基于遥感图像分析的北京植被状态与变化研究
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摘要
植被是生态环境的指示器。生态环境对北京市的政治、经济和文化发展至关重要,所以准确全面的把握北京市近年来的植被状况和变化对未来生态环境的评估和城市整体的规划有着重要意义。本文基于多源遥感数据,综合运用植被变化趋势分析、变化检测、植被提取和GIS等方法,对北京地区1998～2011年间植被变化趋势进行研究,并分析了引起北京市植被变化的主要驱动因子；同时研究了2006到2010的土地利用变化状况；并基于2006年Landsat TM遥感图像使用AdaBoost算法对北京市的植被信息进行提取,研究北京地区植被分布格局。本文的主要工作和结论如下：
1、针对闽值法对线性回归植被趋势分析法中斜率k的分类易受人为因素影响的问题,提出了基于线性显著性检验的植被变化趋势分类方法。通过F假设检验和相关系数检验能将植被变化趋势分为4类(显著退化、退化但不显著、改善但不显著和显著改善)或5类(显著退化、退化但不显著、基本不变、改善但不显著和显著改善),并对应用F假设检验和相关系数的分类结果进行比较,实验结果表明,两者的差异很小,仅为0.01%,并且两者计算量相当,因此可相互替代。
2、线性显著性检验法只能将回归植被趋势分析的斜率k分为4类或5类,更细致的划分(如文献中常用的7类)则又只能使用手工阈值法,针对此问题提了基于克隆选择聚类算法对斜率k进行聚类,解决了植被变化趋势分析中分类数多于5类时的分类问题。实验结果表明,基于克隆选择的聚类算法的DBI明显小于比较算法OTSU、 K-Means和FCM,该方法结果与北京市园林绿化局公布的数据一致,有较高的可信度。
3、针对参数检验要求数据呈正态分布且对噪声敏感的缺点,引入非参数检验Sen+Mann-Kendall法,该方法有良好的抗噪性且对数据分布无要求,实验结果与线性回归植被趋势分析的差异最大仅为2.36%,因此该方法更适合遥感数据噪声普遍存在且数据分布未知条件下的植被变化趋势分析。
4、北京市植被活动在1998～2011年间整体呈增强趋势,约占北京市总面积的76%以上的区域植被变化呈上升趋势。植被变化显著上升的区域集中在北京市城区、怀柔区、密云县和延庆县的大部分地区。值得注意的是,北京市局部地区植被变化呈显著下降趋势,且下降区域呈马蹄状包围着北京城区,该区分布在北京市城区的南部、东部和北部,以昌平、顺义、通州和大兴靠近城区的部分退化最为严重。
5、气温和降水不是北京市植被变化的主要驱动因子,人类活动对北京市的植被变化影响显著,主要表现在两个方面：一方面人类通过植树造林和绿化使植被向着增长的方向发展,主要体现在北京市城区植被变化显著增长；另一方面城市化进程的不断推进,破坏了地表植被,植被退化显著,该区域集中在靠近北京市城区的北部、东部和南部且呈马蹄状环绕北京市城区。
6、基于2006年8月21日和2010年8月21日SPOT VGT NDVI匕京区域遥感数据,使用植被指数变化检测法研究了北京地区年际植被变化状况,研究结果表明北京地区2006年与2010年间植被变化以平稳为主,植被退化和植被增长强的区域只有2%的差异。
7、基于中高分辨率遥感数据,提出了结合植被光谱特征(NDVI和其它波段光谱组合)和决策树弱分类器的AdaBoost植被提取算法,实验结果表明该方法的植被提取总精度和Kappa系数分别达到96.33%和0.93。更多地区的遥感数据测试表明该方法有较好的鲁棒性,因此该方法可应用于其它区域的植被信息提取。
Vegetation cover is one of the primary indicators for ecosystem. As a metropolis and the capital of China, ecosystem and environment are crucial to political, economic and cultural development of Beijing.Therefore, it is very meaningful to understand the recent vegetation change and its status in Beijing comprehensively and accurately for ecological management, urban planning and development of the economy. This study aims at finding the following vegetation aspects in Beijing using vegetation analysis methods, change detection technologies and vegetation extraction algorithms based on the multiple remote sensing imageries:vegetation change trends and their major causes in Beijing from1998to2010, land use and land cover change from2006to2010and vegetation distribution pattern using AdaBoost algorithm based on LandSat TM remote sensing imagery in Beijing in2006. The main work and conclusions are as follows:
1. Aiming at the problem which the manual threshold in linear regression vegetation change analysis method affect dramatically by the human factors, linear significant test was employed to settle the classification of vegetation change trend method. The significant test can divide the vegetation change trend into four classes (significant degradation, degradation but not significant, improvement but not significant and significant improvement) or five classes (significant degradation, degradation but not significant, no trend, improvement but not significant and significant improvement). The F test and correlation coefficient were used to classify the vegetation change trend. The experimental results show that the classification difference between F test and correlation coefficient is very little, only0.01%. At the same time, these two methods are very same in amounts of calculation, so they can substitute for each other.
