Mapping of land degradation from space: a comparative study of Landsat ETM+ and ASTER data

The purpose of this study is to compare the role of spectral and spatial resolutions in mapping land degradation from space‐borne imagery using Landsat ETM+ and ASTER data as examples. Land degradation in the form of salinization and waterlogging in Tongyu County, western Jilin Province of northeast China was mapped from an ETM+ image of 22 June 2002 and an ASTER image recorded on 24 June 2001 using supervised classification, together with several other land covers. It was found that the mapping accuracy was achieved at 56.8% and higher for moderately degraded (e.g. salinized) farmland, and over 80% for severely degraded land (e.g. barren) from both ASTER and ETM+ data. The spatial resolution of the ASTER data exerts only a negligible effect on the mapping accuracy. The 30 m ETM+ outperforms the ASTER image of both 15 m and 30 m resolution in consistently generating a higher overall accuracy as well as a higher user's accuracy for barren land. The inferiority of ASTER data is attributed to the highly repetitive spectral content of its six shortwave infrared bands. It is concluded that the spectral resolution of an image is not as important as the information content of individual bands in accurately mapping land covers automatically.     

Monitoring deforestation and forest degradation using multi-temporal fraction images derived from Landsat sensor data in the Brazilian Amazon

Deforestation is the replacement of forest by other land use while degradation is a reduction of long-term canopy cover and/or forest stock. Forest degradation in the Brazilian Amazon is mainly due to selective logging of intact/un-managed forests and to uncontrolled fires. The deforestation contribution to carbon emission is already known but determining the contribution of forest degradation remains a challenge. Discrimination of logging from fires, both of which produce different levels of forest damage, is important for the UNFCCC (United Nations Framework Convention on Climate Change) REDD+ (Reducing Emissions from Deforestation and Forest Degradation) program. This work presents a semi-automated procedure for monitoring deforestation and forest degradation in the Brazilian Amazon using fraction images derived from Linear Spectral Mixing Model (LSMM). Part of a Landsat Thematic Mapper (TM) scene (path/row 226/068) covering part of Mato Grosso State in the Brazilian Amazon, was selected to develop the proposed method. First, the approach consisted of mapping deforested areas and mapping forest degraded by fires using image segmentation. Next, degraded areas due to selective logging activities were mapped using a pixel-based classifier. The results showed that the vegetation, soil, and shade fraction images allowed deforested areas to be mapped and monitored and to separate degraded forest areas caused by selective logging and by fires. The comparison of Landsat Operational Land Imager (OLI) and RapidEye results for the year 2013 showed an overall accuracy of 94%. We concluded that spatial resolution plays an important role for mapping selective logging features due to their characteristics. Therefore, when compared to Landsat data, the current availability of higher spatial and temporal resolution data, such as provided by Sentinel-2, is expected to improve the assessment of deforestation and forest degradation, especially caused by selective logging. This will facilitate the implementation of actions for forest protection.     

A modeling and spatio-temporal analysis framework for monitoring environmental change using NPP as an ecosystem indicator

We present and describe a modeling and analysis framework for monitoring protected area (PA) ecosystems with net primary productivity (NPP) as an indicator of health. It brings together satellite data, an ecosystem simulation model (NASA-CASA), spatial linear models with autoregression, and a GIS to provide practitioners a low-cost, accessible ecosystem monitoring and analysis system (EMAS) at landscape resolutions. The EMAS is evaluated and assessed with an application example in Yellowstone National Park aimed at identifying the causes and consequences of drought. Utilizing five predictor covariates (solar radiation, burn severity, soil productivity, temperature, and precipitation), spatio-temporal analysis revealed how landscape controls and climate (summer vegetation moisture stress) affected patterns of NPP according to vegetation functional type, species cover type, and successional stage. These results supported regional and national trends of NPP in relation to carbon fluxes and lag effects of climate. Overall, the EMAS provides valuable decision support for PAs regarding informed land use planning, conservation programs, vital sign monitoring, control programs (fire fuels, invasives, etc.), and restoration efforts.     

