The ongoing encroachment of agricultural activities into natural areas is a growing problem for the ecological condition of streams. Stream ecological condition is best measured using both biotic and abiotic parameters that reflect different channel, riparian zone and catchment aspects. Multiple physical-chemical measures of water quality have long been widely used to represent the environmental conditions of water bodies. More recently, physical habitat structure, catchment land use and land cover have been employed to better understand water body conditions. Both water quality and physical habitat structure metrics are usually measured in the field and often have strong predictive power to analyze biological assemblage conditions. On the other hand, remote sensing of catchment land use and land cover provide relatively low-cost environmental information at large spatial extents, minimizing the need for fieldwork and reducing analytical time. Given these considerations, our aim in the present study was to evaluate the degree to which stream environmental conditions could be measured reliably via remote sensing. In particular, we assessed whether a remote sensing index (Normalized Difference Vegetation Index) and land use can be used as reliable surrogates for site habitat condition, channel dimensions, and water quality. We found that our remote sensing variables were not sufficient for predicting stream water quality or habitat structure. Therefore, we recommend using remote sensing indicators only when it is impossible to measure water quality and habitat structure in the field directly.
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http://dx.doi.org/10.1016/j.scitotenv.2021.147617 | DOI Listing |
Sci Rep
January 2025
Department of Wildlife Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, Mississippi State, MS, 39762-9690, USA.
This study addresses the significant issue of rapid land use and land cover (LULC) changes in Lahore District, which is critical for supporting ecological management and sustainable land-use planning. Understanding these changes is crucial for mitigating adverse environmental impacts and promoting sustainable development. The main goal is to evaluate historical LULC changes from 1994 to 2024 and forecast future trends for 2034 and 2044 utilizing the CA-Markov hybrid model combined with GIS methodologies.
View Article and Find Full Text PDFSci Data
January 2025
Remote Sensing Centre for Earth System Research (RSC4Earth), Leipzig University, Leipzig, 04103, Germany.
With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
School of Chemistry and Environment, Guangdong Provincial Observation and Research Station for Tropical Ocean Environment in Western Coastal Water, Guangdong Ocean University, Zhanjiang 524088, China.
Microplastic pollution, a major global environmental issue, is gaining heightened attention worldwide. Marginal seas are particularly susceptible to microplastic contamination, yet data on microplastics in marine sediments remain scarce, especially in the Beibu Gulf. This study presents a large-scale investigation of microplastics in the surface sediments of the Beibu Gulf to deciphering their distribution, sources and risk to marginal seas ecosystems.
View Article and Find Full Text PDFSci Total Environ
January 2025
US Geological Survey, New England Water Science Center, Northborough, MA, USA.
Groundwater-dependent ecosystems in areas with industrial land use are at risk of exposure to a PFAS chemicals. We investigated one such system with several known PFAS source areas, where high and low permeability sediments (glacial) coupled with groundwater-lake and groundwater/surface-water interactions created complex 'source to seep' dynamics. Using heat-tracing and chemical methods, numerous preferential groundwater discharge zones were identified and sampled across the upper Quashnet River stream-wetland system in Mashpee, MA, USA, downgradient of Joint Base Cape Cod (JBCC).
View Article and Find Full Text PDFPlants (Basel)
January 2025
Department of Plant and Soil Sciences, University of Pretoria, Hatfield, Pretoria P.O. Box X20, South Africa.
The global rise in temperatures due to climate change has made it difficult even for specialised desert-adapted plant species to survive on sandy desert soils. Two of Namibia's iconic desert-adapted plant species, and the quiver tree , have recently been shown to be under threat because of climate change. In the current study, three ecologically important Namibian milk bushes were evaluated for their climate change response.
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