Land Consumption Monitoring with SAR Data and Multispectral Indices
| Autor: | Paolo De Fioravante, Luca Congedo, Tania Luti, Nicola Riitano, A Strollo, Valentina Falanga, Ines Marinosci, Michele Munafò, Lorella Mariani |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
land monitoring
010504 meteorology & atmospheric sciences NDVI Science Multispectral image 0211 other engineering and technologies soil sealing semi-automatic classification Sentinel change detection 02 engineering and technology Land cover 01 natural sciences Normalized Difference Vegetation Index 021101 geological & geomatics engineering 0105 earth and related environmental sciences Pixel Decision rule Vegetation Sustainability General Earth and Planetary Sciences Environmental science Cartography Change detection |
| Zdroj: | Remote Sensing, Vol 13, Iss 1586, p 1586 (2021) Remote Sensing; Volume 13; Issue 8; Pages: 1586 |
| ISSN: | 2072-4292 |
| Popis: | 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 7,300 km2 (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 m2 (the pixel size) and over 84.0% of changes above 500 m2. 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|