首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >IDENTIFYING LAND USE AND LAND COVER (LULC) CHANGE FROM 2000 TO 2025 DRIVEN BY TOURISM GROWTH: A STUDY CASE IN BALI
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IDENTIFYING LAND USE AND LAND COVER (LULC) CHANGE FROM 2000 TO 2025 DRIVEN BY TOURISM GROWTH: A STUDY CASE IN BALI

机译:识别土地利用和土地覆盖(LULC)从旅游增长推动的2000年到2025年发生变化:巴厘岛的一项研究案例

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Bali has been open to tourism since the beginning of the 20th century and is known as the first tourist destination in Indonesia. The Denpasar, Badung, Gianyar, and Tabanan (Sarbagita) areas experience the most rapid growth of tourism activity in Bali. This rapid tourism growth has caused land use and land cover (LULC) to change drastically. This study mapped the land-use change in Bali from 2000 to 2025. The land change modeller (LCM) tool in ArcGIS was employed to conduct this analysis. The images were classified into agricultural land, open area, mangrove, vegetation/forest, and built-up area. Some Landsat images in 2000 and 2015 were exploited in predicting the land use and land cover (LULC) change in 2019 and 2025. To measure the accuracy of prediction, Landsat 8 OLI images for 2019 were classified and tested to verify the LULC model for 2019. The Multi-Layer Perceptron (MLP) neural network was trained with two influencing factors: elevation and road network. The result showed that the built-up growth direction expanded from the Denpasar area to the neighbouring areas, and land was converted from agriculture, open area and vegetation/forest to built-up for all observation years. The built-up was predicted growing up to 43 % from 2015 to 2025. This model could support decision-makers in issuing a policy for monitoring LULC since the Kappa coefficients were more than 80% for all models.
机译:自20世纪初以来,巴厘岛已经开放了旅游业,被称为印度尼西亚第一个旅游胜地。 Denpasar,Badung,Gianyar和Tabanan(Sarbagita)地区经历了巴厘岛旅游活动的最迅速增长。这种快速的旅游增长导致土地利用和陆地覆盖(LULC)急剧改变。本研究从2000年到2025年映射了巴厘岛的土地使用变化。ArcGIS中的土地改变制动器(LCM)工具用于进行该分析。这些图像被分为农业用地,开放区域,红树林,植被/森林和建筑区域。 2000年和2015年的一些Landsat图像被利用在预测2019年和2025年的土地利用和陆地覆盖(LULC)变化。为了衡量预测的准确性,2019年的Landsat 8 Oli图像被分类和测试,以验证2019年的LULC模型。多层Perceptron(MLP)神经网络培训,有两个影响因素:高程和道路网络。结果表明,从日本巴萨尔地区扩展到邻近地区的内置增长方向,土地被从农业,开放区域和植被/森林转换为所有观察年份。从2015年到2025年预计建立的预计增长至43%。该模型可以支持决策者在所有型号的Kappa系数超过80%以上发出监测策略。

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