Showing papers in "International Journal of Remote Sensing in 2013"
TL;DR: The theoretical basis of LSE measurements is given, a description of the published methods, and the validation methods, which are of importance in verifying the uncertainty and accuracy of retrieved emissivity.
Abstract: As an intrinsic property of natural materials, land surface emissivity LSE is an important surface parameter and can be derived from the emitted radiance measured from space. Besides radiometric calibration and cloud detection, two main problems need to be resolved to obtain LSE values from space measurements. These problems are often referred to as land surface temperature LST and emissivity separation from radiance at ground level and as atmospheric corrections in the literature. To date, many LSE retrieval methods have been proposed with the same goal but different application conditions, advantages, and limitations. The aim of this article is to review these LSE retrieval methods and to provide technical assistance for estimating LSE from space. This article first gives a description of the theoretical basis of LSE measurements and then reviews the published methods. For clarity, we categorize these methods into 1 semi-empirical or theoretical methods, 2 multi-channel temperature emissivity separation TES methods, and 3 physically based methods PBMs. This article also discusses the validation methods, which are of importance in verifying the uncertainty and accuracy of retrieved emissivity. Finally, the prospects for further developments are given.
370 citations
TL;DR: In this paper, a set of forecasts and data assimilation experiments with the integrated forecasting system of the European Centre for Medium-range Weather Forecasts is performed with the monthly LAI climatology datasets as opposed to a vegetation-dependent constant LAI.
Abstract: Two distinct monthly LAI climatology datasets derived respectively from AVHRR and MODIS sensors are tested. A set of forecasts and data assimilation experiments with the integrated forecasting system of the European Centre for Medium-range Weather Forecasts is performed with the monthly LAI climatology datasets as opposed to a vegetation-dependent constant LAI. The monthly LAI is shown to improve the forecasts of near-surface screen-level air temperature and relative humidity through its effect on evapotranspiration, with the largest impact obtained over needleleaf forests, crops, and grassland. At longer time-scales, the introduction of the monthly LAI is shown to have a positive impact on the model climate particularly during the boreal spring, where the LAI climatology has a large seasonal cycle.
125 citations
TL;DR: In this paper, the authors use multispectral and hyperspectral imagers, light detection and ranging lidar, and radar systems for mapping coastal marshes, submerged aquatic vegetation, coral reefs, beach profiles, algal blooms, and concentrations of suspended particles and dissolved substances in coastal waters.
Abstract: To plan for wetland protection and responsible coastal development, scientists and managers need to monitor changes in the coastal zone, as the sea level continues to rise and the coastal population keeps expanding. Advances in sensor design and data analysis techniques are now making remote-sensing systems practical and cost-effective for monitoring natural and human-induced coastal changes. Multispectral and hyperspectral imagers, light detection and ranging lidar, and radar systems are available for mapping coastal marshes, submerged aquatic vegetation, coral reefs, beach profiles, algal blooms, and concentrations of suspended particles and dissolved substances in coastal waters. Since coastal ecosystems have high spatial complexity and temporal variability, they should be observed with high spatial, spectral, and temporal resolutions. New satellites, carrying sensors with fine spatial 0.4–4 m or spectral 200 narrow bands resolution, are now more accurately detecting changes in coastal wetland extent, ecosystem health, biological productivity, and habitat quality. Using airborne lidars, one can produce topographic and bathymetric maps, even in moderately turbid coastal waters. Imaging radars are sensitive to soil moisture and inundation and can detect hydrologic features beneath the vegetation canopy. Combining these techniques and using time-series of images enables scientists to study the health of coastal ecosystems and accurately determine long-term trends and short-term changes.
123 citations
TL;DR: Building area and building height turned out to be the most influential factors among all the adopted variables, demonstrating that synthesized horizontal and vertical properties of buildings and their relevant spatial characteristics are important in differentiating the four land-use classes.
Abstract: Urban land-use information plays a key role in a wide variety of planning and environmental management processes. The purpose of this study was to develop an automatic method for classifying detailed urban land-use classes with remote-sensing data. Seven land-use parcel attributes, derived from relevant remote-sensing data, were incorporated for classifying four land-use classes, namely office, industrial, civic, and transportation, which were reported as the most difficult ones to classify from previous studies. An experiment was carried out in a study site in Austin, Texas. An overall accuracy of 61.68% and a kappa coefficient of 0.54 were achieved with a decision tree method. Building area and building height turned out to be the most influential factors among all the adopted variables. In addition, the variable of floor area ratio played the second dominant role among the seven variables, demonstrating that synthesized horizontal and vertical properties of buildings and their relevant spatial characteristics are important in differentiating the four classes.
111 citations
TL;DR: In this article, the authors present a summary of SSS retrieval from SMOS observations and show initial results obtained one year after launch, but also indicate that further improvements at various data processing levels are needed and hence are currently under investigation.
Abstract: Soil Moisture and Ocean Salinity SMOS, launched on 2 November 2009, is the first satellite mission addressing sea surface salinity SSS measurement from space. Its unique payload is the Microwave Imaging Radiometer using Aperture Synthesis MIRAS, a new two-dimensional interferometer designed by the European Space Agency ESA and operating at the L-band frequency. This article presents a summary of SSS retrieval from SMOS observations and shows initial results obtained one year after launch. These results are encouraging, but also indicate that further improvements at various data processing levels are needed and hence are currently under investigation.
