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Title: | Assessing long-term deforestation in nam san watershed, loei province, thailand using a dyna-clue model |
Authors: | Waiyasusri K. Wetchayont P. |
Issue Date: | 2020 |
Abstract: | This research analyzed land-use changes (LUC) in the Nam San Watershed (NSW) by applying geoinformatics methods and land-use modeling approach to explore LUC in the past. Landsat satellite images from years 2002, 2007 and 2013 were classified using a maximum likelihood algorithm to create land-use maps. For assessing future LUC over a period of twenty years (2014–2033), land-use simulations were conducted using a dynamic LUC model (Dyna-CLUE model) in two land management scenarios: Scenario 1 is a simple projection of the LUC trend without reservation area, while Scenario 2 projects the LUC trend with reservation area in future periods. NSW land-use maps for 2002–2013 were analyzed using geoinformatics technology. The results revealed that the amount of forested area within the NSW has reduced drastically, from 380.40 km² to 267.23 km², changing to fields and perennial crops, which the logistic regression identified as being influenced by a slope factor. These data was used as a reference for LUC detection with the model simulation in two scenarios. Model results have shown that by 2033, Scenario 1 predicts a significant decrease in the overall forest area, from 72.21 km² to 41.55 km² in Phu Ruea district, and from 107.31 km² to 45.62 km² in Phu Luang district. Whereas Scenario 2 predicts slightly decreasing forest area within the reservation area, but rapid decrease, from 177.86 km² to 28.54 km² outside the reservation area, where the distance to village factor is the main influencer. These findings highlight the importance and the potential of model predictions for planning activities to protect forested areas. © 2020, Russian Geographical Society. All rights reserved. |
URI: | https://ir.swu.ac.th/jspui/handle/123456789/12187 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099566010&doi=10.24057%2f2071-9388-2020-14&partnerID=40&md5=283024cdc74ebc5b58671c9acff10504 |
ISSN: | 20719388 |
Appears in Collections: | Scopus 1983-2021 |
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