Abstract
The expansion of rubber (Hevea brasiliensis) plantations has been a critical driver for the rapid transformation of tropical forests, especially in Thailand. Rubber plantation mapping provides basic information for surveying resources, updating forest subplot information, logging, and managing the forest. However, due to the diversity of stand structure, complexity of the forest growth environment, and the similarity of spectral characteristics between rubber trees and natural forests, it is difficult to discriminate rubber plantation from natural forest using only spectral information. This study evaluated the validity of textural features for rubber plantation recognition at different spatial resolutions using GaoFen-1 (GF-1), Sentinel-2, and Landsat 8 optical data. C-band Sentinel-1 10 m imagery was first used to map forests (including both rubber plantations and natural forests) and non-forests, then the pixels identified as forests in the Sentinel-1 imagery were compared with GF-1, Sentinel-2, and Landsat 8 images to separate rubber plantations and natural forest using two different approaches: a method based on spectral information characteristics only and a method combining spectral and textural features. In addition, we extracted textural features of different window sizes (3 × 3 to 31 × 31) and analyzed the influence of window size on the separability of rubber plantations and natural forests. Our major findings include: (1) the suitable texture extraction window sizes of GF-1, Sentinel-2, and Landsat 8 are 31 × 31, 11 × 11 to 15 × 15, and 3 × 3 to 7 × 7, respectively; (2) correlation (COR) is a robust textural feature in remote sensing images with different resolutions; and (3) compared with classification by spectral information only, the producer’s accuracy of rubber plantations based on GF-1, Sentinel-2, and Landsat 8 was improved by 8.04%, 9.44%, and 8.74%, respectively, and the user’s accuracy was increased by 4.63%, 4.54%, and 6.75%, respectively, when the textural features were introduced. These results demonstrate that the method combining textural features has great potential in delineating rubber plantations.
Highlights
Of the 56.8 million hectares of plantations in the tropics in 2015, 29.9 million ha are in South and Southeast Asia [1]
Rubber plantations were confused with natural forest in the classification that only used spectral bands
The producer’s accuracy of rubber delineation results for Sentinel-2 and Landsat 8 data was less than 82%, and for GF-1 the rubber plantations producer’s accuracy was the lowest with an accuracy of only 79.37%, which implied that the resultant rubber plantation map produced by GF-1 had the largest number of rubber plantations that was misclassified as natural forests
Summary
Of the 56.8 million hectares (ha) of plantations in the tropics in 2015, 29.9 million ha are in South and Southeast Asia [1] These plantations are crucial in global climate change regulation [2] and natural resource protection [1] as they increase carbon dioxide fixation and carbon sink with high growth rates, and provide large quantities of wood and other products and services [3]. While large-scale rubber planting increases the income of local governments and farmers, it causes many ecological and environmental problems, such as the dramatic reduction of tropical rainforest, soil degradation, and loss of biodiversity in rubber planting areas [4,5]. Accurate maps of rubber plantations are required to manage forests effectively and document the expansion of rubber plantations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
