Remote sensing of coastlines: detection,extraction and monitoring

This paper reviews the current status of the use of remote sensing for the detection, extraction and monitoring of coastlines. The review takes the US system as an example. However, the issues at hand can be applied to any other part of the world. Visual interpretation of airborne remote sensing data is still widely and popularly used for coastal delineation. However, a variety of remote sensing data and techniques are available to detect, extract and monitor the coastline. The developed techniques have reached a level of maturity such that they are applied in operational settings.     

Remote sensing technique for mapping salt affected soils

The salt affected soils of Etah, Aligarh, Mainpuri and Mathura districts have been mapped into S1: < 10 per cent of the area covered by salts, S2: 10–30 per cent, S3: 30–50 per cent, S4: 50–75 per cent and S5: > 75 per cent using an integrated approach of image interpretation. The dull white tone of salt affected and sandy soils have posed problems in their discrimination, on TM false colour composite, however, the problem of spectral similarity was solved through integration of interpretation of thermal data (10.4–12.5μm) with TM FCC (bands 2, 3, 4) interpretation. The discrimination of salt affected soils was significantly better on data between March and first week of April because of maximum contrast. Out of l-75m ha of the total geographical area, the salt affected soils account for 11–8 per cent. Another 16–2 per cent is likely to be affected with this problem if similar degraded conditions prevail there. The values of pH: 8–7 and ECe: 1–3 dsm^-1 of S1 soils group them into non saline and mildly alkali class. Similarly low ECe: 4–4dSm^-1 and high pH: 9–8 for S3 soils and high ECe: 24–0 dsm^-1 and very high pH: 10–7 of S4/S5 soils, placed the soils into alkali and sodic classes respectively. The gypsum requirement based on pH values varies from 1 to 15 tha^-1.     

Automatic detection of oil spills from SAR images

A probabilistic method has been developed that distinguishes oil spills from other similar sea surface features in synthetic aperture radar (SAR) images. It considers both the radiometric and the geometric characteristics of the areas being tested. In order to minimize the operator intervention, it adopts automatic selection criteria to extract the potentially polluted areas from the images. The method has an a priori percentage of correct classification higher than 90% on the training dataset; the performance is confirmed on a different dataset of verified slicks. Some analyses have been conducted using images with different radiometric and geometric resolutions to test its suitability with SAR images different from European Remote Sensing (ERS) satellite ones. The system and its ability to detect and classify oil and non‐oil surface features are described. Starting from a set of verified oil spills detected offshore and over the coastline, the ability of SAR to reveal oil spills is tested by analysing wind intensity, deduced from the image itself, and the distance from the coast.     

Remote sensing of land use and vegetation for mesoscale hydrological studies

In this paper, methods for mapping land use changes and vegetation parameters using remote sensing data are presentedin the context of hydrological studies. In the first part, a land use and land cover classification system (RUB-LUCS: Ruhr University Bochum - Land Use and Land Cover Classification System) is developed for providing distributed information for hydrological modelling and for detection of distributed land use changes. Applying this system to Landsat data, land use time series is created for hydrological modelling of effects of man-made changes in the Sauer River Basin. In the second part, equations are established for estimating leaf area indices using vegetation indices calculated from remote sensing data and a two stream approximation model for estimating leaf area indices is applied to the Sauer River Basin. Combining the two approaches, a method has been found for calculating leaf area indices for mesoscale river basins using remote sensing data.     

Remote sensing of vegetation dynamics in West Africa

Vegetation dynamics and the lives of millions of people in West Africa are closely interlinked with each other. The high annual variability of the phenological cycle considerably affects the agricultural population with late rainfalls and droughts, often resulting in serious food crises. On the other hand, the rapidly growing population has a great need for space due to expanding cities and a low agricultural efficiency. This situation, together with a changing climate, has had a strong impact on vegetation dynamics in West Africa and will play a major role in the future. The dynamic nature of vegetation in the region has attracted a lot of remote-sensing-based research in the past 30 years and has lead to heated discussions. This review article gives a comprehensive overview of the studies on remotely sensed vegetation dynamics in West Africa. After an introduction to the specific situation for vegetation dynamics in West Africa, the applied sensors and their suitability for the region are outlined. Research on the assessment of different plant parameters, on phenological metrics as well as on the monitoring of agricultural areas is outlined and discussed. Furthermore, a major part of this review is dedicated to the analyses undertaken to assess vegetation trends in West Africa over the past 30 years and their potential human and climatic causes. Finally, identified research gaps and challenges for future studies are discussed.     

