融合光谱混合分解与面向对象的土地利用/覆被分类

错综复杂的土地利用模式和破碎的地物斑块制约了土地利用/覆被分类的精度和效率。一方面,混合像元模糊了地物的光谱信息,影响了分类精度。另一方面,如何高效利用地物的光谱、形状和纹理特征是当前土地利用/覆被分类的研究热点。为了提高基于遥感技术的土地利用/覆被分类精度,该研究基于Sentinel-2A遥感影像,开展融合光谱混合分解与面向对象的土地利用/覆被分类研究。首先,基于地物的光谱、形状和纹理特征,在3个分割尺度通过NDWI（Normalized Difference Water Index)、NDVI（Normalized Difference Vegetation Index）、SBL（Soil Background Level）等8个特征参数构建了不同地物信息的提取规则。其次,利用光谱混合分解模型提取研究区基质（SL;岩石和土壤）、植被（GV;光合作用叶片）和暗色物质（DA;阴影和水）3类通用端元。最后,尝试融合3端元光谱特征优化地物信息提取规则。研究结果表明：1）基于构建的光谱、形状和纹理的地物信息提取规则,使用模糊函数、阈值法进行土地利用/覆被分类,获得了较高的分类精度,总体精度为80.83%,Kappa系数为0.76。2）融合3端元的光谱特征的提取规则将分类精度提升至90.00%,Kappa系数提升至0.88。3）具有明确物理意义的3端元的融入增强了像元内各组分信息的差异性,弥补了传统光谱指数对植被与土壤间的亮度信息解析度不足的缺陷。该方法能充分利用影像的光谱信息,是一种由易到难、对不确定因素进行逐层剥离的土地利用/覆被信息提取技术。因此,对中高分辨率的多光谱遥感影像十分友好,在土地利用/覆被的精细化分类中有较大应用潜力。     

基于遥感光谱的干旱区土地退化评价体系构建

干旱区土地退化(荒漠化)作为全球面临生态环境挑战之一,对粮食安全、环境质量和区域自然资源管理至关重要。土地退化本质是人与自然因素协同作用下土地利用/覆被类型、数量、结构以及功能的改变而引起的生态服务价值降低,核心是土壤和植被的退化。一方面,人与自然共同作用下的土地利用覆被可以表征土地退化状态,另一方面植被-土壤生境时间序列相互作用过程进一步辅助土地退化过程诊断。因此,该文首先从覆被结构、退化类型和退化程度3个层次建立干旱区土地退化状态评价体系。其次,采用GF-1/WFV时间序列遥感影像,基于多端元光谱混合分解模型建立土地利用/覆被精细分类量化表征下垫面质量属性,并进一步利用植被-生境组分互动特征参数进行功能量化,综合评价民勤2015年退化类型和退化程度。最后,结合地面立地景观照片以及采样点实测数据,对土地退化状态评价结果进行绝对定标和交叉验证。结果表明:遥感评价识别土地退化类型和程度的能力分别为87.5%和78.7%。对于民勤旱地系统,沙化过程、沙-盐化过程是主要的土地退化过程,轻度沙化、中度沙化为主导退化程度。该方法为宽波段遥感国产高分1号卫星在旱地系统土地退化状态信息提取和深入应用提供科学依据和实证研究。     

基于Hyperion和ASTER影像的喀斯特石漠化评价指标提取对比研究

中国西南喀斯特地区以石漠化为特征的生态环境退化现象严重,遥感是获取大尺度石漠化评价指标的主要手段.但目前还没有直接从遥感图像快速、客观有效地提取石漠化评价指标的应用.基于植被指数(NDVI)、石漠化综合光谱指数(KRDSI)和木质素—纤维素吸收指数(LCA),对比分析利用Hyperion 高光谱遥感影像及其模拟的ASTER多光谱遥感影像直接提取石漠化评价指标的可行性和精度.研究表明,利用Hyperion高光谱影像能够比较有效地直接反演绿色植被、干枯植被、裸土等的覆盖信息,但由于不同碳酸盐岩及其不同侵蚀程度的光谱特征差异,直接反演基岩裸露的精度较低;而利用ASTER多光谱影像能够直接有效提取直接反演绿色植被、裸土等的覆盖信息,但由于ASTER影像波段设置的局限性,使其直接提取干枯植被盖度和基岩裸露率的效果较差.     

