湖库水生植被光学遥感监测研究进展和展望

水生植被在湖库生态系统中发挥稳定沉积物、净化水质、平衡水生生态系统等作用,监测水生植被变化对湖库生态环境的监测具有重要意义。梳理了国内外利用高光谱、多光谱光学卫星遥感数据提取湖库水生植被的方法,尤其是针对其中涉及的阈值确定问题进行总结分析,介绍了典型研究区水生植被时空分布和变化以及与水质的关系,最后给出一些水生植被遥感监测的展望。     

应用遥感监测技术分析扬州市五年植被覆盖状况

利用扬州市2006—2010年间卫星遥感数据,对扬州市植被覆盖状况进行了研究。首先对遥感影像进行几何校正,其次对遥感影像进行解译,提取植被覆盖信息,计算扬州市植被覆盖指数,同时对扬州市近5年植被覆盖进行了动态变化分析。研究表明,扬州市近5年林地面积大幅度提高,植被覆盖指数逐年增加,生态环境状况逐年好转。     

Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing

The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The spectral library is used to automate the processing of hyperspectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.     

基于多星遥感影像的太湖北部湖湾春季沉水植被分布动态监测

利用多星遥感影像动态监测了2015年春季太湖北部湖湾沉水植被分布的变化过程,并对水生植被遥感监测中的几个关键问题展开了探讨。决策树方法获取的水生植被分布结果表明,2015年4月28日太湖北部湖湾发现明显沉水植被分布面积约11.2 km~2,与2014年同期相比,新出现的较大面积沉水植被分布区域主要集中在田鸡山西和锡东水厂附近。提出应及时清理打捞,避免水生植被死亡腐烂,威胁水质安全。     

环境DNA宏条形码监测水生态系统变化与健康状态

开展快速可靠的水生态监测并预测其变化趋势,对保护水生态环境具有重要的价值。近年来,环境DNA宏条形码技术(简称环境DNA技术)的快速发展弥补了传统形态学生物监测的缺陷,显著提升了水生生物群落的监测能力。与机器学习、遥感和云服务等技术结合,环境DNA技术不仅能大尺度、高频率、高灵敏度、自动化地获取生态监测信息,而且能准确地识别水生态系统的变化趋势,进而改变对水生态系统的认识与管理方式。因此,研究着重总结了环境DNA技术在水生态监测中的应用,分析了环境DNA技术与机器学习、卫星遥感等跨学科合作的潜在机遇,基于环境DNA技术简单、便捷的优势,提出了社会公民参与水环境保护的生态监测新思路。     

Exploring the relationship between vegetation spectra and eco-geo-environmental conditions in karst region, Southwest China

Remote sensing of local environmental conditions is not accessible if substrates are covered with vegetation. This study explored the relationship between vegetation spectra and karst eco-geo-environmental conditions. Hyperspectral remote sensing techniques showed that there were significant differences between spectral features of vegetation mainly distributed in karst and non-karst regions, and combination of 1,300- to 2,500-nm reflectance and 400- to 680-nm first-derivative spectra could delineate karst and non-karst vegetation groups. Canonical correspondence analysis (CCA) successfully assessed to what extent the variation of vegetation spectral features can be explained by associated eco-geo-environmental variables, and it was found that soil moisture and calcium carbonate contents had the most significant effects on vegetation spectral features in karst region. Our study indicates that vegetation spectra is tightly linked to eco-geo-environmental conditions and CCA is an effective means of studying the relationship between vegetation spectral features and eco-geo-environmental variables. Employing a combination of spectral and spatial analysis, it is anticipated that hyperspectral imagery can be used in interpreting or mapping eco-geo-environmental conditions covered with vegetation in karst region.     

