水土流失的定量遥感及其应用研究进展

简要介绍了水土流失定量遥感的概念，阐述了它的四项基本要求，报道了它的应用研究进展，并为尽快实现中国水土流失定量遥感提出了若干建议。     

区域水土流失遥感与核素示踪联合评价技术研究

水土流失遥感调查具有宏观、快速、效率高的优点,可以实现水土流失时空动态监测;侵蚀137Cs核素示踪技术能够提供独立的土壤侵蚀与堆积数据以及空间分布的信息,与其它方法相比,不仅具有无时空限制、操作简便快捷的优点,而且可提供上世纪60年代以来的多空间尺度(测点到流域)年均净侵蚀量。核素示踪与遥感解译的结合,可实现水土流失评价的宏观与微观、点与面、估算与实测的结合,大大地提高水土流失评价时空分辨率和精准度。文章以锦屏二级水电站工程建设区域为例,将137Cs侵蚀示踪技术和水土流失遥感调查方法相结合,基于GIS技术,对区域内水土流失现状进行了评价。     

南方地区荒坡山地种植龙须草的生态效益研究

通过定位试验，采用定性与定量相结合的方法，研究了荒坡地种植龙须草对土壤理化性状，草地小气候环境和水土流失等的影响。结果表明，种植龙须草能降低土壤容重，提高土壤总孔隙度、非毛管孔隙度和毛管孔隙度，改善土壤通气透水性能。增加土壤贮水量，降低土温，减少水土流失，提高土壤肥力，可取得良好的生态效益。     

南方地区荒坡山地种植龙须草的生态效益研究

通过定位试验 ,采用定性与定量相结合的方法 ,研究了荒坡地种植龙须草对土壤理化性状、草地小气候环境和水土流失等的影响。结果表明 ,种植龙须草能降低土壤容重 ,提高土壤总孔隙度、非毛管孔隙度和毛管孔隙度 ,改善土壤通气透水性能 ,增加土壤贮水量 ,降低土温 ,减少水土流失 ,提高土壤肥力 ,可取得良好的生态效益。     

“3S”技术与澳门邻近水域县浮泥沙信息定量定位研究

遥感（RS）、地理信息系统（GIS）和全球定位系统（GPS）是空间信息定位和定量研究的强有力技术与方法，在进行空间定位和定量研究中“3S”技术各有特点，作者综合各技术优势对澳门邻近水域悬浮泥沙信息进行了定位与定量研究，结果表明，澳门邻近水域的悬浮泥沙有明显的一个高浓度带和一个低浓度带，悬浮泥沙高浓度带分布在澳门地区的东南水域，悬浮泥沙这一分布特点将对位子澳门半岛东南的澳门外港泥沙淤积有重要影响，这一结论对澳门外港的建设有一定指导意义。     

赤红壤水土流失区发展持续农业的存在问题及对策

本文以广东省赤红壤水土流失区为例，综合分析了赤红壤水土流失区发展持续农业的存在问题，在此基础上提出了该区发展持续农业的途径与对策．认为：水土流失区发展持续农业存在的突出问题主要是土壤退化；发展持续农业的根本途径在于走生态农业的道路，建立立体农业生态系统；侵蚀土壤上重建森林植被群落首先要建立先锋群落，然后建立稳定的南亚热带森林植被群落。     

建立国家水土保持信息数据库的基本思路

对国家水土保持信息数据库的内容，功能，数据库系统的结构，组织管理等进行了初步探讨。该数据库系统内容包括：影响水土流失的因子，水土流失状况和水 土保持工程效益等，系统是以网络结构，按照多级别进行组织和管理的，不同级别具有各自独特的责任。     

