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.

中文翻译：

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遥感数据中的纹理特征对不同空间分辨率水平下橡胶林提取的影响

橡胶（巴西橡胶树）的膨胀）人工林一直是热带森林快速转型的关键动力，尤其是在泰国。橡胶园地图提供了基本信息，可用于勘测资源，更新森林子图信息，记录和管理森林。但是，由于林分结构的多样性，森林生长环境的复杂性以及橡胶树和天然林之间光谱特征的相似性，仅使用光谱信息很难将橡胶人工林与天然林区分开。这项研究使用高芬1（GF-1），前哨2和Landsat 8光学数据评估了在不同空间分辨率下橡胶树识别的纹理特征的有效性。首先使用C波段Sentinel-1 10 m图像绘制森林（包括橡胶林和天然林）和非森林的地图，然后将Sentinel-1图像中识别为森林的像素与GF-1，Sentinel-图2和Landsat 8图像使用两种不同的方法将橡胶园和天然林分开：仅基于光谱信息特征的方法以及结合光谱和纹理特征的方法。此外，我们提取了不同窗口大小（3×3到31×31）的纹理特征，并分析了窗口大小对橡胶园和天然林可分离性的影响。我们的主要发现包括：（1）GF-1，Sentinel-2和Landsat 8的合适的纹理提取窗口大小分别为31×31、11×11至15×15和3×3至7×7；（2）相关性（COR）是具有不同分辨率的遥感图像中的稳健纹理特征；（3）与仅按光谱信息分类相比，基于GF-1，Sentinel-2和Landsat 8的橡胶园生产者的准确性分别提高了8.04％，9.44％和8.74％，用户的准确性也得到了提高。引入纹理特征后，分别增加了4.63％，4.54％和6.75％。这些结果表明，结合纹理特征的方法在描绘橡胶园方面具有很大的潜力。引入了纹理特征后，用户的准确性分别提高了4.63％，4.54％和6.75％。这些结果表明，结合纹理特征的方法在描绘橡胶园方面具有很大的潜力。引入了纹理特征后，用户的准确性分别提高了4.63％，4.54％和6.75％。这些结果表明，结合纹理特征的方法在描绘橡胶园方面具有很大的潜力。