Remote sensing of biological soil crust under simulated climate change manipulations in the Mojave Desert

Earth's arid and semiarid ecosystems are subject to novel combinations of disruptive factors and unprecedented rates of change. Biotic soil crust is believed to be sensitive to impacts caused by land use and climate changes. This study examined the potential for spectral detection of different biological soil crusts (BSC: cyanobacteria, moss and lichen) and bare soil components at a long-term manipulative experiment at the Mojave Global Change Facility (MGCF) in southwestern Nevada. We evaluated the potential for spectral detection of experimental treatments using laboratory and field measured reflectance spectra in the second and third year of the experiment, and airborne hyperspectral data obtained in the third year of the manipulations for soil disturbance, increased summer rainfall, and dry nitrogen deposition. Laboratory spectra of individual components of biological soil crust and bare soil measured under controlled laboratory conditions were spectrally different over much of the spectrum and exhibited features at 0.42, 0.50, and 0.68 μm, which could differentiate these materials. Field measured spectra were more similar in overall shape in each of the MGCF treatments and individual BSC could not be distinguished. The field spectra most closely resemble cyanobacteria from laboratory measurements, which are known to cover up to 60% of the inter-shrub spaces. There were significant treatment differences between control, soil disturbance, and irrigation treatments in field spectral measurements and a spectral feature in the 2.00-2.08 μm region could distinguish these treatments. The treatments were also apparent in high spatial resolution (~ 4 m ground IFOV) airborne hyperspectral imagery using a minimum noise fraction (MNF) analysis, although treatments were not distinct in terms of laboratory or field-based specific features. Disturbance treatments were easily apparent in color-infrared imagery although other treatments were not distinguished. Three-band composites from a MNF analysis and classification images of the six most significant MNF bands for treatment differences, revealed disturbed and irrigation treatments and combinations of these with nitrogen treatments can be observed but control treatments were not separated from the untreated background. Nitrogen treatments were generally not significantly different from controls unless combined with irrigation or disturbance treatments. These data suggest that hyperspectral imagery could be used to monitor local and perhaps regional changes in biological soil crust in the southwestern deserts of the United States, even if crust components are not individually detected.     

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 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 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.     

工业园区空气污染监控节点的设计

在工业密集型的陶瓷工业园区,空气污染的监测和控制是一项重要任务。从应用环境出发,设计了一种新型的空气污染监控无线传感器网络节点,该节点基于差分光谱法原理,同时监控多种污染成分,并利用无线多跳网络传输监测数据。实验证明：该方案是可行的。     

An empirical investigation into long-term climate change in Australia

In this paper, we undertake an empirical investigation into the possibility of climate change in Australian centres using average monthly air temperatures for six sites around the country over the period 1901:1–1998:12. By estimating a multivariate structural time series model and carrying out the appropriate tests, it is concluded that the temperature series is I(1). A graphical inspection of the extracted trends reveals that temperature has an upward trend in many centres. Given that the data may involve measurement errors, the results should be treated with caution.     

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.     

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.     

Remote sensing and an experimental Geographic Information System for environmental monitoring,resource planning and management

Abstract

A brief summary is presented of a study on Geographic Information Systems; a full report on the study is being published elsewhere.     

基于无线传感器网络的大气污染监测系统的设计

随着经济的快速发展,人们生活水平提高的同时,大气污染也越来越严重。为此设计了基于无线传感器网络的大气污染监测系统,主要包括监测部分和监控中心的设计。监测部分采用无线传感器网络,监控中心采用VB.NET编程语言。系统不但可以制作出友好的界面,还可以方便高效地实现与SQL Server数据库的链接与操作,具有实时、功耗小、费用低的优点,在大气污染监测中具有一定的实用价值。     

自适应PCNN和改进C-V结合的遥感图像变化检测

为获取保留图像信息较完好的差异图并得到更好的变化检测结果,提出一种基于自适应脉冲耦合神经网络（PC‐NN）和改进Chan‐Vese （C‐V）模型的非监督的不同时相遥感图像的变化检测算法。用差值法、比值法对两幅遥感图像进行差异图获取;用自适应PCNN图像融合算法对两幅差异图进行融合,获取保留图像信息较好的差异图;用基于改进C‐V模型的分割算法对融合后的差异图进行分割,得到变化检测结果图。实验结果表明,该算法具有很好的变化检测效果,总检测精度较高。     

