Hyperspectral and LiDAR remote sensing of fire fuels in Hawaii Volcanoes National Park.

Alien invasive grasses threaten to transform Hawaiian ecosystems through the alteration of ecosystem dynamics, especially the creation or intensification of a fire cycle. Across sub-montane ecosystems of Hawaii Volcanoes National Park on Hawaii Island, we quantified fine fuels and fire spread potential of invasive grasses using a combination of airborne hyperspectral and light detection and ranging (LiDAR) measurements. Across a gradient from forest to savanna to shrubland, automated mixture analysis of hyperspectral data provided spatially explicit fractional cover estimates of photosynthetic vegetation, non-photosynthetic vegetation, and bare substrate and shade. Small-footprint LiDAR provided measurements of vegetation height along this gradient of ecosystems. Through the fusion of hyperspectral and LiDAR data, a new fire fuel index (FFI) was developed to model the three-dimensional volume of grass fuels. Regionally, savanna ecosystems had the highest volumes of fire fuels, averaging 20% across the ecosystem and frequently filling all of the three-dimensional space represented by each image pixel. The forest and shrubland ecosystems had lower FFI values, averaging 4.4% and 8.4%, respectively. The results indicate that the fusion of hyperspectral and LiDAR remote sensing can provide unique information on the three-dimensional properties of ecosystems, their flammability, and the potential for fire spread.     

Characterizing the landscape dynamics of an invasive plant and risk of invasion using remote sensing.

Improved understanding of the spatial dynamics of invasive plant species may lead to more effective land management and reduced future invasion. Here, we identified the spatial extents of nonnative cheatgrass (Bromus tectorum) in the north central Great Basin using remotely sensed data from Landsat MSS, TM, and ETM+. We compared cheatgrass extents in 1973 and 2001 to six spatially explicit landscape variables: elevation, aspect, hydrographic channels, cultivation, roads, and power lines. In 2001, Cheatgrass was 10% more likely to be found in elevation ranges from 1400 to 1700 m (although the data suggest a preferential invasion into lower elevations by 2001), 6% more likely on west and northwest facing slopes, and 3% more likely within hydrographic channels. Over this time period, cheatgrass expansion was also closely linked to proximity to land use. In 2001, cheatgrass was 20% more likely to be found within 3 km of cultivation, 13% more likely to be found within 700 m of a road, and 15% more likely to be found within 1 km of a power line. Finally, in 2001 cheatgrass was 26% more likely to be present within 150 m of areas occupied by cheatgrass in 1973. Using these relationships, we created a risk map of future cheatgrass invasion that may aid land management. These results highlight the importance of including land use variables and the extents of current plant invasion in predictions of future risk.     

辽东湾双台子河口湿地翅碱蓬（Suaeda salsa）生物量遥感反演研究

通过现场采集辽东湾双台子河口翅碱蓬（Suaeda salsa）光谱数据,收集标准样方（1 m×1 m）的翅碱蓬生物量（Biomass）,建立相关植被指数与翅碱蓬生物量的遥感反演回归算法,发现辽宁双台子河口湿地翅碱蓬生物量与植被指数PVI、SAVI和MSAVI的相关系数R2较高,直线回归方程相关系数分别达到0.626、0.698和0.679。同时,利用得到的估算模型,结合1990—2005年的双台子河口的TM影像数据,反演该区域翅碱蓬分布面积和生物量。结果发现：辽东湾双台子河口湿地翅碱蓬面积变化呈先降后升的趋势。通过植被指数（NDVI、RVI、PVI和MSAVI）和生物量的算法反演,发现翅碱蓬生物量曲线与分布面积曲线的变化趋势一致。     

遥感水文耦合模型的研究进展

遥感水文的耦合模型在目前生态环境领域,特别是在水资源的应用和管理中其作用日益重要,具有大流域尺度上快速应用、实时动态监测等优点。结合国内外近年来取得的研究成果,文章综述了遥感水文耦合模型的研究进展。首先介绍了遥感技术在水文学中的应用,讨论了它的分类发展概况,接着介绍了几种主要的遥感水文耦合模型及其应用实例,包括SCS(SoilConservationServices)模型、SiB2(SimpleBiosphereModelversion2)简化生物圈模型、SRM(SnowmeltRunoffModel)融雪径流模型以及SWAT(SoilandWaterAssessmentTool)模型,最后展望了遥感水文耦合模型未来的发展趋势,指出尺度问题上的时空变异性仍是其发展的关键,与GIS(Geographicinformationsystem)及其他空间技术的相结合是其未来发展的重要方向,从而为水文学、水资源的预测评价等研究提供参考。     

