海涂围垦区土壤高光谱特性与土地利用遥感调查研究
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摘要
海涂土壤是浙江省主要的后备土地资源，海涂土壤利用状况及其变化，不仅关系到围垦区社会经济的持续发展，而且还影响到海涂土壤资源的合理开发与利用。为此就必须要科学的、动态的掌握它们的分布、性质及其利用的变化。
本文结合国家自然科学基金项目，选取围垦面积较大、围垦历史较长、在浙江省具有典型代表的上虞市海涂围垦区(杭州湾南岸)为例，综合应用多层次、多类型的遥感数据进行样区海涂土壤高光谱特性与评价研究，并开展围垦区土地利用遥感调查研究。
主要研究内容及其结果如下：
1．研究区内野外采集的土壤样品经自然烘干、过筛后，分别进行土壤理化性质分析和高光谱测试。土壤理化性质分析结果表明海涂土壤总体有机质含量低，电导率和含砂量高，并且随着围垦时间的增长呈现出规律性的变化。从土壤高光谱中选取9个吸收波段与土壤电导率、阳离子交换量和机械组成的Person相关分析表明，海涂土壤砂粒、粉粒含量与各波段相关性显著。
2．依据不同围垦历史，对所有土样进行分组，并对选取的9个吸收波段进行逐步判别分析。判别结果表明，处于不同围垦区并具有不同改良程度的土样，其光谱数据具有很好的类可分性。
3．对研究区系列历史数据预处理，提取围堤信息，根据地面的辅助调查和地方统计资料，统计出近三十年来海涂围垦面积的变化和范围。
4．利用多时相陆地卫星影像进行研究区土地利用遥感调查。首先从ETM+图像中提取围垦坝和围垦范围变化信息，其次根据围垦年代不同对研究区进行空间分区，然后对不同子区采用不同分类方法。对分类后的各子区土地利用类型面积进行统计。研究结果表明，不同时期围垦区的主要农业土地利用类型有着明显的差异，从新围垦区裸露的未利用地和水产养殖塘为主，逐步过渡到棉花田及老围垦区的水稻田和果园。
5．在没有光学遥感数据的辅助下，仅采用多时相的ERS-2 PRI雷达产品，经过雷达预处理、研究区分区后，针对不同子区土地利用类型的复杂程度，分别采用ISODATA非监督分类和BP神经网络分类器进行土地利用类型分类。分类总体精度为77.34％，总体Kappa系数为0.74。结果表明，在类似海涂围垦区，全天时、全天候的雷达遥感数据能够替代多光谱遥感数据进行土地利用遥感调查，并显示出巨大的应用潜力。
The coastal soils are the main land resource in support for Zhejiang province. The exploitation, utilization and its changes of the coastal soils not only relate with the sustained development of local economy, but also impact the subsequent exploitation and available land use plan. Thus, timely and reliable information with regard to the nature, extent and physic-chemical characteristics of the coastal soils is essential.
Combined with a project supported by National Natural Science Foundation of China, a large, reclaimed with long time and representative area in Zhejiang province as the study area (located in Shangyu city and south of Hangzhou gulf), this thesis focused on the studies of hyperspectral characteristic of the coastal soils, estimating reclamation level of soil using laboratory hyperspectra and land use investigation by remote sensing with multi-type and multi-hiberarchy remote sensing data.
The main study contents and corresponding results were as follow:
1. After the natural drying and sieving of soil samples collected from field, physic-chemical property analysis and hyperspectral measurement were carried out in laboratory. The results showed that coastal soils had low organic matter, high electrical conductivity and sand content, and some soil properties changed with reclamation years. The Person correlation analysis showed that there exist good relationships between nine absorption bands and selected soil properties, such as soil sand and silt content.
2. The study area was divided into four sub-zones with different historical years of reclamation. Stepwise Discrimination Analysis (SDA) was applied to estimate the reclamation levels of coastal soil using the nine absorption bands. The results showed that coastal soils with different reclamation years could be classified with satisfied accuracy using selected absorption bands from hyperspectra.
3. Land use investigation by remote sensing were implemented with Multi-temporal Landsat TM and new generation Enhanced Thematic Mapper Plus (ETM+). First, dykes were extracted using a line feature enhanced and resolution sharpen ETM+ image. Basing on the extracted dykes, six sub-zones were defined according to different reclamation year, in order to facilitate subsequent classification of various land use types. Then, different classifiers were applied to the land use
classification for different sub-zones. Finally, a detailed land use map based on a modified land use classification system was produced. The results showed that the dominant agricultural land use type of every sub-zone was changed with the reclamation years, from barren land to aqua-farm pond, to cotton field, and to paddy field and orchard.
4. In the condition of no visible-light images, only multi-temporal ERS-2 PRI SAR images were applied for land use investigation. After the SAR pre-processing, pseudo-color synthesizing of three-temporal SAR images, extracting dykes and partitioning the study area into six sub-zones according to the reclamation years, an ISODATA unsupervised classifier and a BP neural network classifier were used for the land use classification for different sub-zones. The total classification accuracy was up to 77.34%. The study results showed that SAR remote sensing, instead of multi-optical remote sensing, has presented great potential on the agricultural land use investigation in such a coastal region.
