Change detection; Citrus orchard; Crop mapping; Data fusion; Google Earth Engine; Precision agriculture; Ecology, Evolution, Behavior and Systematics; Ecology; Modeling and Simulation; Ecological Modeling; Computer Science Applications; Computational Theory and Mathematics; Applied Mathematics
Abstract :
[en] Nowadays crop mapping as an interdisciplinary hot topic attracted both agriculture and remote sensing researchers' interests. This study proposed an automatic method to map citrus orchards in Juybar, Iran, where planting citrus trees is booming there. In this regard, 148 Sentinel-1, Sentinel-2, and ALOS Digital Surface Model (DSM) tiles are processed in Google Earth Engine to provide a hybrid feature set including initial satellite images, Gray Level Co-occurrence Matrix (GLCM) textural features, and spectral features such as vegetation, built-up, bare-soil indices, and the proposed Vegetation Dynamic Index (VDI). A semi-automatic sample selection paradigm is also developed based on a time-series analysis of 12 monthly Normalized Difference Vegetation Indices (NDVIs), Otsu thresholding, multi-level thresholding (MLT), and using two proposed indices called Evergreenness Index (EGI) and Water-covered or No-vegetation (WCNV) index, and finally human post-revision. The output of the Support Vector Machine (SVM) classification using 60,000 samples and the post-classification operation showed that the classified map has an average overall accuracy (OA) and an average kappa coefficient (KC) equal to 99.7% and 0.992, respectively. The results show that multispectral bands lonely extracted orchards with high accuracy (OA: 99.55%, KC: 0.986), and the rest of the features only made a slight improvement to that. For the year 2019, an area of about 4351 ha is estimated as citrus orchards, which is in accordance with the agriculture department's reports (~4700 ha). The results indicate that from 2016 to 2019, despite a “citrus to non-citrus” land-use conversion of about 754 ha, the citrus orchards area was totally expanded by about 17%. Comparing the results with the Google Earth images indicates the precise extraction of orchards with a 10 m spatial resolution. To use the proposed method for extensive cases or areas with other types of evergreen trees, it is recommended to use high-resolution normalized DSMs (nDSMs) and textural features.
Disciplines :
Environmental sciences & ecology
Author, co-author :
Toosi, Ahmad; School of Surveying and Geospatial Engineering, University College of Engineering, University of Tehran, Tehran, Iran
Javan, Farzaneh Dadrass; School of Surveying and Geospatial Engineering, University College of Engineering, University of Tehran, Tehran, Iran ; Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, Netherlands
Samadzadegan, Farhad; School of Surveying and Geospatial Engineering, University College of Engineering, University of Tehran, Tehran, Iran
Mehravar, Soroosh; School of Surveying and Geospatial Engineering, University College of Engineering, University of Tehran, Tehran, Iran
Kurban, Alishir; Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China ; Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, China ; Sino-Belgian Joint Laboratory for Geo-Information, Urumqi, China ; University of Chinese Academy of Sciences, Beijing, China
Azadi, Hossein ; Université de Liège - ULiège > TERRA Research Centre > Modélisation et développement ; Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China ; Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic ; Faculty of Environmental Science and Engineering, Babeș-Bolyai University, Cluj-Napoca, Romania
Language :
English
Title :
Citrus orchard mapping in Juybar, Iran: Analysis of NDVI time series and feature fusion of multi-source satellite imageries
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