Estimation of storage heat flux in an urban area using ASTER data

The urban heat island phenomenon occurs as a result of the mixed effects of anthropogenic heat discharge, increased use of artificial impervious surface materials, and decreased vegetation cover. These factors modify the heat balance at the land surface and eventually raise the atmospheric temperature. It is important to quantify the surface heat balance in order to estimate the contributions of these factors. The present authors propose the use of storage heat flux to represent the heat flux between the land surface and the inside of the canopy for the heat balance analysis based on satellite remote sensing data. Surface heat fluxes were estimated around the city of Nagoya, Japan using Terra ASTER data and meteorological data. Seasonal and day-night differences in heat balance were compared using ASTER data acquired in the daytime on July 10, 2000, and January 2, 2004 and in the nighttime on September 26, 2003. In the central business and commercial districts, the storage heat flux was higher than those in the surrounding residential areas. In particular, in winter, the storage heat flux in the central urban area was 240 to 290 W m^− 2, which was much larger than the storage heat fluxes in residential areas, which ranged from 180 to 220 W m^− 2. Moreover, the negative storage heat flux in the central urban area was greater at night. This tendency implies that the urban surface stores heat during the daytime and discharges it at night. Extremely large negative storage heat flux occurred primarily in the industrial areas for both daytime and nighttime as a result of the enormous energy consumption by factories.     

A new approach for land surface emissivity estimation using LDCM data in semi-arid areas: exploitation of the ASTER spectral library data set

In this research, a new approach called non-vegetated based emissivity estimation method (NV-method) for estimating land surface emissivity (LSE) on Landsat-8 (known as Landsat Data Continuity Mission, LDCM) data has been proposed for semi-arid areas. At first, a simulation of channel emissivities and reflective bands of basic classes in vegetation and non-vegetated areas is accomplished based on convolving Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral Library with LDCM spectral response functions. Then, four main classes in non-vegetated areas are defined to determine separate emissivity estimate model as a function of reflective bands from basic spectra associated with the main class. The LSEs in mixed and vegetation areas are adopted from the simplified normalized difference vegetation index (NDVI)-based emissivity threshold method (N-method^THM), namely SN-method^THM and improved N-method^THM (IN-method^THM) methods, respectively. The NV-method is empirically tested using LDCM data and the obtained LSEs were compared with two scenes of LSE product of the ASTER. The root mean square error (RMSE) values of computed LSEs by NV-method are 0.46% and 0.81%, for band 10 and 11, respectively, in the first examined scene. While, for the second scene, the RMSE are 0.36% and 0.56% for band 10 and 11, respectively. Moreover, the NV-method were compared with N-method^THM, SN-method^THM, and IN-method^THM in non-vegetated areas. Generally, the obtained results of LSEs by NV-method are better than that of results from the compared methods in non-vegetated areas in terms of statistical measures. Except in rocky class, for which N-method^THM provides better results, the NV-method achieved superior results in soil texture and man-made classes, which are dominating classes in the study area.     

Simultaneous determination of aerosol optical thickness and surface reflectance using ASTER visible to near-infrared data over land

An innovative method for the determination of aerosol optical thickness (AOT) and surface reflectance for operational use of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible to near-infrared data is presented. This method is designed to obtain the atmospheric parameters needed in the correction of the image. This method is based on a simplified radiative transfer equation describing the relation between the ground surface reflectance, AOT and top-of-atmosphere reflectance. By exploiting the ASTER dual-angle view capabilities in band 3N (Nadir) and band 3B (Backwards), surface reflectance and AOT can be retrieved synchronously. Thus, it solves the problem of separating atmospheric radiance from the transmitted radiance of the surface to some extent. After applying this new atmospheric correction method to three areas of ASTER images, Beijing urban city, the Heihe River Basin and Hong Kong of China, ASTER surface reflectance products (AST07) were obtained. AOT values from in situ measurements of CIMEL Electronique 318 Sun Photometers or AERONET (AErosol RObotic NETwork) and surface reflectance in situ measured using an Analytical Spectral Device (ASD) Field Spec spectral radiometer are used for validation. AOT derived from the new method is consistent with in situ station measurements from CIMEL Electronique 318 Sun Photometer and level 2.0 data from AERONET, with correlation coefficient (R ²) of 0.98 and root mean square error of 0.05, whereas Multi-angle Imaging Spectroradiometer AOT products underestimate AERONET AOT and Moderate-Resolution Imaging Spectroradiometer AOT products overestimate AERONET AOT in these regions. More encouraging is the comparison between the corrected surface reflectance, AST07 and ASD measurements. Root mean square error of AST07 and retrieved surface reflectance are as follows: band 1 (556 nm) = 0.04 and 0.05; band 2 (661 nm) = 0.036 and 0.035; band 3 (807 nm) = 0.056 and 0.038, which suggests that compared with AST07 in bands 2 and 3, retrieved surface reflectance has better agreement with measured reflectance from ASD.     