基于GIS与QuickBird影像的小流域土壤侵蚀定量评价

基于GIS技术和QuickBird遥感影像,采用修正的通用土壤流失方程RUSLE作为评价模型,计算了清水沟小流域土壤侵蚀量,并结合土壤侵蚀强度分级标准,生成流域土壤侵蚀强度等级图;利用GIS的叠置分析功能,定量分析了土壤侵蚀与坡度和土地利用之间的关系,并针对不同水保措施预测了水土保持治理效果.结果表明清水沟小流域土壤侵蚀模数为75.02 t·hm-2·a-1,属强度侵蚀区,年水土流失量2.6×104 t;流域64.5%的泥沙来自于占流域面积仅28%的极强度和剧烈侵蚀区域;经分析,流域土壤侵蚀模数与坡度呈显著正相关,不同土地利用类型的土壤侵蚀模数大小排序依次为难利用地＞建设用地＞坡耕地＞园地＞草地＞林地;退耕还林是降低土壤侵蚀强度与减少水土流失量的主要途径.     

土壤遥感分类识别专家系统的结构设计

介绍了土壤遥感分类识别专家系统的设计原理与实现方法，阐述了土壤遥感分类识别专家系统的土壤分类决策判断树的构造原理，讨论了土壤遥感分类识别专家知识表示的基本原则和系统知识库中专家知识的组织方式，以及专家系统推理机的设计原理和推理规则的构建方法。系统采用压缩编码方式存储地学专题图形和遥感图像数据，具有数据与图像的存储更新、查询检索、分析处理、特征提取和信息输出等功能。该系统可以对遥感图像进行土壤类型的分类识别，并对分类精度进行监测与评价。用该系统对新疆天山北麓阜康试验区的土壤分类识别进行了试验研究，并对试验结果进行了讨论与评价。     

我国水土流失的成因,危害及其防治途径

在对以往研究结果进行综合分析的基础上，提出了我国水土流失的成因，危害及防治途径。     

Optimization of ecosystem model parameters using spatio-temporal soil moisture information

Parameters in process-based terrestrial ecosystem models are often nonlinearly related to the water flux to the atmosphere, and they also change temporally and spatially. Therefore, for estimating soil moisture, process-based terrestrial ecosystem models inevitably need to specify spatially and temporally variant model parameters. This study presents a two-stage data assimilation scheme (TSDA) to spatially and temporally optimize some key parameters of an ecosystem model which are closely related to soil moisture. At the first stage, a simplified ecosystem model, namely the Boreal Ecosystem Productivity Simulator (BEPS), is used to obtain the prior estimation of daily soil moisture. After the spatial distribution of 0–10 cm surface soil moisture is derived from remote sensing, an Ensemble Kalman Filter is used to minimize the difference between the remote sensing model results, through optimizing some model parameters spatially. At the second stage, BEPS is reinitialized using the optimized parameters to provide the updated model predictions of daily soil moisture. TSDA has been applied to an arid and semi-arid area of northwest China, and the performance of the model for estimating daily 0–10 cm soil moisture after parameter optimization was validated using field measurements. Results indicate that the TSDA developed in this study is robust and efficient in both temporal and spatial model parameter optimization. After performing the optimization, the correlation (r²) between model-predicted 0–10 cm soil moisture and field measurement increased from 0.66 to 0.75. It is demonstrated that spatial and temporal optimization of ecosystem model parameters can not only improve the model prediction of daily soil moisture but also help to understand the spatial and temporal variation of some key parameters in an ecosystem model and the corresponding ecological mechanisms controlling the variation.     

