GPS遥感测绘方法在土地测绘中的应用研究

土地测绘是查明我国土地资源利用状况和后期规划、利用的基础依据,因此,如何更准确的、快速的获得现阶段土地资源利用状况是当前亟待解决的问题。以GPS遥感测绘方法为研究对象,以某地区土地测绘实例为分析目标,着重讲述GPS遥感测绘方法中的CORS技术在土地测绘中的应用。结果表明:该技术有效的提高了测绘精度、降低了成本、提高了测绘效率,在土地测绘中具有广阔的应用前景。     

Sensitivity of Watershed Runoff under Humid Conditions to Potential Climate Variations

A semidistributed watershed model is applied over the Mahantango Creek catchment in Pennsylvania to estimate future changes in direct runoff under 22 different climate scenarios. It is shown how different subcatchments of the watershed may respond to possible changes in the precipitation and temperature regimes. Subcatchments with the most unfavorable future runoff responses can be identified where possible changes in land use management practices may be suggested.     

基于多元方法耦合的高强度人为扰动复合区土地利用信息提取

高强度人为扰动所带来的地表破碎化使得＂同物异谱＂和＂异物同谱＂现象特别严重,仅靠地物光谱特征统计提取土地利用信息存在明显缺陷与不足,探索符合特定区域的基于多元方法耦合的土地利用信息提取模型显得非常必要,可为复杂土地利用信息提取提供便捷通道。本文首先找出典型土地利用类型的光谱特征和归一化差异型指数变化规律,再运用交互式数据语言耦合光谱信息并建立决策函数,对1993年、2001年和2009年3期TM影像进行决策树分类,以自动提取三峡库区回水淹没与快速城市化重叠区这一高强度人为扰动复合区典型土地利用信息。结果表明：典型地类的波谱响应曲线和归一化差异型指数曲线分布形态符合于地表三大基本地类特征,以此验证了研究中所选取的三种指数的合理性和各地类样区选择的有效性;各地类在TM影像1、2、3波段上因光谱特征变化趋势无明显差异、相关性较强而不易于提取,3、4、5波段光谱特征因差异变化最大、所包含的土地利用信息最为丰富而更易于提取样区典型地类;三种指数及其差值组合指数的曲线分布形态及其值变化与研究区的实际地类变化较为吻合;多元方法耦合的决策树分类比监督分类方法在提取土地利用信息上具有更高的精度,在样区土地利用自动分类中是一种低成本、易实现和推广的有效方法,从1993年、2001年和2009年总体分类精度分别提高了18.46%、15.43%和24.18%。据此,基于多元方法耦合的决策树分类模型能为三峡库区人为扰动复合区土地利用信息提取提供一可操作途径,为整个三峡库区土地利用信息自动提取提供已有方法的整合思路。     

太湖湖滨敏感区的土地利用遥感分类研究

近年来太湖流域水体污染日趋严重,土地利用是重要的环境变化影响因子,对太湖湖滨敏感区土地利用分类研究具有重要意义。研究基于2010年ALOS多光谱遥感影像,以太湖流域上游的武进港、直湖港流域为研究区,根据研究区实际状况和研究目的,建立太湖流域上游湖滨敏感区的土地利用/土地覆被分类系统,并用于该地区的面向对象遥感分类,研究通过影像的多尺度分割,获得不同层次的影像对象,在不同层次设置对应的分类规则,以充分利用影像中地物的光谱、纹理和不同层对象相互关系等信息,从而提高分类效果。研究表明:在面向对象多尺度影像分割的基础上,基于决策树建立多个分类规则的分类方法,能够有效提取建设用地、道路、水体等几类信息,分类总体精度达到88.00%;同时,该地区主要土地利用类型如耕地、农村居民点和城镇居民点的分类精度也较高,这也表明该分类方法对整个太湖流域以及其他平原河网地区的土地利用相关研究具有一定的实用价值。     

兰州市削山造地区域生态变化遥感评价方法

针对现有方法无法准确提取削山造地范围以及定量评价削山造地区域生态变化问题,由此提出了基于三维地形变化的削山造地范围提取方法;同时,基于1991、2001、2009和2016年遥感影像,以绿度、湿度、热度和干度指标构建遥感生态指数（Remote Sensing Ecological Index,RSEI）,对比分析4个时期生态质量时空变化。结果表明：RSEI不仅能够定量反映生态环境质量,而且可以空间化可视化。1991—2016年,兰州市周边削山造地工程面积不断增加,RSEI值由1991年的0.297上升至2016年的0.406,兰州市生态环境质量整体呈上升趋势,但是具有明显的空间差异。生态变差区域集中在兰州主城区北部山区、兰州新区东南部的削山造地工程范围,削山造地工程致使建筑用地和裸土地激增,植被覆盖度降低,表明削山造地工程对区域生态环境质量有一定影响,因此应该控制削山造地工程规模,加强保护生态脆弱区域环境。     

基于面向对象的高分影像分类研究

遥感技术已经成为实现地表信息提取的主要手段。以高分辨率影像为主要数据源,采用面向对象的多尺度分割算法,根据对象的光谱、形状等特征,实现了面向高分遥感数据的土地利用分类算法。该算法结合了面向地物对象和综合对象特征的分类方法,充分发挥了高分辨率影像进行精细地物分类的优势,得到了高精度的分类结果。通过西双版纳纳板河流域国家级自然保护区实例验证表明:该算法总体精度达到88.58%,Kappa系数达到0.77,精度符合应用要求,能够实现土地利用高精度、快速的分类。     

Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments

Remote sensing has considerable potential for providing accurate, up-to-date information in urban areas. Urban remote sensing is complicated, however, by very high spectral and spatial complexity. In this paper, Multiple Endmember Spectral Mixture Analysis (MESMA) was applied to map urban land cover using HyMap data acquired over the city of Bonn, Germany. MESMA is well suited for urban environments because it allows the number and types of endmembers to vary on a per-pixel basis, which allows controlling the large spectral variability in these environments. We employed a hierarchical approach, in which MESMA was applied to map four levels of complexity ranging from the simplest level consisting of only two classes, impervious and pervious, to 20 classes that differentiated material composition and plant species. Lower levels of complexity, mapped at the highest accuracies, were used to constrain spatially models at higher levels of complexity, reducing spectral confusion between materials. A spectral library containing 1521 endmembers was created from the HyMap data. Three endmember selection procedures, Endmember Average RMS (EAR), Minimum Average Spectral Angle (MASA) and Count Based Endmember Selection (COB), were used to identify the most representative endmembers for each level of complexity. Combined two-, three- or four-endmember models - depending on the hierarchical level - were applied, and the highest endmember fractions were used to assign a land cover class. Classification accuracies of 97.2% were achieved for the two lowest complexity levels, consisting of impervious and pervious classes, and a four class map consisting of vegetation, bare soil, water and built-up. At the next level of complexity, consisting of seven classes including trees, grass, bare soil, river, lakes/basins, road and roof/building, classification accuracies remained high at 81.7% with most classes mapped above 85% accuracy. At the highest level, consisting of 20 land cover classes, a 75.9% classification accuracy was achieved. The ability of MESMA to incorporate within-class spectral variability, combined with a hierarchical approach that uses spatial information from one level to constrain model selection at a higher level of complexity was shown to be particularly well suited for urban environments.     

Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study

A significant proportion of high spatial resolution imagery in urban areas can be affected by shadows. Considerable research has been conducted to investigate shadow detection and removal in remotely sensed imagery. Few studies, however, have evaluated how applications of these shadow detection and restoration methods can help eliminate the shadow problem in land cover classification of high spatial resolution images in urban settings. This paper presents a comparison study of three methods for land cover classification of shaded areas from high spatial resolution imagery in an urban environment. Method 1 combines spectral information in shaded areas with spatial information for shadow classification. Method 2 applies a shadow restoration technique, the linear-correlation correction method to create a “shadow-free” image before the classification. Method 3 uses multisource data fusion to aid in classification of shadows. The results indicated that Method 3 achieved the best accuracy, with overall accuracy of 88%. It provides a significantly better means for shadow classification than the other two methods. The overall accuracy for Method 1 was 81.5%, slightly but not significantly higher than the 80.5% from Method 2. All of the three methods applied an object-based classification procedure, which was critical as it provides an effective way to address the problems of radiometric difference and spatial misregistration associated with multisource data fusion (Method 3), and to incorporate thematic spatial information (Method 1).     

The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial representativeness over forested landscapes

A new methodology for establishing the spatial representativeness of tower albedo measurements that are routinely used in validation of satellite retrievals from global land surface albedo and reflectance anisotropy products is presented. This method brings together knowledge of the intrinsic biophysical properties of a measurement site, and the surrounding landscape to produce a number of geostatistical attributes that describe the overall variability, spatial extent, strength of the spatial correlation, and spatial structure of surface albedo patterns at separate seasonal periods throughout the year. Variogram functions extracted from Enhanced Thematic Mapper Plus (ETM+) retrievals of surface albedo using multiple spatial and temporal thresholds were used to assess the degree to which a given point (tower) measurement is able to capture the intrinsic variability of the immediate landscape extending to a satellite pixel. A validation scheme was implemented over a wide range of forested landscapes, looking at both deciduous and coniferous sites, from tropical to boreal ecosystems. The experiment focused on comparisons between tower measurements of surface albedo acquired at local solar noon and matching retrievals from the MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V005) Bidirectional Reflectance Distribution Function (BRDF)/albedo algorithm. Assessments over a select group of field stations with comparable landscape features and daily retrieval scenarios further demonstrate the ability of this technique to identify measurement sites that contain the intrinsic spatial and seasonal features of surface albedo over sufficiently large enough footprints for use in modeling and remote sensing studies. This approach, therefore, improves our understanding of product uncertainty both in terms of the representativeness of the field data and its relationship to the larger satellite pixel.     

基于生态地理分区的5套土地利用覆盖数据的不确定性研究

基于生态地理分区从面积精度和位置精度两个方面定量探讨了5套全球土地利用/覆盖(LULC)数据产品在实际应用中的不确定性,为基于生态地理分区的相关研究选择合理数据集提供参考依据,同时为中国生产全球LULC产品提供有关信息。选择中国地区20个典型生态地理分区为研究对象,采用最小误差频率法,分析5套数据集对各个类型面积估计的不确定性大小及原因;采用混淆矩阵法,基于位置分析5套数据集在类型混分方面的不确定性大小,原因及空间分布规律。结果表明:在生态地理分区尺度,MODIS,Meris300以及Glc2000这3套数据集明显优于Umd和Usgs这两套数据集,并且随着生态地理分区自南向北\,自东向西的空间分布,这3套数据集的不确定性呈减小趋势。对所有生态地理分区而言,Meris300数据集整体估计的稳定性最高,但是估计精度不是最高,并且它对建设用地和水域的估计最有优势。MODIS数据集整体估计精度和稳定性次之,对耕地的估计最有优势。 Glc2000数据集更适用于土地利用/覆盖简单的生态地理分区。研究还发现地形和土地利用/覆盖的复杂程度是引起数据集不确定性的两个重要因素。