The Cochran-Weiss-Shanteau performance index as an indicator of upper limb risk assessment expertise

Ergonomists and many other professionals apply ergonomics principles to musculoskeletal health problems. This study examines whether there are differences when it comes to judgement expertise concerning upper limb disorders (ULDs) between ergonomists and those with less ergonomics training. The Cochran-Weiss-Shanteau (CWS) performance index combines judgement consistency with discrimination into one CWS index. Fifty-eight professionals working in the musculoskeletal health area, from four different professions, judged the likelihood of staff complaining of ULDs in a number of written work scenarios containing ULD risk factors. A student group (n = 148) taking an introductory ergonomics module was used as a reference. The ergonomists scored higher on the CWS index than all of the other groups, performing significantly better than all but the occupational health advisors. Performance improved with increased training level but not with experience. This study suggests that ergonomists are quantifiably different from other ergonomics advisors in their judgement performance in this context. Given the global cost of musculoskeletal disorders, assessing the expertise of those giving ergonomics advice for the management of musculoskeletal health is of great significance. This study presents a method for assessing judgement performance in ULD risk assessment, an important part of musculoskeletal health management.     

Retrieval of land surface temperature and emissivity from ASTER1B data using a dynamic learning neural network

The accuracy of a radiance transfer model neural network (RM-NN) for separating land surface temperature (LST) and emissivity from AST09 (the Advanced Spaceborne and Thermal Emission and Reflection Radiometer (ASTER) Standard Data Product, surface leaving radiance) is very high, but it is limited by the accuracy of the atmospheric correction. This article uses a neural network and radiance transfer model (MODTRAN4) to directly retrieve the LST and emissivity from ASTER1B data, which overcomes the difficulty of atmospheric correction in previous methods. The retrieval average accuracy of LST is about 1.1 K, and the average accuracy of emissivity in bands 11–14 is under 0.016 for simulated data when the input nodes are a combination of brightness temperature in bands 11–14. The average accuracy of LST is under 0.8 K when the input nodes are a combination of water vapour content and brightness temperature in bands 11–14. Finally, the comparison of retrieval results with ground measurement data indicates that the RM-NN can be used to accurately retrieve LST and emissivity from ASTER1B data.     

Scaling of land surface temperature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous terrain area

Land surface temperature (LST) is a key parameter in numerous environmental studies. Surface heterogeneity induces uncertainty in pixel-wise LST. Spatial scaling may account for the uncertainty, however, different approaches lead to differences in scaled values. Satellite-retrieved LST may be representative of the pixel-wise LST and useful for scaling analysis, but the limited accuracy of retrieved values adds uncertainty into the scaled values. Based on the Stefan-Boltzmann (S-B) law, this study proposed scaling approaches for LST over flat and relief areas to explore the combined uncertainties in scaling using satellite-retrieved data. To take advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from these two sensors were examined for part of the Loess Plateau in China. 90-m ASTER LST data were scaled up to 1 km using the proposed approaches, and variation in the LST was generally reduced after scaling. Amongst the sources of uncertainties, surface heterogeneity (emissivity) and different scaling approaches resulted in very minor differences, with a maximum difference of 0.2 K for the upscaled LST. Terrain features, taken as an areal weighting factor, had negligible effects on the upscaled value. Limited accuracy of the retrieved LST was the major uncertainty. The overall LST increased 0.6 K on average with correction for terrain-induced angular effect and 0.4 K for both angular and adjacency effects over the study area. Accounting for terrain correction in scaling is necessary for rugged areas. With terrain correction, the upscaled ASTER LST achieved an agreement of − 0.1 ± 1.87 K and a root mean square error (RMSE) of 1.87 K overall with the 1-km MODIS LST rectified by Wan et al.'s approach [Wan, Z., Zhang, Y., Zhang Q., Li, Z.-L. (2002b), Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment, 83, 163-180]. Refining the rectification approach resulted in a better agreement of − 0.2 ± 1.57 K and a RMSE of 1.58 K.     