103 citations
TL;DR: In this paper, a field survey was performed to characterize the Gobabeb site more closely, and 2 years of LST from MSG/SEVIRI data were compared with in situ LSTs from Gobabbe main station.
Abstract: Land surface temperature LST derived from Meteosat Second Generation/ Spinning-Enhanced Visible and Infrared Imager MSG/SEVIRI data is an operational product of the Land Surface Analysis Satellite Applications Facility LSA SAF. The LST has a temporal resolution of 15 minutes, a sampling distance of 3 km at nadir, and a targeted accuracy of better than 2 K. Gobabeb Namibia is one of Karlsruhe Institute of Technology's KIT's four dedicated stations for LST validation. In March 2010, a field survey was performed to characterize the Gobabeb site more closely. SAF LST and in situ LST obtained over a period of 3 days from additional measurements with a telescopic mast on the Namib gravel plains were in good agreement with each other bias 1.0 K. For the same period, the bias between SAF LST and Gobabeb main station LST was even smaller 0.4 K. A mobile measurement system was set up by fixing the telescopic mast to a four-wheel drive. Around solar noon, LST from in situ measurements along a 40 km track and LST from Gobabeb main station had a bias of 0.4 K and a standard deviation of 1.2 K, which means that in situ LSTs at Gobabeb main station are representative for large parts of the gravel plains. Exploiting this relationship, 2 years of LST from MSG/SEVIRI were compared with in situ LST from Gobabeb main station. The magnitude of the monthly biases between the two data sets was generally less than 1.0 K and root mean square errors were below 1.5 K. Furthermore, the bias appears to exhibit a seasonality, which could be accounted for in future validation work.
94 citations
TL;DR: In this article, the authors evaluated the surface urban heat island SUHI effect in the city of Madrid Spain from thermal infrared TIR remote-sensing data obtained from the Dual-use European Security IR Experiment DESIREX campaign that was carried out during June and July 2008 in Madrid.
Abstract: The surface urban heat island SUHI effect is defined as the increased surface temperatures in urban areas in contrast to cooler surrounding rural areas. In this article, the evaluation of the SUHI effect in the city of Madrid Spain from thermal infrared TIR remote-sensing data is presented. The data were obtained from the framework of the Dual-use European Security IR Experiment DESIREX campaign that was carried out during June and July 2008 in Madrid. The campaign combined the collection of airborne hyperspectral and in situ measurements. Thirty spectral and spatial high-resolution images were acquired with the Airborne Hyperspectral Scanner AHS sensor in a 11, 21, and 4 h UTC scheme. The imagery was used to retrieve the SUHI effect by applying the temperature and emissivity separation TES algorithm. The results show a nocturnal SUHI effect with a highest value of 5 K. This maximum value agrees within 1 K with the highest value of the urban heat island UHI observed using air temperature data AT. During the daytime, this situation is reversed and the city becomes a negative heat island.
93 citations
TL;DR: In this paper, a land surface temperature LST data set generated and provided in near-real-time or offline based on infrared data from sensors onboard different geostationary GEO satellites is presented and implemented.
Abstract: This article provides a description of a land surface temperature LST data set generated and provided in near-real-time or offline based on infrared data from sensors onboard different geostationary GEO satellites: Meteosat Second Generation MSG, Geostationary Operational Environmental Satellite GOES, and Multifunction Transport Satellite MTSAT. Given the different characteristics of the imagers onboard each GEO platform, different algorithmic methodologies for the retrieval of LST are presented and implemented – namely the Generalized Split-Window GSW algorithm and the Dual Algorithm DA in its mono-and dual-channel forms – using semi-empirical functions that relate LST to top-of-atmosphere brightness temperatures in infrared window channels. The assumptions and physics underlying each methodology, as well as the uncertainties of LST estimates, are discussed. The formulations are trained using a data set of radiative transfer simulations for a wide range of atmospheric and surface conditions. The performance of each algorithm is then assessed by comparing its output against an independent set of simulations, suggesting that product uncertainties range from 2°C for GSW and the two-channel algorithm to 4°C for the one-channel algorithm. Finally, LST retrievals from different GEO satellites are merged into a single field. In overlapping areas, the average discrepancies between LST products derived from GOES and from the Spinning Enhanced Visible and Infrared Imager SEVIRI onboard MSG are within 1°C during night-time, but may reach 3°C during daytime. Over those areas, the merged LST field is obtained as a weighted average of available LST retrievals for the same time slot, taking into account the respective error bar.
87 citations
TL;DR: In this paper, an object-based classification of high-resolution imagery, guided by field data and ecological and geomorphological principles, can produce consistent, accurate benthic maps at four hierarchical spatial scales for coral reefs of various sizes and complexities.
