Remote Sensing Techniques Of Appet Orehard Information Extraction Based On Multi-source Data In Hilly Areas

Remote sensing technology has been widely used in agriculture. With the development ofinformation technology, massive data was provided for crop planting area extraction by more andmore multi-resolution and multi-spectral remote sensing image.There is large cultivated area of apple in China, and Shandong Province is one of the mainplangting areas. Dynamic monitoring of dominant apple cultivated area and acquisiton of orchardarea and distribution is significant to Chinese apple industry sustainable development.Taking Qixia City as the research region, using different spatial resolution RS image,measured spectral data and GPS survey data, combined with the NDVI and DEM information, thispaper determined the apple orchard distribution range by the various kinds of classification methodsand conducted a systemic study on appropriate extraction methods for different remote sensing data.The main contents and conclusions are following:⑴Optimal temporal selection for apple orchard classificationBased on six CBERS images in apple growth season, the13kinds of vegetation index of appleorchard, other orchards and cultivated land were calculated, and then, analysis of variance was done.The results showed that F test statistic value in April and May was higher than other periods'. Itproved that the remote sensing images of apple florescence can be used to effectively identify theapple orchard in theory. At the same time, the extraction accuracy from ALOS data, CBERS dataand TM data in apple florescence was satisfying.⑵Apple orchard information extraction method using ALOS dataIn the paper, BP artificial neural network was used to extract apple orchards for ALOSspectrum and ALOS spectrum with DEM data. It showed that BP neural network classifier based onALOS spectrum and DEM data was prior, which the area precision was better, the user accuracyand production accuracy were higher89%. It proved that DEM data was an essential geograhic datafor extraction of apple orchard.⑶Apple orchard information extraction method using CBERS dataIn this section, the apple florescence CBERS and multi-temporal CBERS were used as datasource, and vegetation index was adopted for the apple orchard extraction. Conclusions arefollowings:Comparisons of seven kinds of vegetation index and band ratio index showed that the area andspatial precision of RVI-BAND1/BAND2were the best respectively using the apple florescenceCBERS, followed by RDVI-BAND1/BAND2and MSAVI-BAND1/BAND2. Comparisons of spatial estimation with different vegetation indexes indicated that PVI-SARVI was prior to RVIbased on multi-temporal CBERS.⑷Apple orchard information extraction method using TM dataThis paper determined the apple orchard distribution by the decision tree classification methodand the linear spectral unmixing model. Based on measured spectral endmembers, the WaveletTransform was used to improve linear unmixing models. Three spectral mixture analysis methodsincluding improved linear spectral unmixing model based on measured data, linear spectralunmixing model based on measured data, and linear spectral unmixing model based on TM datawere employed to extract the apple orchard information. The results showde that: after accurateatmospheric and topographic correction, the apple orchard information can be effectively extractedby using the improved linear spectral mixture model based on measured data, and the area precisionwas best; the correlation between NDVI of abundance image and average NDVI of ALOS data wasbetter, with R2higher than0.81.