As the reflectivity is very low in near-infrared spectral band for water, it is easy to extract water bodies in remote
sensing images with the use of NDVI (Normalized Difference Vegetation Index).The problem is that we always have to try
several times to find the appropriate threshold to separate water bodies from land. In this paper, a particular method was
developed to solve this problem by automatically determining the threshold that was used to extract the specific water body.
We select both Chaohu Lake and Taihu Lake as the study regions. First, we generate NDVI image of the study region from
GF-1 data after several pre-processing procedures. Then we resize the NDVI image to make it contain approximately the
same number of water and land pixels. Because the NDVI value is lower for water than that for land, there will appear two
peaks in the histogram which we derived from the resized NDVI image. The threshold locates at the lowest place between
the two peaks can be chosen as the proper threshold used for land/water delineation. With the use of a reasonable threshold
range we can finally get the threshold by calculating the minimum value in it, and extract the water body successfully.
Ulva prolifera, a kind of green macroalgae, is nontoxic itself, however, its bloom has bad effects on the marine
environment, coastal scene, water sports and seashore tourism. Monitoring of the Ulva prolifera by remote sensing
technology has the advantages of wide coverage, rapidness, low cost and dynamic monitoring over a long period of time.
The GF-1 satellite was launched in April 2013, which provides a new suitable remote sensing data source for monitoring
the Ulva prolifera. At present, segmenting image with a threshold is the most widely used method in Ulva prolifera
extraction by remote sensing data, because it is simple and easy to operate. However, the threshold value is obtained
through visual analysis or using a fixed statistical value, and could not be got automatically. Facing this problem, we
proposed a new method, which can obtain the segmentation threshold automatically based on the local maximum gradient
value. This method adopted the average NDVI value of local maximum gradient points as the threshold, and could get an
appropriate segmentation threshold automatically for each image. The preliminary results showed that this method works
well in monitoring Ulva prolifera by GF-1 WFV data.