Abstract: Coral reef maps at various spatial scales and extents are needed for mapping, monitoring, modelling, and management of these environments. High spatial resolution satellite imagery, pixel <10 m, integrated with field survey data and processed with various mapping approaches, can provide these maps. These approaches have been accurately applied to single reefs 10–100 km2, covering one high spatial resolution scene from which a single thematic layer e.g. benthic community is mapped. This article demonstrates how a hierarchical mapping approach can be applied to coral reefs from individual reef to reef-system scales 10–1000 km2 using object-based image classification of high spatial resolution images guided by ecological and geomorphological principles. The approach is demonstrated for three individual reefs 10–35 km2 in Australia, Fiji, and Palau; and for three complex reef systems 300–600 km2 one in the Solomon Islands and two in Fiji. Archived high spatial resolution images were pre-processed and mosaics were created for the reef systems. Georeferenced benthic photo transect surveys were used to acquire cover information. Field and image data were integrated using an object-based image analysis approach that resulted in a hierarchically structured classification. Objects were assigned class labels based on the dominant benthic cover type, or location-relevant ecological and geomorphological principles, or a combination thereof. This generated a hierarchical sequence of reef maps with an increasing complexity in benthic thematic information that included: ‘reef’, ‘reef type’, ‘geomorphic zone’, and ‘benthic community’. The overall accuracy of the ‘geomorphic zone’ classification for each of the six study sites was 76–82% using 6–10 mapping categories. For ‘benthic community’ classification, the overall accuracy was 52–75% with individual reefs having 14–17 categories and reef systems 20–30 categories. We show that an object-based classification of high spatial resolution imagery, guided by field data and ecological and geomorphological principles, can produce consistent, accurate benthic maps at four hierarchical spatial scales for coral reefs of various sizes and complexities.
81 citations
TL;DR: In this article, the authors evaluated different vegetation indices for the remote estimation of the fractional vegetation cover in two crop types, maize and soybean, with contrasting canopy architectures and leaf structures.
Abstract: Many algorithms have been developed for the remote estimation of vegetation fraction in terms of combinations of spectral bands, derivatives of reflectance spectra, neural networks, inversion of radiative transfer models, and several multispectral statistical approaches. The most widespread type of algorithm used is the mathematical combination of visible and near-infrared reflectance, in the form of spectral vegetation indices. The general objective of this study is to evaluate different vegetation indices for the remote estimation of the fractional vegetation cover in two crop types, maize and soybean, with contrasting canopy architectures and leaf structures. The noise equivalent of vegetation indices was used as an indicator of sensitivity and accuracy of vegetation fraction estimation. Among the indices tested, the enhanced vegetation index EVI2, wide dynamic range vegetation index WDRVI, green-and red-edge normalized difference vegetation index NDVI were found to be accurate in estimating vegetation fraction. These results were obtained using reflectance data acquired with close-range sensors i.e. spectroradiometers mounted 6 m above the top of canopy. WDRVI was able to estimate vegetation fraction in both crops with no re-parameterization with RMSE below 6% and mean normalized bias below 2%.
74 citations
TL;DR: The object-oriented classification method described here proved effective for separating vegetation types defined by life form, area, or shape without using additional remote-sensing data sources with different resolutions or any ancillary data such as digital elevation models.
Abstract: In this article, we demonstrate an object-oriented method for detailed urban vegetation delineation by using 1 m resolution, four-band digital aerial photography as the only input data. A hierarchical classification scheme was developed to discriminate vegetation types at both coarse and fine scales. The processes of vegetation extraction include the examination of spectral and spatial relationships, object geometry, and the hierarchical relationship of image objects. The advantages of four different segmentation methods were combined to identify feature similarities, both among image objects and with their neighbours. Image growth took place if those neighbours satisfied a series of criteria given a set of features of class-defined objects. Object-based classification results demonstrated higher accuracy than those using pixel-based classification methods. The object-oriented method achieved overall classification accuracies of 87.5%, 90.5%, and 90.5% at three different levels of class hierarchy, and very high producer's accuracies were demonstrated in the classes of tree, crop, and different types of grass. The object-oriented classification method described here proved effective for separating vegetation types defined by life form, area, or shape without using additional remote-sensing data sources with different resolutions or any ancillary data such as digital elevation models.
TL;DR: In this paper, the authors derived the building frontal area index (FAI), a parameter for estimating aerodynamic resistance of the urban surface as a predictor of wind ventilation, from a three-dimensional building database.
Abstract: In densely urbanized regions, the local climate is greatly influenced by the urban morphology, including interactions between buildings, space, and human activities. The Kowloon Peninsula in Hong Kong, with some of the greatest urban population densities in the world, represents an extreme case of the influence of the built environment on climate, with high-rise buildings, narrow street canyons, and little green space. In this study, the building frontal area index FAI, a parameter for estimating aerodynamic resistance of the urban surface as a predictor of wind ventilation, was derived from a three-dimensional building database. The relationship between FAI and urban heat island intensity UHII from an Advanced Spaceborne Thermal Emission and Reflection Radiometer ASTER satellite image was calculated at different scales. The highest correlation r = 0.574, n = 4900 was obtained at 100 m resolution, which suggests that the optimum operational scale of FAI is 100 m resolution for the study area, i.e. the scale and size at which FAI impacts on the urban climate. The presence/trend of any higher correlation at different resolutions was tested using the nonparametric Mann–Kendall test, and the results show that the statistic Z -value generated from the test is smaller than the hypothesis significance levels of 90%, 95%, and 99%; thus, the hypothesis of having a higher correlation at any scale other than 100 m resolution is rejected. Planning authorities may use the FAI generated at 100 m resolution for designing wind ventilation corridors across Hong Kong at this scale, especially when temperature and air quality in the inner city are of major concern. However, for applications to other cities with different standard morphologies, the FAI–UHII relationship should be re-evaluated.