2. The significant test only can divide the vegetation change trends into four or five classes. The more subdivision, for instance seven classes, is return to use the method of manual threshold. A clonal selection clustering algorithm was introduced to subdivision in vegetation change trends. The experimental results show that the DBI of the proposed approach is significantly less than the compared algorithms OTSU, K-Means and FCM. The results are feasible because it is consistent with the data released by the Beijing Gardening and Greening Bureau.
3. For the shortcomings of the parameter test, which the data should be satisfied the normal distribution and it is also sensitive to the noise, Sen+Mann-Kendall approach was used. This method has good property in resistance of the noise and it is also no requirement in data distribution. The experimental results indicate that the classification difference between Sen+Mann-Kendall and linear regression vegetation change analysis is only2.36%, so Sen+Mann-Kendall is more suitable for the noise existing commonly in remote sensing imagery and unknown distribution of the data than the other approaches.
4. The overall vegetation change trends are increasing in Beijing from1998to2011and the increasing regions are76%of the total areas. The vegetation's change is significantly increased in the following areas:the urban of Beijing city, Yanqing county, Huairou and Pinggu district. On the contrary, the vegetation's decreasing areas locate at the north, east and south of the urban district in Beijing and surrounds like a Horseshoe-shaped, especially in the neighbor on urban area regions of ChangPing, ShunYi and DaXing.
5. Temperature and precipitation are not the major driving factors on vegetation change. Vegetation change was affected by human factors remarkably. This affection is showed in the following two aspects:plants and green increase the vegetation cover such as the urban area of Beijing, in contrast to the rapid urbanization destroyed the vegetation on the earth surface, vegetation degraded significantly.
6. Interannual changes in vegetation conditions in Beijing were also studied using the vegetation index approach based on the SPOT VEGETATION NDVI data from August,212006to August,212010. The experimental results indicated that vegetation change was stable from2006to2010. The difference between degradation and improved regions is only2%.
7. Vegetation extraction approach based on AdaBoost algorithm was proposed combined the property of spectral of vegetation (NDVI and combination of bands) and weaker classifiers of decision tree using high resolution remote sensing data. The experimental results showed that the overall accuracy and Kappa reached96.33%and0.93respectively. The proposed algorithm is robust according to the more testing data. Therefore, the proposed approach can be applied to other areas of vegetation information extraction.
1、针对闽值法对线性回归植被趋势分析法中斜率k的分类易受人为因素影响的问题,提出了基于线性显著性检验的植被变化趋势分类方法。通过F假设检验和相关系数检验能将植被变化趋势分为4类(显著退化、退化但不显著、改善但不显著和显著改善)或5类(显著退化、退化但不显著、基本不变、改善但不显著和显著改善),并对应用F假设检验和相关系数的分类结果进行比较,实验结果表明,两者的差异很小,仅为0.01%,并且两者计算量相当,因此可相互替代。
2、线性显著性检验法只能将回归植被趋势分析的斜率k分为4类或5类,更细致的划分(如文献中常用的7类)则又只能使用手工阈值法,针对此问题提了基于克隆选择聚类算法对斜率k进行聚类,解决了植被变化趋势分析中分类数多于5类时的分类问题。实验结果表明,基于克隆选择的聚类算法的DBI明显小于比较算法OTSU、 K-Means和FCM,该方法结果与北京市园林绿化局公布的数据一致,有较高的可信度。
3、针对参数检验要求数据呈正态分布且对噪声敏感的缺点,引入非参数检验Sen+Mann-Kendall法,该方法有良好的抗噪性且对数据分布无要求,实验结果与线性回归植被趋势分析的差异最大仅为2.36%,因此该方法更适合遥感数据噪声普遍存在且数据分布未知条件下的植被变化趋势分析。
4、北京市植被活动在1998～2011年间整体呈增强趋势,约占北京市总面积的76%以上的区域植被变化呈上升趋势。植被变化显著上升的区域集中在北京市城区、怀柔区、密云县和延庆县的大部分地区。值得注意的是,北京市局部地区植被变化呈显著下降趋势,且下降区域呈马蹄状包围着北京城区,该区分布在北京市城区的南部、东部和北部,以昌平、顺义、通州和大兴靠近城区的部分退化最为严重。
5、气温和降水不是北京市植被变化的主要驱动因子,人类活动对北京市的植被变化影响显著,主要表现在两个方面：一方面人类通过植树造林和绿化使植被向着增长的方向发展,主要体现在北京市城区植被变化显著增长；另一方面城市化进程的不断推进,破坏了地表植被,植被退化显著,该区域集中在靠近北京市城区的北部、东部和南部且呈马蹄状环绕北京市城区。
6、基于2006年8月21日和2010年8月21日SPOT VGT NDVI匕京区域遥感数据,使用植被指数变化检测法研究了北京地区年际植被变化状况,研究结果表明北京地区2006年与2010年间植被变化以平稳为主,植被退化和植被增长强的区域只有2%的差异。
7、基于中高分辨率遥感数据,提出了结合植被光谱特征(NDVI和其它波段光谱组合)和决策树弱分类器的AdaBoost植被提取算法,实验结果表明该方法的植被提取总精度和Kappa系数分别达到96.33%和0.93。更多地区的遥感数据测试表明该方法有较好的鲁棒性,因此该方法可应用于其它区域的植被信息提取。
