The development of remote sensing has enabled the acquisition of information on land-cover change at different spatial scales. However, a tradeoff between spatial and temporal resolutions normally exists. Fine-spatial-resolution images have low temporal resolutions, whereas coarse-spatial-resolution images have high temporal repetition rates. A novel superresolution change detection method (SRCD) is proposed to detect land-cover changes at both fine spatial and temporal resolutions with the use of a coarse-resolution image and a fine-resolution land-cover map acquired at different times. SRCD is an iterative method that involves endmember estimation, spectral unmixing, land-cover fraction change detection, and superresolution land-cover mapping. Both the land-cover change/no-change map and from-to change map at fine spatial resolution can be generated by SRCD. In this paper, SRCD was applied to a synthetic multispectral image, a Moderate-Resolution Imaging Spectroradiometer multispectral image, and a Landsat-8 Operational Land Imager multispectral image. The land-cover from-to change maps are found to have the highest overall accuracy (higher than 85%) in all of the three experiments. Most of the changed land-cover patches, which were larger than the coarse-resolution pixel, were correctly detected.
While deforestation in the tropics is a major source of atmospheric CO2 so the regeneration of these forests is a major sink of CO2. To determine the magnitude of this sink and assess the carbon balance of tropical forests at regional to global scales an accounting model is required that multiplies carbon flux by the area of each forest regeneration stage. The only feasible tool with which to estimate the area of forest regenerative stages at regional to global scales is remote sensing from spaceborne sensors. The role of remote sensing in studies of tropical deforestation and forest regeneration is reviewed and results from a study focused on tropical forests in Ghana, West Africa, are presented. Emphasis is placed on tropical forest regeneration, which may be characterized typically by increases in leaf biomass, wood biomass and canopy roughness. Remotely sensed red and near infrared radiation can be used to estimate leaf biomass; microwave backscatter can be used to estimate leaf biomass, wood biomass and canopy roughness and multi-angle measurements of radiation can be used to estimate canopy roughness. This paper focused attention on three limitations with the most widely used of these approaches, the use of reflected red and near infrared radiation: (i) the availability of satellite sensor data for tropical forests; (ii) the relationships between remotely sensed data in red and near infrared wavelengths and forest measures that relate to leaf biomass; and (iii) the feasibility of estimating the coverage of forest within a pixel for accurate estimation of
A strong relationship between night-time light (NTL) data and the areal extent of urbanized regions has been observed frequently. As urban regions have an important vertical dimension, it is hypothesized that the strength of the relationship with NTL can be increased by consideration of the volume rather than simply the area of urbanized land. Relationships between NTL and the area and volume of urbanized land were determined for a set of towns and cities in the UK, the conterminous states of the USA and countries of the European Union. Strong relationships between NTL and the area urbanized were observed, with correlation coefficients ranging from 0.9282 to 0.9446. Higher correlation coefficients were observed for the relationship between NTL and urban building volume, ranging from 0.9548 to 0.9604; The difference in the correlations obtained with volume and with area was statistically significant at the 95% level of confidence. Studies using NTL data may be strengthened by consideration of the volume rather than just area of urbanized land.
The relevance vector machine (RVM), a Bayesian extension of the support vector machine (SVM), has considerable potential for the analysis of remotely sensed data. Here, the RVM is introduced and used to derive a multi‐class classification of land cover with an accuracy of 91.25%, a level comparable to that achieved by a suite of popular image classifiers including the SVM. Critically, however, the output of the RVM includes an estimate of the posterior probability of class membership. This output may be used to illustrate the uncertainty of the class allocations on a per‐case basis and help to identify possible routes to further enhance classification accuracy.
No abstract is provided for this article.
This article introduces a strategic initiative, COST Action TD1202, focused on the role of citizen sensors in mapping. It outlines the Action's scope, aims and current status. In particular, the article outlines the potential of citizen science in mapping activities and indicates the scope of current work undertaken by the Action's four working groups. It is stressed that the Action is at an early stage and that it is open to new members.
No abstract is provided for this article.