TL;DR: In this article, a sensitivity study was conducted for bare soil to investigate the interrelationship between the evolution of land surface temperature LST and SSM, and the coefficients of the linear model were found to be independent of the soil type for a given atmospheric condition.
Abstract: Land surface soil moisture SSM is a fundamental variable in the hydrological cycle and is an important parameter in investigations on water and energy balances at the Earth's surface. Many efforts have been made to derive SSM from remotely sensed thermal infrared data. Using the Noah land surface model LSM and the Gaussian emulation machine for sensitivity analysis GEM-SA software, a sensitivity study was conducted for bare soil to investigate the interrelationship between the evolution of land surface temperature LST and SSM. Based on the diurnal cycles of LST and net surface shortwave radiation, eight parameters intuitively related to SSM were defined, and a sensitivity analysis SA was performed in the presence and absence of atmospheric variation. The results provided insight into the relationships between the eight parameters and various environmental factors such as soil physical parameters, soil moisture, albedo, and atmospheric parameters. For instance, the results suggested that the surface air temperature had a significant effect on the LST, especially the maximum, minimum, and average daytime temperatures. For a given atmospheric forcing data set, the LST rising rate normalized by the difference in the net surface shortwave radiation during the mid-morning T N was the parameter most sensitive to the SSM, contributing 80.72% to the total variance. In addition, the time at which the daily maximum temperature occurred t d, the daily minimum temperature, and the LST nocturnal decay coefficient were strongly related to the soil type. Using a linear combination of T N and t d, a method was proposed to retrieve the SSM, and the coefficients of the linear model were found to be independent of the soil type for a given atmospheric condition. Compared with the actual SSM values used in the Noah LSM simulation, the root mean square error RMSE of the SSM retrieved from our proposed method was within 0.04 m3 m−3 for all the 20 clear days evaluated in the present study.
TL;DR: The MISTIGRI project as discussed by the authors is a microsatellite that combines a high spatial resolution ∼50 m with a daily revisit in the thermal infrared TIR, which is an experimental mission devoted to demonstrate the potential of such TIR data for future operational missions.
Abstract: This article presents the MISTIGRI project of a microsatellite developed by the French space agency Centre National d'Etudes Spatiales CNES in cooperation with Spain Image Processing Laboratory of the University of Valencia and Centro para el Desarrollo Tecnologico Industrial CDTI. MISTIGRI is a mission that has the originality of combining a high spatial resolution ∼50 m with a daily revisit in the thermal infrared TIR. MISTIGRI is an experimental mission devoted to demonstrate the potential of such TIR data for future operational missions. The scientific goals and expected applications of the mission are described: they encompass the monitoring of i agricultural areas and related hydrological processes, ii urban areas, and iii coastal areas and continental waters. Then, the specifications on spatial resolution, revisit frequency, overpass time, and spectral configuration are justified. The strategy of the mission is based on the combination with a network of long-term experimental sites. It will also make possible observing some areas facing rapid climatic change. The choice of the orbit is presented. Finally, we give rapid overviews of both the instrumental concept and the proposed mission architecture.
TL;DR: A method that employs multi-temporal interpolated lidar data to perform change detection and change-type determination via geometric analysis and achieves accuracy as high as 80%.
Abstract: Change detection of objects, such as buildings, is essential for map updating. Traditionally, detection is usually performed through spectral analysis of multi-temporal images. This article proposes a method that employs multi-temporal interpolated lidar data. The objective of this study is to perform change detection and change-type determination via geometric analysis. A shape difference map is generated between the digital surface models in two different time periods. The areas with small shape differences are treated as non-changed areas and are excluded from the segmentation. The object's properties are then applied to determine the change types. Experimental results demonstrate that the proposed scheme achieves accuracy as high as 80%. Most of the errors from this study occurred in small or vegetation areas.
TL;DR: In this paper, the authors investigated the effect of grazing on vegetation degradation in the steppe biome of Mongolia and found that unpalatable species had invaded into the grazed areas, substituting the native grasses.
Abstract: Space and ground observations were applied to explore the ability of remote sensing techniques to assess the effect of grazing on vegetation degradation. The steppe biome of Mongolia was used as the study area, in which several pairs of sites were investigated – each pair comprised an ungrazed fenced-off area and a heavily grazed area. For each pair, the enhanced vegetation index EVI, computed from Landsat-7 Enhanced Thematic Mapper Plus ETM+ data, along with field-observed biophysical variables e.g. plant density, species composite, above-ground biomass AGB, and percentage cover and plant spectral reflectance data were collected. As expected, plant density, AGB, and percentage cover values were significantly higher in the ungrazed areas than in the adjacent grazed ones. However, unexpectedly, the grazed areas had significantly higher EVI values than the ungrazed areas. It was found that unpalatable species had invaded into the grazed areas, substituting the native grasses. These invasive species, mostly characterized by denser leaf structure, induced higher spectral responses in the near infrared NIR region of the electromagnetic spectrum. EVI is the preferred vegetation index to use for detecting this phenomenon, since it is more sensitive to variations in leaf cellular structural as expressed in the NIR rather than the red portion of the spectrum. The current study contradicts the general assumption that the higher the vegetation index value, the better the grazing conditions.
