基于GF-2光譜特征的石漠化信息自動提取
發(fā)布時間：2019-04-07 19:58
【摘要】：石漠化(Stony Desertification),是指土地處在熱帶氣候、亞熱帶濕潤氣候、半濕潤氣候條件下,以及喀斯特石山區(qū)巖溶極其發(fā)育的背景下,由于自然因素和人類不合理的經(jīng)濟生產(chǎn)、生活活動等因素導(dǎo)致的土地劣化現(xiàn)象。石漠化是土地退化的一種極端形式,導(dǎo)致缺水、少土和土質(zhì)貧瘠,石漠化發(fā)展最直接的后果就是土地資源的喪失。石漠化與沙漠化、水土流失一起成為我國的三大土地生態(tài)災(zāi)害。本研究以貴州省貴陽市觀山湖區(qū)為研究對象,以2016年觀山湖區(qū)的GF-2遙感數(shù)據(jù)為基礎(chǔ),通過分析GF-2光譜特征提取觀山湖區(qū)石漠化小班數(shù)據(jù)、第二次貴州全省石漠化調(diào)查資料及附屬資料成果數(shù)據(jù)、進行實地特征點調(diào)查所獲取的最新石漠化現(xiàn)狀數(shù)據(jù),經(jīng)多次實驗驗證之后,最終選取了一種能區(qū)分GF-2影像中石漠化土地與非石漠化土地的波段運算算法,通過這套算法對GF-2影像進行處理之后,再進行分類。并應(yīng)用IDL語言編制了一個石漠化信息自動提取模塊,應(yīng)用此模塊在ENVI軟件中自動提取GF-2遙感影像的石漠化信息。應(yīng)用此分類方法和自動提取模塊,最后獲得了貴陽市觀山湖區(qū)第三次石漠化監(jiān)測的相關(guān)成果,并對觀山湖區(qū)石漠化進行分級和空間分布特征分析。在此基礎(chǔ)上,對觀山湖區(qū)石漠化監(jiān)測結(jié)果進行了綜合分析,得出了觀山湖區(qū)石漠化的成因與動態(tài)變化(與第一次、第二次監(jiān)測結(jié)果進行對比分析)。研究成果可為觀山湖區(qū)、貴陽市乃至貴州省的石漠化監(jiān)測和治理提供科學(xué)的參考依據(jù)。本論文的主要研究成果包括:1)通過對GF-2數(shù)據(jù)進行0IF指數(shù)分析,結(jié)合聯(lián)合熵驗證,確定波段2+波段3+波段4組合包含豐富的信息量,是觀山湖區(qū)石漠化信息提取的最佳波段組合。2)依據(jù)貴州省第三次石漠化監(jiān)測技術(shù)標(biāo)準(zhǔn)與GF-2影像的可分性,確定了貴陽市觀山湖區(qū)石漠化等級劃分體系:即非石漠化、潛在石漠化、重度石漠化、中度石漠化、輕度石漠化。3)采用最大似然法對GF-2遙感影像進行分類,分類后的道路與建筑用地界限分明,與真彩色圖像契合度極高的同時石漠化斑塊的分類效果較好。同時開展面向?qū)ο笮畔⑻崛⊙芯?此時觀山湖區(qū)的GF-2遙感影像分割合并的參數(shù)為分割尺度=47,合并尺度=95。4)分別對最大似然法與基于樣本的面向?qū)ο笫畔⑻崛∵M行精度評價,其中最大似然法(經(jīng)過波段運算)的最終分類精度為83.2309%,Kappa系數(shù)為0.6752;最大似然法(未經(jīng)過波段運算)的最終分類精度為77.8021%,Kappa系數(shù)為0.6147;基于樣本的面向?qū)ο笫畔⑻崛∽罱K分類精度為74.362%,Kappa系數(shù)為0.6233,與。經(jīng)過波段運算后,最大似然法分類的結(jié)果提高了 8.8689個百分點。5)利用IDL語言編寫了基于GF-2光譜特征的觀山湖區(qū)石漠化信息自動提取模塊,從而完成了石漠化信息提取的模塊化。模塊的運行能快速準(zhǔn)確地提取石漠化信息,并與ENVI實現(xiàn)有效對接,提高石漠化信息提取的效率。此模塊包括波段運算模塊與最大似然法模塊兩個部分。6)制作出了觀山湖區(qū)石漠化現(xiàn)狀和程度分布圖,并綜合分析了觀山湖區(qū)石漠化監(jiān)測區(qū)地類分布情況、石漠化與潛在石漠化土地分布情況和石漠化強度分布情況。觀山湖區(qū)非石漠化面積8165.5hm2,潛在石漠化面積9462.6 hm2,石漠化面積3904.6 hm2。石漠化土地中,輕度石漠化面積1351.8 hm2,中度石漠化面積2148.5 hm2,重度石漠化面積404.3 hm2。
[Abstract]:Stony Destification means that the land is under the condition of tropical climate, subtropical humid climate, semi-humid and humid climate, and in the background that the karst in the karst area is extremely developed, due to the natural factors and the unreasonable economic production of the human, Land degradation caused by factors such as living activities. Rocky desertification is an extreme form of land degradation, resulting in a lack of water, less soil and poor soil, and the most direct consequence of the development of rocky desertification is the loss of land resources. Stone desertification and desertification and water and soil loss become the three major land ecological disasters in China. This study is based on the GF-2 remote sensing data of the Guanyan Lake region of Guiyang, Guizhou Province. Based on the data of GF-2 remote sensing data in the region of Guanshan Lake in 2016, the data of the data and the data on the data of the data and the data on the data of the data of the rock and stone in the whole province of Guizhou Province are analyzed by the analysis of the GF-2 spectrum. The present data of the latest stone in the field characteristic point survey is carried out. After many experiments and verification, a band operation algorithm for distinguishing the land of the stone and the non-stone land in the GF-2 image is finally selected, and after the GF-2 image is processed by the algorithm, the classification is carried out. And an automatic extraction module for stone information is prepared by using the IDL language, and the block information of the GF-2 remote sensing image is automatically extracted in the ENVI software by using the module. The classification