Abstract
Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and object-based approaches. A total of 80 variables derived from each WorldView-2 band with pan-sharpening, standardization, principal components and gray level co-occurrence matrix (GLCM) texture indices transformation, were set as the input variables. For pixel-based image analysis (PBIA), 34 variables were selected, including seven principal components, 21 GLCM texture indices and six original WorldView-2 bands. Results showed that support vector machine (SVM) had the highest tea crop classification accuracy (OA = 84.70% and KIA = 0.690), followed by random forest (RF), maximum likelihood algorithm (ML), and logistic regression analysis (LR). However, the ML classifier achieved the highest classification accuracy (OA = 96.04% and KIA = 0.887) in object-based image analysis (OBIA) using only six variables. The contribution of this study is to create a new framework for accurately identifying tea crops in a subtropical region with real-time high-resolution WorldView-2 imagery without field survey, which could further aid agriculture land management and a sustainable agricultural product supply.
Highlights
In recent years, due to global warming, many regions of the Earth have suffered from drastic climate change, which has increased the frequency of extreme climate events
In order to control the production surface area and the amount of production, governments would generally monitor the sensitive crops with prices that fluctuate according to the weather, disasters, or public preferences so as to prevent price collapses, which could cause financial damage to farmers
For more detailed information about the producer’s accuracy (PA) and user’s accuracy (UA) values of each classifier, we found that the PA value of maximum likelihood algorithm (ML) classifier was much higher than others (Figure 6a) since over 50% of study area was interpreted as tea tree LULC
Summary
Due to global warming, many regions of the Earth have suffered from drastic climate change, which has increased the frequency of extreme climate events. Does this cause certain inconveniences for the lives of human beings, but it threatens our lives and property. It is important to have a comprehensive understanding of the acreage of crop production and the market situation of various crops in order to propose important contingency measures in response to the economic losses and potential food crises caused by extreme climate phenomena. In the sub-tropical region in Taiwan, for example, tea is a sensitive crop of high economic value and a featured export product. According to the United Nations Food and Agriculture Organization (FAO), the global production of tea grew from
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