Photography Estimates Of Vegetation Coverage And Its Relationship With Vegetation Index

Vegetation coverage (VC) is the most important index to measure the vegetation status of the earth's surface and the major factor to affect the soil erosion. The change of vegetation coverage indicates the changes of global and regional entironment. Moreover, it is necessary to improve the measurement and its precision of vegetation coverage for the development of all relative fields. Since the improvement of techniques and the need of applications, both the measurement of the earth's surface and the retrieval models of remote sensing all put forward new demands for the researches on relative fields.Based on the project of national natural finance "research on information extraction, monitoring and identification of land use/land cover for SPOT images"(number: 40371053), this paper choose NanJing City and the farmland around it as the research area.We also choose NDVI, the most widely used index as the index of vegetation information of remote sensing from all kinds of vegetation indexes extracted from the SPOT and the ETM+ images of this area. With the field survey and the remote sensing imaging synchronously, we worked for more than 20 days and went hundreds of kilometers to get the longitude/latitude data of GCPs and all the samples by means of GPS with sub-meter precision.Meanwhile, we combined the digital photography method with traditional ocular method to get VC of the samples.Eventually, we set up relative models of this area based on different satellite images, different kinds of vegetation and different density of vegetation with the precisions above 80%.The achievements of the paper include:1 DataDuring the hard field survey, we got the data of hundreds of samples for the two satellite images including VC, VC photos, longitude and latitude of each sample center, types of vegetation and types of land use, as well as GCP data which used as geometrical correction references.2 Field measurement methodsWe successfully designed and founded the vegetation coverage collection system (VCCS) based on the digital camera and DGPS; According to different satellite images and vegetation, we adopted"5 points method"for SPOT monolayer vegetation, "5 pairs method"for SPOT multilayer vegetation and"5 sections method"for ETM+ data respectively. For multilayer vegetation, we set up calculating models to calculate VC of the samples from"upward and downward"VC; All photos of VC were taken vertically.3 Processing of the digital imagesWe designed different models of decision tree for photos taken from upwards and downwards according to the spectrum characters of visible light. By this way, we could extract VC information from photo data quickly and exactly and overcome the shortage of traditional classify method. Based on the differences of the location of the center point in each pixel on remote sensing images, we classified the samples into A, B and C types and processed in "adjacent field". The precision of correspondence to pixel space of samples was improved notably.4 Relative modelsWe founded different VC-VI relative models successfully including CM model based on vegetation types for SPOT image, DM model based on vegetation density for SPOT image and AM model based on the whole study area for SPOT image and ETM+ image. In the CM model for SPOT image, we divided the vegetation into four kinds including grass, shrub, forest and crop according to vegetation region and its structure. We also build four relative models of linearity and nonlinearity respectively, while the relativity of nonlinear models is better than the linear models (y=VC, x=NDVI):Grass y=4.8253x2+2.0624x+0.3579 (R2=0.6636)Shrub y=-4.562x2-K).899x+0.5546 (R2=0.6356)Forest y=-2.0552x2+0.4631x-K).8612 (R2=0.7145)Crop y=1.4706x2+1.3582x+0.4734 (R2=0.7391)We tested the models below from application and precision:1) The DM model for SPOT imageWe set up linear and nonlinear regressive models according to different grade of vegetation density of SPOT samples and tested three kinds of nonlinear models as follows. The whole precision is 86.7045%.?the sparse vegetation y=-0.649x2+0.5616x+0.2762 (R2=0.7120)?the normal density vegetation y=-0.5031 x2+0.3788x+0.5578 (R2=0.7437)?the dense vegetation y=-0.1644x2+0.3397x+0.8661 (R2=0.6851)2) The AM model for SPOT imageIt is a VC abstraction model for the whole image without classification for SPOT image. We take NDVI from the SPOT image as the independent variable and AM model as the band operation equation to abstract the VC data.We founded one simple equation, one quadratic equation, one cubic equation and one biquadrate equation respectively and tested the cubic equation which performed better with the precision of 86.7447%. The model is:y=-6.4515x3-0.5789x2+2.2346x+0.5697 (R2=0.7128 )3) The AM model for ETM+ imageIt is a VC abstraction model for the whole image without classification for ETM+ image. We take NDVI from the ETM+ image as the independent variable and AM model as the band operation equation to abstract the VC data. We founded one simple equation, one quadratic equation, one cubic equation and one biquadrate equation respectively and tested the cubic equation which performed better with the precision of 88.5053%. The model is:y=-6.4515x3-0.5789x2+2.2346x+0.5697 (R2=0.8804)...