Rapid urbanization in developing and transitional regions has created significant challenges in monitoring land use change and environmental degradation. Remote sensing (RS) has emerged as a vital tool for assessing urban growth and its ecological impacts due to its capability to provide consistent, multi-temporal, and large-scale data. However, conventional RS methods often rely on single-resolution imagery, which limits the ability to capture both detailed urban features and broader environmental trends simultaneously. Additionally, challenges such as high processing costs, skill requirements, and fragmented collaboration between Remote Sensing and Geographic Information Systems (GIS) communities further hinder comprehensive urban analysis. This study proposes a novel multi-resolution remote sensing methodology that integrates high, medium, and low-resolution satellite data for a holistic assessment of urban dynamics. The proposed approach enhances urban feature detection and environmental monitoring through spatial data fusion, supervised classification, and change detection techniques. The workflow includes data acquisition from diverse sources, pre-processing (correction, enhancement), classification using machine learning algorithms (e.g., Random Forest), accuracy assessment through ground-truth validation, and visualization via thematic maps. Experimental results demonstrate the method’s effectiveness in capturing urban sprawl, vegetation loss, and pollution indicators in rapidly urbanizing regions. The integrated multi-resolution framework outperforms single-source analysis in both accuracy and spatial coverage. This approach supports decision-making for sustainable urban planning by delivering high-quality geospatial insights. Overall, the proposed methodology bridges the gap between RS and GIS applications and serves as a scalable model for future urban environmental monitoring initiatives.

Precise positioning is available from global navigation satellite systems (GNSS). Unfortunately, satellite signals are weak and a strong interference signal can create a GNSS denial region. The solution requires spatially separating the valid signals from the interference signal. The traditional approach involves constructing a beampattern that puts a sidelobe null in the direction of the interference signal while putting valid signals into the mainlobe or sidelobes. This approach is seen as suboptimal because of array constraints on the beampattern. An alternative approach is presented here that uses linear algebra methods. Physically, all the signals are mapped onto a receive antenna array vector. The signal space approach inverts this mapping which results in a signal vector that completely decouples the valid signals from the interference signal. This allows only the valid signals to be used for positioning. Implementation of this alternative approach yields an adaptive set of antenna array weights calculated using linear algebra methods to decouple all the signals.

Land use Land cover (LULC) has undergone progressive changes worldwide over the years. However, there is limited information available about these changes in Oba Hills Forest Reserve, Nigeria. The existing spatial analysis of the forest excluded important land use classes like settlements. Therefore, this study aimed at assessing the dynamics of LULC in Oba Hills Forest Reserve between 1987 and 2019. Images from Landsat 5, Landsat 7, and Landsat 8 for the years 1987, 2001, 2013, and 2019 were obtained and subjected to preprocessing and classification using the maximum likelihood algorithm, change detection, and Normalized Differential Vegetation Index (NDVI). The coordinates of specific benchmark locations and other points were acquired for ground-truthing and developing Digital Elevation Model (DEM). Three distinct LULC classes were identified: forest, bare land (including open spaces, agriculture, rocks, and grasslands), and built-up areas. The forest cover in the reserve gradually decreased from 56% in 1987 to 47% in 2019, resulting in a total area loss of 455.4 hectares. Correspondingly, the other LULC classes experienced exponential expansion. Bare land increased from 44% in 1987 to 52% in 2019, while the built-up area expanded by 57.28 hectares. These changes are attributed to prevalent anthropogenic activities such as agriculture, grazing, logging, firewood collection, and population growth within the catchment area. The declining NDVI values in the forest reserve, from 0.52 to 0.44 within the years of assessment, further substantiated the substantial loss of forest cover. The DEM and topographical map highlighted notable steep slopes and elevations of up to over 550 m above sea level (asl) within the reserve, which have implications for forest growth and dynamics. In conclusion, this study reveals extensive rates of forest cover changes into bare land, primarily for agriculture, and settlements, and offers further recommendations to reverse the trend.

This paper studies the effect of breaking single-class building data into multi-class building data for semantic segmentation under end-to-end architecture such as UNet, UNet++, DeepLabV3, and DeepLabv3+. Although, the already existing semantic segmentation methods for building detection work on the imagery of developed world, where the buildings are highly structured and there is a clearly distinguishable space present between the building instances, the same methods do not work as effectively on the developing world where there is often no clear differentiable spaces between instances of building thus reducing the number of detected instances. Hence as a noble approach, we have added building contours as new class along with building segmentation data, and detected the building contours and the inner building regions, hence giving the precise number of buildings existing in the input imagery especially in the convoluted areas where the boundary between the buildings are often hard to determine even for human eyes. Breaking down the building data into multi-class data increased the building detection precision and recall. This is useful in building detection where building instances are convoluted and are difficult for bare instance segmentation to detect all the instances.

