Remote Sens., Volume 4, Issue 11 (November 2012) – 18 articles , Pages 3265-3637

With an increasing demand of fresh water resources in arid/semi-arid parts of the world, researchers and practitioners are relying more than ever on remote sensing techniques for monitoring and evaluating crop water status and for estimating crop water use or crop actual evapotranspiration (ET_a). In this present study, infrared thermometry was used in conjunction with a few weather parameters to develop non-water-stressed and non-transpiring baselines for irrigated maize in a semi-arid region of Colorado in the western USA. A remote sensing-based Crop Water Stress Index (CWSI) was then estimated for four hourly periods each day during 5 August to 2 September 2011 (29 days). The estimated CWSI was smallest during the 10:00–11:00 a.m. and largest during the 12:00–13:00 p.m. hours. Plotting volumetric water content of the topsoil vs. CWSI revealed that there is a high correlation between the two parameters during the analyzed period. CWSI values were also used to estimate maize actual transpiration (T_a). T_a estimates were more influenced by crop biomass rather than irrigation depths alone, mainly due to the fact that the effects of deficit irrigation were largely masked by the significant precipitation during the growing season. During the study period, applying an independent remotely sensed energy balance model showed that maize ET_a was 159 mm, 30% larger than CWSI-T_a (122 mm) and 9% smaller than standard-condition maize ET (174 mm). Full article

With more than 80% of Brazilians living in cities, urbanization has had an important impact on climatic variations. São José dos Campos is located in a region experiencing rapid urbanization, which has produced a remarkable Urban Heat Island (UHI) effect. This effect influences the climate, environment and socio-economic development on a regional scale. In this study, the brightness temperatures and land cover types from Landsat TM images of São José dos Campos from 1986, 2001 and 2010 were analyzed for the spatial distribution of changes in temperature and land cover. A quantitative approach was used to explore the relationships among temperature, land cover areas and several indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-up Index (NDBI). The results showed that urban and bare areas correlated positively with high temperatures. Conversely, areas covered in vegetation and water correlated positively with low temperatures. The indices showed that correlations between the NDVI and NDWI and temperature were low (<0.5); however, a moderate correlation was found between the NDBI and temperature. Full article

This paper proposes the optimization relaxation approach based on the analogue Hopfield Neural Network (HNN) for cluster refinement of pre-classified Polarimetric Synthetic Aperture Radar (PolSAR) image data. We consider the initial classification provided by the maximum-likelihood classifier based on the complex Wishart distribution, which is then supplied to the HNN optimization approach. The goal is to improve the classification results obtained by the Wishart approach. The classification improvement is verified by computing a cluster separability coefficient and a measure of homogeneity within the clusters. During the HNN optimization process, for each iteration and for each pixel, two consistency coefficients are computed, taking into account two types of relations between the pixel under consideration and its corresponding neighbors. Based on these coefficients and on the information coming from the pixel itself, the pixel under study is re-classified. Different experiments are carried out to verify that the proposed approach outperforms other strategies, achieving the best results in terms of separability and a trade-off with the homogeneity preserving relevant structures in the image. The performance is also measured in terms of computational central processing unit (CPU) times. Full article

CO₂ sequestration of the forests in Oita Prefecture, Japan, was estimated using satellite remote sensing data. First, hybrid classification of the optical LANDSAT ETM+ data was performed using GIS to produce a detailed land cover map. CO₂ sequestration for each forest type was calculated using the sequestration rates per unit area multiplied by the forest areas obtained from the land cover map This results in 3.57 MtCO₂/yr for coniferous, 0.77 MtCO₂/yr for deciduous broadleaf, and 2.25 MtCO₂/yr for evergreen broadleaf, equivalent to a total of 6.60 MtCO₂/yr for all the forest covers in Oita. Then, two different methodologies were used to improve these estimates by considering tree ages: the Normalized Difference Vegetation Index (NDVI) and the stem volume methods. Calculation using the NDVI method shows the limitation of this method in providing detailed estimations for trees older than 15 years, because of NDVI saturation beyond this age. In the stem volume method, tree ages were deduced from stem volume values obtained by using PALSAR backscattering data. Sequestration based on tree age forest subclasses yields 2.96 MtCO₂/yr (coniferous) and 0.31 MtCO₂/yr (deciduous broadleaf forests). These results show the importance of using not only detailed forest types, but also detailed tree age information for more realistic CO₂ sequestration estimates. In so doing, overestimation of the sequestration capacity of forests could be avoided, and the information on the status and location of forest resources could be improved, thereby leading to sounder decision making in sustainable management of forest resources. Full article

