Li Yao, Zhang Li-fu, Huang Chang-ping, Wang Jin-nian, Cen Yi
Guang Pu Xue Yu Guang Pu Fen Xi. 2016 May;36(5):1406-11.
Algal bloom highly impacts the ecological balance of inland lakes. Remote sensing provides real-time and large-scale observations, which plays an increasingly significant role in the monitoring of algal bloom. Various Vegetation Indices (VIs) derived from satellite images have been used to monitor algae. With threshold segmentation of VI, the area of algal bloom can be extracted from images. However, the result of threshold segmentation only reflects the condition of algae when images were generated. Compared to separated VI data obtained at a particular moment of time, temporal spectral VI data contains phonological information of algae, which may be used to evaluate algal bloom more accurately and comprehensively. This study chose MODIS NDVI data of the Lake Taihu from 2001 to 2013, and constructed temporal spectral data for each year. Then, we determined the feature temporal spectra of severe cyanobacteria bloom, moderate cyanobacteria bloom, slight cyanobacteria bloom and aquatic plants, and separated these four kinds of objects using SVM (Support Vector Machine) algorithm, getting the spatial distribution and area of them. In order to compare the results of our method with traditional threshold segmentation method, we chose 8 separated NDVI images from the temporal spectral data of 2007. With the threshold 0.2 and 0.4, cyanobacteria bloom was classified into three degrees: severe cyanobacteria bloom, moderate cyanobacteria bloom, and slight cyanobacteria bloom. By comparison, it showed that our method reflected cyanobacteria bloom more comprehensively, and could distinguish cyanobacteria and aquatic plants using the phonological information provided by NDVI temporal spectra. This study provides important information for monitoring the algal bloom trends and degrees of inland lakes, and temporal spectral method may be used in the forecast of algal bloom in the future.
藻华对内陆湖泊的生态平衡有重大影响。遥感可提供实时的大规模观测，在藻华监测中发挥着越来越重要的作用。从卫星图像中提取的各种植被指数（VIs）已被用于监测藻类。通过对植被指数进行阈值分割，可从图像中提取藻华面积。然而，阈值分割的结果仅反映了图像生成时藻类的状况。与特定时刻获取的离散植被指数数据相比，植被指数的时间序列光谱数据包含藻类的物候信息，可用于更准确、全面地评估藻华。本研究选取了2001年至2013年太湖的MODIS归一化植被指数（NDVI）数据，构建了每年的时间序列光谱数据。然后，确定了重度蓝藻水华、中度蓝藻水华、轻度蓝藻水华和水生植物的特征时间光谱，并使用支持向量机（SVM）算法对这四类对象进行分离，得到它们的空间分布和面积。为了将我们的方法与传统阈值分割方法的结果进行比较，我们从2007年的时间序列光谱数据中选取了8幅离散的归一化植被指数图像。以0.2和0.4为阈值，将蓝藻水华分为重度蓝藻水华、中度蓝藻水华和轻度蓝藻水华三个等级。通过比较表明，我们的方法能更全面地反映蓝藻水华情况，并可利用归一化植被指数时间序列光谱提供的物候信息区分蓝藻和水生植物。本研究为监测内陆湖泊藻华趋势和程度提供了重要信息，时间序列光谱方法未来可能用于藻华预测。
