Land use and land cover (LULC) change influences many issues such as the climate, ecological environment, and economy. In this study, the LULC transitions in the Yellow River Basin (YRB) were analyzed based on the GlobeLand30 land use data in 2000, 2010, and 2020. The intensity analysis method with hypothetical errors calculation was used, which could explain the deviations from uniform land changes. The strength of the evidence for the deviation was revealed even though the confusion matrixes of the LULC data at each time point for the YRB were unavailable.

Estimates of the area or percent area of the land cover classes within a study region are often based on the reference land cover class labels assigned by analysts interpreting satellite imagery and other ancillary spatial data. Different analysts interpreting the same spatial unit will not always agree on the land cover class label that should be assigned. Two approaches for accommodating interpreter variability when estimating the area are simple averaging (SA) and latent class modeling (LCM).

This study explores the influence of land-use and land cover (LULC) changes on the temperature over North India (NI) and North-Eastern India (NEI) during 1981–2006 by subtracting the reanalysis temperature from the observed temperature (observation minus reanalysis (OMR) method). The normalized difference vegetation index (NDVI) data of the AVHRR satellite for the period 1981–2006 were analyzed to understand the type of LULC changes during this period and their linkage with the temperature change over the two regions.

The headwaters of the Blue Nile River in Ethiopia contain fragile mountain ecosystems and are highly susceptible to land degradation that impacts water quality and flow dynamics in a major transboundary river system. This study evaluates the status of land use/cover (LULC) change and key drivers of change over the past 31 years through a combination of satellite remote sensing and surveying of the local understanding of LULC patterns and drivers.

The increasing pressure from land cover change exacerbates the negative effect on ecosystems and ecosystem services (ES). One approach to inform holistic and sustainable management is to quantify the ES provided by the landscape. Using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, this study quantified the sediment retention capacity and water yield potential of different land cover in the Santee River Basin Network in South Carolina, USA. Results showed that vegetated areas provided the highest sediment retention capacity and lowest water yield potential.

In the tropics, the domestic water supply depends principally on ecosystem services, including the regulation and purification of water by humid, dense tropical forests. The Yangambi Biosphere Reserve (YBR) landscape is situated within such forests in the Democratic Republic of Congo (DRC). Surprisingly, given its proximity to the Congo River, the YBR is confronted with water issues. As part of its ecosystem function, the landscape is expected to reduce deterioration of water quality.

Land use and land cover (LULC) changes are regarded as one of the key drivers of ecosystem services degradation, especially in mountain regions where they may provide various ecosystem services to local livelihoods and surrounding areas. Additionally, ecosystems and habitats extend across political boundaries, causing more difficulties for ecosystem conservation.

Population growth rates in Sub-Saharan East Africa are among the highest in the world, creating increasing pressure for land cover conversion. To date, however, there has been no comprehensive assessment of regional land cover change, and most long-term trends have not yet been quantified. Using a designed sample of satellite-based observations of historical land cover change, we estimate the areas and trends in nine land cover classes from 1998 to 2017 in Ethiopia, Kenya, Uganda, Malawi, Rwanda, Tanzania, and Zambia.

The influence of landscape on nutrient dynamics in rivers constitutes an important research issue because of its significance with regard to water and land management. In the current study spatial and temporal variability of N-NO3 and P-PO4 concentrations and their landscape dependence was documented in the Świder River catchment in central Poland.

In some cloudy and rainy regions, the cloud cover is high in moderate-high resolution remote sensing images collected by satellites with a low revisit cycle, such as Landsat. This presents an obstacle for classifying land cover in cloud-covered parts of the image. A decision fusion scheme is proposed for improving land cover classification accuracy by integrating the complementary information of MODIS (Moderate-resolution Imaging Spectroradiometer) time series data with Landsat moderate-high spatial resolution data. The multilevel decision fusion method includes two processes.

In Indonesia, land cover change for agriculture and mining is threatening tropical forests, biodiversity and ecosystem services. However, land cover change is highly dynamic and complex and varies over time and space. In this study, we combined Landsat-based land cover (change) mapping, pixel-to-pixel cross tabulations and expert knowledge to analyze land cover change and forest loss in the West Kutai and Mahakam Ulu districts in East Kalimantan from 1990-2009.