In this paper, we report a geospatial assessment of the selected mine sites in the Plateau State, Nigeria. The aim of this work is to determine the impact of mining on the terrain as well as the Land Use/Land Cover (LULC) of the host communities. The Shuttle Radar Topographic Mission (SRTM) is used for the terrain mapping. The derived impact of mining on LULC between 1975 and 2014 is determined...

The accuracy of the Land Use/Land Cover (LULC) data derived from remote sensing images is critical for many applications. Classification error is caused by the interaction of numerous factors, including landscape characteristics, sensor resolution, spectral overlap, preprocessing algorithms, and classification procedures. The purpose of this paper is to analyze the impacts of landscape characte...

Land Use and Land Cover (LULC) classification is vital for environmental and ecological applications. Sentinel-2 is a new generation land monitoring satellite with the advantages of novel spectral capabilities, wide coverage and fine spatial and temporal resolutions. The effects of different spatial resolution unification schemes and methods on LULC classification have been scarcely investigate...

We evaluate the return on investment (ROI) from public land conservation in the state of Minnesota, USA. We use a spatially-explicit modeling tool, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), to estimate how changes in land use and land cover (LULC), including public land acquisitions for conservation, influence the joint provision and value of multiple ecosystem serv...

The Heihe River Basin (HRB) is the second largest inland river basin in arid Northwest China. The expanding agricultural irrigation, growing industrialization, and increasing urban development in the middle reach have depleted much of the river flow to the lower reach, degrading the corresponding ecosystems. Since the enactment of the State Council of China's new HRB water allocation policy in ...

GIS methodologies in combination with Remote Sensing data and techniques are fundamental to analyse and characterise Land Use/Land Cover (LULC) and their evolutionary dynamics. The case study here described, has been conducted in two study-areas: the Serre Regional natural Park and the socalled Conca of Avellino (Southern Italy). This study is part of a wider research allowed to understand how ...

Land use and land cover (LULC) maps are remote sensing products that are used to classify areas into different landscapes. Data fusion for remote sensing is becoming an important tool to improve classical approaches. In addition, soft computing techniques such as machine learning or evolutive computation are often applied to improve the final LULC classification. In this paper, a method based o...

Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC) information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed pixel influence....

Hyperspectral images and light detection and ranging (LiDAR) data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC) classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method based on adaptive differential evolution, namely ODF-ADE, for...

Urban areas play an important role in the global carbon cycle, and human-induced carbon emissions from urban areas urgently need to be reduced. Therefore, understanding the relationship between carbon sources and sinks is the first step toward mitigating the effect of urban areas on climate change. Combined with the land use and land cover (LULC) empirical coefficients and statistical methods, ...