We present a fixed-lag ensemble Kalman smoother for estimating emissions for a global aerosol transport model from remote sensing observations. We assimilate AERONET AOT and AE as well as MODIS Terra AOT over ocean to estimate the emissions for dust, sea salt and carbon aerosol and the precursor gas SO2. For January 2009, globally dust emission decreases by 26% (to 3,244 Tg/yr), sea salt emission increases by 190% (to 9073 Tg/yr), while carbon emission increases by 45% (to 136 Tg/yr), compared with the standard emissions. Remaining errors in global emissions are estimated at 62% (dust), 18% (sea salt) and 78% (carbons), with the large errors over land mostly due to the sparseness of AERONET observations. The new emissions are verified by comparing a forecast run against independent MODIS Aqua AOT, which shows significant improvement over both ocean and land. This paper confirms the usefulness of remote sensing observations for improving global aerosol modelling. Full article

In this study we examine the relationship between remotely sensed, in situ and modelled land surface temperature (LST) over a heterogeneous land-cover (LC) enclosed in alpine terrain. This relationship can help to understand to what extent the remotely sensed data can be used to improve model simulations of land surface parameters such as LST in mountainous areas. LST from the MODerate resolution Imaging Spectro-radiometer (MODIS), the modelled surface skin temperature by the Weather Research and Forecasting (WRF) mesoscale numerical model and the in situ measurements of surface temperature are used in the analysis. The test-site is located in a mountain valley in the Southern Alps of New Zealand. Geospatial analysis in GIS is used to relate pixels, grid-cells and points from the MODIS LST, model simulations and the in situ data, respectively. Differences between LST from MODIS, the WRF model and the in situ data are presented with respect to surface LC at different times of day. Initial results from regression analysis of the three datasets showed a goodness of fit R2 coefficient of 0:77 for the model simulations and 0:35 for the MODIS LST. These values improved significantly when time-lags were considered and the few outliers were removed, giving R2 values of 0:80 for the model and 0:73 for the MODIS LST. These results show that the WRF model correlates better with the in situ measurements over various LC types in this region compared with the MODIS LST. Longer time-series, however, are required to draw more robust conclusions about the applicability of the MODIS LST product for improving WRF simulations over alpine complex terrain. Full article

Kettle holes, small inland water bodies usually less than 1 ha in size, are subjected to pollution, drainage, and structural alteration by intensive land use practices. This study presents the analysis of spectral signatures from kettle holes based on in situ water sampling and reflectance measurements in application for chlorophyll estimation. Water samples and surface reflectance from kettle holes were collected from 6 ponds in 15 field campaigns (5 in 2007 and 10 in 2008), resulting in a total of 80 spectral datasets. We assessed the existing semi-empirical algorithms to determine chlorophyll content for different types of kettle holes using seasonal and cross-seasonal volume reflectance and derivative spectra. Based on this analysis and optical properties of water leaving reflectance from kettle holes, the following typology of the remote signal interpretation was proposed: Submerged vegetation, Phytoplankton dominated and Mixed type. Full article

Mapping the spatial distribution of plant species in savannas provides insight into the roles of competition, fire, herbivory, soils and climate in maintaining the biodiversity of these ecosystems. This study focuses on the challenges facing large-scale species mapping using a fusion of Light Detection and Ranging (LiDAR) and hyperspectral imagery. Here we build upon previous work on airborne species detection by using a two-stage support vector machine (SVM) classifier to first predict species from hyperspectral data at the pixel scale. Tree crowns are segmented from the lidar imagery such that crown-level information, such as maximum tree height, can then be combined with the pixel-level species probabilities to predict the species of each tree. An overall prediction accuracy of 76% was achieved for 15 species. We also show that bidirectional reflectance distribution (BRDF) effects caused by anisotropic scattering properties of savanna vegetation can result in flight line artifacts evident in species probability maps, yet these can be largely mitigated by applying a semi-empirical BRDF model to the hyperspectral data. We find that confronting these three challenges—reflectance anisotropy, integration of pixel- and crown-level data, and crown delineation over large areas—enables species mapping at ecosystem scales for monitoring biodiversity and ecosystem function. Full article

