Study On The Ecotone Of Tree Species And Its Environmental Factors Extracted By Aerial-ground-space Remote Sensing

Pinus yunnanensis and P.kesiya var.langbianensis are two important coniferous timber tree species in Yunnan Province,accounting for more than 30% of the distribution area of forest land in Yunnan,which are characteristic forest types in southwest China.In the past,surveys on the demarcation among tree species were mainly conducted by ground surveys and local historical documents which are low accuracy and high cost.Remote sensing technology of ground,aerial and space were integrated to exert advantages of ground sample surveys,aerial images with high-spectrum and spatial resolution,and satellite remote sensing images at large regional scales.It could solve tree species identification and distribution problems more effectively.The research on the ecotone of tree species has great significance to the investigation of forest resources,introduction and origin of tree species.Based on the ground sample survey and the vector data of the second class forest-survey,hyperspectral image data acquired by the CAF-Li CHy aerial system AISA Eagle II was used to select the original hyperspectral,first-order differential,and second-order differential images to analyze the spectral characteristics band of P.yunnanensis and P.kesiya var.langbianensis forests.Using the method of multifactoranalysis of variance,the effects of topography,solar altitude,and other environmental factors on canopy spectra of P.kesiya var.langbianensis forest were analyzed and a spectral library was established.According to the characteristic bands obtained from hyperspectral image,high spatial resolution Gaofen 2 image was selected as the satellite image for large-scale tree species recognition.Quantitative evaluation combined with qualitative analysis was used to analyze the fusion effects of Brovey,PC,Gram-Schmidt transform,and NNDiffuse methods on Gaofen 2 panchromatic and multispectral images.The maximum likelihood and BP neural network classifiers were used to identify the P.yunnanensis and P.kesiya var.langbianensis in the original multispectral and fused images to generate a distribution map of the two tree species.Based on the topographic factors extracted from DEM imagery,the relationship between the distribution of P.yunnanensis and P.kesiya var.langbianensis and elevation,slope,and aspect was studied,respectively.Inaddition,the influence of the distribution of P.yunnanensis and P.kesiya var.langbianensis was analyzed.The main conclusions are as follows:(1)Spectral reflectance of P.yunnanensis and P.kesiya var.langbianensis forest canopy reflects the reflectance of green vegetation.In the visible wavelength range,the reflectance is generally low(less than 0.070),showing a tendency to decline after a small increase.The reflectivity of 0.89 ?m reaches the highest point in the range of 0.4-1.0 ?m wave band,and the P.kesiya is 0.327,the P.yunnanensis is 0.235.The canopy of the P.kesiya var.langbianensis is generally reflected higher than P.yunnanensis.(2)We analyze the influence of slope direction,solar elevation angle and other factors on canopy spectra of P.kesiya var.langbianensis forest,and found that the spectral reflectance of shady slope forest canopy is generally higher than that of sunny slope.Besides the north slope,northeast slope and south slope,southeast slope peak band reflectance have asignificantly differenceat the 0.05 level.According to the solar elevation angle,the highest reflection 0.89 ?m band is taken as an example.The east slope,northeast slope,and southeast slope of the light surface are more reflective than the west slope of backlight surface,northwest slope,and southwest slope.The rates increased by 23%,17%,and 14%.The maximum difference in reflectivity was between west and east,reaching 0.0671.The difference between northeast and northwest,southeast and southwest reached to 0.0538,0.0426,respectively.(3)Combining with visual interpretation,the six indicators of mean,mean deviation,standard deviation,correlation coefficient,information entropy,and optimal index were used to qualitatively and quantitatively evaluate the effect of Gaofen 2 image fusion.The experimental results showed that PC,Gram-Schmidt and NNDiffuse can reflect the original spectral information of the image well.The Gram-Schmidt method has the smallest mean deviation and the highest spectral fidelity that is the closest to the original image,but there are some images missing;NNDiffuse method has the highest index value of 660.68.The image contains more information and NNDiffuse is the best image fusion method.(4)The maximum likelihood and BP neural network classifier were used to classify the original multispectral and fusion images.It is found that the classification accuracy of BP neural network is better than maximum likelihood,and the recognition effect of P.yunnanensis and P.kesiya var.langbianensis forest is better.Using BP neural network classifier,multispectral images have the highest classification accuracy,with an overall accuracy of 74.63% and a Kappa coefficient of 0.6077.Concretely,80.4% and 79.43% of the ratios of P.yunnanensis and P.kesiya var.langbianensis can be accurately distinguished;NNDiffuse fusion method classification accuracy is the highest in all fused image,the overall accuracy is 72.17%,and the Kappa value is 0.5488.The fused image changes the spectrum of the tree species to a certain extent,which reduces the classification accuracy.(5)Based on the classification results of P.yunnanensis and P.kesiya var.langbianensis forests,and combining the data with the second class forest-survey,the ecotone of P.yunnanensis and P.kesiya var.langbianensis were divided,and P.yunnanensis and P.kesiya var.langbianensis in Yunnan Province were found in Honghe-Yuanjiang-Xinping-Shuangbai-Chuxiong-Nanhua-Nanxun-Fengqing-Yunxian-Linyi-Shuangjiang-Wuyuan-Hummer-Zhenkang-Longling-Tengchong line adjacent to the distribution.Combining with the analysis of mountains,rivers and other factors,it was found that the interlaced belts of P.yunnanensis and P.kesiya var.langbianensis forests are mainly in the Ailao Mountain and Lancang Rivers.P.kesiya var.langbianensis mainly distributes in the west of Ailao Mountain.and located east of Lancang River,P.yunnanensis is mainly distributed in the east of Ailao Mountain and west of the Lancang River.(6)DEM images were used to extract topographical factors such as elevation,slope and aspect,and the effect of topographic factors on the distribution of P.yunnanensis and P.kesiya var.langbianensis was analyzed.It was found that 51.97% P.yunnanensis distributed in the range of 1500-2000 m above sea level;58.59% P.kesiya var.langbianensis distributed in the elevation of 1000-1500 m.The differences in the distribution areas of slopes and slopes between P.yunnanensis and P.kesiya var.langbianensis is smaller,which shows a tendency of increasing first and then decreasing with the increase of slope.The distribution of sunny slopes is larger than that of shady slope.