玛纳斯河流域盐渍化灌区生态环境遥感监测研究

土壤盐渍化已成为全球性问题,给生态环境及农业生产带来严重的威胁。为了快速、准确评价土壤盐渍化给区域生态坏境带来的影响,该文提出了新的完全基于遥感数据的遥感生态指数(SSEI,Soil Salinization Ecology Index)来监测玛纳斯河流域盐渍化灌区生态环境变化。该指数利用主成分分析的方法耦合与土壤盐渍化相关的土壤盐度、地表反照率、植被覆盖度和土壤湿度四大地表参数,指数构建是数据本身性质所决定,不同于以往遥感与非遥感指数加权叠加易受人为影响。研究结果表明:耦合与盐渍化信息相关的各遥感指数得到的生态指数,能够对土壤盐渍化影响区域的生态环境进行快速、定量、客观的监测。将该指数应用到新疆玛纳斯河流域灌区,结果表明在近26年优和良等级生态环境面积增加了12.89%,这说明灌区生态环境有所改善。该研究对土壤盐渍化监测与评价具有一定参考意义。     

干湿季节下基于遥感和电磁感应技术的塔里木盆地北缘绿洲土壤盐分的空间变异性

土壤盐渍化问题是制约干旱半干旱区植被生长最主要的生态环境地质问题,也是影响绿洲农业生产的障碍性问题.而将遥感与近感技术相结合,是当前评价、监测及预报土壤盐渍化程度的先进方法.以新疆塔里木盆地北缘的渭干河-库车河三角洲绿洲为例,以遥感数据和解译后的电磁感应数据为基础数据源,利用解译后的数据结合GIS和地统计学知识以及野外实测所得到的土壤电导率和盐分资料,分别采用泛克里格(Universal Kriging)、光谱指数回归(Spectral Index Regression)和回归残差泛克里格(Regression-Universal Kriging)3种方法研究了该地区两个关键季节(干季和湿季)土壤盐分的空间变异特征.研究结果表明:研究区的土壤浸提液电导率EC1∶5和土壤盐分呈现显著相关,可以用EC1∶5来代替土壤的全盐量进行分析;电磁感应仪(EM38)所测各季节土壤表观电导率与EC1∶5的相关系数均达到1％显著水平,以表观电导率垂直读数(EMv)和水平读数(EMH)为自变量的多元回归模型拟合效果较好;研究区各季节的表层土壤电导率的空间分布均表现为强相关性,说明土壤采样点间的内部结构性良好,采用能够充分考虑到干旱区表层土壤电导率空间变异的尺度依赖性的球状套合模型,能够更好的拟合土壤表观电导率的空间结构;经过精度比较,回归残差泛克里格法为最优预测方法,这表明将遥感和电磁感应技术相结合,能够有效的提高预测与评估土壤盐分空间分布的精度,为精确地进行土壤盐分预测以及土壤次生盐渍化的防控提供了一定的依据.     

基于遥感的塔里木盆地北缘绿洲干湿季土地盐渍化监测

土壤盐渍化是导致干旱区土地退化、抑制植被生长、影响区域农业生产的最主要的生态环境地质问题.利用遥感数据准确、快速地获取区域盐渍化土壤的动态变化信息对于土壤盐渍化监测具有重要意义.本文以盐渍化现象严重的新疆渭干河-库车河三角洲绿洲为研究区,以2011年4月15日和2011年9月22日成像的两期Landsat-TM多光谱遥感数据为数据源,结合对研究区实地考察所采集的实测数据,通过提取改进型归一化差异水体指数、归一化植被指数以及K-L变换后所提取的第3主成分等参数作为特征量,利用决策树分类方法分别建立了研究区两个关键季节（干季和湿季）的土壤盐渍化信息提取模型,并绘制了两个季节的土壤盐渍化信息分类图.结果表明： 该方法对干季和湿季盐渍地信息的提取精度分别达到87.2%、85.3%,识别精度较高;采用该方法可以有效地对盐渍地变化信息及其空间分布状况进行监测,可为干旱区盐渍地的综合治理和绿洲土地资源的合理利用提供科学依据.     