Linking richness, community variability, and invasion resistance with patch size

The influence of community dynamics on the success or failure of an invasion is of considerable interest. What has not been explored is the influence of patch size on the outcomes of invasions for communities with the same species pool. Here we use an empirically validated spatial model of a marine epibenthic community to examine the effects of patch size on community variability, species richness, invasion, and the relationships between these variables. We found that the qualitative form of the relationship between community variability and species richness is determined by the size of the model patch. In small patches, variability decreases with species richness, but beyond a critical patch size, variability increases with increasing richness. This occurs because in large patches large, long-lived colonies attain sufficient size to minimize mortality and dominate the community, leading to decreased species richness and community variability. This mechanism cannot operate on smaller patches where the size of colonies is limited by the patch size and mortality is high irrespective of species identity. Further, invasion resistance is strongly correlated with community variability. Thus, the relationship between species richness and invasion resistance is also determined by patch size. These patterns are generated largely by an inverse relationship between colony size and mortality, and they depend on the spatial nature and patch size of the community. Our results suggest that a continuum of possible relationships can exist between species richness, community variability, invasion resistance, and area. These relationships are emergent behaviors generated by the individual properties of the particular component species of a community.     

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.     

Quantification and reduction of erroneous differences between images in remote sensing

The reliability of image data, along with the difficulty of accurately comparing images acquired at different times or from different sensors, is a generic problem in remote sensing. Measurement repeatability errors occur frequently and can substantially reduce the system’s ability to reliably quantify real spectral and spatial change in a target. This paper outlines methodologies for quantifying and reducing erroneous differences between monochrome and multispectral or hyperspectral images. [Each of these image types is acquired by an instrument that collects light photons across a variable range of the electromagnetic spectrum (often referred to as an image band). The hyperspectral image is often referred to as a hyperspectral cube with XY spatial dimensions and many Z bands (spectral demotions)]. In this paper, we specifically discuss the Pixel Block Transform (PBT), the Spectral Averaging Transform (SAT), and the Wavelet Transform (WAVEL). We briefly address sensor fusion. Results indicate that the PBT is a powerful cross-noise and repeatability error reducing tool, applicable to monochrome, multispectral, and hyperspectral images. The SAT is as powerful as the PBT in reducing error but is suitable only for hyperspectral imagery. WAVEL can reduce some of the finer scale noise, but it is not as powerful in reducing cross-noise as PBT or SAT and it requires some trial and error for selecting the appropriate wavelet function. It is important that those involved in developing statistically sound relationships between remotely sensed imagery and other data sources understand the problems and their solutions to prevent wasting time or developing relationships that are statistically insignificant or unstable, or that lead to faulty conclusions.     

Monitoring of abandoned quarries by remote sensing and in situ surveying

In Italy quarrying causes relevant environmental damages and alterations to the land and the ecosystems. Despite the present Italian legislation requiring the restoration of the sites after exploitation, most of the quarries, both the abandoned and the still operational ones, are not restored.The objective of this work is to indicate a monitoring methodology in order to survey the present state of the quarry sites and their evolution in time, which are the basic data needed to implement an adequate land reclamation project.Such methodology has been applied to several abandoned limestone quarries in the Latina province (close to Rome), characterised by a typical Mediterranean vegetation, but it can be applied to any other kind of litology and vegetation.The land monitoring has been realised both by using remote sensing techniques, supported by a Geographic Information System of the studied area, and by in situ surveying. The in situ surveying was able to assess the capability of the remote sensing model to describe the state of each site.     

Sea-level rise and coastal vulnerability: an assessment of Andhra Pradesh coast,India through remote sensing and GIS

The eustatic sea-level rise due to global warming is predicted to be about 18 to 59 cm by the 2100 (IPCC 2007), which necessitates identification and protection of vulnerable sections of coasts. Assessment of vulnerability level of Andhra Pradesh (AP) coast as an example is demonstrated in this study using five physical variables, namely coastal geomorphology, coastal slope, shoreline change, mean spring tide range, and significant wave height. A coastal vulnerability index was prepared by integrating the differentially weighted rank values of the five variables, based on which the coastline is segmented into low-, moderate-, high-, and very high risk categories. About 43% of the 1,030-km-long AP coast is under very high-risk, followed by another 35% under high-risk if the sea level rises by ~0.6 m displacing more than 1.29 million people living within 2.0 m elevation in 282 villages in the region.     