A tool for rapid assessment of erosion risk to support decision-making and policy development at the Ngenge watershed in Uganda

This study tests a rapid, user-friendly method for assessing changes in erosion risk, which yields information to aid policy development and decision-making for sustainable natural resources management. There is currently a lack of timely, up-to-date and current information to support policy development on sustainable natural resources management in Uganda. The study was carried out in the Ngenge watershed, a typical catchment in the Ugandan Highlands, characterised by deforestation in favour of subsistence agriculture without adequate soil and water conservation measures. The watershed is experiencing soil erosion, sedimentation and flooding problems which are threatening agricultural productivity and food security. Sustainable management of environmental resources is needed to ensure a livelihood for the rural population which is dependent on the land. Historical erosion risk was evaluated in three steps using multi-temporal satellite data. First, current erosion risk was assessed by combining slope and vegetation cover during periods of high intensity rainfall. The data used for the assessment was obtained from public (free) satellite images. Erosion risk was then linked to land use and finally to the change in vegetation cover over the years 1980-2000. The analysis of erosion risk using rainfall, slope and NDVI (Normalised Difference Vegetative Index) as a proxy for vegetation cover gives an indication of the current erosion risk in the area. The results of historical vegetation cover change analysis indicate an overall increase in areas under erosion risk in the study area from 1980 to 2000. This method of erosion risk mapping provides a quick and straightforward means for identifying priority areas for interventions for soil and water resource management. Considering that resources are limited, the interventions to be appropriate have to be focused mainly on areas affected by degradation.     

综合季相节律和特征光谱的冬小麦种植面积遥感估算

及时准确地获取区域和国家尺度的作物种植面积和空间分布具有重要意义。针对目前中低分辨率遥感数据相结合方法的局限,提出一种新的作物类型识别方法。首先基于MODIS NDVI数据的时间优势,提取研究区各类植被的NDVI时间序列曲线,从而分析冬小麦在季相节律上的识别特征,构建冬小麦识别模型。再将MODIS像元分类处理,纯耕地像元利用冬小麦的季相节律特征识别;耕地与其他植被的混合像元利用混合像元分解的思想提取耕地组分的NDVI时间序列,从而进行识别,进一步根据空间关系将识别结果重新定位到中分辨率尺度上;冬小麦与其他作物的混合像元覆盖区则利用TM遥感影像的光谱差异加以区分。在伊洛河流域主要农业区,以冬小麦为识别对象,结果表明识别精度达到96.3%。该方法为作物种植信息的提取提供了新的解决问题的途径,也对其他类型作物的识别也具有重要的参考价值。     

煤矸石山不同植被恢复模式对土壤侵蚀和养分流失的影响

 针对煤矸石山复垦地水土流失严重的问题,在山西阳煤集团三矿280煤矸石山坡面上选取7种植被恢复模式,以裸地为对照,设置水土流失试验样方,观测不同植被恢复模式对土壤侵蚀和养分流失的影响。结果表明：与裸地相比,不同椬被恢复模式均对矸石山坡面土壤侵蚀和养分流失具有明显控制效果;下坡段的乔灌混交模式防治坡面侵蚀的作用优于上坡段的草本和灌木;减少水土流失的效果上坡段为荆条 > 紫花苜蓿 > 沙打旺 >草木樨,下坡段为刺槐+柠条、刺槐+荆条和榆树+紫穗槐,但这3种植被模式对减少水土流失的差异不显著;通过偏相关分析,影响养分流失的因索依次为土壤侵蚀量 > 径流量 > 坡度 > 坡长;不同植被恢复模式对煤矸石风化物中速效养分质量系数也具有明显的影响。     

