Spherical harmonic expansion is a commonly applied mathematical representation of the earth’s gravity field. This representation is implied by the potential coeffcients determined by using elements/parameters of the field observed on the surface of the earth and/or in space outside the earth in the spherical harmonic expansion of the field. International Centre for Gravity Earth Models (ICGEM) publishes, from time to time, Global Gravity Field Models (GGMs) that have been developed. These GGMs need evaluation with terrestrial data of different locations to ascertain their accuracy for application in those locations. In this study, Bouguer gravity anomalies derived from a total of eleven (11) recent GGMs, using sixty sample points, were evaluated by means of Root-Mean-Square difference and correlation coeficient. The Root-Mean-Square differences of the computed Bouguer anomalies from ICGEMwebsite compared to their positionally corresponding terrestrial Bouguer anomalies range from 9.530mgal to 37.113mgal. Additionally, the correlation coe_cients of the structure of the signal of the terrestrial and GGM-derived Bouguer anomalies range from 0.480 to 0.879. It was observed that GECO derived Bouguer gravity anomalies have the best signal structure relationship with the terrestrial data than the other ten GGMs. We also discovered that EIGEN-6C4 and GECO derived Bouguer anomalies have enormous potential to be used as supplements to the terrestrial Bouguer anomalies for Enugu State, Nigeria.

Digital Elevation Models (DEMs) depict the configuration of the earth surface and are being applied in many areas in earth and environmental sciences. In this study, the accuracy of the Advanced Land Observing Satellite World 3D Digital Surface Model version 2.1 (ALOS W3D30), the Shuttle Radar Topography Mission Digital Elevation Model version 3.0 (SRTM30) and the Advanced Space borne Thermal Emission and Reflection Radiometer Global DEM version 2.0 (ASTER GDEM2) was statistically assessed using high accuracy GPS survey data. Root-Mean-Square errors of ~5.40 m, ~7.47 m and ~20.03 m were obtained for ALOS W3D30, SRTM30 and ASTER GDEM2 respectively. In further analyses, we discovered that ALOS W3D30 and SRTM30 were much more accurate in regions where the height intervals were within 201 m-400 m and >801 m. ALOS W3D30 proved to be the most accurate DEM that best represents the topography of the earth's surface and could be used for some earth and environmental applications in Nigeria. We recommend that this study should serve as a guide in the use of any of these DEMs for earth and environmental applications in Nigeria.

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Assessment of forest health is very vital because forests form the largest terrestrial ecosystems on earth. The greenness of vegetation is one of the essential factors used in evaluating the health of forest reserves. This study is aimed at assessing the health of fifteen forest reserves in Southeastern part of Nigeria using meteorological data and MOD13A1-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Related portions of the monthly MOD13A1 data, derived for the years 2010, 2014, and 2018, were downloaded, and the monthly mean values of the vegetation indices (NDVI and EVI) were estimated for each of the forest reserves using the Spatial Analysis Module in ArcGIS software. The computed monthly mean values of NDVI range from 0.094 to 0.790 while that of EVI ranges from 0.11 to 0.624 and the rainfall data range from 0 to 780.2 mm/month within the period of study. Analyses of the correlation coefficients between monthly rainfall data and NDVI, monthly rainfall data and EVI, and that of NDVI and EVI range from −0.827 to 0.584; −0.715 to 0.914, and 0.598 to 0.980. The obtained results indicate that some of the forest reserves are moderately healthy while some areas are under great stress. We can conclude that satellite remote sensing is a veritable tool in the assessment, management, and monitoring of forest health especially where there is little or no terrestrially acquired forest inventory data.

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