Optical remote sensing of coastal plumes and run-off in the Mediterranean region

Sea surface colour data, derived from the Coastal Zone Colour Scanner (CZCS) archive, have been used to assess the space/time variability of coastal plumes and run-off in the Mediterranean Sea. A time series of 2645 scenes, collected by the CZCS from 1979 to 1985, was processed to apply sensor calibration algorithms, correct for atmospheric contamination, and derive chlorophyll-like pigment concentration. Individual images, remapped on a 1-km² pixel grid, were generated for each available day, and then mean values calculated pixel by pixel to form monthly, seasonal and annual composites. The results obtained must be taken with caution, due to the CZCS limitations in the quantitative assessment of bio-optical pigments when high concentrations of dissolved organics or suspended sediments are present, e.g. along littorals or within plumes. Marked differences appear in the distribution of water constituents between coastal zones and open sea, northern and southern near-coastal areas, western and eastern sub-basins. The oligotrophic character of the basin contrasts with areas of high concentration related to river plumes—Ebro (Ebre), Po, Rhone, Nile—, coastal run-off patterns, and persistent mesoscale features (e.g. coastal filaments and eddies). Seasonal variability appears to be high, with higher concentrations occurring over most of the basin in the cold season, when climatic conditions are favourable to coastal run-off and vertical mixing. Atmospheric forcing (wind and rainfall over continental margins) could play an important role in establishing the observed space/time distribution of water constituents. The impact of continental interactions (fluvial and coastal run-off), or that of exchanges between coastal zone and open sea, could have paramount influence on the biogeochemical fluxes in the entire basin.     

Evaluating long-term hydrological impacts of regional urbanisation in Hanyang,China, using a GIS model and remote sensing

China has experienced a rapid urbanisation, especially since the 1980s; however, the environmental impacts of this process are not fully investigated. Hanyang (Hubei Province, south China) was selected as a typical case to investigate runoff and non-point source (NPS) pollution impacts of urbanisation. A water quality simulation model (L-THIA) was applied to determine the long-term implications of different degrees of regional urbanisation impacts on NPS pollutants. Land-use patterns in 1987, 1998 and 2003 were analysed to evaluate the temporal variation of urbanisation, and the precipitation dataset from 1975 to 2003 was used to estimate the mean annual runoff and NPS pollutants. The contributions of different land-use categories to average annual runoff and NPS pollutant production were assessed by the means of a regression model. Results show that urban/impervious lands increased by 30.4% between 1987 and 2003, with the most increase occurring in 1998–2003. Industrial and forestlands have the most and least impact, respectively, on mean annual runoff and NPS pollutants. A combination of L-THIA and the regression model was found to be useful as a decision support tool for regional and urban planning from the perspectives of water quality control.     

Study of the dynamics of estuarine and coastal waters using remote sensing: the Tinto-Odiel estuary, SW Spain

This paper deals with the application of satellite images to characterize some aspects of the circulation dynamics of the Tinto-Odiel estuary using turbidity patterns as ‘natural tracers’. 15 images (Landsat TM and Spot HRV) were processed to provide synoptic, instantaneous views of the circulation patterns under different environmental conditions. In addition, a comparison was made between results of oceanographic field work, using biplanes and fluorescent tracers, and satellite image turbidity patterns used as ‘ground truth’ data for specific hydroclimatic situations. This approach allowed (1) the identification and mapping of dynamic processes of interest during a theoretical tidal cycle, (2) the elaboration of additional information on the ‘flow schemes’ at the mouth of the estuary with improved spatial and temporal resolution, and (3) the supply of basic data to improve the knowledge of exchange processes between estuarine and coastal waters. The results of this study are considered to be useful for the management of the estuarine system.     

Study of the dynamics of estuarine and coastal waters using remote sensing: the Tinto-Odiel estuary,SW Spain

This paper deals with the application of satellite images to characterize some aspects of the circulation dynamics of the Tinto-Odiel estuary using turbidity patterns as ‘natural tracers’. 15 images (Landsat TM and Spot HRV) were processed to provide synoptic, instantaneous views of the circulation patterns under different environmental conditions. In addition, a comparison was made between results of oceanographic field work, using biplanes and fluorescent tracers, and satellite image turbidity patterns used as ‘ground truth’ data for specific hydroclimatic situations. This approach allowed (1) the identification and mapping of dynamic processes of interest during a theoretical tidal cycle, (2) the elaboration of additional information on the ‘flow schemes’ at the mouth of the estuary with improved spatial and temporal resolution, and (3) the supply of basic data to improve the knowledge of exchange processes between estuarine and coastal waters. The results of this study are considered to be useful for the management of the estuarine system.     

