GIS-based Characterization of Land Use, Land Cover Patterns and Microclimate of Agricultural and Agroforestry Landscapes in a Rainforest Zone of Nigeria

Abstract

There is increasing need for development of sustainable land use and landscape management practices to avert accelerating trends of land, water and ecosystem degradation and for climate mitigation. This study characterized land use and land cover patterns and microclimate of permanent (forest, agroforestry, fallow, cocoa, oil palm, citrus and ornamental plant field) and annual crop land use systems in a rainforest zone of Nigeria using space-based remote sensing technology. The goal is to evaluate land use and land cover patterns and microclimate along agricultural and agroforestry landscapes in a rainforest zone of Nigeria. Land use types were: permanent (forest, agroforestry, fallow, cocoa, oil palm, citrus and ornamental plant field) and annual cropland. Vegetation indices (Normalized Difference Vegetation Index: NDVI, Normalized Difference Water Index: NDWI and Soil Adjusted Vegetation Index: SAVI) were deployed for characterizing land use vegetation cover patterns in relation to vigour and health in addition to responses to weather variables (temperature and rainfall). The NDVI intensities of vegetation cover from the land use types showed differences in vigour and health of vegetation during the rainy and dry seasons of 2017 to 2019. The NDWI of vegetation cover intensity indicates differences in moisture conditions of vegetation cover, the vegetation of the land use systems had more water content (received more rainfall) in 2017 compared to 2018 and 2019 during the rainy season while during the dry season of 2019, NDWI intensity was highest compare to 2018 and 2017. NDVI and NDWI also showed that vegetation cover of permanent land uses had better vigour and health compared to annual (maize) field. SAVI was applied to correct NDVI of vegetation cover patterns of land use types with reference to canopy gaps (soil brightness within canopy especially in spots where vegetation cover is sparse). High SAVI intensities were obtained during rainy compared to the low values during dry season (sparse vegetation cover). Decreasing order of SAVI intensities were agroforestry, oil palm, ornamental plant field, citrus, cocoa, fallow land and maize crop field. Result from the correlations among vegetation indices (NDVI, NDWI and DSAVI) were strong association (R2 = 1) among the years and seasons. The strong R2 values imply that less than 10% of changes in NDWI (the explanatory variables) can be explained by changes in NDVI and SAVI (the dependent variable). Temperature and rainfall differed within months and years of study. Temperatures were highest for March, April and May while rainfall was highest for September of 2017 and 2018 and in October, 2019. Significantly lower rainfall amounts were received for January, February, November and December. The vegetation indices (NDVI, NDWI and SAVI) indicated vigour and water contents of the land use types within seasons and years as well as responses to weather variables (rainfall and temperature in particular). The biophysical findings from this study may advance capacities to cope with climate change challenges and ecosystem conservation. Information generated will find use as strategies for ecologically sound and sustainable land use systems and policy for mainstreaming climate mitigation in in the study area.