method and the automatic extraction module are applied, and the relevant results of the third stone-rock monitoring in the Guanshan Lake region of Guiyang are finally obtained, and the characteristics of the classification and the spatial distribution of the stone-stone in the Guanshan Lake region are analyzed. On the basis of this, a comprehensive analysis of the results of the monitoring of the rock-stone in the Great Lakes region is carried out, and the genetic and dynamic changes of the stone-stone in the Great Lakes region are obtained (compared with the first and the second monitoring results). The research results can provide scientific reference for the monitoring and management of the stone-rock in the Great Lakes region, Guiyang and the Guizhou province. The main research results of this paper are as follows:1) By performing the 0 IF index analysis on the GF-2 data, the combined entropy verification is combined to determine that the band 2 + band 3 + band 4 combination contains a rich amount of information, It is the best wave band combination of the information extraction of the stone-stone in the Guanshan Lake region.2) According to the separability of the third stone-stone monitoring technology standard and the GF-2 image in Guizhou, the division system of the stone-stone level in the Guanyan Lake region of Guiyang is determined: that is, the non-stone-stone, the latent stone-stone and the severe stone-stone. The classification of GF-2 remote sensing image was carried out by using the maximum likelihood method, and the classification of the road and the building was clear, and the classification effect of the stone plaque was better than that of the true color image. At the same time, the object-oriented information extraction is carried out. At this time, the parameters of the GF-2 remote sensing image segmentation and merging in the Guanshan Lake region are the division scale = 47, the combined scale = 95.4), and the accuracy evaluation is carried out on the maximum likelihood method and the sample-based object-oriented stone extraction information respectively, The final classification accuracy of the maximum likelihood method (through band operation) is 83.2309%, the Kappa coefficient is 0.6752, the final classification accuracy of the maximum likelihood method (not through band operation) is 77.8021%, the Kappa coefficient is 0.6147, and the final classification accuracy of the sample-based object-oriented stone extraction information is 74.362%. The Kappa coefficient was 0.6233, compared to. After the wave band operation, the result of the maximum likelihood classification is increased by 8.8689%.5) The automatic extraction module of the stone-level information based on the GF-2 spectral characteristic is prepared by using the IDL language, thus the modularization of the information extraction of the stone information is completed. The operation of the module can extract the stone information quickly and accurately, and can effectively interface with the ENVI to improve the efficiency of the stone extraction information extraction. The module includes two parts of the band operation module and the maximum likelihood module. The distribution of the land and the distribution of the stone and stone. The area of the non-stone area in the Guanshan Lake area is 8165.5hm2, the potential stone area is 9462.6hm2, and the stone area is 3904.6hm2. In the land of Shifang, the area of the light stone is 1351.8hm2, the area of the medium stone is 2148.5hm2, and the area of the severe stone is 404.3hm2.