基于MODIS数据的河南省冬小麦产量遥感估算模型

小麦是世界上最重要的粮食作物,小麦生产对中国的粮食保障起着十分重要的作用,及时、准确、大范围对小麦产量进行监测预报,对于农学经济发展和粮食政策制定具有极为重要的现实意义。对作物产量进行遥感监测的原理是建立在其遥感特征基础之上的,通过建立作物长势指标与遥感信息的定量关系,可实现对作物产量的监测预报。文章基于2009年MODIS遥感数据和气象数据,利用Arcgis和ENVI提取纯小麦像元,并提取纯小麦像元对应的NDVI、NPP和LAI,获取分县NDVI、NPP和LAI均值,利用统计软件对产量数据和分县遥感参数均值进行数据整理和分析,建立了河南省冬小麦产量估算模型。以往研究多采用遥感图像上某像元和地面调查点进行研究,具有很大的不确定性,文章以县为单位,对冬小麦平均单产和县域内冬小麦种植像元遥感参数的均值进行相关研究,提高了模型模拟精度。同时文章选用多种遥感参数和多项气象因子建立估产模型,避免了针对一个参数进行估产的局限性。在最佳时相的选择上,根据冯美辰（2010）以往的研究结果,从4月以后,5月8日和4月20Et植被指数和产量相关性最大,4月份之前冬小麦处于返青到拔节期,对产量来说还有很多不确定闪素,因此文章选用5月8El和4月20日进行冬小麦估产研究。结果表明,5月8日的估产模型优于4月20日,加入气象冈子的遥感气象估产模型优于只采用遥感参数进行估产的遥感模型。利用2010年产量数据对模型精度进行检验,遥感气象模型预测精度在70．2％N99．7％之间,平均精度为90．7％;遥感模型预测精度在68．1％到95．5％之间,平均精度为83．9％。表明遥感气象模型模拟精度更高,其精度可以满足大面积估产要求,可以对产量预报提供科学参考。     

基于遥感与GIS的土壤侵蚀强度快速估测方法

针对土壤侵蚀问题,提出了一种基于遥感数据、气象数据、土壤数据的区域土壤侵蚀快速估测方法。首先,通过对土壤侵蚀问题形成原因和影响因素的分析,确定土壤侵蚀敏感性因子;其次,利用遥感(RS)、GIS技术提取土壤侵蚀敏感性因子信息;然后,在GIS系统支持下,运用主成分分析方法,进行土壤侵蚀强度分级;以北京密云水库周边区域为例,进行了土壤侵蚀估测方法的应用,并对该区域的土壤侵蚀空间特征进行分析。研究结果表明:(1)区域土壤侵蚀快速估测方法与实际情况有较好的一致性;(2)密云水库周边地区土壤侵蚀以微度和轻度侵蚀为主,所占面积比例为67.51%,中度以上侵蚀仅占面积的1.46%;(3)土壤侵蚀受坡度、地貌影响明显,中度侵蚀主要发生在低山、丘陵区以耕地为主的土地利用上以及坡度在大于25°的阳坡地带。     

Developments in remote sensing, dynamic modelling and GIS applications for integrated coastal zone management

The International Institute for Aerospace Survey and Earth Sciences (ITC) has a research programme that should result in an integrated environmental coastal zone management system through three subprojects. The programme aims to develop methodologies and tools for assessing coastal zone changes, and for the evaluation of scenarios for coastal zone management, based on a spatio-temporal Geographical Information System (GIS) working platform which integrates remote sensing data, physical-morphodynamic and eco-hydrologic modelling, and a decision support system. The first subproject develops methodologies for the generation of optimum Remote Sensing (RS) data sets, leading to better interpretation and complementary use of conventional and new remote sensing imagery. It also integrates RS, GIS, and modelling through hypothesis generation, parameter estimation, evaluation and validation. The second subproject facilitates qualitative and quantitative analysis and prediction of the physical aspects of coastal landscape development under the influence of natural processes and human impacts. This subproject is based on the application of remote sensing and dynamic modelling. The third subproject leads to a spatio-temporal working platform which supports data integration of RS and in-situ measurements, and qualitative and quantitative analysis for the prediction of coastal landscape development. Both support decision making in Integrated Coastal Zone Management.     

Ten ways remote sensing can contribute to conservation

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain‐referral survey. We then used a workshop‐based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real‐time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing‐derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?     

Assessing the carbon balance of circumpolar Arctic tundra using remote sensing and process modeling.

