Li, W.; Pan, J.; Peng, W.; Li, Y.; Li, C. Garlic Crops’ Mapping and Change Analysis in the Erhai Lake Basin Based on Google Earth Engine. Agronomy2024, 14, 755.
Li, W.; Pan, J.; Peng, W.; Li, Y.; Li, C. Garlic Crops’ Mapping and Change Analysis in the Erhai Lake Basin Based on Google Earth Engine. Agronomy 2024, 14, 755.
Li, W.; Pan, J.; Peng, W.; Li, Y.; Li, C. Garlic Crops’ Mapping and Change Analysis in the Erhai Lake Basin Based on Google Earth Engine. Agronomy2024, 14, 755.
Li, W.; Pan, J.; Peng, W.; Li, Y.; Li, C. Garlic Crops’ Mapping and Change Analysis in the Erhai Lake Basin Based on Google Earth Engine. Agronomy 2024, 14, 755.
Abstract
Garlic, as an important economic crop in China, still has room for improvement in terms of identification using remote sensing technology, Among them, high-precision classification of garlic has become an important subject. The Erhai Lake is an important freshwater lake in China. Under the influence of technology and policies, significant changes have occurred in the cultivation of garlic crops. this study constructed multidimensional features for crop classification suitable for Google Earth Engine, and Propose a method for identifying garlic crops using sample and feature datasets under limited conditions. The results indicate that: 1) In the land-use classification of the Erhai Lake Basin, the importance ranking of characteristic bands, from high to low, is as follows: spectral features, vegetation features, texture features, and terrain features. 2) The Random Forest method based on feature selection demonstrates high classification accuracy in land-use classification within the Erhai Lake Basin in Yunnan Province. The overall classification accuracy reaches 95.79%, with a Kappa coefficient of 0.9481. 3) The expansion direction of garlic cultivation in the Erhai Lake Basin initially strengthened and then weakened from 1999 to 2023. The vertical development of garlic cultivation reached saturation, showing a slow trend towards horizontal expansion between 2005 and 2018. The planting distribution in various townships in the Erhai Lake Basin gradually shifted from a relatively uniform distribution to an upstream development in the basin. This study utilizes the Google Earth Engine (GEE) cloud computing platform and machine learning algorithms to compensate for the lack of statistical data on garlic cultivation in the Erhai Lake Basin. Simultaneously, it accurately, rapidly, and efficiently extracts planting information, demonstrating significant potential for practical applications.
Keywords
GEE; Erhai Lake Basin; Garlic Extract; Feature Selection; Random Forest
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