Effects of land markets and land management on ecosystem function: A framework for modelling exurban land-change

This paper presents the conceptual design and application of a new land-change modelling framework that represents geographical, sociological, economic, and ecological aspects of a land system. The framework provides an overarching design that can be extended into specific model implementations to evaluate how policy, land-management preferences, and land-market dynamics affect (and are affected by) land-use and land-cover change patterns and subsequent carbon storage and flux. To demonstrate the framework, we implement a simple integration of a new agent-based model of exurban residential development and land-management decisions with the ecosystem process model BIOME-BGC. Using a stylized scenario, we evaluate the influence of different exurban residential-land-management strategies on carbon storage at the parcel level over a 48-year period from 1958 to 2005, simulating stocks of carbon in soil, litter, vegetation, and net primary productivity. Results show 1) residential parcels with management practices that only provided additions in the form of fertilizer and irrigation to turfgrass stored slightly more carbon than parcels that did not include management practices, 2) conducting no land-management strategy stored more carbon than implementing a strategy that included removals in the form of removing coarse woody debris from dense tree cover and litter from turfgrass, and 3) the removal practices modelled had a larger impact on total parcel carbon storage than our modelled additions. The degree of variation within the evaluated land-management practices was approximately 42,104 kg C storage on a 1.62 ha plot after 48 years, demonstrating the substantial effect that residential land-management practices can have on carbon storage.     

Utility of AVHRR data for land degradation assessment: a case study

AVHRR data are widely used to monitor vegetation greenness and to provide a gross measure of primary production throughout the world. This paper examines whether AVHRR data can be used for determining the extent of land degradation in arid rangelands under commercial grazing using models of vegetation dynamics and animal grazing behaviour developed for Landsat-MSS data. These models are applied after large rainfall events and either search for systematic change in average vegetation cover across relatively uniform landscapes with increasing distance from stock watering points or examine the magnitude of vegetation response to rainfall for each pixel.

We applied the models where previous work with Landsat-MSS had demonstrated the extent of grazing impact. An index of vegetation cover using adjusted AVHRR channel 1 values produced trends in wet period average vegetation cover with increasing distance from water similar to, but less pronounced than, those obtained with MSS data. NDVI produced inconsistent and often ambiguous results when compared with the MSS data. AVHRR-derived vegetation indices were unusable in degradation assessment procedures which require pixel-scale vegetation response models. The large AVHRR pixel, even in LAC mode, creates difficulties in detecting grazing impact. Landscape changes as a result of grazing occur at a finer scale and are therefore subsumed within the pixel. Misregistration of multi-temporal images further reduces the ability to detect grazing impact on a pixel basis when such change is occurring within the pixel.

We conclude that despite their cost attractiveness, AVHRR data are inappropriate for the reliable detection of grazing impact using grazing gradient methods in the large paddocks of arid rangelands.     

Mapping wetlands using ASTER data: a comparison between classification trees and logistic regression

This study compared a non‐parametric and a parametric model for discriminating among uplands (non‐wetlands), woody wetlands, emergent wetlands and open water. Satellite images obtained on 6 March 2005 and 16 October 2005 from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and geographic information system (GIS) data layers formed the input for analysis using classification and regression tree (CART^®) and multinomial logistic regression analysis. The overall accuracy of the CART model was 73.3%. The overall accuracy of the logit model was 76.7%. The accuracies were not statistically different from each other (McNemar χ ² = 1.65, p = 0.19). The CART producer's accuracy of the emergent wetlands was higher than the accuracy from the multinomial logit (57.1% vs. 40.7%), whereas woody wetlands identified by the multinomial logit model presented a producer's accuracy higher than that from the CART model (68.7% vs. 52.6%). A McNemar test between the two models and National Wetland Inventory (NWI) maps showed that their accuracies were not statistically different. Overall, these two models provided promising results, although they are not sufficiently accurate to replace current methods of wetland mapping based on feature extraction in high‐resolution orthoimagery.     