Vegetation cover is one of the primary indicators for ecosystem. As a metropolis and the capital of China, ecosystem and environment are crucial to political, economic and cultural development of Beijing.Therefore, it is very meaningful to understand the recent vegetation change and its status in Beijing comprehensively and accurately for ecological management, urban planning and development of the economy. This study aims at finding the following vegetation aspects in Beijing using vegetation analysis methods, change detection technologies and vegetation extraction algorithms based on the multiple remote sensing imageries:vegetation change trends and their major causes in Beijing from1998to2010, land use and land cover change from2006to2010and vegetation distribution pattern using AdaBoost algorithm based on LandSat TM remote sensing imagery in Beijing in2006. The main work and conclusions are as follows:
1. Aiming at the problem which the manual threshold in linear regression vegetation change analysis method affect dramatically by the human factors, linear significant test was employed to settle the classification of vegetation change trend method. The significant test can divide the vegetation change trend into four classes (significant degradation, degradation but not significant, improvement but not significant and significant improvement) or five classes (significant degradation, degradation but not significant, no trend, improvement but not significant and significant improvement). The F test and correlation coefficient were used to classify the vegetation change trend. The experimental results show that the classification difference between F test and correlation coefficient is very little, only0.01%. At the same time, these two methods are very same in amounts of calculation, so they can substitute for each other.
2. The significant test only can divide the vegetation change trends into four or five classes. The more subdivision, for instance seven classes, is return to use the method of manual threshold. A clonal selection clustering algorithm was introduced to subdivision in vegetation change trends. The experimental results show that the DBI of the proposed approach is significantly less than the compared algorithms OTSU, K-Means and FCM. The results are feasible because it is consistent with the data released by the Beijing Gardening and Greening Bureau.
3. For the shortcomings of the parameter test, which the data should be satisfied the normal distribution and it is also sensitive to the noise, Sen+Mann-Kendall approach was used. This method has good property in resistance of the noise and it is also no requirement in data distribution. The experimental results indicate that the classification difference between Sen+Mann-Kendall and linear regression vegetation change analysis is only2.36%, so Sen+Mann-Kendall is more suitable for the noise existing commonly in remote sensing imagery and unknown distribution of the data than the other approaches.
4. The overall vegetation change trends are increasing in Beijing from1998to2011and the increasing regions are76%of the total areas. The vegetation's change is significantly increased in the following areas:the urban of Beijing city, Yanqing county, Huairou and Pinggu district. On the contrary, the vegetation's decreasing areas locate at the north, east and south of the urban district in Beijing and surrounds like a Horseshoe-shaped, especially in the neighbor on urban area regions of ChangPing, ShunYi and DaXing.
5. Temperature and precipitation are not the major driving factors on vegetation change. Vegetation change was affected by human factors remarkably. This affection is showed in the following two aspects:plants and green increase the vegetation cover such as the urban area of Beijing, in contrast to the rapid urbanization destroyed the vegetation on the earth surface, vegetation degraded significantly.
6. Interannual changes in vegetation conditions in Beijing were also studied using the vegetation index approach based on the SPOT VEGETATION NDVI data from August,212006to August,212010. The experimental results indicated that vegetation change was stable from2006to2010. The difference between degradation and improved regions is only2%.
7. Vegetation extraction approach based on AdaBoost algorithm was proposed combined the property of spectral of vegetation (NDVI and combination of bands) and weaker classifiers of decision tree using high resolution remote sensing data. The experimental results showed that the overall accuracy and Kappa reached96.33%and0.93respectively. The proposed algorithm is robust according to the more testing data. Therefore, the proposed approach can be applied to other areas of vegetation information extraction.
引文
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