TL;DR: In this article, the authors present a short description of each form of soil degradation, including data regarding known extents and rates, and then review the methods with respect to direct and indirect modelling approaches.
Abstract: Agricultural land degradation is a global problem that severely hampers the production of food needed to sustain the growing world population. Mapping of soil degradation by remote sensing is instrumental for understanding the spatial extent and rate of this problem. Methods aimed at detecting soil loss, soil drying, and soil-quality deterioration have been demonstrated in numerous studies utilizing passive and active remote sensors. This review presents a short description of each form of soil degradation, including data regarding known extents and rates, and then reviews the methods with respect to direct and indirect modelling approaches. Two types of obstacles to achieving wide regional detection of soil degradation are revealed. The first concerns the complex and non-unique relationships between remote-sensing indicators and different soil properties, such as roughness, soil moisture SM, soil salinity, and organic matter content. The second concerns the difficulties involved in acquiring data on subsurface soil properties. In view of these difficulties, we recommend expanding the use of phenomenological models capable of estimating soil-degradation potential according to combinations of environmental conditions, thus enabling remote-sensing efforts to be focused on local areas where the environmental threat is highest. The second avenue for improving the contribution of remote sensing on a wide regional scale involves the application of integrative methods, such as those based on hyperspectral, multisensory, and multitemporal approaches, as well as those that incorporate environmental information such as topography, soil types, and precipitation.
TL;DR: In this paper, a new empirical model relating brightness temperatures (T b) from the Special Sensor Microwave Imager (SSM/I) and q 10 is developed, which is an extension of the author's previous q 10 model.
Abstract: A new method is developed to estimate daily turbulent air–sea fluxes over the global ocean on a 0.25° grid. The required surface wind speed (w 10) and specific air humidity (q 10) at 10 m height are both estimated from remotely sensed measurements. w 10 is obtained from the SeaWinds scatterometer on board the QuikSCAT satellite. A new empirical model relating brightness temperatures (T b) from the Special Sensor Microwave Imager (SSM/I) and q 10 is developed. It is an extension of the author's previous q 10 model. In addition to T b, the empirical model includes sea surface temperature (SST) and air–sea temperature difference data. The calibration of the new empirical q 10 model utilizes q 10 from the latest version of the National Oceanography Centre air–sea interaction gridded data set (NOCS2.0). Compared with mooring data, the new satellite q 10 exhibits better statistical results than previous estimates. For instance, the bias, the root mean square (RMS), and the correlation coefficient values estimat...
TL;DR: In this article, the authors proposed a framework to fill the white ribbon area of a coral reef system by integrating multiple elevation data sets acquired by a suite of remote-sensing technologies into a seamless digital elevation model.
Abstract: Hydrographers have traditionally referred to the nearshore area as the ‘white ribbon’ area due to the challenges associated with the collection of elevation data elevation hereafter refers to both topography and bathymetry in this highly dynamic transitional zone between terrestrial and marine environments. Accordingly, available information in this zone is typically characterized by a range of data sets from disparate sources. In this article, we propose a framework to fill the white ribbon area of a coral reef system by integrating multiple elevation data sets acquired by a suite of remote-sensing technologies into a seamless digital elevation model DEM. A range of data sets are integrated, including field-collected global positioning system GPS elevation points, topographic and bathymetric light detecting and ranging lidar, single and multibeam echosoundings, nautical charts, and bathymetry derived from optical remote-sensing imagery. The proposed framework ranks data reliability internally, thereby avoiding the requirements to quantify absolute error and results in a high-resolution, seamless product. Nested within this approach is an effective spatially explicit technique for improving the accuracy of bathymetry estimates derived empirically from optical satellite imagery through modelling the spatial structure of residuals. The approach was applied to data collected on and around Lizard Island in northern Australia. Collectively, the framework holds promise for filling the white ribbon zone in coastal areas characterized by similar data availability scenarios.
TL;DR: A review of the literature on remote sensing of rangelands can be found in this paper, which discusses recent developments with respect to mapping thematic classes of vegetation and vegetative cover, mapping biophysical properties such as primary production, and monitoring land use changes, including those driven by anthropogenically enhanced processes such as soil erosion.
Abstract: Rangelands in temperate areas provide food to herds of domesticated animals and, therefore, provide the infrastructure for two major industries: a the meat industry that feeds large populations around the globe; and b the wool industry that uses fibre from sheep. In the semiarid zone, rangelands have a socio-economic role as they support the economy and culture of pastoral societies. However, despite their importance, rangelands are under constant threat due to encroachment by humans and invasion by noxious plants, due to degradation and erosion processes and due to drought effects. Remote sensing can be used to identify and monitor the threats to ecological processes in rangelands and, thus, to their ecological sustainability. This article provides a review of the scientific literature on the remote sensing of rangelands and discusses recent developments with respect to mapping thematic classes of vegetation and vegetative cover, mapping biophysical properties such as primary production, and monitoring land-use changes, including those driven by anthropogenically enhanced processes such as soil erosion. In the light of the reviewed studies, we expect that future research on monitoring rangeland sustainability with remote sensing will focus on hyperspectral measurements of the spectra of rangeland plant species, on lidar measurements of canopy height, and on synthetic aperture radar for biomass assessment. In the long-term, more predictive or at least heuristic modelling of degradation scenarios due to erosion, invasion of noxious species, and land-use transformations can be anticipated.