基于植被/温度特征的黄土高原地表水分季节变化

地表水分是监测土地退化的一个重要指标,是气候、水文、生态、农业等领域的主要参数。选择陕北黄土高原地区作为研究区域,首先采用基于植被覆盖特征的植被状态指数（Vegetation Condition Index,简称VCI）和基于地表温度特征（Land Surface Temperature,简称Ts）的温度状态指数（Temperature Condition Index,简称TCI）分别评价了区域地表水分状况的季节变化。在此基础上分析了植被指数与地表温度特征线性关系的季节变化规律,计算了基于两者经验关系的地表干燥度指数（Temperature Vegetation Dryness Index,TVDI）。该指数对Ts／NDVI特征空间的生态特征的解释,对土壤和作物的水分含量具有综合的指示意义。文中利用该指数综合评价了研究区域地表水分状况的时空分布差异,进一步对VCI、TCI与TVDI相关关系的季节变化进行比较分析,并结合气候因子进行了相关验证,从而对不同指数的应用范围做出判定。研究结果表明,单独采用TCI或VCI表征地表水分会受到明显的季节影响,而TVDI能在不同季节综合体现植被覆盖和地表温度特征对地表水分的影响,从而能较好的反映区域地表水分状况的时空变化特征。各区域的TVDI值季节分布上皆为4—7月份高于10-翌年1月份,但各区TVDI值的季节变化则存在显著不同,而各流域内部TVDI值的空间变异性也存在季节差异,其中在10月份较为显著。     

干旱区内陆河流域尾闾绿洲土壤盐渍化动态预测

针对干旱区尾闾绿洲土壤盐渍化严重发生的现状,运用遥感和GIS技术,基于天山北麓三工河流域平原尾闾绿洲农区0~20 cm耕作层土壤盐渍化特征,利用空间概率模型预测了其今后的发生状态。结果表明:经过长时间的土地利用,土壤盐渍化程度有所加剧;通过空间概率模型χ~2检验,1982—2005年的转移概率模拟值与实际值可靠性要高于1982—1999年的转移概率。模型模拟结果显示,2010年以后到达稳定状态,人工景观的面积增加,自然景观的面积减少,其中轻盐化耕地面积减少,中盐化和重盐化耕地面积增加,在长时间范围内,土壤盐渍化依旧是制约研究区绿洲耕地生产力的主要因素。     

基于流域尺度的土壤盐分空间变异特征——以渭干河-库车河流域三角洲绿洲为例

土壤中水溶性盐的分析,是发展研究盐渍土盐分动态监测与预报技术的重要基础工作。针对目前干旱半干旱区广泛存在的绿洲土壤次生盐渍化问题,基于GIS和地统计学方法,研究了渭干河—库车河流域三角洲绿洲盐渍化土壤特征(土壤含盐量)的空间变异性。研究结果表明:渭-库绿洲土壤含盐量的空间变异性为中等变异,且随土壤深度增加而减弱。各层土壤的合理采样数为在95%置信水平,20%误差下的合理采样数目。高斯模型能够较好的拟合10-30cm及30-50cm层土壤含盐量的空间结构。各层土壤盐分空间变异性受到结构性与随机性因素共同作用的影响,呈强空间相关性。套合结构模型考虑到0-10cm层土壤盐分空间变异的尺度依赖性,能够更好的拟合0-10cm层土壤含盐量的空间结构。Kriging插值以及空间等值线分布趋势图能够直观的表现研究区内土壤盐分含量的空间分布状况和变化情况。研究为土壤特征变量空间变异分析与评价提供了普适性较强的实现方法,为构建盐渍土定量动态监测模型以及盐渍土的改良利用提供一定的科学依据。     

中国遥感干旱指数时空特征及其对气候和地表覆盖变化的响应

干旱严重影响植被生长,威胁粮食安全,基于遥感计算的植被状态指数（Vegetation Condition Index,VCI）、温度状态指数（Temperature Condition Index,TCI）和植被健康指数（Vegetation Health Index,VHI）是常用的干旱指数,被广泛应用于干旱监测。为了探究近年来我国干旱特征及其对气候和地表覆盖变化的响应,分析了2003-2016年期间VCI、TCI和VHI的时空变化特征;采用最小二乘（OLS）和偏相关分析方法分析了这些指数对气候和地表覆盖变化的响应。基于上述干旱指数计算的干旱频率表明,中温带中部和南温带等地区干旱发生频率高,干旱指数变化趋势表明在2003-2016年期间中国大部分地区干旱缓解,但在中温带、南温带和高原气候区等局部地区干旱加剧;总体而言,干旱指数随着年平均温度的上升和年降水量的降低而减小,VHI与温度和降水量的相关性在不同气候区的一致性优于VCI和TCI;裸土的减少和植被的增加导致干旱指数增大,树木转变为低矮植被干旱指数降低。     