Identification of eroded areas using remote sensing in a badlands landscape on marls in the central Spanish Pyrenees

Identification of erosion areas on a regional scale can be very useful for environmental planning, and can help reduce land degradation and sediment yield to streams and reservoirs. Remote sensing techniques were used to determine erosion and erosion risk areas in a badlands landscape in the Ésera River catchment (Spanish Pyrenees). The size, sparse vegetation cover, and high erosion level in the badlands, enabled good visual and digital discrimination relative to other land covers and surfaces. The maximum likelihood algorithm was used for obtaining a spectral distance map to the bare soil signature characteristic of badlands on marls. The ROC (receiver operating characteristic) curve analysis was applied to this map for obtaining an optimum classification of the badlands. Two alternative classification thresholds were set to determine erosion areas and areas at risk of erosion. Two classification performance statistics, the model's sensitivity and specificity, were calculated as a means of expressing the uncertainty–omission and commission errors–associated to both maps. Most erosion risk areas coincided with low vegetation cover surrounding the badland areas.     

Mapping and monitoring land degradation risks in the Western Brazilian Amazon using multitemporal Landsat TM/ETM+ images

Mapping and monitoring land degradation in areas under human‐induced stresses have become urgent tasks in remote sensing whose importance has not yet been fully appreciated. In this study, a surface cover index (SCI) is developed to evaluate and map potential land degradation risks associated with deforestation and accompanying soil erosion in a Western Brazilian Amazon rural settlement study area. The relationships between land‐use and land‐cover (LULC) types and land degradation risks as well as the impacts of LULC change on land degradation are examined. This research indicates that remotely sensed data can be effectively used for identification and mapping of land degradation risks and monitoring of land degradation changes in the study area. Sites covered by mature forest and advanced successional forests have low land degradation risk potential, while some types of initial successional forests, agroforestry/perennial agriculture and pasture have higher risk potential. Deforestation and associated soil erosion are major causes leading to land degradation, while vegetation regrowth reduces such problems. Copyright © 2006 John Wiley & Sons, Ltd.     

水蚀流域土地覆被格局土壤保持能力空间分布式评价

以丹江口水库以上的汉江流域为例,利用植被覆盖度、坡度加权的上游有效汇流面积空间分布型式和驱动-阻力耦合空间分布拟合函数2个指标,评价水蚀流域土地覆被格局土壤保持能力,并基于研究区14个子流域水文站泥沙资料进行验证.结果表明：汉江干流沿岸河谷盆地和丹江口水库周边丘陵区子流域是研究区城镇和农田的主要分布区,植被覆盖度低,距离汉江较近,当前覆被格局土壤保持能力相对较低,是河流泥沙的主要直接来源地;有效汇流面积空间分布型式和驱动-阻力耦合空间分布拟合函数能有效反映流域泥沙输出,可作为评价流域土地覆被格局土壤保持能力的指标.该评价方法基于土地覆被格局与土壤侵蚀的作用机制,简单易行,可为评价土地覆被格局土壤保持能力、土地覆被格局变化的土壤侵蚀效应提供一种简捷的途径.     

