4.7 Article

Land Consumption Monitoring with SAR Data and Multispectral Indices

期刊

REMOTE SENSING
卷 13, 期 8, 页码 -

出版社

MDPI
DOI: 10.3390/rs13081586

关键词

soil sealing; semi-automatic classification; Sentinel; NDVI; land monitoring; change detection

资金

  1. Italian Institute for Environmental Protection and Research (ISPRA)
  2. Italian Space Agency (ASI) [F82F17000010005]

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This study aims to develop a methodology to reduce the effort of photointerpretation in detecting land consumption changes in Italy. The results show that the developed methodology reduces the area to be photointerpreted to 2.4% of the total Italian surface, efficiently detecting the majority of larger changes.
Land consumption is the increase in artificial land cover, which is a major issue for environmental sustainability. In Italy, the Italian Institute for Environmental Protection and Research (ISPRA) and National System for Environmental Protection (SNPA) have the institutional duty to monitor land consumption yearly, through the photointerpretation of high-resolution images. This study intends to develop a methodology in order to produce maps of land consumption, by the use of the semi-automatic classification of multitemporal images, to reduce the effort of photointerpretation in detecting real changes. The developed methodology uses vegetation indices calculated over time series of images and decision rules. Three variants of the methodology were applied to detect the changes that occurred in Italy between the years 2018 and 2019, and the results were validated using ISPRA official data. The results show that the produced maps include large commission errors, but thanks to the developed methodology, the area to be photointerpreted was reduced to 7300 km(2) (2.4% of Italian surface). The third variant of the methodology provided the highest detection of changes: 70.4% of the changes larger than 100 m(2) (the pixel size) and over 84.0% of changes above 500 m(2). Omissions are mainly related to single pixel changes, while larger changes are detected by at least one pixel in most of the cases. In conclusion, the developed methodology can improve the detection of land consumption, focusing photointerpretation work over selected areas detected automatically.

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