This study compiled a wide range of modern and historic geospatial datasets to examine ecological and geomorphic change at Diego Garcia Atoll across a 38-year period (1967–2005). This remarkable collection of spatially referenced information offered an opportunity to advance our understanding of the nature and extent of environmental change that has taken place with the construction of the military airbase at Diego Garcia. Changes assessed included movements of the lagoon rim shorelines, changes in the terrestrial vegetation on the lagoon rim and amendments to the bathymetry of the lagoon basin through dredging activities. Data compiled included detailed shoreline and vegetation maps produced as part of the H.M.S. Vidal Indian Ocean Expedition (1967), three Ikonos satellite images acquired in 2005 that collectively covered the complete Atoll area, a ground truthing field dataset collected in the northern section of the lagoon for the purpose of seafloor mapping (2005), observational evidence of shoreline erosion including photographs and descriptions of seawater inundations and bathymetric soundings from five independent surveys of the lagoon floor (1967, 1985, 1987, 1988 and 1997). Results indicated that much of the change along the lagoon rim is associated with the expansion of the inner lagoon shoreline as a result of the construction of the military airbase, with an estimated increase in land area of 3.01 km² in this portion of the atoll rim. Comparisons of 69 rim width transects measured from 1967 and 2005 indicated that shorelines are both eroding (26 transects) and accreting (43 transects). Within a total vegetated area of 24 km², there was a notable transition from Cocos Woodland to Broadleaf Woodland for a land area of 5.6 km². From the hydrographic surveys, it was estimated that approximately 0.55 km³ of carbonate sediment material has been removed from the northwest quadrant of the lagoon, particularly in the vicinity of the Main Passage. As no previous record of benthic character exists, a complete benthic habitat map of the atoll was derived through classification of the three IKONOS satellite images. Management implications arising from this overall appraisal of geomorphic and ecological change at Diego Garcia included the need for ongoing monitoring of shoreline change at a representative set of sites around the atoll rim, monitoring of the water flow regime through the northern channels between the open ocean and the lagoon basin and an ongoing mapping campaign to record periodic changes in the character of the benthic surface ecology. Full article

An understanding of the distribution and extent of marine habitats is essential for the implementation of ecosystem-based management strategies. Historically this had been difficult in marine environments until the advancement of acoustic sensors. This study demonstrates the applicability of supervised learning techniques for benthic habitat characterization using angular backscatter response data. With the advancement of multibeam echo-sounder (MBES) technology, full coverage datasets of physical structure over vast regions of the seafloor are now achievable. Supervised learning methods typically applied to terrestrial remote sensing provide a cost-effective approach for habitat characterization in marine systems. However the comparison of the relative performance of different classifiers using acoustic data is limited. Characterization of acoustic backscatter data from MBES using four different supervised learning methods to generate benthic habitat maps is presented. Maximum Likelihood Classifier (MLC), Quick, Unbiased, Efficient Statistical Tree (QUEST), Random Forest (RF) and Support Vector Machine (SVM) were evaluated to classify angular backscatter response into habitat classes using training data acquired from underwater video observations. Results for biota classifications indicated that SVM and RF produced the highest accuracies, followed by QUEST and MLC, respectively. The most important backscatter data were from the moderate incidence angles between 30° and 50°. This study presents initial results for understanding how acoustic backscatter from MBES can be optimized for the characterization of marine benthic biological habitats. Full article

Tidal flats provide habitat for biodiversity, protection from storm surges and sea level rise, and a range of other ecosystem services. However, no simple method exists for mapping tidal flats over large (>1,000 km) extents, and consequently their global status and distribution remain poorly understood. Existing mapping methods are restricted to small areas with known tidal regimes because tidal flats are only fully exposed for a brief period around low tide. Here we present a method for mapping tidal flats over very large areas and demonstrate its utility by mapping the tidal flats of China, the Democratic People’s Republic of Korea and the Republic of Korea. We (i) generated tide height predictions at the acquisition time of all Landsat Archive images of our study area using a validated regional tide model, (ii) selected suitable images acquired in the upper and lower 10% of the tidal range, (iii) converted high and low tide images to a land and water class image derived from the Normalized Differenced Water Index (NDWI) and, (iv) subtracted the high tide classified image from the low tide classified image, resulting in delineation of the tidal flat. Using this method, we mapped the tidal flats for 86.8% of the study area coastline (13,800 km). A confusion matrix for error assessment indicated an accuracy of >85% for the resulting tidal flat map. Our method enables coastal morphology to be mapped and monitored at continental scales, providing critical data to inform coastal adaptation measures for sea level rise, for monitoring coastal habitat loss and for developing ecosystem-based coastal conservation measures. Full article