Effect of vegetation cover on soil erosion in a mountainous watershed

We applied the Revised Soil Loss Equation (RUSLE) to assess levels of soil loss in a Geographic Information System (GIS). In this study, we used the k-NN technique to estimate vegetation cover by integrating Landsat ETM+ scenes and field data with optimal parameters. We evaluated the root mean square errors and significance of biases at the pixel level in order to determine the optimal parameters. The accuracy of vegetation cover estimation by the k-NN technique was compared to that predicted by a regression function using Landsat ETM+ bands and field measurements as well as to that predicted by the Normalized Difference Vegetation Index (NDVI). We used a regression equation to calculate the cover management (C) factor of the RUSLE from vegetation cover data. On the basis of the quantitative model of soil erosion, we explored the relationship between soil loss and its influencing factors, and identified areas at high erosion risk. The results showed that the k-NN method can predict vegetation cover more accurately for image pixels at the landscape level than can the other two methods examined in this study. Of those factors, the C-factor is one of the most important affecting soil erosion in the region. Scenarios with different vegetation cover on high-risk areas showed that greater vegetation cover can considerably reduce the loss of soil erosion. The k-NN technique provides a new method to estimate the C-factor for RUSLE erosion mapping. The quantitative model of different vegetation cover scenarios provides information on how vegetation restoration could reduce erosion.     

南方花岗岩红壤区不同土地利用类型坡地产流与侵蚀产沙研究

基于自然坡面径流小区,分析了南方花岗岩红壤区5种典型土地利用下坡面地表径流与侵蚀产沙规律.结果表明,不同地类地表径流与侵蚀产沙分异规律明显.裸露荒地、坡耕地、早地和疏林地的地表径流分别是典型林分林地的1 354.9%,1 099.9%,712.3%和285.4%;不同地类土壤侵蚀强度排序为裸露荒地＞坡耕地＞旱地＞疏林地＞典型林分林地,说明植被能较好地调蓄地表径流和减少侵蚀产沙.在不同降雨强度和雨型条件下,不同土地利用的坡面产流与侵蚀产沙也表现出显著的差异.裸露荒地、坡耕地、旱地、疏林地是研究区主要的侵蚀产沙地类.基于径流小区次降雨条件下径流和侵蚀产沙观测资料,建立了不同地类坡面径流与侵蚀产沙的回归方程.     

基于蒸散发模型的定量遥感缺水指数

该文针对西北地区存在着严重的缺水问题,以黑河中游区域范围内的张掖市内的盈科绿洲及荒漠为研究对象,运用ASTER卫星遥感数据,根据地表能量平衡原理,建立作物缺水指数模型。在前人研究基础上,对缺水指数模型涉及的2个参数进行了改进:1)在植被覆盖区,利用半干旱地区基于亚象元的土壤蒸发和植物蒸腾双层模型,剥离土壤的影响,获取缺水指数模型中的植被潜热通量;2)为了更精确地提取地表信息,利用遗传算法对该区进行混合像元分解,获取模型中的地表组分温度参量。通过地表缺水指数估算干旱半干旱区土壤含水率,模拟结果与地表同步实测值土壤水比较,误差精度分布在2.17%～3.58%,表明该方法是可行的。     

Monitoring of sustainable land management using remotely sensed vegetation cover and variable tolerable soil erosion targets across New South Wales,Australia

The monitoring of land management practices is vital for the protection of soil resource and the environment. Here, we present a new approach to monitor sustainable land management using widely available remotely sensed data, such as MODIS fractional vegetation cover data. The method is based on the concept of maintaining sufficient vegetation cover to prevent hillslope water erosion beyond tolerable soil erosion targets. The targets were based on long-term natural erosion rates plus a small constant and are spatially and temporally variable, not static as in most reported studies to date. Where vegetation cover is more than that required to prevent nontolerable erosion under normal conditions for that calendar month, the site (pixel) is deemed to be managed sustainably. Monthly indices are then combined to form a yearly sustainable land management index (SLMI), presented as raster maps with a spatial resolution of 100 m. We explored this new method through case studies over New South Wales (NSW), Australia, over the period 2010 to 2021, with a particular examination of 2020. Results were further stratified by land uses and natural resource management regions, which revealed useful data and trends. The method is offered as an example of the potential use of readily available vegetation cover data to quantitatively assess and monitor levels of sustainable land management across landscapes. We believe it overcomes the limitations of previous methods to monitor vegetation cover and land management from remote-sensed data alone. Other users are encouraged to adapt the broad approach to meet local requirements.     