Estimation of Vegetation Coverage in Semi-arid Sandy Land Based on Multivariate Statistical Modeling Using Remote Sensing Data

The estimation of vegetation coverage is essential in the monitoring and management of arid and semi-arid sandy lands. But how to estimate vegetation coverage and monitor the environmental change at global and regional scales still remains to be further studied. Here, combined with field vegetation survey, multispectral remote sensing data were used to estimate coverage based on theoretical statistical modeling. First, the remote sensing data were processed and several groups of spectral variables were selected/proposed and calculated, and then statistically correlated to measured vegetation coverage. Both the single- and multiple-variable-based models were established and further analyzed. Among all single-variable-based models, that is based on Normalized Difference Vegetation Index showed the highest R (0.900) and R ² (0.810) as well as lowest standard estimate error (0.128024). Since the multiple-variable-based model using multiple stepwise regression analysis behaved much better, it was determined as the optimal model for local coverage estimation. Finally, the estimation was conducted based on the optimal model and the result was cross-validated. The coefficient of determination used for validation was 0.867 with a root-mean-squared error (RMSE) of 0.101. The large-scale estimation of vegetation coverage using statistical modeling based on remote sensing data can be helpful for the monitoring and controlling of desertification in arid and semi-arid regions. It could serve for regional ecological management which is of great significance.     

遥感监测土壤湿度综述

遥感技术具有大面积同步观测，时效性、经济性强等特点，为大面积动态监测土壤湿度提供了可能。本文对近年来国内外遥感监测土壤湿度的理论、方法的发展和应用进行了回顾，重点介绍了目前已经比较成熟和广泛应用的基于可见光与热红外波段的植被指数方法以及在干旱、半干旱地区的应用，通过对比分析了各种遥感监测方法的优缺点，指出了土壤湿度遥感监测方法存在的不足，展望了土壤湿度遥感监测方法的发展趋势。     

水质参数的遥感反演和遥感监测

遥感技术由于具有快速、宏观、低成本和周期性的优点,便于探测水质的时空变化,已成为水质参数监测的重要手段。目前能够直接进行遥感反演的水质参数主要是悬浮物浓度、叶绿素a浓度、可溶解性有机物等光学活性物质,并已经建立了许多反演模型。但是这些模型直接用于水质的遥感监测仍存在一些问题。今后,利用3S技术将地面观测和遥感观测结合起来,可望推动水色遥感的实际应用。     

A Model for Simulating the Soil Organic Carbon Pool of Steppe Ecosystems

In this research, the improved Terrestrial Ecosystem Regional (TECO-R) model was adapted to steppe ecosystems and then utilized to simulate the soil organic carbon pool in the period from 1989 to 2011 (excluding 1994, 2002, 2009, and 2010) for a typical steppe in Xilingol League of Inner Mongolia in China. The improved TECO-R model is an ecological model in combination of remote sensing data, which allows the spatial scale for the analysis of soil organic carbon which is not limited to vegetation or soil type. The spatial and temporal resolution advantages of remote sensing image can be well utilized in this model. The results indicate that in addition to an accurate simulation of the soil carbon pool of a steppe ecosystem, the vegetation aboveground carbon pool, grazing intensity of herbivores, mowing coefficient, litter carbon pool, root carbon pools of different vegetation layers, root-shoot ratio, actual residence time of different carbon pools, and allocation coefficients of different carbon pools in corresponding years are also available from the TECO-R model. Some of the above data are difficult to obtain through macro-observation but can be simulated with the TECO-R model by combining the model with input data; this is very important for a correct understanding of the feedback relationships between the steppe ecosystem’s carbon cycle and climate change (e.g., global warming) and human activities such as grazing.     

Airborne observations of vegetation and implications for biogenic emission characterization

Measuring hydrocarbons from aircraft represents one way to infer biogenic emissions at the surface. The focus of this paper is to show that complementary remote sensing information can be provided by optical measurements of a vegetation index, which is readily measured with high temporal coverage using reflectance data. We examine the similarities between the vegetation index and in situ measurements of the chemicals isoprene, methacrolein, and alpha-pinene to estimate whether the temporal behavior of the in situ measurements of these chemicals could be better understood by the addition of the vegetation index. Data were compared for flights conducted around Houston in August and September 2000. The three independent sets of chemical measurements examined correspond reasonably well with the vegetation index curves for the majority of flight days. While low values of the vegetation index always correspond to low values of the in situ chemical measurements, high values of the index correspond to both high and low values of the chemical measurements. In this sense it represents an upper limit when compared with in situ data (assuming the calibration constant is adequately chosen). This result suggests that while the vegetation index cannot represent a purely predictive quantity for the in situ measurements, it represents a complementary measurement that can be useful in understanding comparisons of various in situ observations, particularly when these observations occur with relatively low temporal frequency. In situ isoprene measurements and the vegetation index were also compared to an isoprene emission inventory to provide additional insight on broad issues relating to the use of vegetation indices in emission database development.     