This paper reviews the current status of using remote sensing and process-based modeling approaches to assess the contemporary and future circumpolar carbon balance of Arctic tundra, including the exchange of both carbon dioxide and methane with the atmosphere. Analyses based on remote sensing approaches that use a 20-year data record of satellite data indicate that tundra is greening in the Arctic, suggesting an increase in photosynthetic activity and net primary production. Modeling studies generally simulate a small net carbon sink for the distribution of Arctic tundra, a result that is within the uncertainty range of field-based estimates of net carbon exchange. Applications of process-based approaches for scenarios of future climate change generally indicate net carbon sequestration in Arctic tundra as enhanced vegetation production exceeds simulated increases in decomposition. However, methane emissions are likely to increase dramatically, in response to rising soil temperatures, over the next century. Key uncertainties in the response of Arctic ecosystems to climate change include uncertainties in future fire regimes and uncertainties relating to changes in the soil environment. These include the response of soil decomposition and respiration to warming and deepening of the soil active layer, uncertainties in precipitation and potential soil drying, and distribution of wetlands. While there are numerous uncertainties in the projections of process-based models, they generally indicate that Arctic tundra will be a small sink for carbon over the next century and that methane emissions will increase considerably, which implies that exchange of greenhouse gases between the atmosphere and Arctic tundra ecosystems is likely to contribute to climate warming.     

A simple soil organic-matter model for biomass data assimilation in community-level carbon contracts.

Soil carbon (C) sequestration has been proposed as a transitional win-win strategy to help replenish organic-matter content in depleted agricultural soils and counter increases in atmospheric greenhouse gases. Data assimilation and remote sensing can reduce uncertainty in sequestered C mass estimates, but simple soil organic carbon (SOC) models are required to make operational predictions of tradeable amounts over large, heterogenous areas. Our study compared the performance of RothC26.3 and a reduced compartmental model on an 11-year fertilizer trial in subhumid West Africa. Root mean square error (RMSE) differences of 0.05 Mg C/ha between models on total SOC predictions suggest that for contractual purposes, SOC dynamics can be simulated by a two-pool structure with labile and stable components. Faster (seasonal) and slower (semicentennial and beyond) rates can be approximated by constants as instantaneous and infinite decay. In these systems, simulations indicate that cereal residue incorporation holds most potential for mitigation of transient C loss associated with recent land conversion to agriculture.     

干旱区绿洲土壤盐渍化程度遥感定量评价

土壤盐渍化是制约渭干河-库车河三角洲绿洲内部植被生长最主要的生态环境地质问题,也是影响区域农业生产的第一障碍性问题.利用Landsat TM遥感数据,构建了多维向量空间下的11个遥感定量指标.首先,采用因子分析方法分析不同程度的盐渍化与这11个指标之间的相关性,并建立因子得分模型.其次,采用Fisher逐步判别分析法从这些指标中筛选出8个与盐渍化程度密切相关的指标:氧化铁、归一化盐分指数、地表反照率、植被指数、TM1、TM5、地表温度、湿度指标,并对这些指标建立判别方程,然后对渭干河—库车河三角洲绿洲典型样区盐渍化程度进行评价,将所有的实地样本分别代入判别方程,通过分析,发现判别方程的精度较高,与实际情况较吻合.轻度盐渍化的判别精度为80.3%,中度盐渍化的判别精度为68.75%,重度盐渍化的判别精度为83.65%.中度盐渍化土地的判别精度与轻度盐渍化、重度盐渍化土地的判别精度相比较低,这与中度盐渍化的光谱特征与其它地物易混淆有关.总之,所选取的指标简单,易于获取,有利于盐渍化的定量分析与评价.     

城镇化与绿地退化对城市热环境影响研究

城市的发展和城市化进程的加速,导致建设用地的迅速膨胀和生态景观绿地的急剧减少,最终改变了城市热环境。以广州南部快速发展区为例,基于Landsat6遥感影像数据定量分析城市化及绿地系统的变化情况及其城市地表热场和热岛效应空间分布情况,探讨城市热场及热岛效应受城市化和绿地退化（以NDVI为表征）影响的定量关系。研究表明,广州南部地区的热环境状况与其地形特征之间有较密切的联系,城市热环境的分布变化与城镇建设用地和NDVI的分布变化有一定的相关性,特别是与NDVI呈现线性负相关,即地表温度随着归一化植被指数的增大而降低。结果表明,城镇化与绿地退化是导致城市热岛分布变化的影响因素之一。最后从生态角度出发,提出通过构建城市点状绿岛和带状生态廊道来改善城市热环境的可能性。