基于光谱特征空间的农田植被区土壤湿度遥感监测

土壤湿度遥感动态监测在农业生产中具有重要作用。近年来,多种基于光谱特征空间的土壤水分监测指数被陆续提出,并得到广泛关注和应用,但当前多数监测指数未考虑混合像元的影响。该文针对垂直干旱指数(perpendicular drought index,PDI)在农田植被覆盖区监测精度降低问题,分析了植被覆盖下的PDI误差分布规律,引入垂直植被指数(perpendicular vegetation index,PVI)作为植被覆盖表征量,在PVI-PDI二维空间对PDI模型进行调整,提出了适于植被覆盖的植被调整垂直干旱指数(vegetation adjusted perpendicular drought index,VAPDI),并利用内蒙古明安镇研究区实测土壤湿度数据,对PDI与VAPDI进行了比较和验证。结果表明,在裸土、麦茬、土豆、豇豆4种植被覆盖类型中,PDI与土壤实测含水率的决定系数分别为0.630、0.504、0.571、0.543,VAPDI与土壤实测含水率的决定系数分别为0.599、0.523、0.602、0.585。VAPDI在植被区的误差略小于PDI,一定程度上克服了植被覆盖对监测精度的影响。通过PDI和VAPDI空间分布图的比较也说明,VAPDI对土壤湿度的差别有更好的区分能力,在中尺度土壤表层水分遥感反演方面具有一定的优势。该研究可为农田土壤湿度遥感监测方法选择及监测误差分析提供参考依据。     

A FRAMEWORK FOR NATIONAL ASSESSMENT OF LAND DEGRADATION IN THE DRYLANDS: A CASE STUDY OF SOMALIA

Land degradation is a gradual, negative environmental process that is accelerated by human activities. Its gradual nature allows degradation to proceed unnoticed, thus reducing the likelihood of appropriate and timely control action. Presently, there are few practical frameworks to help countries design national strategies and policies for its control. The study presented here developed a framework for the national assessment of land degradation. This framework is envisaged to support governments in formulating policies on land degradation. It uses time‐series remote sensing data to identify the rate and extent of land degradation, local experts to identify prevalent degradation types and drivers of the degradation and field observations to validate the overall assessment. Its simplicity, use of freely downloadable input data and self‐triangulation of the assessment methods make it suitable for rapid assessment of land degradation on a national scale. It was tested in Somalia, where it exhibited accuracy greater than 60 per cent when assessing land degradation. This framework is relevant for designing national strategies and policies that address land degradation and provides an opportunity for accurate identification of areas to target with comprehensive local assessment. Testing of the framework in Somalia showed that about one‐third of the country was degraded because of loss of vegetation cover, topsoil loss and to the decline of soil moisture. Overgrazing, excessive tree cutting and poor agronomic practices in agricultural areas were identified as the primary drivers of the country's land degradation. These drivers are encouraged by the prevailing communal land tenure practices, poor governance and civil war. Copyright © 2011 John Wiley & Sons, Ltd.     

区域荒漠化评价中植被的指示性及盖度分级标准研究--以毛乌素沙区为例

在分析国内外有关荒漠化评价指标体系研究现状和存在问题的基础上，提出了以植被作为主要评价因子的观点；以我国10大沙区之一的毛乌素沙区为对象，通过研究沙地、梁地、滩地和河谷地等不同地貌类型的荒漠化发生发展过程，得到主要植物指示群落的相应演替、变化规律；通过地表风沙运动与风沙流的实地观测，考查了植被对地表的保护性，并确定出风蚀荒漠化的植被盖度分级标准。     