Monitoring global change: Comparison of forest cover estimates using remote sensing and inventory approaches

Satellite-based remote sensing offers great potential for frequent assessment of forest cover over broad spatial scales, however, calibration and validation using ground-based surveys are needed. In this study, forest cover estimates for the United States from a recently developed land surface cover map generated from satellite remote sensing data were compared to state-level inventory data from the U.S. National Resources Planning Act Timber Database. The land cover map was produced at the U.S. Geological Survey EROS Data Center and is based on imagery from the AVHRR sensor (spatial resolution 1.1 km). Vegetation type was classified using the temporal signal in the Normalized Difference Vegetation Index derived from AVHRR data. Comparisons revealed close agreement in the estimate of forest cover for extensively forested states with large polygons of relatively similar vegetation such as Oregon. Larger forest cover differences were observed in other states with some regional patterns in the level of agreement apparent.Comparisons in inventory- and remote sensing-based estimates of current forested area with potential vegetation maps indicated the magnitude of past land use change and the potential for future changes. The remote sensing approach appears to hold promise for conducting surveys of forest cover where inventory data are limited or where rates of vegetation change, due to human or climatic factors, are rapid.     

高分一、二号卫星遥感数据在生态环境监测中的应用

从水环境质量监测、水体信息提取、植被资源监测、城市土地覆盖识别、大气环境监测5个方面综述了高分一号(GF-1)、高分二号(GF-2)卫星遥感数据在区域生态环境监测领域的应用,分析了相关研究的应用方向和重点,表明GF-1、GF-2卫星遥感数据在该领域具有良好的适用性和较大的应用潜力。     

The application of satellite data for the quantification of mangrove loss and coastal management in the Godavari estuary, East Coast of India

The mangrove formations of Godavari estuary are due to silting over many centuries. The estuary covers an area of 62,000 ha of which dense Coringa mangrove forest spread in 6,600 ha. Satellite sensor data was used to detect change in the mangrove cover for a period of 12 years (1992-2004). It was found that an area of about 1,250 ha of mangroves was destroyed by anthropogenic interference like aquaculture, and tree felling etc. It was found that mangrove's spectral response/digital number (DN) value is much lower than non-mangrove vegetation such as plantation and paddy fields in SWIR band. By taking this as an advantage, spectral data was utilized for clear demarcation of mangroves from nearby paddy fields and other vegetation. Simpson's diversity index, which is a measure of biodiversity, was found to be 0.09, showing mangroves dominance. Ecological parameters like mud-flats/swamps, mangrove cover alterations, and biodiversity status are studied in detail for a period of 12 years. The increase in mangrove front towards coast was delineated using remote sensing data. The major advantages of remote sensing data is monitoring of change periodically. The combination of moderate and high-resolution data provided detailed coastal land use maps for implementing coastal regulation measures. The classification accuracy has been achieved is 90%. Overall, simple and viable measures are suggested based on multi-spectral data to sustain this sensitive coastal ecology.     

On applications of remote sensing for environmental monitoring

Modern airborne and satellite remote sensing techniques offer attractive opportunities to coastal monitoring systems. Improvements of the evaluation of larger scales phenomena and processes due to the synopticity of the remote sensing data are of particular interest. However, some uncertainties and limitations about remote sensing must be considered. Microwave, infrared and visible radiation methods and their applications are briefly discussed and some applications are demonstrated. Special attention is paid to the remote sensing of various pollutants in the sea, in particular with respect to oil pollution.Promising developments of the remote sensing methods for coastal monitoring are to be expected from the European remote sensing satellite missions ERS 1 and ERS 2.Combination of these observations with simultaneous in situ measurements from ships (sea truth) appears to be most advantageous for the interpretation of the collected data.     