Remote sensing of biomass of salt marsh vegetation in France

Abstract

Spectral data (gathered using a hand-held radiometer) and harvest data were collected from four salt marsh vegetation types in Brittany, France, to develop equations predicting live aerial biomass from spectral measurements. Remote sensing estimates of biomass of the general salt marsh community (GSM) and of Spartina alternifiora can be obtained throughout the growing season if separate biomass prediction equations are formulated for different species mixtures (for the GSM) and for different canopy types (for S. alternifiora). Results suggest that remote sensing will not be useful for predicting Halimione portulacoides biomass, but can be used to estimate Puccinellia maritima biomass early in the growing season.     

Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems

The objective of this paper is to review research conducted over the past decade on the application of multi-temporal remote sensing for monitoring changes of Arctic tundra lands. Emphasis is placed on results from the National Science Foundation Land-Air-Ice Interactions (LAII) program and on optical remote sensing techniques. Case studies demonstrate that ground-level sensors on stationary or moving track platforms and wide-swath imaging sensors on polar orbiting satellites are particularly useful for capturing optical remote sensing data at sufficient frequency to study tundra vegetation dynamics and changes for the cloud prone Arctic. Less frequent imaging with high spatial resolution instruments on aircraft and lower orbiting satellites enable more detailed analyses of land cover change and calibration/validation of coarser resolution observations.The strongest signals of ecosystem change detected thus far appear to correspond to expansion of tundra shrubs and changes in the amount and extent of thaw lakes and ponds. Changes in shrub cover and extent have been documented by modern repeat imaging that matches archived historical aerial photography. NOAA Advanced Very High Resolution Radiometer (AVHRR) time series provide a 20-year record for determining changes in greenness that relates to photosynthetic activity, net primary production, and growing season length. The strong contrast between land materials and surface waters enables changes in lake and pond extent to be readily measured and monitored.     

Remote sensing of vegetation change near Inco's Sudbury mining complexes

Abstract

Using LANDSAT data for different years between 1973 and 1983, vegetation change maps were produced for two areas in the vicinity of Inco's mining complexes. Field checking showed that for more than 80 per cent of the sites inspected, causes for the vegetation changes recorded on the maps could be determined. The system used provides a cost-effective method of monitoring major vegetation changes over a number of years, but it is not suitable for monitoring slow, progressive, vegetation changes over short periods.     

遥感图像边缘特征提取与融合方法研究

针对单一传感器的局限性,通过多传感器融合技术,最大限度地获取对目标场景的边缘信息描述。由于光学图像和SAR图像成像机理不同,即便配准精确的图像提取出来的边缘也会出现不重合的情况。首先采用离散域Canny算法提取光学图像边缘特征,用ratio算法提取SAR图像边缘特征,通过不变矩算法对两幅边缘特征图中的边缘进行匹配融合,得到了比单一图像边缘特征图更完整更清晰的边缘特征图,获取了更多的目标场景的信息描述。     

Remote sensing and GIS for detecting changes in the aquatic vegetation of a rehabilitated lake

Remote sensing and geographical information system (GIS) methods combined with ground estimations were used to assess the effects of rehabilitation on the aquatic vegetation of a shallow, eutrophic lake in Finland. Aerial photograph interpretation was used to study the distribution of aquatic vegetation before (1953, 1996) and after (2001) rehabilitation in 1997. A digital elevation model was derived to relate the change in the aquatic vegetation to water depth. In addition, changes in the biomass of the most abundant species of the lake, Common Club-rush (Schoenoplectus lacustris), were studied by means of a regression analysis relating the ground estimations to the reflectance values (R ²=0.889, p@lt;0.001). The results indicated that the objective of the rehabilitation--to stop the overgrowth process--has at least temporarily been achieved. After rehabilitation the most noticeable change had taken place in the area covered by floating-leaved vegetation. Greater proportional changes in the aquatic vegetation areas had occurred in the deep rather than in the shallow areas. A decrease in biomass of Common Club-rush was estimated to be 30% due to rehabilitation. The use of remote sensing and GIS provided valuable information on temporal and spatial changes in the aquatic vegetation, and the methods could be applied more extensively for lake monitoring purposes.     

Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects

Remote sensing provides a broad view of landscapes and can be consistent through time, making it an important tool for monitoring and managing protected areas. An impediment to broader use of remote sensing science for monitoring has been the need for resource managers to understand the specialized capabilities of an ever-expanding array of image sources and analysis techniques. Here, we provide guidelines that will enable land managers to more effectively collaborate with remote sensing scientists to develop and apply remote sensing science to achieve monitoring objectives. We first describe fundamental characteristics of remotely sensed data and change detection analysis that affect the types and range of phenomena that can be tracked. Using that background, we describe four general steps in natural resource remote sensing projects: image and reference data acquisition, pre-processing, analysis, and evaluation. We emphasize the practical considerations that arise in each of these steps. We articulate a four-phase process that guides natural resource and remote sensing specialists through a collaborative process to articulate goals, evaluate data and options for image processing, refine or eliminate unrealistic paths, and assess the cost and utility of different options.     

A multirobot platform based on autonomous surface and underwater vehicles with bio-inspired neurocontrollers for long-term oil spills monitoring

This paper describes the BUSCAMOS-Oil monitoring system, which is a robotic platform consisting of an autonomous surface vessel combined with an underwater vehicle. The system has been designed for the long-term monitoring of oil spills, including the search for the spill, and transmitting information on its location, extent, direction and speed. Both vehicles are controlled by two different types of bio-inspired neural networks: a Self-Organization Direction Mapping Network for trajectory generation and a Neural Network for Avoidance Behaviour for avoiding obstacles. The systems’ resilient capabilities are provided by bio-inspired algorithms implemented in a modular software architecture and controlled by redundant devices to give the necessary robustness to operate in the difficult conditions typically found in long-term oil-spill operations. The efficacy of the vehicles’ adaptive navigation system and long-term mission capabilities are shown in the experimental results.     

Cover: Remote sensing based detection of defensive minefields

Crop surface temperature (CST) is an important parameter to monitor crop status. Satellite data in thermal region provide an opportunity to estimate CST over large regions at frequent intervals. In the present study, various split‐window algorithms are employed to estimate CST over rice areas in irrigation projects of Krishna basin, South India using multi‐resolution MODIS satellite data. NDVI is used to approximate the mean pixel emissivity, by taking known values for emissivity and NDVI for pure vegetation and bare soil pixels. Diurnal ground measurements are made to evaluate satellite‐derived CST. CST values obtained using the Sobrino method have been found to be closer to the ground‐measured values compared with other algorithms, as it takes into account view angle, atmospheric transmittance, and water vapour corrections. It has been observed that the error in estimating CST is comparatively lower for well‐grown crops.     

Remote sensing monitoring of ocean colour in Pearl River estuary

The study attempts to provide additional understanding of the long-term evolution of water quality in the Pearl River estuary and Hong Kong coastal areas by monitoring the ocean colour from remote sensing images. The extensively inter-connected river network of Pearl River estuary has a large catchment area and its environmental deterioration due to rapid economic growth is significant. The data received by Landsat satellites, over a time period of more than 20 years, were used to evaluate the evolution of water clarity. The study shows the historical varying of sea colour in these areas and image processing methods for extracting the ocean colour features of Landsat images are suggested. The algorithm of 'Gradient Transition' was used to calibrate the result images and the comparing method was used to process and analyse remote sensing images of Case 2 water.     