干旱区不同景观单元土壤盐分的变化特征

针对内陆河流域空间土壤盐渍化问题,以新疆三工河流域为例,通过调查、取样分析,研究了冲洪积扇和冲洪积平原2个水文地质带上一个生长季5-10月0～10、10～20cm土壤盐分积聚特征及其主导因素.结果表明:冲洪积扇土壤盐分含量明显低于冲洪积平原,盐分含量的变异系数均>100%,属于强变异性;灌溉景观土壤盐分的聚积在冲洪积扇和冲洪积平原相似,盐分含量均减小,而非灌溉景观的差异较大,冲洪积扇区土壤盐分含量减小,土壤盐渍化程度减弱;而冲洪积平原区土壤盐分含量升高,盐渍化程度增强.冲洪积扇区土层间盐分变化关系紧密相关(P<0.01),降雨和灌溉对盐分都具有淋洗作用,促进了盐分含量的减少;冲洪积平原区,蒸发对非灌溉景观0～10 cm土层盐分聚积起到促进作用,而10～20 cm土层盐分聚积受上层土壤盐分和地下水位共同作用.

Abstract：

Aiming at the problem of soil salinization in inland river basin, a case study was con-ducted in the Sangong River catchment of Xinjiang, China. The characteristics and dominant af-fecting factors of salt accumulation in 0-10 cm and 10-20 cm soil layers in two hydro-geological zones, i. e. , alluvial-proluvial delta and alluvial plain, were studied in a growth season (from May to October), and the precipitation, evaporation, groundwater table, and irrigation were also taken into account. The soil salt content was obviously lower in alluvial-proluvial delta than in al-luvial plain, but its coefficient of variation in the two zones was all higher than 100%, indicating a great variability. The soil salt accumulation in irrigated landscape in the two zones had a similar decreasing trend, but that in non-irrigated landscape had greater difference, with a decreased soil salinization in alluvial-proluvial delta, and an increased soil salinization in alluvial plain. In allu-vial-proluvial delta, the salt contents in 0-10 cm and 10-20 cm soil layers had a close relation-ship (P <0. 01), and there was a clear process of salt leaching by precipitation and irrigation, leading to the decrease of soil salt content. In alluvial plain, the salt accumulation in 0-10 cm soil layer in non-irrigated landscape was promoted by evaporation, and that in 10-20 cm soil lay-er was co-affected by the salt content in 0-10 cm soil layer and the groundwater table.     

农业旱情遥感监测的一种改进方法及其应用

从地表蒸散的角度出发,利用基于Priestley-Taylor公式与地表温度 植被指数(LST-VI)三角形特征空间的半经验蒸散模型,对农业干旱遥感监测方法进行改进,推导得到简化型蒸散胁迫指数(SESI).利用2008、2009年3-11月的MODIS陆地标准产品数据,构造了3种特征空间建模计算了SESI,对京津冀平原地区开展了农业旱情监测试验,并与温度植被干旱指数(TVDI)进行比较.结果表明: SESI有效地简化了基于地表蒸散估算的遥感干旱监测方法,对土壤表层水分(10、20 cm)有着良好的指示作用.该方法春、秋季监测效果优于夏季,且不同时相SESI的可比性优于TVDI.将SESI指数应用于大面积农业旱情连续监测具有一定可行性.     

基于电磁感应技术的土壤剖面盐分空间分布建模研究

土壤盐渍化问题是干旱半干旱地区农业发展的主要障碍,也是制约荒漠植物生长状况的关键因素之一,严重影响到绿洲生态环境的稳定与安全。研究土壤剖面盐分的分布情况,能及时探究盐渍化对生态的影响。以渭干河-库车河三角洲绿洲为研究靶区,利用电磁感应仪技术与传统采样方法获取该地区典型地块的土壤电导率,剖析其剖面分布特征,在建立磁感式表观电导率和土壤样本实测电导率之间的线性混合模型的基础上,采用自然邻近插值方法解析和评估研究区土壤剖面盐分的空间分布特征。结果表明:研究区土壤电导率具有较强的表聚性与空间变异强度,土壤主体属于中度盐渍化类型;基于各深度层土壤电导率与磁感表观电导率所构建的3种线性混合模型均能达到0.01的显著性水平,其中磁感表观电导率两种模式相结合解译模型预测精度最高;自然邻近法插值结果直观反映研究区土壤剖面盐分的空间分布状况,与水平模式和垂直模式相结合的土壤盐分解译模型相结合则能够更有效的提高土壤盐分空间分布的预测精度。研究结果表明,借助构建的土壤盐分解译模型可对研究区土壤盐渍化空间分布情况进行快速监测与评估,为该区土壤盐渍化的防治提供了一定的技术支撑。     