Monitoring secondary forest regrowth is a priority in forest restoration strategies. A site history helps in understanding the present status of natural regeneration in the three landscapes impacted by bauxite mining. Nonetheless, high rainfall in bauxite residue storage areas can facilitate natural regeneration of forest. This research analyzed the natural regeneration of forest after thirty years of different land use histories at three bauxite mining areas of the Upper Demerara—Berbice region of Guyana. There are no man made forest plantations in the three landscapes being reviewed. The methodology included: 1) the selection of three sampling landscapes with different land use histories 2) the generation Land Use/Land Cover maps using KMeans unsupervised classification of satellite images in each landscape and 3) the assessment of landscape structure of the land cover classes for year 2020 at class and landscape level using landscape metrics. The assessment of landscape structure of the land cover classes was carried-out with landscape metrics for the comparisons at class and landscape level. Principal component analysis enables the identification of main patterns among landscape-level metrics and land cover classes. Discriminant classification of the landscape classes was analyzed with the different metrics. The results suggest that Normalized Difference Vegetation Index and KMeans unsupervised classification can be used to evaluate the difference in natural forest regeneration among landscapes with differing land use histories. The landscape metrics revealed secondary stages of forest succession. The Landscape Shape Index and Edge Density were the most significant for landscape differentiation. The result of the various land uses reveals a mosaic of early, intermediate, and late successional sequences.

Kano State is one of the frontline states in northern Nigeria that have suffered land degradation. This degradation results from urbanization and anthropogenic influence. In Danbatta Local Government Area (LGA), persistent degradation of the land has adversely affected the environment and the economy and there is lack of studies on land degradation in the area. Therefore, this study assessed land degradation in Dambatta Local Government Area (LGA) of Kano State paying special attention to the causes and effects of the reduction in the lands’ actual or potential uses. The images used for the research analysis were obtained from National Space Research and Development Agency (NASRDA), Abuja. The data sets were captured by Landsat Multispectral Scanner/Thematic Mapper (MSS/TM) 1997, Landsat Enhanced Thematic Mapper Plus (ETM+) 2007 and Operational Land Imager (OLI) 2017. Post classification change detection technique was conducted in ILWIS 5.2, and later converted to shape files where it was imported to Arc Map 10.2 GIS software. The results showed Normalized Difference Vegetation Index (NDVI) ranges from −0.056 to 0.18 in 1997, −0.07 to 0.11 in 2007 and −0.128 to 0.217 in 2017. This depicts that there has been a progressive loss in vegetation cover in Dambatta LGA over a period of 20 years with corresponding acceleration in bare lands and developed areas. The Land Surface Temperature (LST) results generally show a continuous and constant increase in surface temperature from the developed and urban areas to the undeveloped and rural areas. The LST results also show that no area under consideration in the study area experienced an extreme temperature (≥44˚C) during the period of study. In 2017, a large part of the study area fell within the higher temperature zones (≥40˚C) and other areas fell into mid-temperature zones (35˚C - 40˚C). This high surface temperature resulted from increase in bare land, high insolation, urbanization, anthropogenic influences and large distribution of imperviousness or geographical relief of the area. It was suggested that afforestation and sustainable development should be encouraged and strengthened in the area. In addition, studies should be carried out on the appropriate solution to the land degradation problem in Dambatta LGA, Kano State. Furthermore, Local Government Areas neighboring Dambatta LGA should also be analyzed on the issue of land degradation.

This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (ere used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands i.e. Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985oC to 46.1314oC, and, 18.3437oC to 30.9693oC respectively. Results of Kumasi also show a higher range of temperatures from 32.6986oC to 19.1077oC during the dry season. In the wet season, temperatures ranged from 26.4142oC to -0.898728oC. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.

Geophysical surveying is crucial in the investigation of mineral resources in poorly exposed areas such as SE-Cameroon, a region known for its gold mineral potential. In this paper, gravity survey is carried out in the Batouri area, SE-Cameroon based on land gravity data from the Centre-south Cameroon. Therefore, an analytical polynomial separation program, based on least-square fitting of a third-degree polynomial surface to the Bouguer anomaly map, was used to separate the regional/residual components in gravity data. This technique permitted to better understand the disposition of the deep and near surface structures responsible of the observed anomalies in the Batouri area. Spectral analysis and 2.5D modelling of two profiles P1 (SW-NE) and P2 (N-S) selected from the residual anomaly map provided depths to basement. These depths constrain the gravity models along the profiles, indicating a variable thickness of the sedimentary infill with an approximate anomaly of -33 mGal. The 2.5D model of the basement shows a gravity body, with a signature suggesting two close and similar masses, which characterize the quartz-bearing formations associated here to granite and gneiss. Our work highlights a main heavy gravity: Gwe-Batouri anomaly, containing the major part of auriferous deposits located along the NE-SW direction. Further, three tectonic sub-basins bounded by normal faults have been highlighted at Guedal, Gwe, and Belimban, in the south of Guedal-Belimban depression. They are associated with the extension tectonics, more or less vertical tangential cuts and accidents that have affected the region. A correlation with previous results from tectonic, lithological and gold mineralization activities proves the relevance of the study and the need to intensify geophysical surveying in the area.

Poverty has always been one of the topics concerned by governments and researchers all over the world, especially in developing countries. Remote sensing image is widely used in poverty estimation because of its large area observation, timeliness and periodicity. In this study, we explore the applicability of convolution neural network (CNN) combined with remote sensing image in regional poverty estimation. In the 2016 economic indicators estimation of Guizhou Province, China, the Pearson coefficient of per capita GDP (PCGDP) reached 0.76, which means that the image features extracted by CNN can explain the change of PCGDP of county level economic indicators up to 76%. Compared with other methods, our method still has high precision. Based on these results, we found that convolutional neural network combined with remote sensing image can be used in regional poverty estimation.