TL;DR: Zhang et al. as mentioned in this paper evaluated the changes in spatial patterns of urban ecological security in Xuzhou City during its transformation from a coal mining industrial city to a modern one with decreasing mining production.
Abstract: Research in urban ecology has recently focused on the field of ecological security. Ecological security should be assessed at different scales, from global to understand some processes of global change to regional to understand processes in specific areas to urban to consider the processes of urban development. Remote sensing RS and geographical information systems GISs are widely utilized in ecological security evaluation, with remotely sensed images applied to determine the structure and quantitative indicators of urban ecological systems and GIS to manipulate, analyse, and visualize multisource information in urban ecology. This article focuses on urban ecological security assessment based on multitemporal RS information and GIS. As a case study, the changes in spatial patterns of urban ecological security in Xuzhou City are evaluated during its transformation from a coal mining industrial city to a modern one with decreasing mining production. The popular ‘pressure–state–response’ conceptual model is used to construct an evaluation framework according to geographical properties and data availability. A hierarchical weighted model is adopted to take different factors into account and determine the state of ecological security in the study area. Using Landsat Thematic Mapper/Enhanced Thematic Mapper Plus TM/ETM+ images as the main RS data source, the derived parameters, such as vegetation index, urban thermal information, and landscape pattern, are integrated to depict the structure and properties of the urban ecological system. Other ancillary information, including mining pressure, water pollution pressure, population density, and ecological flexibility are collected and managed by ArcGIS. The GIS is then used to manage ecological factors derived from remotely sensed data, statistical reports, and geographical data, to evaluate and analyse urban ecological security, and to generate quantitative evaluation and spatial patterns of ecological security. The resulting maps of urban ecological security situations in 1987, 1994, 2000, 2005, and 2007 are analysed in both temporal and spatial dimensions. By analysing urban ecological security in both temporal and spatial dimensions, it is concluded that the ecological security of Xuzhou City is under increasing pressure. Influenced by the pressure of urbanization and mining industry development, the unsafe areas of Xuzhou City have greatly expanded, and with accelerating speed. Finally, suggestions for improving urban ecological security are provided.
TL;DR: In this paper, the authors applied remote sensing to the detection of grasses and broadleaf weeds among cereal and broad-leaf crops, and achieved an overall classification accuracy of 87 ± 5.57% for >5% vegetation coverage.
Abstract: Weed control is commonly performed by applying selective herbicides homogeneously over entire agricultural fields. However, applying herbicide only where needed could have economical and environmental benefits. The objective of this study was to apply remote sensing to the detection of grasses and broadleaf weeds among cereal and broadleaf crops. Spectral relative reflectance values at both leaf and canopy scales were obtained by field spectroscopy for four plant categories: wheat, chickpea, grass weeds, and broadleaf weeds. Total reflectance spectra of leaf tissues for botanical genera were successfully classified by general discriminant analysis GDA. The total canopy spectral classification by GDA for specific narrow bands was 95 ± 4.19% for wheat and 94 ± 5.13% for chickpea. The total canopy spectral classification by GDA for future Vegetation and Environmental Monitoring on a New Micro-Satellite VENμS bands was 77 ± 8.09% for wheat and 88 ± 6.94% for chickpea, and for the operative satellite Advanced Land Imager ALI bands was 78 ± 7.97% for wheat and 82 ± 8.22% for chickpea. Within the critical period for weed control, an overall classification accuracy of 87 ± 5.57% was achieved for >5% vegetation coverage in a wheat field, thereby providing potential for implementation of herbicide applications. Qualitative models based on wheat, chickpea, grass weed, and broadleaf weed spectral properties have high-quality classification and prediction potential that can be used for site-specific weed management.
TL;DR: In this article, an area-weighted aggregation algorithm was used to aggregate SEVIRI and MODIS LST/LSE products into the same spatial resolution, according to the quality control QC criterion and the view angle, the crossvalidation was completed under clear-sky conditions and within a view angle difference of less than 5° for the two instruments to prevent land surface anisotropic effects.
Abstract: Land surface temperature LST and land surface emissivity LSE are two key parameters in global climate study. This article aims to cross-validate LST/LSE products retrieved from data of the Spinning Enhanced Visible and Infrared Imager SEVIRI on board the first geostationary satellite, Meteosat Second Generation MSG, with Moderate Resolution Imaging Spectroradiometer MODIS LST/LSE version 5 products over the Iberian Peninsula and over Egypt and the Middle East. Besides time matching, coordinate matching is another requirement of the cross-validation. An area-weighted aggregation algorithm was used to aggregate SEVIRI and MODIS LST/LSE products into the same spatial resolution. According to the quality control QC criterion and the view angle, the cross-validation was completed under clear-sky conditions and within a view angle difference of less than 5° for the two instruments to prevent land surface anisotropic effects. The results showed that the SEVIRI LST/LSE products are consistent with MODIS LST/LSE products and have the same trend over the two study areas during both the daytime and the night-time. The SEVIRI LST overestimates the temperature by approximately 1.0 K during the night-time and by approximately 2.0 K during the daytime compared to MODIS products over these two study areas. The SEVIRI LSE underestimates by about 0.015 in 11 μm and by about 0.025 in 12 μm over the Iberian Peninsula. However, both LSEs agree and show a difference of less than 0.01 over Egypt and the Middle East.