Evaluating the performance of remote sensed rain-use efficiency as an indicator of ecosystem functioning in semi-arid ecosystems

Monitoring ecological functioning is a significant step towards detecting changes in ecosystem attributes that could be linked to desertification processes in drylands. The remote sensing proxies of ecological functioning, attract substantial attention due to its advantage on large spatial and increasingly long temporal scales. Remote sensed Vegetation Indices (VIs) have been proposed as the approach to plant productivity to be indicators of ecosystem functioning in local drylands. However, VIs are easily affected by rainfall, a limiting source in arid and semi-arid areas. Therefore, they may not be suitable indicators of ecosystem functioning when applied at large scales, with different rainfall regimes. To overcome the influence of precipitation, the performance of the remote sensing Rain-Use Efficiency (RUE, defined as aboveground net primary production divided by rainfall) was evaluated in 78 global drylands (of which 74 are located in semi-arid areas), as an indicator of multiple ecosystem functions, quantified by ecological multifunctionality index (EMI, integrated by carbon, nitrogen and phosphorus cycles). The correlation analysis showed that during the growing season, the linear relationships of summed EVI (Enhanced Vegetation Index) and RUE with EMI are both significant positive. However, RUE explained more variation (about 44%) in EMI than summed EVI (about 32%) did. The results obtained by partial correlation analysis by controlling the rainfall showed that correlation coefficient between summed EVI and EMI, increased about 20%, while correlation coefficient between RUE and EMI increased very slightly (about 3%). Similar results were also found by using the Normalized Difference Vegetation Index (NDVI). These facts indicated that both remote sensed VIs and RUE could be indicators of ecological multifunctioning. However, RUE was better due to its robustness to rainfall. Also, we must take care that the core assumptions related to the RUE should be fulfilled before using it as an indicator. The relationships between RUE and nutrient cycles showed that in comparison to phosphorus cycle, the carbon and nitrogen cycles had an apparent higher weight in determining the relationship between RUE and EMI. Our findings support the use of remote sensed RUE to monitor ecosystem functioning which could be linked with alternative dryland states and early detection of desertification in drylands.     

Crseharacterization of land surface thermal structure from NOAA-AVHRR data over a northern ecosystem

This paper focuses on the estimation and analysis of surface thermal parameters (emissivities and surface temperatures) in a nordic environment (Québec, Canada). The land cover in this region varies from boreal forest in the south to tundra in the north. The thermal parameters are estimated from two variants of a new model that combines the radiances of the short wave infrared (SWIR) spectral band [advanced very high resolution radiometer (AVHRR) channel 3: 3.55–3.93 μm] and the thermal bands (AVHRR channel 4: 10.5–11.5 μm; and AVHRR channel 5: 11.5–12.5 μm). The study, carried out for images acquired on different dates, reveals that, in most situations, the two approaches allow the separation of emissivities and surface temperatures. Analysis of the variations of the estimated emissivities in relation to surface patterns shows that they are slightly variable in spectral bands 4, and 5, with values generally greater than 0.95. Variations are more important in the SWIR channel, where values less than 0.90 appear, especially in urban areas. In general, surface emissivities increase with the density of the vegetation cover. Moreover, for densely vegetated areas, SWIR surface reflectivities, which can be derived from emissivities, appear to be well correlated with the reflectivities of the AVHRR visible channel. As with emissivities, variations of the estimated land surface temperatures (LST) in relation to vegetation density, characterized by the normalized difference vegetation index (NDVI), were considered. The relations between the two parameters (LST and NDVI) show essentially two opposite directions of linear variations (positive and negative correlations). In the light of the main results obtained, the synergistic use of the different spectral regions (visible, near, mid-, and thermal infrareds) could be very useful in the parameterization of boreal ecosystems.     

The development of an operational procedure for burned-area mapping using object-based classification and ASTER imagery

Although burned-area mapping at a regional level is traditionally based on the use of Landsat data, the potential gap in the sensor's data collection emphasizes the need to find alternative data sources to be used in the operational mapping of burned areas. This work aims to investigate whether it is possible to develop a transferable object-based classification model for burned-area mapping using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. The initial step in the investigation involved the development of an object-based classification model for accurately mapping burned areas in central Portugal using an ASTER image, and subsequently an examination of its performance when mapping a burned area located on the island of Rhodes, Greece, using a different ASTER image. Results indicate that the combined use of object-based image analysis and ASTER imagery can provide an alternative operational tool that could be used to identify and map burned areas and thus fill a potential gap in Landsat data collection.     