Developing and applying novel spectral feature parameters for classifying soil salt types in arid land

Soil salinization is a major desertification process that threatens especially the stability of arid ecosystems. There is an urgent need for intensive monitoring and quick assessment of salinization through remote sensing as a tool for combating desertification in such ecosystems. Recent researches have revealed that in order to retrieve soil salt contents accurately from hyperspectral reflectance, a pre-knowledge of salt types is required, which greatly outlines the spectral features of saline soil reflectance. In this study, a set of feature parameters have been developed after a thorough investigation of spectral responses to different soil salt types and salt contents for quick and accurate classification of soil salt types. The application has been validated using three independent datasets composed from: laboratory experiments (dataset I), in-situ field measurements (dataset II), and satellite-borne Hyperion image (dataset III). For comparison, four other common classification algorithms have been validated using the same datasets. The results showed that the new approach proposed in this study performed well with not only single-type but also multiple-type salts for which the four common algorithms performed rather fairly. Furthermore, validating using datasets II and III showed that the newly proposed approach had a stable performance while the other four failed, indicating the advantage of the new approach. The feature parameters developed in this study hence provide a novel and efficient approach for salt type classification from reflectance spectra, and we foresee its potential applications on large-scale soil salt type mapping towards better understanding soil salinity characterization from remote sensing data.     

Integrating remote sensing and local ecological knowledge to monitor rangeland dynamics

Rangelands are among the most extensive anthropogenic landscapes on earth, supporting nearly 500 million people. Disagreements over the extent and severity of rangeland degradation affect pastoralist livelihoods, especially when impacts of drought and over-grazing are confounded. While vegetation indices (such as NDVI, or Normalized Difference Vegetation Index) derived from remotely sensed imagery are often used to monitor rangelands, their strategic integration with local ecological knowledge (LEK) is under-appreciated. Here, we explore these complementary approaches in Kyrgyzstan’s pasture-rich province of Naryn, where disagreements regarding pasture degradation could greatly benefit from additional information. We examine a time series of MODIS satellite imagery (2000–2015) to characterize browning trends in vegetation as well as to distinguish between climate- and grazing-induced trends. We also compare and contrast measured trends with LEK perceptions of pasture degradation. To do so, we first examine statistical trends in NDVI as well as in NDVI residuals after de-trending with meteorological data. Second, we use participatory mapping to identify areas local pasture managers believe are overgrazed, a particularly useful approach in lieu of reliable historical stocking rates for livestock in this region. Lastly, we compare the strengths and weaknesses of LEK and remote sensing for landscape monitoring.Browning trends were widespread as declining trends in NDVI (and NDVI residuals) covered 24% (and 9%) of the landscape, respectively. Local managers’ perceptions of pasture degradation better reflected trends seen in NDVI than in climate-controlled NDVI residuals, suggesting patterns in the latter are less apparent to managers. Our approach demonstrated great potential for the integration of two inexpensive and effective methods of rangeland monitoring well-suited to the country’s needs. Despite limitations due to terrain, our approach was most successful within the semi-arid steppe where pasture degradation is believed to be most severe. In many parts of the world, sources of long-term spatially extensive data are rare or even non-existent. Thus, paired LEK and remote sensing can contribute to comprehensive and informative assessments of land degradation, especially where contentious management issues intersect with sparse data availability. LEK is a valuable source of complementary information to remote sensing and should be integrated more routinely and formally into landscape monitoring. To aid this endeavor, we synthesize advice for linking LEK and remote sensing across diverse landscape situations.     

基于TM遥感影像的陕北黄土区结构化植被因子指数提取

根据结构化植被因子指数的概念,以TM影像为信息源,探讨了利用遥感技术提取陕北黄土区结构化植被因子指数（C_s）的途径与方法.结果表明：在陕北黄土区,C_s能更好地描述植被群落的水土保持效益,其与绿度植被指数（归一化植被指数NDVI、修正土壤调整植被指数MSAVI）和黄度植被指（归一化差异衰败指数NDSVI、归一化耕作指数NDTI）等单一的遥感植被指数虽然均存在良好的相关关系,但用绿度与黄度植被指数相结合可综合反映植被的水土保持功能,能较好地克服单一指数在描述植被控制水土流失中的不足;MSAVI、NDTI分别是基于遥感影像提取C_s较为理想的绿度和黄度植被指数;根据群落结构化植被因子指数与遥感植被指数的关系推算区域尺度上的结构化植被因子指数是可行的,但由于不同地区植物物候期的差异,要使该方法在其他地区适用,仍需开展相应的率定和验证工作.     