【學(xué)位授予單位】：中南林業(yè)科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：X171;X87
，
本文編號：2454377
[Abstract]:Stony Destification means that the land is under the condition of tropical climate, subtropical humid climate, semi-humid and humid climate, and in the background that the karst in the karst area is extremely developed, due to the natural factors and the unreasonable economic production of the human, Land degradation caused by factors such as living activities. Rocky desertification is an extreme form of land degradation, resulting in a lack of water, less soil and poor soil, and the most direct consequence of the development of rocky desertification is the loss of land resources. Stone desertification and desertification and water and soil loss become the three major land ecological disasters in China. This study is based on the GF-2 remote sensing data of the Guanyan Lake region of Guiyang, Guizhou Province. Based on the data of GF-2 remote sensing data in the region of Guanshan Lake in 2016, the data of the data and the data on the data of the data and the data on the data of the data of the rock and stone in the whole province of Guizhou Province are analyzed by the analysis of the GF-2 spectrum. The present data of the latest stone in the field characteristic point survey is carried out. After many experiments and verification, a band operation algorithm for distinguishing the land of the stone and the non-stone land in the GF-2 image is finally selected, and after the GF-2 image is processed by the algorithm, the classification is carried out. And an automatic extraction module for stone information is prepared by using the IDL language, and the block information of the GF-2 remote sensing image is automatically extracted in the ENVI software by using the module. The classification method and the automatic extraction module are applied, and the relevant results of the third stone-rock monitoring in the Guanshan Lake region of Guiyang are finally obtained, and the characteristics of the classification and the spatial distribution of the stone-stone in the Guanshan Lake region are analyzed. On the basis of this, a comprehensive analysis of the results of the monitoring of the rock-stone in the Great Lakes region is carried out, and the genetic and dynamic changes of the stone-stone in the Great Lakes region are obtained (compared with the first and the second monitoring results). The research results can provide scientific reference for the monitoring and management of the stone-rock in the Great Lakes region, Guiyang and the Guizhou province. The main research results of this paper are as follows:1) By performing the 0 IF index analysis on the GF-2 data, the combined entropy verification is combined to determine that the band 2 + band 3 + band 4 combination contains a rich amount of information, It is the best wave band combination of the information extraction of the stone-stone in the Guanshan Lake region.2) According to the separability of the third stone-stone monitoring technology standard and the GF-2 image in Guizhou, the division system of the stone-stone level in the Guanyan Lake region of Guiyang is determined: that is, the non-stone-stone, the latent stone-stone and the severe stone-stone. The classification of GF-2 remote sensing image was carried out by using the maximum likelihood method, and the classification of the road and the building was clear, and the classification effect of the stone plaque was better than that of the true color image. At the same time, the object-oriented information extraction is carried out. At this time, the parameters of the GF-2 remote sensing image segmentation and merging in the Guanshan Lake region are the division scale = 47, the combined scale = 95.4), and the accuracy evaluation is carried out on the maximum likelihood method and the sample-based object-oriented stone extraction information respectively, The final classification accuracy of the maximum likelihood method (through band operation) is 83.2309%, the Kappa coefficient is 0.6752, the final classification accuracy of the maximum likelihood method (not through band operation) is 77.8021%, the Kappa coefficient is 0.6147, and the final classification accuracy of the sample-based object-oriented stone extraction information is 74.362%. The Kappa coefficient was 0.6233, compared to. After the wave band operation, the result of the maximum likelihood classification is increased by 8.8689%.5) The automatic extraction module of the stone-level information based on the GF-2 spectral characteristic is prepared by using the IDL language, thus the modularization of the information extraction of the stone information is completed. The operation of the module can extract the stone information quickly and accurately, and can effectively interface with the ENVI to improve the efficiency of the stone extraction information extraction. The module includes two parts of the band operation module and the maximum likelihood module. The distribution of the land and the distribution of the stone and stone. The area of the non-stone area in the Guanshan Lake area is 8165.5hm2, the potential stone area is 9462.6hm2, and the stone area is 3904.6hm2. In the land of Shifang, the area of the light stone is 1351.8hm2, the area of the medium stone is 2148.5hm2, and the area of the severe stone is 404.3hm2.
【學(xué)位授予單位】：中南林業(yè)科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：X171;X87
，
本文編號：2454377
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