Abstract
Quickly and accurately understanding the spatial distribution of regional rubber resources is of great practical significance. Using the unique phenological characteristics of rubber trees derived from remotely sensed data is a common effective method for monitoring rubber trees. However, due to the lack of high-quality images available during the key phenological period, it is still very difficult to apply this method in practical applications. PlanetScope data with high temporal (daily) resolution have great advantages in acquiring high-quality images, but these images have not been previously used to monitor rubber plantations. In this paper, multitemporal PlanetScope images were used as data sources, and the spectral features, index features, first principal components, and textural features of the images were comprehensively utilized. Four classification methods, including a pixel-based random forest (RF) approach, pixel-based support vector machine (SVM) approach, object-oriented RF approach and object-oriented SVM approach, were utilized to discuss the feasibility of using PlanetScope data to monitor rubber forests. The results showed that the optimal time window for monitoring rubber forests in the study area spanned from the 49th day to the 65th day of 2019 according to the MODIS-NDVI analysis. The contribution rate of the difference in the modified simple ratio (dMSR) feature was largest among all considered features for all pixel-based and object-oriented methods. The object-oriented RF/SVM classification method achieved the best classification results with an overall accuracy of 93.87% and a Kappa index of agreement (KIA) of 0.92. The highest producer’s accuracy and user’s accuracy obtained with this method were 95.18% for rubber plantations. The results of this study show that it is feasible to use PlanetScope data to perform rubber monitoring, thus effectively solving the problem of missing images in the optimal rubber monitoring period; additionally, this method can be extended to other real-life applications.
Highlights
Rubber trees (Hevea brasiliensis) are important sources of natural rubber and wood products that meet commodity production requirements
Due to differences in annual temperatures and precipitation, the time of rubber tree defoliation differs annually, and the degree of defoliation of rubber tree leaves at different defoliation stages affects the remote sensing monitoring results
Since this study focused on the identification of rubber plantation through remote sensing, we did not conduct an in-depth exploration of the estimation of dirt roads
Summary
Rubber trees (Hevea brasiliensis) are important sources of natural rubber and wood products that meet commodity production requirements. Rubber tree development in the region of study is often affected by local government policies and the potential economic benefits. It is crucial to conduct accurate and up-to-date monitoring and mapping of the rubber plantation distribution to ensure the health of the rubber industry in the region of study. Compared with traditional manual survey methods, remote sensing technologies have the ability to survey large study areas and rapidly acquire ground object information; these technologies have been widely used in various agricultural fields. The monitoring of rubber forests based on remote sensing technology is recently new, and extensive research is lacking. The first report about rubber mapping with remote sensing was published in 2002 [1], and only a total of 24 documents focusing on this field were
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