This article presents an environmental remote sensing application using a UAV that is specifically aimed at reducing the data gap between field scale and satellite scale in soil erosion monitoring in Morocco. A fixed-wing aircraft type Sirius I (MAVinci, Germany) equipped with a digital system camera (Panasonic) is employed. UAV surveys are conducted over different study sites with varying extents and flying heights in order to provide both very high resolution site-specific data and lower-resolution overviews, thus fully exploiting the large potential of the chosen UAV for multi-scale mapping purposes. Depending on the scale and area coverage, two different approaches for georeferencing are used, based on high-precision GCPs or the UAV’s log file with exterior orientation values respectively. The photogrammetric image processing enables the creation of Digital Terrain Models (DTMs) and ortho-image mosaics with very high resolution on a sub-decimetre level. The created data products were used for quantifying gully and badland erosion in 2D and 3D as well as for the analysis of the surrounding areas and landscape development for larger extents. Full article

Detailed information from global remote sensing has greatly advanced ourunderstanding of Earth as a system in general and of agricultural processes in particular.Vegetation monitoring with global remote sensing systems over long time periods iscritical to gain a better understanding of processes related to agricultural change over longtime periods. This specifically relates to sub-humid to semi-arid ecosystems, whereagricultural change in grazing lands can only be detected based on long time series. Byintegrating data from different sensors it is theoretically possible to construct NDVI timeseries back to the early 1980s. However, such integration is hampered by uncertainties inthe comparability between different sensor products. To be able to rely on vegetationtrends derived from integrated time series it is therefore crucial to investigate whether vegetation trends derived from NDVI and phenological parameters are consistent acrossproducts. In this paper we analyzed several indicators of vegetation change for a range ofagricultural systems in Inner Mongolia, China, and compared the results across differentsatellite archives. Specifically, we compared two of the prime NDVI archives—AVHRR Global Inventory Modeling and Mapping Studies (GIMMS) and SPOT Vegetation (VGT)NDVI. Because a true accuracy assessment of long time series is not possible, we furthercompared SPOT VGT NDVI with NDVI from MODIS Terra as a benchmark. We foundhigh similarities in interannual trends, and also in trends of the seasonal amplitude andintegral between SPOT VGT and MODIS Terra (r > 0.9). However, we observedconsiderable disagreements in NDVI-derived trends between AVHRR GIMMS and SPOTVGT. We detected similar discrepancies for trends based on phenological parameters, suchas amplitude and integral of NDVI curves corresponding to seasonal vegetation cycles.Inconsistencies were partially related to land cover and vegetation density. Differentpre-processing schemes and the coarser spatial resolution of AVHRR GIMMS introducedfurther uncertainties. Our results corroborate findings from other studies that vegetationtrends derived from AVHRR GIMMS data not always reflect true vegetation changes. Amore thorough understanding of the factors introducing uncertainties in AVHRR GIMMStime series is needed, and we caution against using AVHRR GIMMS data in regionalstudies without applying regional sensitivity analyses. Full article

We neglected to state that the radar data from Tokushima and Anan is owned by the Ministry of Land, Infrastructure, Transport and Tourism, Shikoku Regional Development Bureau, Komatsushima port and airport office, Japan. Lipa et al. [1] describe results on tsunami detection using data measured by two radars located at Tokushima and Anan on the Kii channel. This data is owned by the Ministry of Land, Infrastructure, Transport and Tourism, Shikoku Regional Development Bureau, Komatsushima port and airport office, Japan. Locations of the radars are shown in Figure 4(a,c) [1]. Results of the data analysis are given in Section 3.1.2, plotted in Figure 6 and listed in Table 1 [1]. [...] Full article

Direct field observations of fine-scaled biological processes and interactions of the benthic community of corals and associated reef organisms (e.g., feeding, reproduction, mutualistic or agonistic behavior, behavioral responses to changing abiotic factors) usually involve a disturbing intervention. Modern digital camcorders (without inflexible land-or ship-based cable connection) such as the GoPro camera enable undisturbed and unmanned, stationary close-up observations. Such observations, however, are also very time-limited (~3 h) and full 24 h-recordings throughout day and night, including nocturnal observations without artificial daylight illumination, are not possible. Herein we introduce the application of modern standard video surveillance technology with the main objective of providing a tool for monitoring coral reef or other sessile and mobile organisms for periods of 24 h and longer. This system includes nocturnal close-up observations with miniature infrared (IR)-sensitive cameras and separate high-power IR-LEDs. Integrating this easy-to-set up and portable remote-sensing equipment into coral reef research is expected to significantly advance our understanding of fine-scaled biotic processes on coral reefs. Rare events and long-lasting processes can easily be recorded, in situ-experiments can be monitored live on land, and nocturnal IR-observations reveal undisturbed behavior. The options and equipment choices in IR-sensitive surveillance technology are numerous and subject to a steadily increasing technical supply and quality at decreasing prices. Accompanied by short video examples, this report introduces a radio-transmission system for simultaneous recordings and real-time monitoring of multiple cameras with synchronized timestamps, and a surface-independent underwater-recording system. Full article