TL;DR: In this paper, the authors compared one-and two-source energy balance OSEB and TSEB models in the estimates of surface energy components using Landsat imagery and surface measurements acquired from an experimental field at Yucheng Station in Northern China.
Abstract: This article compares one-and two-source energy balance OSEB and TSEB models in the estimates of surface energy components using Landsat imagery and surface measurements acquired from an experimental field at Yucheng Station in Northern China. Compared to surface measurements, similar performance between the TSEB and OSEB models has been observed for estimated surface net radiation and soil heat flux. The root mean square difference RMSD is within 14–39 W m−2 in both the TSEB and OSEB models. The residual energy E R correction method yields the best agreement in comparisons of the sensible H and latent LE heat fluxes estimated using both the TSEB and OSEB models to the eddy covariance EC system measurements. The TSEB model is shown to greatly outperform the OSEB model in reproducing surface H and LE measurements. Cirrus clouds are likely responsible for the surface temperature retrieved from the enhanced thematic mapper plus ETM+ sensor being lower than air temperature on days of the year DOYs 178 and 218 of 2009. This atmospheric stability is contrary to the unstable atmosphere that the EC measurements observe. If data on these two days are excluded and the E R correction method is applied, when comparing the estimated H and LE to the EC measurements, RMSD is within 55 W m−2 in the TSEB model and is larger than 97 W m−2 in the OSEB model.
TL;DR: In this article, an evaluation of the thermal data acquired by the Advanced Very High Resolution Radiometer AVHRR instrument to quantify the spatial temperature dynamics of London is presented, where a total of 81 cloud-free AVHII scenes from summers between 1996 and 2006 were analysed in association with air temperature measurements from four London weather stations in order to characterize the year-on-year temperature dynamics.
Abstract: This article presents an evaluation of the thermal data acquired by the Advanced Very High Resolution Radiometer AVHRR instrument to quantify the spatial temperature dynamics of London. A total of 81 cloud-free AVHRR scenes from summers between 1996 and 2006 were analysed in association with air temperature measurements from four London weather stations in order to characterize the year-on-year temperature dynamics of London. The data were employed to investigate the viability of using AVHRR scenes to distinguish a heatwave year from background years using the commonly employed urban heat island intensity UHII metric. Results show that AVHRR thermal data are highly sensitive to local meteorological and diurnal effects, requiring temporal averaging to the monthly and seasonal scales to provide robust data for a comparison between different years. Resulting UHII scenes highlight the spatial variability of intensity across London. However, comparison of UHII scenes between summers indicates that the UHII metric is a relatively poor means by which to distinguish between a heatwave summer in London and the 75th percentile, median, and 25th percentile summer temperatures of the time series investigated.
TL;DR: In this article, the effect of abrupt changes in solar radiation due to varying cloudy conditions on T C and CWSI for olive trees was investigated in a commercial orchard where five irrigation levels were applied.
Abstract: Cloud cover drastically and instantaneously reduces net radiation and available energy. Appearance of clouds will therefore alter the surface energy balance and elicit response of plant canopy temperature T C. The attenuated shortwave radiation and altered T C during the presence of clouds may subsequently affect the crop water stress index CWSI. Therefore, to correctly interpret T C measurements, the effect of clouds must be understood. The objective of this work was to study the effect of abrupt changes in solar radiation due to varying cloudy conditions on T C and CWSI for olive trees. Results from two separate experiments are presented, both comparing different levels of water status of Barnea olive trees. The first experiment was conducted in a commercial orchard where five irrigation levels were applied. Thermal images were acquired simultaneously with stomatal resistance measurements on a day with clear skies. The second experiment was conducted on single trees planted in lysimeters. Irrigation was withheld for five of 15 trees for 6 days until they were severely stressed. Thereafter, irrigation was resumed to levels higher than the transpiration rates. Throughout the stress and recovery periods, water status measurements were conducted daily between 12:00 and 14:00 on all trees. On the day of maximum stress, thermal images of well-watered and stressed trees were acquired every minute throughout a time sequence during which large fluctuations in radiation due to cloud cover were observed. The most pronounced result of this study was the greater response of stressed, compared to well-watered, trees to abrupt changes in radiation intensity. When solar radiation was high, the CWSI of stressed trees reached 0.8, while the CWSI of well-watered trees was near 0. When solar radiation dropped due to clouds, the CWSI of the stressed trees decreased to ∼0.3, while that of well-watered trees continued to fluctuate around 0. This finding implies that application of thermal imagery for water status detection would require very high radiometric resolution and constant reference measurements. For routine monitoring in commercial olive orchards, this could be facilitated by strategic maintenance of a few well-watered trees.
TL;DR: In this paper, a field test at Padre Island National Seashore of a mobile lidar scanner mounted on a sport utility vehicle and integrated with a position and orientation system was conducted to assess the vertical and horizontal accuracy of data collected by the mobile terrestrial lidar system.