Mapping of debris-covered glaciers in the Garhwal Himalayas using ASTER DEMs and thermal data

Mapping of debris-covered glaciers using remote-sensing techniques is recognized as one of the greatest challenges for generating glacier inventories and automated glacier change analysis. The use of visible (VIS) and near-infrared (NIR) bands does not provide sufficient continual information to detect debris-covered ice with remote-sensing data. This article presents a semi-automated mapping method for the debris-covered glaciers of the Garhwal Himalayas based on an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation model (DEM) and thermal data. Morphometric parameters such as slope, plan curvature and profile curvature were computed by means of the ASTER DEM and organized in similar surface groups using cluster analysis. A thermal mask was generated from a single band of an ASTER thermal image, while the clean-ice glaciers were identified using a band ratio based on ASTER bands 3 and 4. Vector maps were drawn up from the output of the cluster analysis, the thermal mask and the band ratio mask for the preparation of the final outlines of the debris-covered glaciers using geographic information system (GIS) overlay operations. The semi-automated mapped debris-covered glacier outline of Gangotri Glacier derived from 2006 ASTER data varied by about 5% from the manually outlined debris-covered glacier area of the Cartosat-1 high-resolution image from the same year. By contrast, outlines derived from the method developed using the 2001 ASTER DEM and Landsat thermal data varied by only 0.5% from manually digitized outlines based on Indian Remote Sensing Satellite (IRS)-1C panchromatic (PAN) data. We found that post-depositional sedimentation by debris flow/mass movement was a great hindrance in the fully automated mapping of debris-covered glaciers in the polygenetic environment of the Himalayas. In addition, the resolution of ASTER stereo data and thermal band data limits the automated mapping of small debris-covered glaciers with adjacent end moraine. However, the results obtained for Gangotri Glacier confirm the strong potential of the approach presented.     

Estimation of subpixel land surface temperature using an endmember index based technique: A case examination on ASTER and MODIS temperature products over a heterogeneous area

Land surface temperature (LST) is a key parameter in numerous environmental studies. Surface heterogeneity induces uncertainty in estimating subpixel temperature. To take an advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from the two sensors were examined for a portion of suburb area in Beijing, China. We selected Soil-Adjusted Vegetation Index (SAVI), Normalized Multi-band Drought Index (NMDI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI) as representative remote sensing indices for four land cover types (vegetation, bare soil, impervious and water area), respectively. By using support vector machines, the overall classification accuracy of the four land cover types with inputs of the four remote sensing indices, extracted from ASTER visible near infrared (VNIR) bands and shortwave infrared (SWIR) bands, reached 97.66%, and Kappa coefficient was 0.9632. In order to lower the subpixel temperature estimation error caused by re-sampling of remote sensing data, a disaggregation method for subpixel temperature using the remote sensing endmember index based technique (DisEMI) was established in this study. Firstly, the area ratios and statistical information of endmember remote sensing indices were calculated from ASTER VNIR/SWIR data at 990 m and 90 m resolutions, respectively. Secondly, the relationship between the 990 m resolution MODIS LST and the corresponding input parameters (area ratios and endmember indices at the 990 m resolution) was trained by a genetic algorithm and self-organizing feature map artificial neural network (GA-SOFM-ANN). Finally, the trained models were employed to estimate the 90 m resolution subpixel temperature with inputs of area ratios and endmember indices at the 90 m resolution. ASTER LST product was used for verifying the estimated subpixel temperature, and the verified results indicate that the estimated temperature distribution was basically consistent with that of ASTER LST product. A better agreement was found between temperatures derived by our proposed method (DisEMI) and the ASTER 90 m data (R² = 0.709 and RMSE = 2.702 K).     

