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ArticleProjection modeling-based geospatial analysis of land use-land cover change at Hasdeo River Watershed, Chhattisgarh, India
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.The land-use change in the Hasdeo River watershed has been observed with all its subwatersheds. The changing patterns may portend localized impairment to forest and agricultural watershed. In this study, Land-use land-cover (LULC) change was modeled using terrset modeling software. The Hasdeo river watershed (geographical extent of 10,396.373 km2) is a part of the Mahanadi River basin in Chhattisgarh, India. Hasdeo River originates from Sonhat (Koriya district, Chhattisgarh, India) and is submerged into the river Mahanadi. It flows in the stretch of 330 km from north to south direction. This river has eight subwatersheds with rich forest diversity and perennial water resources. IRS-1D & P6 LISS3 images from the years 2000 and 2013 were used to investigate the LULC pattern. This has been used for the prediction of LULC change patterns for the years 2035 and 2050 based on the Markov model. The result of the project LULC map for the year 2000-2035 and 2000-2050 shows that the dense forest area will decrease by 12.30% and 15.68% respectively. The settlement area will significantly increase by 20.13% (2035) and 34.90% (2050) and will be the dominant land-use type in the watershed. It shows that population pressure will directly affect forest vegetation and agriculture activities. This study will be helpful for the effective sustainability approach for maintaining the proper LULC pattern of LULC pattern of land-use change in the watershed. This changing pattern will also influence the farming pattern in the catchment area of the Hasdeo River watershed. Keywords: Adaptive and integrated management, Deforestation and forest degradation, Landscape management, Monitoring and data collection, Sustainable forest management ID: 3487496 -
ArticleUsing Standardized Time Series Land Cover Maps to Monitor the SDG Indicator “Mountain Green Cover Index” and Assess Its Sensitivity to Vegetation Dynamics 2021
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No results found.SDG indicators are instrumental for the monitoring of countries’ progress towards sustainability goals as set out by the UN Agenda 2030. Earth observation data can facilitate such monitoring and reporting processes, thanks to their intrinsic characteristics of spatial extensive coverage, high spatial, spectral, and temporal resolution, and low costs. EO data can hence be used to regularly assess specific SDG indicators over very large areas, and to extract statistics at any given subnational level. The Food and Agriculture Organization of the United Nations (FAO) is the custodian agency for 21 out of the 231 SDG indicators. To fulfill this responsibility, it has invested in EO data from the outset, among others, by developing a new SDG indicator directly monitored with EO data: SDG indicator 15.4.2, the Mountain Green Cover Index (MGCI), for which the FAO produced initial baseline estimates in 2017. The MGCI is a very important indicator, allowing the monitoring of the health of mountain ecosystems. The initial FAO methodology involved visual interpretation of land cover types at sample locations defined by a global regular grid that was superimposed on satellite images. While this solution allowed the FAO to establish a first global MGCI baseline and produce MGCI estimates for the large majority of countries, several reporting countries raised concerns regarding: (i) the objectivity of the method; (ii) the difficulty in validating FAO estimates; (iii) the limited involvement of countries in estimating the MGCI; and (iv) the indicator’s limited capacity to account for forest encroachment due to agricultural expansion as well as the undesired expansion of green vegetation in mountain areas, resulting from the effect of global warming. To address such concerns, in 2020, the FAO introduced a new data collection approach that directly measures the indicator through a quantitative analysis of standardized land cover maps (European Space Agency Climate Change Initiative Land Cover maps—ESA CCI-LC). In so doing, this new approach addresses the first three of the four issues, while it also provides stronger grounds to develop a solution for the fourth issue—a solution that the FAO plans to present to the Interagency and Expert Group on SDG Indicators (IAEG-SDG) at its autumn 2021 session. -
PresentationLesotho Land Cover (2017–2021) Dashboard. A Google Earth engine dashboard to monitor land cover change and land degradation 2022
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