Abstract: The higher point density and mobility of terrestrial laser scanning light detection and ranging lidar is desired when extremely detailed elevation data are needed for mapping vertically orientated complex features such as levees, dunes, and cliffs, or when highly accurate data are needed for monitoring geomorphic changes. Mobile terrestrial lidar scanners have the capability for rapid data collection on a larger spatial scale compared with tripod-based terrestrial lidar, but few studies have examined the accuracy of this relatively new mapping technology. For this reason, we conducted a field test at Padre Island National Seashore of a mobile lidar scanner mounted on a sport utility vehicle and integrated with a position and orientation system. The purpose of the study was to assess the vertical and horizontal accuracy of data collected by the mobile terrestrial lidar system, which is georeferenced to the Universal Transverse Mercator coordinate system and the North American Vertical Datum of 1988. To accomplish the study objectives, independent elevation data were collected by conducting a high-accuracy global positioning system survey to establish the coordinates and elevations of 12 targets spaced throughout the 12 km transect. These independent ground control data were compared to the lidar scanner-derived elevations to quantify the accuracy of the mobile lidar system. The performance of the mobile lidar system was also tested at various vehicle speeds and scan density settings e.g. field of view and linear point spacing to estimate the optimal parameters for desired point density. After adjustment of the lever arm parameters, the final point cloud accuracy was 0.060 m east, 0.095 m north, and 0.053 m height. The very high density of the resulting point cloud was sufficient to map fine-scale topographic features, such as the complex shape of the sand dunes.
TL;DR: In this paper, the authors show that sprawl is high and this sprawl has evenly dispersed distribution across the space with absorption of land increasing over a period of time, but at the same time the absorbed land requires more planning and proper utilization as evident from lower LCR values.
Abstract: The results show that sprawl is high. This sprawl has evenly dispersed distribution across the space with absorption of land increasing over a period of time, but at the same time the absorbed land requires more planning and proper utilization as evident from lower LCR values. Such an understanding is a prerequisite for the sustainable planning required to counteract the perceived negative social, economic, and environmental impacts of urban sprawl.
TL;DR: In this article, a Raman lidar system is used to monitor the aerosol depolarization features of the urban atmosphere at the Andalusian Centre for Environmental Research CEAMA, in Granada, southeastern Spain.
Abstract: A Raman lidar system is used to monitor the aerosol depolarization features of the urban atmosphere at the Andalusian Centre for Environmental Research CEAMA, in Granada, southeastern Spain. The lidar system was upgraded in 2010 to enable the application of the ±45° calibration method, which does not require any external optical device. We analyse the method and classify the atmospheric aerosol following the criteria based on depolarization. Backscatter coefficient, backscatter-related Angstrom exponent a β, volume linear depolarization ratio δv, and particle linear depolarization ratio δp profiles are studied in Saharan dust and biomass burning smoke events during the summer of 2010. The strength of these events was visualized in the aerosol optical depth AOD series obtained by Sun and star photometers operated at CEAMA. During the analysed events, the AOD at 440 nm ranged between 0.2 and 0.3, although the Angstrom exponent a AOD retrieved by the Sun photometer was considerably lower during the Saharan dust event a AOD = 0.4 ± 0.1 than during the biomass burning event a AOD = 1.4 ± 0.1. Regarding a β profiles, a β values were similar along the vertical profiles and comparable to a AOD values for each event. In contrast, the particle linear depolarization ratio δp at 532 nm showed an opposite behaviour to a β, changing along the vertical profiles. In fact, the aerosol layers located in the free troposphere showed mean values of δp of 0.13 ± 0.08 and 0.03 ± 0.01 in the Saharan dust and biomass burning events, respectively. These results show that the use of depolarization techniques enables an accurate aerosol typing and the understanding of the layer's composition in the atmosphere.
TL;DR: In this paper, two scaling models, a component-based and a pixel-based model, are proposed on the basis of the Taylor series expansion with corresponding textural and contextural parameters to correct for the scaling effects among LAI products at different scales.
Abstract: This article addresses the major scaling problems in leaf area index LAI retrieval for a heterogeneous surface associated with 1 the nonlinearity in the relationships between remotely sensed reflectances and LAI products, 2 the discontinuity caused by the mixture of contrasting cover types that is categorized as the dominating type within a large-scale pixel, and 3 the algorithm for the dominant cover type being used for the retrieval of the LAI in that large-scale mixed pixel. Through mathematical analysis, two scaling models a component-based model and a pixel-based model are proposed on the basis of the Taylor series expansion with the corresponding textural and contextural parameters i.e. variance–covariance matrices and component fractions to correct for the scaling effects among LAI products at different scales. These models express the magnitude of the scaling effects for the nonlinear and discontinuous situations as a function of 1 the degree of nonlinearity quantified by the second derivative of the retrieval function, 2 the spatial heterogeneity quantified by variance–covariance matrices, and 3 the component fractions in the large-scale mixed pixel. To evaluate the proposed scaling models, a scaling correction test is performed and analysed on a SPOT Systeme Pour l'Observation de la Terre image for two vegetation types. The component fractions have proven to be the main reason for the scaling effects in a mixed pixel. Compared to the results before scaling, using either of the two proposed models greatly reduces the retrieval errors that the scaling effects cause. The relative scaling effects of the LAI may be up to 55% in an uncorrected, large-scale mixed pixel. However, the relative scaling errors can be as low as 2% with the intra-component textural parameters and about 13% with the intra-pixel textural parameters. Because the scaling effects can be corrected for the spatial heterogeneity caused either by density changes within the same cover or by cover type changes, our work indicates that the proposed scaling models are promising and feasible.