No abstract

Abstract. General environmental management, which involves monitoring and modeling, requires the information of the Land surface temperature (LST) status of area concerned. Land surface temperature has gained relevance recognition over the years and there is need to develop approaches that can determine LST using satellite images. This study was conducted in Akure which has experienced rapid urbanization in recent time. The study utilized Landsat data of 1984, 1990, 2000, 2003, 2014 and 2016. The temperature data were derived from Landsat images using remote sensing algorithms for assessing LST from thermal infrared (TIR) data (bands 6 and 10). These data were processed and analyzed using tools in Idrisi and ArcGIS software systems. Surface temperatures derived from Landsat data were validated with ground meteorological data. The results revealed parabolic increase in temperature over the years and the changing pattern was investigated by adopting existing bio-spectral phenomenon Models. The validation operation revealed average bias value of ±0.31 between remote sensing-and ground-based data. This implies that remote sensing technique is reliable and therefore could be employed for large scale temperature mapping. The results could be used in mitigating urban heat island effects such as heat-related stress and ill-timed human deaths.

This study examined the effect of forest degradation on livelihood returns in

Forest stress or health has become a topic of serious interest to researchers in recent times. It reached a crescendo consequent upon the renewed interest in climate change’s effects, resilience and mitigation. Forest stress is majorly a climate-related occurrence that can only be managed but not totally eradicated. Therefore, this study is aimed at assessing thermal induced stress in Akure forest reserve in Ondo State, Nigeria, using the instrumentality of Remote Sensing and Geographic Information System (GIS). The temperature data used for this study was extracted from Landsat 8 (OLI) imagery obtained from United States Geological Survey (USGS) database between the years 2016 and 2020. A land use land cover change detection analysis of the study area revealed that between 2016 and 2020, forest and waterbody decreased from 4344.59 to 2971.71 ha and 187.28 to 178.23 ha respectively while shrubs and bare land increased from 1472.13 to 2533.05 ha and 578.16 to 899.34 ha respectively. Forest stress and health of the study area was assessed using vegetation indices and land surface temperature LST). The result reveals changes in mean LST across the four lands cover types during the study period. It ranges between 22.28 oC (water body) in 2016 to 28.99 oC (bare land) in 2018. The spatial trend of Vegetation Health Index (VHI) was used to delineate the health of the forest. The study reveals that the spectral changes in the biophysical characteristics of the forest could not be solely attributed to temperature variability. Other climatic parameters and soil related variables must have contributed to notice stress in the forest reserve. However, this study has brought to the fore the robustness of geospatial technologies on the study of forest stress, health and drought.

The role of soil moisture in the survival and growth of trees cannot be overemphasized and it contributes to the net productivity of the forest. However, information on the spatial distribution of the soil moisture content regarding the tree volume in forest ecosystems especially in Nigeria is limited. Therefore, this study combined spatial and ground data to determine soil moisture distribution and tree volume in the International Institute of Tropical Agriculture (IITA) forest, Ibadan. Satellite images of 1989, 1999, 2009 and 2019 were obtained and processed using topographic and vegetation-based models to examine the soil moisture status of the forest. Satellite-based soil moisture obtained was validated with ground soil moisture data collected in 2019. Tree growth variables were obtained for tree volume computation using Newton's formular. Forest soil moisture models employed in this study include Topographic Wetness Index (TWI), Temperature Dryness Vegetation Index (TDVI) and Modified Normalized Difference Wetness Index (MNDWI). Relationships between index-based and ground base Soil Moisture Content (SMC), as well as the correlation between soil moisture and tree volume, were examined. The study revealed strong relationships between tree volume and TDVI, SMC, TWI with R 2 values of 0.91, 0.85, and 0.75, respectively. The regression values of 0.89 between in-situ soil data and TWI and 0.83 with TDVI ascertain the reliability of satellite data in soil moisture mapping. The decision of which index to apply between TWI and TDVI, therefore, depends on available data since both proved to be reliable. The TWI surface is considered to be a more suitable soil moisture prediction index, while MNDWI exhibited a weak rela-