Information about species mapping is an essential approach for the management of forests and sustainable practices of conservation. Remote sensing data have proven to be an asset for the assessment of the spatial distribution of species over time. We used time-series data from a single sensor (PlanetScope) and dual-sensor (PlanetScope with Sentinel-2) to map Dalbergia sissoo, a timber species, found on both sides of the Jakhan river in Dehradun district of Lesser Himalaya. The dimensionality of the temporal images was reduced by normalized difference vegetation index (NDVI) and class-based sensor independent NDVI (CBSI-NDVI). Separability analysis was conducted to find optimal dates of the data set using three measures of separability, and Euclidean distance was found to be the best measure of separability for both the indices. Due to limitations of the classifiers in handling mixed pixels, a fuzzy-based modified possibilistic c-means (MPCM) algorithm was tested to extract a single class, i.e., Shisham (Dalbergia sissoo) tree. We used the conventional mean and individual sample as mean (ISM) as training parameter concepts in the MPCM supervised classification approach. We found that the ISM approach was able to handle the heterogeneity within the class for both vegetation indices. It was seen that PlanetScope data were able to spatially map the target class in a better way, whereas combined data of PlanetScope and Sentinel-2 helped to reduce the spectral overlap between target and nontarget classes. An accuracy assessment was performed using mean membership difference, variance, and entropy where variance and entropy showed that NDVI-based ISM approach outperformed the CBSI-NDVI-based approach. Both single-and dual-sensor time-series data showed good classification results for single species extraction.

中文翻译：

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研究小喜马拉雅地区黄檀制图的双传感器时间序列遥感数据

关于物种绘图的信息是森林管理和可持续保护实践的基本方法。遥感数据已被证明是评估物种随时间的空间分布的资产。我们使用来自单传感器 (PlanetScope) 和双传感器 (PlanetScope with Sentinel-2) 的时间序列数据来绘制 Dalbergia sissoo 的地图，这是一种在小喜马拉雅地区 Dehradun 区 Jakhan 河两岸发现的木材物种。通过归一化差分植被指数 (NDVI) 和基于类别的传感器独立 NDVI (CBSI-NDVI) 降低了时间图像的维度。进行可分离性分析以使用三个可分离性度量来找到数据集的最佳日期，并且发现欧几里德距离是两个指标的可分离性的最佳度量。由于分类器在处理混合像素方面的局限性，测试了基于模糊的改进的可能性 c 均值 (MPCM) 算法来提取单个类，即 Shisham (Dalbergia sissoo) 树。我们在 MPCM 监督分类方法中使用常规均值和单个样本均值 (ISM) 作为训练参数概念。我们发现 ISM 方法能够处理两个植被指数的类内异质性。可以看出，PlanetScope 数据能够以更好的方式对目标类进行空间映射，而 PlanetScope 和 Sentinel-2 的组合数据有助于减少目标类和非目标类之间的光谱重叠。使用平均隶属度差、方差、和熵，其中方差和熵表明基于 NDVI 的 ISM 方法优于基于 CBSI-NDVI 的方法。单传感器和双传感器时间序列数据均显示出良好的单物种提取分类结果。