Remotely sensed imagery is an attractive source of information for mapping and monitoring land cover. Fine spatial resolution imagery is typically acquired infrequently, but fine temporal resolution systems commonly provide coarse spatial resolution imagery. Sub-pixel land cover change mapping is a method that aims to use the advantages of these multiple spatial and temporal resolution sensing systems. This method produces fine spatial and temporal resolution land cover maps, by updating fine spatial resolution land cover maps using coarse spatial resolution remote sensing imagery. A critical issue for sub-pixel land cover change mapping is downscaling coarse spatial resolution fraction maps estimated by soft classification to a fine spatial resolution land cover map. The relationship between a historic fine spatial resolution map and a contemporary fine spatial resolution map to be estimated at a more recent date plays an important role in the downscaling procedure. A change strategy based on the assumption that the change for each land cover class in a coarse spatial resolution pixel is unidirectional was shown to be a promising means to describe this relationship. This paper aims to assess this change strategy by analyzing the factors that affect the accuracy of the change strategy, using six subsets of the National Land Cover Database (NLCD) of USA. The results show that the spatial resolution of coarse pixels, the time interval of the previous fine resolution land cover map and the current coarse spatial resolution images, and the thematic resolution of the used land cover class scheme have considerable influence on the accuracy of the change strategy. The accuracy of the change strategy decreases with the coarsening of spatial resolution, an increase of time interval, and an increase of thematic resolution. The results also indicate that, when the historic land cover map has a 30 m resolution, like the NLCD, the average accuracy of the change strategy is still as high as 92% when the coarse spatial resolution data used had a resolution of ~1000 m, confirming the effectiveness of the change strategy used in sub-pixel land cover change mapping for use with popular remote sensing systems.
No abstract is provided for this article.
The performance of the NOAA AVHRR derived V13 vegetation index, relative to the NDVI, for monitoring impacts of the 1997-1998 ENSO related drought stress on Sabah rainforests was explored. Results demonstrated that the V13 index, which incorporates MIR reflectance, was more strongly correlated to rainfall values of this ENSO event than the more widely used NDVI. Moreover, maximum correlation was achieved at a longer lag period using the V13 than the NDVI, demonstrating its possible utility in a forecasting and monitoring system of ENSO related drought impacts on Sabah rainforests.
RÉSUMÉLes pixels mixtes dominent souvent les données de télédétection à faible résolution spatiale utilisées couramment dans la cartographie de la couverture du sol aux échelles régionale et globale. Malheureusement, les pixels mixtes ne peuvent être pris en considération de façon adéquate au moyen des techniques conventionnelles de classification d'image et conséquemment, la représentation spatiale des classes de couverture du sol et les estimations de leur étendue dérivées de telles classifications peuvent être erronées. Le démixage des pixels par classes permet de déterminer la distribution de la couverture du sol dans des sous-images à partir desquelles on peut dériver des estimations plus exactes de l'étendue des classes. Les problèmes rencontrés avec les méthodes de démixage limitent leur application et nous proposons ici une approche alternative basée sur un réseau artificiel de neurones. La méthode de démixage a été appliquée à la détermination de la composition par classes sur des données AVHRR au Brésil, avec l'emphase sur la précision avec laquelle on peut estimer l'étendue des classes qui se manifestent le plus fréquemment au niveau du souspixel dans l'ensemble de données. Les résultats montrent une correspondance étroite entre l'étendue estimée et observée des classes dans les pixels AVHRR (les coefficients de corrélation variant de 0,30–0,74, tous significatifs à de seuils de confiance de 99%). De plus, l'étendue estimée des classes pour le site d'étude était généralement beaucoup plus près de l'étendue observée des classes que les estimations dérivées de l'approche basée sur la classification conventionnelle.
This paper describes an attempt to characterize the flash flood potential in the wadi El-Alam, on the Red Sea coast of Egypt. Many important basin characteristics and morphometric parameters were defined from a digital elevation model (DEM). The range of hydrograph characteristics was estimated and the flood-vulnerable sites along the Idfu-Marsa Alam road identified.
Dams play a significant role in altering the spatial pattern of temperature in rivers and contribute to thermal pollution, which greatly affects the river aquatic ecosystems. Understanding the temporal and spatial variation of thermal pollution caused by dams is important to prevent or mitigate its harmful effect. Assessments based on in-situ measurements are often limited in practice because of the inaccessibility of water temperature records and the scarcity of gauges along rivers. By contrast, thermal infrared remote sensing provides an alternative approach to monitor thermal pollution downstream of dams in large rivers, because it can cover a large area and observe the same zone repeatedly. In this study, Landsat Enhanced Thematic Mapper Plus (ETM+) thermal infrared imagery were applied to assess the thermal pollution caused by two dams, the Geheyan Dam and the Gaobazhou Dam, located on the Qingjiang River, a tributary of the Yangtze River downstream of the Three Gorges Reservoir in Central China. The spatial and temporal characteristics of thermal pollution were analyzed with water temperatures estimated from 54 cloud-free Landsat ETM+ scenes acquired in the period from 2000 to 2014. The results show that water temperatures downstream of both dams are much cooler than those upstream of both dams in summer, and the water temperature remains stable along the river in winter, showing evident characteristic of the thermal pollution caused by dams. The area affected by the Geheyan Dam reaches beyond 20 km along the downstream river, and that affected by the Gaobazhou Dam extends beyond the point where the Qingjiang River enters the Yangtze River. Considering the long time series and global coverage of Landsat ETM+ imagery, the proposed technique in the current study provides a promising method for globally monitoring the thermal pollution caused by dams in large rivers.