利用遥感光谱法进行农田土壤水分遥感动态监测

自 1 997年 4月至 1 998年 1 0月 ,在甘肃省定西县进行了大面积 0～ 5 0 cm土层农田土壤水分按每 1 5 d本底资料实际观测 ,对此间收到的 5幅 TM与 7幅 NOAA卫片数据资料进行了加工处理 ,并对地面光谱资料也进行了观测。在光谱反演与光谱和土壤水分相关性分析基础上 ,利用遥感技术和地理信息系统 ,初步建立了典型试验区 ( 3× 3km2 )遥感信息与土壤含水量之间的遥感光谱相关监测模型 ,做出了观测区土壤水分含量分布图和得到了大面积农田土壤水分宏观动态监测结果 ,并同地面实测土壤水分进行了精度校正。研究结果表明 ,文中提出的“光学植被盖度”概念 ,对土壤水分遥感监测研究是有益的 ,利用遥感光谱法和数学统计方法求出了有关物理参数 ,初步建立了 TM与 NOAA光谱水分监测模型 ,其模型监测 0～2 0 cm土层含水量的精度达到 90 %以上 ,实际监测土壤水分精度达到 72 .3% ;在遥感监测 2 0～ 5 0 cm土层土壤含水量中 ,利用遥感模型监测土壤水分精度达到 80 %以上 ,实际遥感监测精度达到 60 %左右 ,其结果可有效指导干旱半干旱雨养农业区春耕时间和动态监测大面积土壤墒情 ,可为农业生产提供科学依据。另外 ,经地面大量观测表明 ,一般来说 ,当土壤含水量为田间最大持水量的 5 5 %～ 85 %时 ,从生长状况和经济     

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE)

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques.     

Remote sensing variables as predictors of habitat suitability of the viscacha rat (<Emphasis Type="Italic">Octomys mimax</Emphasis>), a rock-dwelling mammal living in a desert environment

Identifying high-quality habitats across large areas is a central goal in biodiversity conservation. Remotely sensed data provide the opportunity to study different habitat characteristics (e.g., landscape topography, soil, vegetation cover, climatic factors) that are difficult to identify at high spatial and temporal resolution on the basis of field studies. Our goal was to evaluate the applicability of remotely sensed information as a potential tool for modeling habitat suitability of the viscacha rat (Octomys mimax), a rock-dwelling species that lives in a desert ecosystem. We fitted models considering raw indices (i.e., green indices, Brightness Index (BI) and temperature) and their derived texture measures on locations used by and available for the viscacha rat. The habitat preferences identified in our models are consistent with results of field studies of landscape use by the viscacha rat. Rocky habitats were well differentiated by the second-order contrast of BI, instead of BI only, making an important contribution to the global model by capturing the heterogeneity of the substratum. Furthermore, rocky habitats are able to maintain more vegetation than much of the surrounding desert; hence, their availability might be estimated using SATVI (Soil Adjusted Total Vegetation Index) and its derived texture measures: second-order contrast and entropy. This is the first study that evaluates the usefulness of remotely sensed data for predicting and mapping habitat suitability for a small-bodied rock dwelling species in a desert environment. Our results may contribute to conservation efforts focused on these habitat specialist species by using good predictors of habitat quality.     

干旱监测指数研究

日益严重的全球化干旱问题已经成为各国科学家和政府部门共同关注的热点,它直接威胁着人类的生存环境.而干旱监测,尤其是遥感干旱监测,一直是科学界公认的难题.本文对传统的干旱监测指数如帕尔默干旱指数、作物湿度指数、标准降水指数、地表水分供应指数的优缺点进行评述;将遥感干旱监测指数分成2类,一类是基于地表反射率和发射率的干旱监测指数;另一类是基于地表水和能量平衡模型的干旱监测指数,详细介绍了这2类干旱遥感监测指数的原理、方法以及适用范围.对各种干旱监测指数存在的问题以及干旱监测的发展趋势进行了探讨.