本文结合国家自然科学基金项目，选取围垦面积较大、围垦历史较长、在浙江省具有典型代表的上虞市海涂围垦区(杭州湾南岸)为例，综合应用多层次、多类型的遥感数据进行样区海涂土壤高光谱特性与评价研究，并开展围垦区土地利用遥感调查研究。
主要研究内容及其结果如下：
1．研究区内野外采集的土壤样品经自然烘干、过筛后，分别进行土壤理化性质分析和高光谱测试。土壤理化性质分析结果表明海涂土壤总体有机质含量低，电导率和含砂量高，并且随着围垦时间的增长呈现出规律性的变化。从土壤高光谱中选取9个吸收波段与土壤电导率、阳离子交换量和机械组成的Person相关分析表明，海涂土壤砂粒、粉粒含量与各波段相关性显著。
2．依据不同围垦历史，对所有土样进行分组，并对选取的9个吸收波段进行逐步判别分析。判别结果表明，处于不同围垦区并具有不同改良程度的土样，其光谱数据具有很好的类可分性。
3．对研究区系列历史数据预处理，提取围堤信息，根据地面的辅助调查和地方统计资料，统计出近三十年来海涂围垦面积的变化和范围。
4．利用多时相陆地卫星影像进行研究区土地利用遥感调查。首先从ETM+图像中提取围垦坝和围垦范围变化信息，其次根据围垦年代不同对研究区进行空间分区，然后对不同子区采用不同分类方法。对分类后的各子区土地利用类型面积进行统计。研究结果表明，不同时期围垦区的主要农业土地利用类型有着明显的差异，从新围垦区裸露的未利用地和水产养殖塘为主，逐步过渡到棉花田及老围垦区的水稻田和果园。
5．在没有光学遥感数据的辅助下，仅采用多时相的ERS-2 PRI雷达产品，经过雷达预处理、研究区分区后，针对不同子区土地利用类型的复杂程度，分别采用ISODATA非监督分类和BP神经网络分类器进行土地利用类型分类。分类总体精度为77.34％，总体Kappa系数为0.74。结果表明，在类似海涂围垦区，全天时、全天候的雷达遥感数据能够替代多光谱遥感数据进行土地利用遥感调查，并显示出巨大的应用潜力。
The coastal soils are the main land resource in support for Zhejiang province. The exploitation, utilization and its changes of the coastal soils not only relate with the sustained development of local economy, but also impact the subsequent exploitation and available land use plan. Thus, timely and reliable information with regard to the nature, extent and physic-chemical characteristics of the coastal soils is essential.
Combined with a project supported by National Natural Science Foundation of China, a large, reclaimed with long time and representative area in Zhejiang province as the study area (located in Shangyu city and south of Hangzhou gulf), this thesis focused on the studies of hyperspectral characteristic of the coastal soils, estimating reclamation level of soil using laboratory hyperspectra and land use investigation by remote sensing with multi-type and multi-hiberarchy remote sensing data.
The main study contents and corresponding results were as follow:
1. After the natural drying and sieving of soil samples collected from field, physic-chemical property analysis and hyperspectral measurement were carried out in laboratory. The results showed that coastal soils had low organic matter, high electrical conductivity and sand content, and some soil properties changed with reclamation years. The Person correlation analysis showed that there exist good relationships between nine absorption bands and selected soil properties, such as soil sand and silt content.
2. The study area was divided into four sub-zones with different historical years of reclamation. Stepwise Discrimination Analysis (SDA) was applied to estimate the reclamation levels of coastal soil using the nine absorption bands. The results showed that coastal soils with different reclamation years could be classified with satisfied accuracy using selected absorption bands from hyperspectra.
3. Land use investigation by remote sensing were implemented with Multi-temporal Landsat TM and new generation Enhanced Thematic Mapper Plus (ETM+). First, dykes were extracted using a line feature enhanced and resolution sharpen ETM+ image. Basing on the extracted dykes, six sub-zones were defined according to different reclamation year, in order to facilitate subsequent classification of various land use types. Then, different classifiers were applied to the land use
classification for different sub-zones. Finally, a detailed land use map based on a modified land use classification system was produced. The results showed that the dominant agricultural land use type of every sub-zone was changed with the reclamation years, from barren land to aqua-farm pond, to cotton field, and to paddy field and orchard.
4. In the condition of no visible-light images, only multi-temporal ERS-2 PRI SAR images were applied for land use investigation. After the SAR pre-processing, pseudo-color synthesizing of three-temporal SAR images, extracting dykes and partitioning the study area into six sub-zones according to the reclamation years, an ISODATA unsupervised classifier and a BP neural network classifier were used for the land use classification for different sub-zones. The total classification accuracy was up to 77.34%. The study results showed that SAR remote sensing, instead of multi-optical remote sensing, has presented great potential on the agricultural land use investigation in such a coastal region.
引文
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