: 10.1016/S0034-42 57(02)00042-1.

Lunetta, R. S., Knight, J. F, Ediriwickrema, J., Lyon, J. G, Worthy, L. D (2006). Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sensing of Environment 105: 142-154. doi: 10.1016/j.rse.2006.06.018.

Lyon, J. G, Yuan, D., Lunetta, R. S, Elvidge, C. D. (1998). A change detection experiment using vegetation indices.Photogrammetric Engineering and Remote Sensing 64 (2): 143-150. URL: http://www.asprs.org/a/publications/pers/98journal/february/1998_feb_143- 50.pdf

NRC. (1999): Global Environmental Change: Research Pathways for the Next Decade. National Research Council, National Academy Press, Washington, DC. 1999. URL: http://www.nap.edu/openbook.php?isbn=0309064201. Assessed on September 10, 2011.

Pu, R., Gong, P., Tian, Y., Miao, X., Carruthers, R. I, Anderson, G. L (2008). Invasive species change detection using artificial neural networks and CASI hyperspectral imagery. Environmental Monitoring and Assessment 140 (1-3): 15-32. - doi: 10.1007/s10661- 007-9843-7.

Sader, S.A, Bertrand, M., Wilson, E. H (2003). Satellite change detection of forest harvest patterns on an Industrial forest landscape. Forest Science 49 (3): 341-353.

Shafer, S. L., Bartlein, P. J., Thomson, R. S. (2001). Potential Changes in the distributions of western north America tree and shrub taxa under future climate scenariors. Ecosystems 4, 200-215.

Schowengerdt, R. A. (1997). Remotesensing: models and methods for image processing (2nd edn).Academic Press, San Diego, CA, USA. [ISBN 0126289816]

Sepehry, A., Liu, G. (2006). Flood Induced land cover change detection using multitemporalETM+ imagery. In: Proceedings of the “2nd Workshop of the EARseL SIG on Land Use and Land Cover: Application and Development” (Braun M ed). Center for Remote Sensing of Land Surfaces, European Association of Remote Sensing Laboratories and Universitat Bonn, Bonn, Germany, pp. 399-406.

Stave, J., Oba, G., Stenseth, N. C. (2001). Temporal changes in woody-plant use and the ekwar indigenous tree management system along the Turkwel River, Kenya. Environmental Conservation, 28:150–159.

Wilson, E. H, Sader, S. A (2002).Detection of forest harvest type using multiple dates of TM imagery.Remote Sensing of Environment 80:385-396. - doi: 10.1016/S0034- 4257(01)00318-2

Xiao, X. M., Zhang, Q.; Braswell, B. (2004): Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of environment, n.91, 2004, p. 256-270.

Xie, Y. (2008). Remote sensing imagery in vegetation mapping: A review. Journal of Plant Ecology, 1(1), 2008, p.9-23 (doi: 10.1093/jpe/rtm005).