基于多时相遥感观测的板栗林分布提取研究

板栗林在欧亚、北美等地广泛分布，具有良好的生态价值和经济效益。我国板栗产量居世界首位，是重要的经济树种。使用遥感影像建立板栗林空间分布提取方法能够为其科学管理和高效经营提供定量数据，但树种分类是遥感分类的难点，并且针对板栗林的遥感提取研究较少。以河北省宽城满族自治县为研究区，结合MODIS高时间分辨率特征和Landsat数据较高空间分辨率的特征，研究板栗林提取的最佳时相以及分类特征，并采用多时相观测基于支持向量机算法实现板栗林的提取。结果表明：①4月至6月各地类光谱差异最大，是板栗林提取的关键物候期；②蓝、绿、红、近红外和短波红外波段地表反射率是分类的有效波段，NDI、NDVI、NDWI、RSI和RVI等植被指数增强了植被信息，是板栗林提取的有效分类特征；③单一时相板栗林分类中，生长季前期6月精度最高，生长季后期9月次之，非生长季1月分类结果较差；④结合生长季6月、9月和非生长季1月遥感影像的分类精度最佳，板栗林制图和用户精度分别为89.90%和87.25%。与林业局板栗林面积统计数据相比，精度可达93.45%。

Extraction of Chestnut Forest Distribution based on Multi-temporal Remote Sensing Observations

Chestnut forest is widely distributed in Europe， Asia and North America， and provides notable ecological and economic benefits. Chestnut is an important economic tree species in China， with its production ranks first in the world. The method of extracting the spatial distribution of chestnut forest based on remote sensing image can provide quantitative data for its scientific management. However， the classification of tree species is difficult in remote sensing classification and there are few reports on extraction of chestnut forest based on remote sensing data. Taking Kuancheng county of Hebei province as the research area， this paper integrates MODIS high temporal resolution observations and Landsat high spatial resolution images to select the optimal time phase and classification characteristics， and then chestnut forest was mapped based on multi-temporal Landsat OLI images using Support Vector Machine. The results showed that： （1）the spectral differences were the largest among different vegetation types from April to June， followed by September， which are the key phenological periods for chestnut forest extraction， and January helps to distinguish chestnut forest and evergreen forest； （2）Reflectances in blue， green， red， near-infrared and short-wave infrared bands are the effective bands of classification. NDI， NDVI， NDWI， RSI and RVI vegetation indexes enhance the information of vegetation growth state and coverage， which are effective classification features； （3）In the classification with single temporal image， the accuracy was highest in early growing season in July， followed by late growing season in September， and poor in non-growing season in January； （4）Integrating the images of June， September and January perform best， and the mapping accuracy and user accuracy of chestnut are up to 89.90% and 87.25%. The accuracy can reach 93.45% when compared with the statistics data of chestnut forest area of local forestry bureau in 2018.