Environmental monitoring, using the techniques of remote sensing (RS) and geographic information systems (GIS), allows the production of time efficient, cost-effective, and reliable surveillance and tracking data. Anthropogenic activities appear to be the major trigger of environmental changes, including land use and land cover (LULC) changes, while natural causes have only a minor impact in most cases. The Omayed Biosphere Reserve (OBR) stands as one of the Egyptian protected areas most highly affected by massive unplanned human activities. Thus, the main objective of this study is to determine the spatio-temporal changes in the OBR over a 35-year period using five Landsat (5 ETM images and 8 OLI-TIRS) imageries, with the specific aim of measuring change rates, trends, and magnitudes of LULC changes between 1984 and 2019 with the topography for planning and selection of developmental strategies. The Normalised Difference Vegetation Index is used to identify the vegetation characteristics of different eco-regions and delivers useful information for the study of vegetation health and density. Normalised Difference Built-up Index can likewise be used to quote built-up areas. Unsupervised classification was used to classify LULC patterns. Six classes were recognised: water bodies, coastal sand, urban areas, cultivated land, newly reclaimed areas, and bare soil. Our results reveal that about 33.55% of OBR land cover has transformed into other forms. Cultivated land and urban regions increased by about 143.5 km and 56.17 km from 1984 to 2019, respectively. Meanwhile, bare soil decreased to around 209.5 km in 2019. In conclusion, the conversion of bare soil into urban land and cultivated areas is the major change in the last 35 years in the OBR. Over the past three decades, the OBR has faced radical and imbalanced changes in its natural habitats. Therefore, monitoring and management of LULC changes are crucial for creating links between policy decisions, regulatory actions, and following LULC activities in the future, especially as many potential risks still exist in the remaining regions of the OBR.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s11356-020-10208-1 | 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 Rep
January 2025
Department of Geography, School of Environment, Education and Development, The University of Manchester, Arthur Lewis Building, Oxford Road, Manchester, M13 9PL, UK.
Urban woodland composition and configuration have strong associations with land surface temperatures (LST), but the evidence is contradictory due to different spatial scales, regional climate zones, woodland types and urban contexts. In this study, we analyse associations between urban woodland and LST within and between five cities in different Köppen climate zones. Our consistent methodology is framed around local climate zones and conducted at a fine spatial scale.
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 Environ Manage
January 2025
School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, Norfolk, NR4 7TJ, UK; Instituto Juruá, Manaus, Brazil.
Over recent decades, forest fire prevalence has increased throughout the tropics, necessitating improved understanding of the landscape-scale drivers of fire occurrence. Here, we use MapBiomas land-cover and fire scar data to evaluate relationships between forest fragmentation, land-use, and forest fire prevalence in a typically consolidated Amazonian agricultural frontier: Portal da Amazonia, Mato Grosso, Brazil. Using zero-/zero-one-inflated Beta regressions, we investigate effects of forest patch (area, shape, surrounding forest cover) and landscape-scale variables (forest edge length, land-cover composition) on forest fire occurrence and density between 1985 and 2021.
View Article and Find Full Text PDFSci Total Environ
January 2025
School of Biological Sciences, University of Adelaide, Adelaide, SA 5000, Australia; The Environment Institute, University of Adelaide, Adelaide, SA 5000, Australia; Center for Macroecology, Evolution, and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark; Center for Global Mountain Biodiversity, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark. Electronic address:
Human overexploitation contributed strongly to the loss of hundreds of bird species across Oceania, including nine giant, flightless birds called moa. The inevitability of anthropogenic moa extinctions in New Zealand has been fiercely debated. However, we can now rigorously evaluate their extinction drivers using spatially explicit demographic models capturing species-specific interactions between moa, natural climates and landscapes, and human colonists.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!