The effect of river damming on vegetation: is it always unfavourable? A case study from the River Tiber (Italy)

River damming leads to strong hydromorphological alterations of the watercourse, consequently affecting river vegetation pattern. A multitemporal and spatial analysis of the dam effect on composition, structure and dynamic of the upstream vegetation was performed on Tiber River at Nazzano-dam (Rome). The main research questions were as follows: How does plant landscape vary over time and along the river? Where does the dam effect on vegetation end? How does naturalistic importance of the vegetation affected by damming change over time? Data collection was performed mapping the vegetation in aerial photos related to the period before (1944), during (1954) and after dam construction (1984, 2000). The plant landscape has significantly changed over time and along the river, particularly as a result of the dam construction (1953). The major vegetation changes have involved riparian forests and macrophytes. Dam effect on vegetation is evident up to 3 km, and gradually decreases along an attenuation zone for about another 3 km. Despite the fact that the damming has caused strong local hydromorphological modification of the river ecosystem transforming it into a sub-lacustrine habitat, it has also led to the formation of wetlands of considerable naturalistic importance. Indeed, in these man-made wetlands, optimal hydrological conditions have been created by favouring both the expansion of pre-existing riparian communities and the rooting of new aquatic communities, albeit typical of lacustrine ecosystems. Some of these plant communities have become an important food resource, refuge or nesting habitats for aquatic fauna, while others fall into category of Natura 2000 habitats. Therefore, river damming seems to have indirectly had a “favourable” effect for habitat conservation and local biodiversity.     

Seagrass resource assessment using remote sensing methods in St. Joseph Sound and Clearwater Harbor,Florida, USA

In the event of a natural or anthropogenic disturbance, environmental resource managers require a reliable tool to quickly assess the spatial extent of potential damage to the seagrass resource. The temporal availability of the Landsat 5 Thematic Mapper (TM) imagery provided a suitable option to detect and assess damage of the submerged aquatic vegetation (SAV). This study examined Landsat TM imagery classification techniques to create two-class (SAV presence/absence) and three-class (SAV estimated coverage) SAV maps of the seagrass resource. The Mahalanobis Distance method achieved the highest overall accuracy (86%) and validation accuracy (68%) for delineating the seagrass resource (two-class SAV map). The Maximum Likelihood method achieved the highest overall accuracy (74%) and validation accuracy (70%) for delineating the seagrass resource three-class SAV map. The Landsat 5 TM imagery classification provided a seagrass resource map product with similar accuracy to the aerial photointerpretation maps (validation accuracy 71%). The results support the application of remote sensing methods to analyze the spatial extent of the seagrass resource.     

塔里木河重要生态功能区生态环境质量评价

采用长时间多源遥感数据对塔里木河重要生态功能区进行了土地利用变化、植被指数分析,同时结合多年地面调查监测数据,系统分析了区域生态环境变化情况,并对近五年区域生态环境质量开展了评价,环境质量变化值ΔI为2.58,生态环境质量略有下降,其中环境状况指标和植被覆盖率指数起主导作用。     

基于神东中心区植被覆盖变化的多时相遥感监测

准确、快速地获取植被覆盖信息是矿区生态恢复和建设的关键与重点。以神东中心区为研究对象,利用2002、2005、2007、2010、2012年Landsat TM/ETM+和HJ1A-CCD1五景同期遥感数据,采用像元二分模型法,归一化植被指数(NDVI)值反演植被覆盖度,对研究区生态环境变化规律进行分析。结果表明,神东中心区平均植被覆盖度整体呈上升趋势,区内绝大部分地表覆盖程度得到改善,改善区面积达64.01%,退化区面积只有15.34%。该方法快速、定量地反映矿区植被覆盖及变化情况,为矿区生态环境动态监测和治理提供技术支持。     

Agricultural practices in grasslands detected by spatial remote sensing

The major decrease in grassland surfaces associated with changes in their management that has been observed in many regions of the earth during the last half century has major impacts on environmental and socio-economic systems. This study focuses on the identification of grassland management practices in an intensive agricultural watershed located in Brittany, France, by analyzing the intra-annual dynamics of the surface condition of vegetation using remotely sensed and field data. We studied the relationship between one vegetation index (NDVI) and two biophysical variables (LAI and fCOVER) derived from a series of three SPOT images on one hand and measurements collected during field campaigns achieved on 120 grasslands on the other. The results show that the LAI appears as the best predictor for monitoring grassland mowing and grazing. Indeed, because of its ability to characterize vegetation status, LAI estimated from remote sensing data is a relevant variable to identify these practices. LAI values derived from the SPOT images were then classified based on the K-Nearest Neighbor (KNN) supervised algorithm. The results points out that the distribution of grassland management practices such as grazing and mowing can be mapped very accurately (Kappa index?=?0.82) at a field scale over large agricultural areas using a series of satellite images.