Thermal imaging of Nisyros volcano (Aegean Sea) using ASTER data: estimation of radiative heat flux

A time series of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images (AST08) have been processed to allow radiative heat flux estimations of the Nisyros volcano. ASTER night-time images were acquired on four different dates: 7 April 2001, 13 June 2002, 26 October 2002 and 23 July 2005. The results show a steady state energy release with heat fluxes ranging from 15 to 30 W m^?2 over the craters of Kaminakia, Polyvotis and Stefanos, respectively. It is suggested that this range of values indicates a background heat flux of this volcano following the unrest of 1995–1997, and that the volcano has entered again into the quiet phase. Also, on the basis of the average spatial extent of the thermal anomaly a total radiative heat flux of 36 MW was estimated at the moment of ASTER overpass. Heat flux values for Nisyros are in good agreement with other published estimates derived from low-temperature fumarolic volcanoes (Stromboli and Vulcano, Italy) using Landsat TM data. It is also evidenced that the ASTER radiometer can be used as an important imaging tool for the monitoring of geophysical properties associated to volcanic activity, as is the volcanic heat flux.     

Comparison of atmospheric correction methods using ASTER data for the area of Crete,Greece

The purpose of atmospheric correction is to produce more accurate surface reflectance and to potentially improve the extraction of surface parameters from satellite images. To achieve this goal the influences of the atmosphere, solar illumination, sensor viewing geometry and terrain information have to be taken into account. Although a lot of information from satellite imagery can be extracted without atmospheric correction, the physically based approach offers advantages, especially when dealing with multitemporal data and/or when a comparison of data provided by different sensors is required. The use of atmospheric correction models is limited by the need to supply data related to the condition of the atmosphere at the time of imaging. Such data are not always available and the cost of their collection is considerable, hence atmospheric correction is performed with the use of standard atmospheric profiles. The use of these profiles results in a loss of accuracy. Therefore, site-dependent databases of atmospheric parameters are needed to calibrate and to adjust atmospheric correction methods for local level applications. In this article, the methodology and results of the project Adjustment of Atmospheric Correction Methods for Local Studies: Application in ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) (ATMOSAT) for the area of Crete are presented. ATMOSAT aimed at comparing several atmospheric correction methods for the area of Crete, as well as investigating the effects of atmospheric correction on land cover classification and change detection. Databases of spatio-temporal distributions of all required input parameters (atmospheric humidity, aerosols, spectral signatures, land cover and elevation) were developed and four atmospheric correction methods were applied and compared. The baseline for this comparison is the spatial distribution of surface reflectance, emitted radiance and brightness temperature as derived by ASTER Higher Level Products (HLPs). The comparison showed that a simple image based method, which was adjusted for the study area, provided satisfactory results for visible, near infrared and short-wave infrared spectral areas; therefore it can be used for local level applications. Finally, the effects of atmospheric correction on land cover classification and change detection were assessed using a time series of ASTER multispectral images acquired in 2000, 2002, 2004 and 2006. Results are in agreement with past studies, indicating that for this type of application, where a common radiometric scale is assumed among the multitemporal images, atmospheric correction should be taken into consideration in pre-processing.     

Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data

In this study we implemented a comprehensive analysis to validate the MODIS and GOES satellite active fire detection products (MOD14 and WFABBA, respectively) and characterize their major sources of omission and commission errors which have important implications for a large community of fire data users. Our analyses were primarily based on the use of 30 m resolution ASTER and ETM+ imagery as our validation data. We found that at the 50% true positive detection probability mark, WFABBA requires four times more active fire area than is necessary for MOD14 to achieve the same probability of detection, despite the 16× factor separating the nominal spatial resolutions of the two products. Approximately 75% and 95% of all fires sampled were omitted by the MOD14 and WFABBA instantaneous products, respectively; whereas an omission error of 38% was obtained for WFABBA when considering the 30-minute interval of the GOES data. Commission errors for MOD14 and WFABBA were found to be similar and highly dependent on the vegetation conditions of the areas imaged, with the larger commission errors (approximately 35%) estimated over regions of active deforestation. Nonetheless, the vast majority (> 80%) of the commission errors were indeed associated with recent burning activity where scars could be visually confirmed in the higher resolution data. Differences in thermal dynamics of vegetated and non-vegetated areas were found to produce a reduction of approximately 50% in the commission errors estimated towards the hours of maximum fire activity (i.e., early-afternoon hours) which coincided with the MODIS/Aqua overpass. Lastly, we demonstrate the potential use of temporal metrics applied to the mid-infrared bands of MODIS and GOES data to reduce the commission errors found with the validation analyses.