Remote sensing provides a progressive record of vegetation change in northern KwaZulu-Natal,South Africa,from 1944 to 2005

Remote sensing, together with geographic information systems (GISs), provide a powerful tool for monitoring environmental processes. Aerial and satellite photographs enable the analysis of an entire landscape and, using multi-temporal sets of photographs, one is able to follow processes over time. In our study, we used multi-temporal sets of aerial photographs, taken between 1944 and 2005, to observe the phenomenon of woody plant encroachment in four neighbouring study sites in northern KwaZulu-Natal, South Africa. We also analysed rainfall and temperature trends. Woody plant encroachment was evident in all sites. We suggest possible drivers behind the bush encroachment; in particular, climatic trends. However, encroachment did not relate directly to rainfall and temperature trends. We also speculate on the effects of herbivory and fire, as well as rising atmospheric CO₂ concentrations and its effect on C₃ and C₄ plants. We suggest that increasing atmospheric CO₂ concentrations could be the overriding driver of woody plant encroachment in our study area, with other drivers, such as rainfall, temperature, fire, herbivory, and land use, playing a modifying role.     

Remote sensing data change detection based on the CI test of Bayesian networks

In recent years, the Bayesian network approach has been used as a data-mining tool in many information fields, but it has rarely been used to process remote sensing data. In this paper, we introduce a Bayesian network classifier for remote sensing data change detection. Using the conditional independence (CI) test we can find out relationships among the attributes and construct a Bayesian network that incorporates these relationship constraints.After geometric correction and radiometric normalization, a Bayesian network change detection system based on CI test algorithm is developed and applied to two temporal Landsat TM data acquired in 1994 and 2003 of Beijing area, and the overall change detection classification accuracy can get 92%. The experimental results show that Bayesian network is a newly effective approach for remote sensing data change detection.     

Autoregressive model-based fuzzy clustering and its application for detecting information redundancy in air pollution monitoring networks

Fuzzy clustering enables the simultaneous membership of objects in two or more clusters. This is particularly pertinent where time series are concerned, because very often patterns of time series change over time. Thus, a time series might belong to different clusters over different periods of time, in which case, crisp clustering is unable to capture this multi-cluster membership. In this paper, we adopt a Fuzzy C-Medoids approach to clustering time series based on autoregressive estimates of models fitted to the time series. We illustrate very good performance of this approach in a range of simulation studies. By means of two applications, we also show the usefulness of this clustering approach in the air pollution monitoring, by considering air pollution time series, i.e., CO time series, CO₂ time series and NO time series monitored on world and urban scales. In particular, we show that, by considering in the clustering process, the autoregressive representation of these air pollution time series, we are able to detect possible information redundancy in the monitoring networks and then, decreasing the number of monitoring stations, to reduce the monitoring costs and then to increase the monitoring efficiency of the networks.     

Remote sensing and GIS activities in UNESCO

The role of UNESCO is to promote international peace and security through cooperation in the fields of education, science and culture. In view of eradicating illiteracy, overcoming unsustainability of natural resources, confronting environmental problems, safeguarding historical monuments, and preserving natural and cultural heritage, UNESCO develops international research programmes and makes best possible use of new advances in technology, namely remote sensing and GIS, in order to effectively carry out activities in the framework of its programmes. Part one describes remote sensing and GIS use in international programmes such as the Man and the Biosphere (MAB) programme for biosphere reserve development and ecosystem monitoring, management and conservation; the International Hydrological Programme (IHP) for surface and ground water research management; the International Geological Correlation Programme (IGCP) for the creation of a data base on the spectral signatures of rocks and soils; and in several projects concerning ocean investigation and climate observation conducted in the framework of the Intergovernmental Oceanographic Commission (IOC). In part two constraints of using the remote sensing and GIS technology are addressed and remaining difficulties in accessing and exchanging data, training issues and costs are discussed.     

Remote sensing in sea search and rescue

A classified United States Army line scanner (Texas Instruments Model AAS-24) mounted in a Mohawk aircraft was tested for its air-sea search and rescue potential. Within the environmental parameters indicated the instrument proved effective in identifying both small boats and individual swimmers. Positive identification was made during daylight and night sorties when the location of swimmers was known and in daylight trials when their location was unknown.