Integrated remote sensing and GIS techniques for landslide hazard zonation: a case study Wadi Watier area,South Sinai,Egypt

Natural hazard analysis involves mapping and identifying future hazardous zones through the analysis of the controls influencing hazard initiation and occurrence. One of such natural hazard is the landslide. Landslides are amongst the most costly and damaging natural hazards especially in mountain regions and are triggered mainly by seismic activity and/or rainfall. The aim of the present study is to integrate Remote Sensing (RS) and Geographic Information System (GIS) tools to create thematic layers for assessment and the estimation of landslide hazard zones in and surrounding the Wadi Watier area, South Sinai, Egypt. Various factors, variables and/or parameters can be derived from thematic layers such as lithology, structural lineaments, land-cover/land-use, terrain analysis and earthquakes. Intensity risk layers were created by using ERDAS Imagine 9.2, ARC GIS 9.2 and ARC INFO 7.2.1 software. Enhanced Thematic Mapper (ETM+7) Landsat satellite images were used to discriminate and extract structural lineaments, lithology and land-use/land-cover variables for the study area. The Digital Elevation Model (DEM) was generated from digitized topographic maps to produce terrain analysis maps such as; slope, aspect, height elevation, and 3D. The weighting score rating system based on the relative importance of various causal factors derived from RS data and other thematic layers was used for landslide hazard zonation (LHZ). Based on these data, a simple algorithm was created to classify the area into different risk zones. By overlaying all hazard layers a final landslide hazard map was produced. Using trial and error and statistical methods the weight score rating values have been readjusted. GIS integration with RS data can greatly facilitate classifying landslide hazard zones into low risk, moderate risk and high risk by using a slicing operation. Seismic data are integrated with final the LHZ to generate a LHZ scenario map for the future and to draw up an action plan of mitigation measures to avoid the damage, loss of life and socio-economic impacts in the study area.     

Crustacean habitat potential mapping in a tidal flat using remote sensing and GIS

To make a macrofaunal (crustacean) habitat potential map, the spatial distribution of ecological variables in the Hwangdo tidal flat, Korea, was explored. Spatial variables were mapped using remote sensing and a geographic information system (GIS) combined with field observations. A frequency ratio (FR) and logistic regression (LR) model were employed to map the macrofauna potential area for the Ilyoplax dentimerosa, a crustacean species. Spatial variables affecting the tidal macrofauna distribution were selected based on abundance and biomass and used within a spatial database derived from remotely sensed data of various types of sensors. The spatial variables included the intertidal digital elevation model (DEM), slope, distance from a tidal channel, tidal channel density, surface sediment facies, spectral reflectance of the near infrared (NIR) bands and the tidal exposure duration. The relation between the I. dentimerosa and each spatial variable was calculated using the FR and LR. The species was randomly divided into a training set (70%) to analyse habitat potential using FR and LR and a test set (30%) to validate the predicted habitat potential map. The relations were overlaid to produce a habitat potential map with the species potential index (SPI) value for each pixel. The potential habitat maps were compared with the surveyed habitat locations such as validation data set. The comparison results showed that the LR model (accuracy is 85.28%) is better in prediction than the FR (accuracy is 78.96%) model. The performance of models gave satisfactory accuracies. The LR provides the quantitative influence of variables on a potential habitat of species; otherwise, the FR shows the quantitative influence of a class in each variable. The combination of a GIS-based frequency ratio and logistic regression models and remote sensing with field observations is an effective method to determine locations favorable for macrofaunal species occurrences in a tidal flat.     

Evolution of spit morphology: a case study using a remote sensing and statistical based approach

The dynamics and factors responsible for morphological changes of spits viz., Uliyargoli-Padukere, Oddu Bengre and Kodi Bengre, southern Karnataka, India, are investigated using multi-dated satellite images and topographic maps during the last 95-years (1910–2005). Variations and overall rate of changes in length, area and coefficient of determination (R²) of each spit are calculated separately for two periods (1910 and 1967 as base years) to find out whether there is any significant trend in the case of change in length and area in all the three spits that are under study. Linear trend lines are fitted using a least squares method and the statistical significance is considered at 80 % level of confidence. The results recorded significant changes in spit morphology, especially in length and area if 1910 and 1967 are considered separately as base years, are may be due to non-availability of data set between 1910 and 1967 period. The study reveals that coastal processes, such as SW-monsoon influenced strong currents and longshore drifts are the main process for formation and growth of spits, whereas rivers influence/drift also plays significant role. The statistical uncertainty estimation in spits morphology is prevalent wherever the coast is affected by human interventions. The study demonstrates that combined use of satellite imagery and statistical techniques can be effectively used to understand the evolution of spits morphology.     