We have investigated for forest plantations in Chile the stand-level retrieval of canopy height (CH) and growing stock volume (GSV) using Airborne Laser Scanner (ALS), ALOS PALSAR and Landsat. In a two-stage up-scaling approach, ensemble regression tree models (randomForest) were used to relate a suite of ALS canopy structure indices to stand-level in situ measurements of CH and GSV for 319 stands. The retrieval of CH and GSV with ALS yielded high accuracies with R²s of 0.93 and 0.81, respectively. A second set of randomForest models was developed using multi-temporal ALOS PALSAR intensities and repeat-pass coherences in two polarizations as well as Landsat data as predictor and stand-level ALS based estimates of CH and GSV as response variables. At three test sites, the retrieval of CH and GSV with PALSAR/Landsat reached promising accuracies with R²s in the range of 0.7 to 0.85. We show that the combined use of multi-temporal PALSAR intensity, coherence and Landsat yields higher retrieval accuracies than the retrieval with any of the datasets alone. Potential limitations for the large-area application of the fusion approach included (1) the low sensitivity of ALS first/last return data to forest horizontal structure, affecting the retrieval of GSV in less managed types of forest, and (2) the dense ALS sampling required to achieve high retrieval accuracies at larger scale. Full article

Thermal infrared (TIR) imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform and often the TIR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes that are at significantly finer spatial scales. Consequently, thermal sharpening techniques have been developed to sharpen TIR imagery to shortwave band pixel resolutions, which are often fine enough for field-scale applications. A classic thermal sharpening technique, TsHARP, uses a relationship between land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) developed empirically at the TIR pixel resolution and applied at the NDVI pixel resolution. However, recent studies show that unique relationships between temperature and NDVI may only exist for a limited class of landscapes, with mostly green vegetation and homogeneous air and soil conditions. To extend application of thermal sharpening to more complex conditions, a new data mining sharpener (DMS) technique is developed. The DMS approach builds regression trees between TIR band brightness temperatures and shortwave spectral reflectances based on intrinsic sample characteristics. A comparison of sharpening techniques applied over a rainfed agricultural area in central Iowa, an irrigated agricultural region in the Texas High Plains, and a heterogeneous naturally vegetated landscape in Alaska indicates that the DMS outperformed TsHARP in all cases. The artificial box-like patterns in LST generated by the TsHARP approach are greatly reduced using the DMS scheme, especially for areas containing irrigated crops, water bodies, thin clouds or terrain. While the DMS technique can provide fine resolution TIR imagery, there are limits to the sharpening ratios that can be reasonably implemented. Consequently, sharpening techniques cannot replace actual thermal band imagery at fine resolutions or missions that provide high quality thermal band imagery at high temporal and spatial resolution critical for many agricultural, land use and water resource management applications. Full article

In this study we tested the feasibility of the thermal infrared (TIR) wavelength region (within the atmospheric window between 8 and 11.5 μm) together with the traditional solar reflective wavelengths for quantifying soil properties for coarse-textured soils from the Australian wheat belt region. These soils have very narrow ranges of texture and organic carbon contents. Soil surface spectral signatures were acquired in the laboratory, using a directional emissivity spectrometer (μFTIR) in the TIR, as well as a bidirectional reflectance spectrometer (ASD FieldSpec) for the solar reflective wavelengths (0.4–2.5 μm). Soil properties were predicted using multivariate analysis techniques (partial least square regression). The spectra were resampled to operational imaging spectroscopy sensor characteristics (HyMAP and TASI-600). To assess the relevance of specific wavelength regions in the prediction, the drivers of the PLS models were interpreted with respect to the spectral characteristics of the soils’ chemical and physical composition. The study revealed the potential of the TIR (for clay: R² = 0.93, RMSEP = 0.66% and for sand: R² = 0.93, RMSEP = 0.82%) and its combination with the solar reflective region (for organic carbon: R² = 0.95, RMSEP = 0.04%) for retrieving soil properties in typical soils of semi-arid regions. The models’ drivers confirmed the opto-physical base of most of the soils’ constituents (clay minerals, silicates, iron oxides), and emphasizes the TIR’s advantage for soils with compositions dominated by quartz and kaolinite. Full article