RÉSUMÉLes méthodes classiques reconnues pour la classification d'images, comme la classification par maximum de vraisemblance, ne sont pas toujours appropriées pour traiter, de nos jours, les données provenant de sources multiples en raison du volume élevé et de la diversité de ces données. Dans le présent article, deux méthodes sont examinées comme solution de rechange à la méthode de classification par maximum de vraisemblance (MV) pour la classification d'images provenant de sources multiples; il s'agit d'un algorithme de classification par raissonement véridique (RV) selon la théorie de Dempster-Shafer et de deux algorithmes de classification par réseaux neuronaux artificiels (RNA). Chaque méthode a été mise à l'épreuve dans des classifications distinctes de la couverture d'un sol de type alpin et de la profondeur des couches actives du pergélisol dans une région montagneuse du sud-ouest du Yukon, à l'aide d'un ensemble de données SPOT, de données sur la texture des images et de variables géomorphométriques tirées d'un modèle numérique d'élévation. La comparaison du niveau d'exactitude global moyen a démontré que la méthode de classification par RV offrait un niveau d'exactitude de 10 % supérieur à celui de la méthode de classification par MV et que la méthode de classification par RNA présentait un niveau d'exactitude de 5 % supérieur à celui de la méthode par RV. Les coefficients d'agrément de Kapp les plus élevés obtenus pour la couverture du sol furent 0,79, 0,90 et 0,96 avec la méthode par MV, la méthode par RV et la méthode par RNA respectivement. Des différences importantes du niveau d'exactitude des deux algorithmes de classification par RNA ont été observées dans plusieurs cas. Ces différences sont attribuables aux problèmes posés par la détermination des paramètres optimaux d'entraînement des réseaux neuronaux à l'aide de données d'entrée subjectives. Pour ce qui est de la durée de calcul, l'algorithme de classification par RV s'est révélé plusieurs ordres de grandeur plus rapide que les algorithmes de classification par RNA. Les conclusions que tirent les auteurs de ces expériences sont les suivantes : l'algorithme de classification par RV et les algorithmes de classification par RNA présentent des capacités supérieures à la méthode de classification par MV pour traiter des données provenant de sources multiples; l'algorithme de classification par raisonnement véridique est plus rapide et moins subjectif que la méthode de classification par réseaux neuronaux, cependant que cette dernière, en raison de son niveau d'exactitude plus élevé, est celle que les auteurs recommandent pour l'instant. La méthode, en effet, a donné les résultats escomptés compte tenu de l'avancement de la recherche dans le domaine des réseaux neuronaux, par rapport à la méthode relativement nouvelle du raisonnement véridique dont les applications en télédétection en sont aux premiers stades de développement; il s'avère justifié de mener des recherches plus poussés pour développer les algorithmes de classification par raisonnement véridique ainsi que pour déterminer les paramètres d'entrée optimaux des algorithmes de classification par réseaux neuronaux dans le but d'améliorer le niveau d'objectivité de cette méthode; enfin, les méthodes de classification par RV et par RNA semblent plus appropriées à l'analyse d'images provenant de sources multiples et à la classification de phénomènes environnementaux complexes.
Malaysia’s shoreline is dynamic and constantly changing. Although the shoreline may be mapped accurately from fine spatial resolution imagery, this is an impractical approach for use over large areas. An alternative approach using coarse spatial resolution satellite sensor imagery, addressed here, is to fit a shoreline boundary at sub-pixel scale. This paper investigates the effects of utilizing relatively coarse spatial resolution satellite sensor imagery to produce accurate shoreline maps. For the purposes of this research a 1m spatial resolution IKONOS satellite sensor image was used to define the actual location of the shoreline. This image was degraded to spatial resolutions of 16 m and 32 m, comparable to that of widely used civilian remote sensors. The coarse spatial resolution images derived were used in the evaluation of four methods for shoreline mapping. The conventional method based on hard classification provided an inaccurate and inappropriate representation of the shoreline. Super-resolution methods based on sub-pixel information derived from a soft classification provided accurate and realistic prediction of the shoreline. The most accurate prediction of the shoreline, with RMSE less than 2.1 m and 5.2 m for all the shorelines at 16 m and 32 m spatial resolutions respectively, were derived from a method based on simulated annealing.