遥感与GIS在蝗虫生境研究中的应用进展

蝗虫生境是蝗虫赖以生活、生存的环境。利用遥感技术获取与蝗虫密切相关的各种生境因子,并结合GIS技术研究和阐明蝗虫的生境因子对东亚飞蝗的产卵、孵化、成虫、迁飞等生育周期的影响机理,是实现对蝗虫监测、预测预警的基础。文章首先分析了各种蝗虫生境因子对蝗虫发生的影响机理,认为这些生境因子对蝗灾发生的影响不是孤立地起作用的,仅靠单个生境因子进行蝗灾的预测是不全面的,而遥感和GIS技术方便的获取、管理和分析多种生境因子数据,在大尺度蝗灾发生监测和预警中起到不可替代的作用。通过对遥感与GIS在蝗虫的不同发育阶段（蝗卵发育阶段、蝗蝻发育阶段和成虫阶段）的应用及其进展的分析,认为对蝗虫不同生育阶段的遥感监测需采用不同的参数和技术才能达到更好的效果,最后提出了遥感与GIS在蝗虫生境监测方面的未来发展方向。     

Shoreline changes in response to sea level rise along Digha Coast,Eastern India: an analytical approach of remote sensing,GIS and statistical techniques

Shoreline is one of the rapidly changing linear features of the coastal zone which is dynamic in nature. The issue of shoreline changes due to sea level rise over the next century has increasingly become a major social, economic and environmental concern to a large number of countries along the coast, where it poses a serious problem to the environment and human settlements. As a consequence, some coastal scientists have advocated analyzing and predicting coastal changes on a more local scale. The present study demonstrates the potential of remote sensing, geospatial and statistical techniques for monitoring the shoreline changes and sea level rise along Digha coast, the eastern India. In the present study, multi-resolution and multi temporal satellite images of Landsat have been utilized to demarcate shoreline positions during 1972, 1980, 1990, 2000, and 2010. The statistical techniques, linear regression, end-point rate and regression coefficient (R²) have been used to find out the shoreline change rates and sea level change during the periods of 1972–2010. Monthly and annual mean sea level data for three nearby station viz., Haldia, Paradip and Gangra from 1972 to 2006 have been used to this study. Finally, an attempt has been made to find out interactive relationship between the sea level rise and shoreline change of the study area. The results of the present study show that combined use of satellite imagery, sea level data and statistical methods can be a reliable method in correlating shoreline changes with sea level rise.     

遥感与GIS支持下的城市绿地信息提取方法研究--以深圳市为例

城市绿地作为城市结构中的自然生产力主体，在城市系统中起着重要作用。传统的城市绿地调查多采用人工普查并结合统计学方法进行城市绿地调查，需要投入大量的人力和资金，但得到的数据精度和现时性都较差，而采用高分辨率遥感卫星数据与地理信息系统相结合的方法，可以达到对城市绿地遥感数据的快速特征提取和准确的空间统计分析。文章采用基于遥感和GIS相结合的方法，并以深圳市为例，进行了城市绿地特征提取方法的研究和应用分析。     

Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India

Fire is the most spectacular natural disturbance that affects the forest ecosystem composition and diversity. Fire has a devastating effect on the landscape and its impact is felt at every level of the ecosystem and it is possible to map forest fire risk zone and thereby minimize the frequency of fire. There is a need for supranational approaches that analyze wide scenarios of factors involved and global fire effects. Fires can be monitored and analyzed over large areas in a timely and cost effective manner by using satellite imagery. Also Geographical Information System (GIS) can be used effectively to demarcate the fire risk zone map. Bhadra wildlife Sanctuary located in Kamataka, India was selected for this study. Vegetation, slope, distance from roads, settlements parameters were derived for a study area using topographic maps and field information. The Remote Sensing (RS) and Geographical Information System (GIS)-based forest fire risk model of the study area appeared to be highly compatible with the actual fire-affected sites. The temporal satellite data from 1989 to2006 have been analyzed to map the burnt areas. These classes were weighted according to their influence on forest fire. Four categories of fire risk regions such as Low, Moderate, High and Very high fire intensity zones were identified. It is predicted that around 10.31% of the area falls undermoderate risk zone.