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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. -
ArticleSatellite remote sensing-based forest resources assessment methods for effective management and sustainable development of forests by generation of information on forests and trees outside forest cover
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Satellite based remote sensing methods have proved to be an effective and scientifically proven method for managing and conserving forest data and resources at periodic time intervals. The forest resources monitoring methods provide useful data to forest managers for sustainable forest management at different scale and forest management units. Over the years the scientific management of forest have been a subject globally discussed incorporating the role of environmentalist, conservationist and communities associated with the forest. It has been an unhidden fact that forests have suffered tremendous pressure in developing countries on the pretext of development. It is through effective monitoring and communication of forest information and knowledge that the concerned provincial governments are forced to take remedial measures for protecting the forests. Apart from the government owned forests, termed as Recorded Forest Areas(RFA) in India, Trees outside forests(TOF) are well acknowledged as an important component of forest resources. The ToF, which basically exist as block, linear and scattered plantations on earth are captured using LISS-III sensor of Indian Remote Sensing Satellite. For the national level scale mapping, all patches of area 1hectare and above are considered for estimation. For mapping of ToF patches of size between 0.1-1hectare, high resolution data from LISSIV sensor(5.8metres resolution) is analyzed. It has been now a well-established fact that trees outside RFAs, although in small proportion, contribute significantly to forest conservation and meeting the demand of people towards minor forest produce, firewood etc. The exercise on forest change detection using a hybrid method, is effective in identification of significant forest change. The assessment of forests and ToFs using satellite data and advance image processing tools may be helpful in effective management and long term sustainability of forests in developing countries. Keywords: [Recorded Forest Area, Trees Outside Forest, National Forest Inventory, FSI, Neural Network, Machine Learning] ID: 3622277 -
ArticleProgression of hyperspectral remote sensing for estimation of forest health to comply with SDGs
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Forests offer many crucial services to sustain life on Earth as they produce timber, maintain hydrological status and biodiversity conservation, sequesters carbon dioxide (CO2), which play a role in mitigating climate change.Forest’s health is an important parameter to maintain in the dire face of tremendous anthropogenic and natural pressure over many parts of the world. Remote sensing technology offers an opportunity to assess forest health at a local, regional to a global scale. New advancements such as hyperspectral remote sensing (HRS) can provide improved forest health estimation and monitoring. The present study classified hyperspectral Airborne Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) data into four broad classes forests, agriculture, fallow land and water bodies. We used the supervised classification method spectral angle mapper (SAM) to classify hyperspectral image based on endmembers produced. Then, we derived the greenness index, leaf pigment index, canopy water and light use efficiency index and dry or senescent carbon index parameters to monitor the health status of forest tropical of the Shoolpaneshwar wildlife sanctuary (SWS), Gujarat, India. The classification map shows that the dense forest cover in SWS is mainly found in the inner parts of SWS, while the outer parts are occupied with agriculture and fallow land. The health parameters mapsrevealed that the outside zones have low forest health, while the inner forest of SWS has good health. This research effectively uses advanced remote sensing hyperspectral data for forest health monitoring, which helps planning and sustainable forest management. Keywords: Forest health, hyperspectral, AVIRIS-NG, Remote sensing ID: 3623691
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