基于微波遥感极化目标分解的土地覆盖/土地利用分类

为有效利用微波遥感影像进行土地覆盖/土地利用分类,该研究以内蒙古河套灌区解放闸灌域为研究区域,采用春耕后试验区Radarsat-2全极化数据,利用极化目标分解方法提取得到了散射熵、平均散射角、反熵、平均特征值、单次反射特征值相对差异度、二次反射特征值相对差异度。结合实地数据,分析了各参数对于耕地、裸地、含植被水体、建筑等类别的可分离性。根据分析结果选取平均散射角、平均特征值、单次反射特征值相对差异度为分类特征变量,通过最小距离法计算了决策边界,最后结合树分类器对试验区影像进行了分类。整体分类精度93.89%,分类Kappa系数为0.914。结果表明,利用平均散射角可有效区分表面散射与二次散射及体散射;平均特征值可有效区分含植被水体与建筑物;单次反射特征值相对差异度参数可有效区分耕地与裸地。利用极化目标分解方法结合决策树分类器可精确地进行土地覆盖/土地利用分类。     

以土地利用为基础的安徽省生态评价遥感信息模型

[目的]建立更科学合理的生态评价遥感信息模型,为研究区的可持续发展提供参考依据。[方法]利用NDVI(归一化植被指数)和像元二分模型监测植被覆盖度作为地表植被覆盖信息的评价指标;用热惯量法反演研究区的土壤含水量作为研究区地表植被覆盖信息与土壤肥力的评价指标;采用目视解译获得土壤质地的编码分布图并进行影像化处理,将土壤中物理性黏粒含量作为评价土壤板结情况指标;利用线性光谱混合分解模型提取研究区的裸地比率作为土地荒漠化程度的评价指标。[结果]从土地利用的角度构建了生态评价指标体系,建立了以像元为单位的生态评价定量化遥感信息模型。[结论]植被覆盖度因子对研究区生态评价的影响程度相对较大;在生态评价过程中本研究新建模型总体评价精度比传统方法提高了15%。     

长江三峡库区土地覆被类型对坡面产沙的影响

 为探讨长江上游三峡库区的产沙时空变异与环境变化的关系,选择重庆四面山为试验地点,分析不同土地覆被类型对坡面产沙的影响。针对当地不同地类,设置30个小型坡面土壤流失观测区,分析不同土地覆被坡面的产沙差异。结果表明,当地坡面流失的泥沙主要来源于坡耕地和裸地,25°以上陡坡耕地侵蚀的泥沙量,约占坡面产沙总量的50%。土地覆被状况对于泥沙的产生有重要影响,同等条件下,若林地土地覆被率达到70%～80%,则能够减少71.05%～95.58%的泥沙产生量。     

黄河源高寒草甸退化秃斑地土壤基本特征及其风蚀规律

[目的]阐明高寒草甸不同类型退化秃斑地的土壤基本特征及其风蚀规律,揭示土壤物理力学特性与土壤风蚀之间的交互影响作用,进而为黄河源区生态植被的修复和保护提供理论依据。[方法]以黄河源7种草甸退化秃斑地及高原鼢鼠鼠丘为研究对象,结合野外调查和原位试验,对不同秃斑地土壤物理力学特征、土壤风蚀规律的差异性及土壤风蚀量与可蚀性因子间的关联度和相关性等进行了分析和讨论。[结果](1)不同退化秃斑地土壤基本物理力学特性指标差异较显著,7种秃斑地的平均土壤含水率、密度、紧实度和黏聚力分别是高原鼢鼠鼠丘的1.3倍、2.6倍、14.2倍和5.0倍,秃斑地中风毛菊的恢复生长对土壤的保水固土能力最强,香薷草和臭蒿相对较弱。(2)风蚀60 min后高原鼢鼠鼠丘土壤的总流失量是退化秃斑地的1.3～4.4倍;风蚀量的增加幅度随风蚀时间呈先增后减的趋势,风蚀作用的前5～10 min是土壤风蚀的敏感期;不同退化秃斑地土壤风蚀量与植被恢复类型有关,且与土壤物理力学指标值之间呈反比例关系。(3)退化秃斑地土壤风蚀量与植被覆盖度、土壤含水率和土壤紧实度之间均呈线性负相关关系,与土壤黏聚力间呈指数函数负相关关系;土壤风蚀量与覆...