Remote sensing of savanna vegetation changes in Eastern Zambia 1972-1989

The use of the historical Landsat Multi-Spectral Scanner (MSS) archive to monitor changes in savanna vegetation between 1972 and 1989 in the South Luangwa National Park region, Eastern Zambia, was investigated. Land-cover types in the region were mapped and major changes in land cover from 1972 to 1989 were detected from MSS data. Woody canopy cover, which provides a quantitative measure of woodland structure, was estimated for woodland vegetation from MSS data using a linear relationship between woody canopy cover and red reflectance. The canopy cover changes estimated from MSS data agreed with those measured from multitemporal aerial photographs (r=0.94). Woody canopy cover changed significantly in the region from 1972 to 1989 and revealed strong spatial patterns of deforestation in Colophospermum mopane woodland on alluvial soils and vegetation regrowth of valley miombo vegetation and riverine woodland. This information on the spatial patterns of canopy cover change from 1972 to 1989 suggests certain criteria that any causative process must satisfy, and it provides a baseline for the National Park and Wildlife Services to manage the natural resources in the region. The canopy cover estimated from MSS data also provides an important input to biophysical and climatic process models for estimating the impact of vegetation structure on vegetation and climate processes.     

A toxicity estimation system for individual hydrocarbons in the monitoring loop of emergency oil spills on water bodies

Risk control under emergency oil spills requires establishing rapidly the fact of oil release in water bodies, estimating the volume of spilled oil, localizing and shifting the resulting pollution, as well as predicting the dangerous influence of oil spills on a biota and population. This paper proposes a detection system for the above-mentioned characteristics of oil spills, which includes contact and remote sensors and employs bioinformation technologies for predicting the toxicity of individual hydrocarbons as oil components.     

Remote sensing and GIS for an oil spill contingency plan,Ras-Mohammed,Egypt

Ras-Mohammed area located at the southern tip of Sinai, is an important and unique environment which has valuable resources that should be carefully maintained. This area is threatened by numerous anthropogenic activities. The most important risk is that associated with possible oil spills from tankers crossing the Suez canal

The main objective of this study is to prepare an oil spill contingency plan for the study area. SPOT-XS data were used to derive valuable information about the different resources of the area, especially the shallow coral reef communities

SPOT images, ground surveys and GIS analysis have enabled an accurate evaluation of environmental sensitivity. This helped to put forward a proposal of an oil spill contingency plan, in order to protect environmentally sensitive areas such as Ras-Mohammed.     

Remote sensing of vegetation water content from equivalent water thickness using satellite imagery

Vegetation water content (VWC) is one of the most important parameters for the successful retrieval of soil moisture content from microwave data. Normalized Difference Infrared Index (NDII) is a widely-used index to remotely sense Equivalent Water Thickness (EWT) of leaves and canopies; however, the amount of water in the foliage is a small part of total VWC. Sites of corn (Zea mays), soybean (Glycine max), and deciduous hardwood woodlands were sampled to estimate EWT and VWC during the Soil Moisture Experiment 2005 (SMEX05) near Ames, Iowa, USA. Using a time series of Landsat 5 Thematic Mapper, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Wide Field Sensor (AWiFS) imagery, NDII was related to EWT with R² of 0.85; there were no significant differences among land-cover types. Furthermore, EWT was linearly related to VWC with R² of 0.87 for corn and 0.48 for soybeans, with a significantly larger slope for corn. The 2005 land-cover classification product from the USDA National Agricultural Statistics Service had an overall accuracy of 92% and was used to spatially distribute VWC over the landscape. SMEX05 VWC versus NDII regressions were compared with the regressions from the Soil Moisture Experiment 2002 (SMEX02), which was conducted in the same study area. No significant difference was found between years for corn (P = 0.13), whereas there was a significant difference for soybean (P = 0.04). Allometric relationships relate the size of one part of a plant to the sizes of other parts, and may be the result from the requirements of structural support or material transport. Relationships between NDII and VWC are indirect, NDII is related to canopy EWT, which in turn is allometrically related to VWC.     

一种有保护装置的光纤光栅漏油报警传感器

采用机械限位原理,设计和组装了一种有保护装置的光纤Bragg光栅漏油传感器.该传感器在检测过程中,通过限制敏感材料的膨胀长度能有效防止光纤光栅因承受较大的应力而断裂,同时,能克服分布式光纤光栅漏油检测系统中相邻信号之间的串扰,并具有较短的响应时间,其漏油报警响应时间小于4min.