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Land Use/Land Cover Evaluation Using Trajectory Maps Based on Landsat TM/OLI in Southwest China
Author(s) -
Wenfeng Gong,
Tiedong Liu,
Tao Liu,
Xuanyu Duan,
YueYang Liu,
Philip Stott
Publication year - 2022
Publication title -
nature environment and pollution technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.154
H-Index - 11
eISSN - 2395-3454
pISSN - 0972-6268
DOI - 10.46488/nept.2022.v21i01.004
Subject(s) - trajectory , land cover , grassland , land use , china , remote sensing , period (music) , beijing , environmental science , physical geography , geography , ecology , physics , archaeology , astronomy , acoustics , biology
This study mainly aims to detect the county-level spatio-temporal variability of LULCC (Land Use and Land Cover Change) spatial patterns in Southwest China. Multi-temporal Remote Sensing (RS) images (Landsat TM/OLI in 2000, 2005, 2010, and 2015) were applied to extract land use/cover types at each of the four-time nodes using the Support Vector Machine (SVM) method. Then, the trajectory map methodology was adopted to identify the spatio-temporal distribution characteristics of LULCC patterns throughout the given time series. According to the results, the area of unused land decreased continuously, 0.094% total. An evident decline of grassland by 2.17% was documented, and a notable increase was observed in forestland by 63.94 km2 during the period from 2000 to 2015. Water bodies, built-up land, and unused land showed no significant change throughout the study period. The conversion from grassland to forestland and vice-versa (code 13 or 31) was prominent due to an adjustment made to local forestry policy during the first two periods (2000-2005 and 2005-2010), accounting for 17.55% and 17.56% of the study region, respectively. The anaphase trajectory transition type occupied the smallest area of all the trajectory maps. By contrast, the repetitive trajectory was the leading land-use transition type and covered the largest area. Trajectory analysis provides an effective approach for detecting the spatio-temporal changes in LULCC patterns.

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