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Remote Sens., Volume 7, Issue 4 (April 2015) , Pages 3426-4972

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Open AccessEditorial
Innovative Technologies for Terrestrial Remote Sensing
Remote Sens. 2015, 7(4), 4968-4972; https://doi.org/10.3390/rs70404968
Received: 22 April 2015 / Accepted: 22 April 2015 / Published: 22 April 2015
Cited by 1 | Viewed by 2100 | PDF Full-text (1442 KB) | HTML Full-text | XML Full-text
Abstract
Characterizing and monitoring terrestrial, or land, surface features, such as forests, deserts, and cities, are fundamental and continuing goals of Earth Observation (EO). EO imagery and related technologies are essential for increasing our scientific understanding of environmental processes, such as carbon capture and [...] Read more.
Characterizing and monitoring terrestrial, or land, surface features, such as forests, deserts, and cities, are fundamental and continuing goals of Earth Observation (EO). EO imagery and related technologies are essential for increasing our scientific understanding of environmental processes, such as carbon capture and albedo change, and to manage and safeguard environmental resources, such as tropical forests, particularly over large areas or the entire globe. This measurement or observation of some property of the land surface is central to a wide range of scientific investigations and industrial operations, involving individuals and organizations from many different backgrounds and disciplines. However, the process of observing the land provides a unifying theme for these investigations, and in practice there is much consistency in the instruments used for observation and the techniques used to map and model the environmental phenomena of interest. There is therefore great potential benefit in exchanging technological knowledge and experience among the many and diverse members of the terrestrial EO community. [...] Full article
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Open AccessArticle
Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image
Remote Sens. 2015, 7(4), 4948-4967; https://doi.org/10.3390/rs70404948
Received: 23 January 2015 / Accepted: 20 April 2015 / Published: 22 April 2015
Cited by 6 | Viewed by 2763 | PDF Full-text (2116 KB) | HTML Full-text | XML Full-text
Abstract
Road damage detection and assessment from high-resolution remote sensing image is critical for natural disaster investigation and disaster relief. In a disaster context, the pairing of pre-disaster and post-disaster road data for change detection and assessment is difficult to achieve due to the [...] Read more.
Road damage detection and assessment from high-resolution remote sensing image is critical for natural disaster investigation and disaster relief. In a disaster context, the pairing of pre-disaster and post-disaster road data for change detection and assessment is difficult to achieve due to the mismatch of different data sources, especially for rural areas where the pre-disaster data (i.e., remote sensing imagery or vector map) are hard to obtain. In this study, a knowledge-based method for road damage detection and assessment solely from post-disaster high-resolution remote sensing image is proposed. The road centerline is firstly extracted based on the preset road seed points. Then, features such as road brightness, standard deviation, rectangularity, and aspect ratio are selected to form a knowledge model. Finally, under the guidance of the road centerline, the post-disaster roads are extracted and the damaged roads are detected by applying the knowledge model. In order to quantitatively assess the damage degree, damage assessment indicators with their corresponding standard of damage grade are also proposed. The newly developed method is evaluated using a WorldView-1 image over Wenchuan, China acquired three days after the earthquake on 15 May 2008. The results show that the producer’s accuracy (PA) and user’s accuracy (UA) reached about 90% and 85%, respectively, indicating that the proposed method is effective for road damage detection and assessment. This approach also significantly reduces the need for pre-disaster remote sensing data. Full article
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Open AccessArticle
A Test of the New VIIRS Lights Data Set: Population and Economic Output in Africa
Remote Sens. 2015, 7(4), 4937-4947; https://doi.org/10.3390/rs70404937
Received: 30 September 2014 / Accepted: 8 April 2015 / Published: 22 April 2015
Cited by 17 | Viewed by 2789 | PDF Full-text (716 KB) | HTML Full-text | XML Full-text
Abstract
The present study analyses the new Visible Infrared Imaging Radiometer Suite (VIIRS) lights data to determine whether it can provide more accurate proxies for socioeconomic data in areas with poor quality data than proxies based on stable lights. Our analysis indicates that VIIRS [...] Read more.
The present study analyses the new Visible Infrared Imaging Radiometer Suite (VIIRS) lights data to determine whether it can provide more accurate proxies for socioeconomic data in areas with poor quality data than proxies based on stable lights. Our analysis indicates that VIIRS lights are a promising supplementary source for standard measures on population and economic output at a small scale, especially for low population and economic density areas in Africa. The current analysis also suggests that in comparison to stable lights generated by the DMSP-OLS system, data generated by the VIIRS system provide more information to estimate population than output index. However, further analysis and formal statistical models are needed to evaluate the usefulness of VIIRS lights versus other lights products. With more advanced methods, there is also a potential to generate a synthetic index by combining different lights products to produce a better proxy measure for other indexes. Full article
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
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Open AccessArticle
Users’ Assessment of Orthoimage Photometric Quality for Visual Interpretation of Agricultural Fields
Remote Sens. 2015, 7(4), 4919-4936; https://doi.org/10.3390/rs70404919
Received: 19 December 2014 / Revised: 12 March 2015 / Accepted: 10 April 2015 / Published: 21 April 2015
Cited by 3 | Viewed by 2172 | PDF Full-text (4028 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Land cover identification and area quantification are key aspects of implementing the European Common Agriculture Policy. Legitimacy of support provided to farmers is monitored using the Land Parcel Identification System (LPIS), with land cover identification performed by visual image interpretation. While the geometric [...] Read more.
Land cover identification and area quantification are key aspects of implementing the European Common Agriculture Policy. Legitimacy of support provided to farmers is monitored using the Land Parcel Identification System (LPIS), with land cover identification performed by visual image interpretation. While the geometric orthoimage quality required for correct interpretation is well understood, little is known about the photometric quality needed for LPIS applications. This paper analyzes the orthoimage quality characteristics chosen by authors as being most suitable for visual identification of agricultural fields. We designed a survey to assess users’ preferred brightness and contrast ranges for orthoimages used for LPIS purposes. Survey questions also tested the influence of a background color on the preferred orthoimage brightness and contrast, the preferred orthoimage format and color composite, assessments of orthoimages with shadowed areas, appreciation of image enhancements and, finally, consistency of individuals’ preferred brightness and contrast settings across multiple sample images. We find that image appreciation is stable at the individual level, but preferences vary across respondents. We therefore recommend that LPIS operators be enabled to personalize photometric settings, such as brightness and contrast values, and to choose the displayed band combination from at least four spectral bands. Full article
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Open AccessArticle
Comparative Assessment of Satellite-Retrieved Surface Net Radiation: An Examination on CERES and SRB Datasets in China
Remote Sens. 2015, 7(4), 4899-4918; https://doi.org/10.3390/rs70404899
Received: 27 January 2015 / Revised: 11 April 2015 / Accepted: 14 April 2015 / Published: 21 April 2015
Cited by 18 | Viewed by 2288 | PDF Full-text (5772 KB) | HTML Full-text | XML Full-text
Abstract
Surface net radiation plays an important role in land–atmosphere interactions. The net radiation can be retrieved from satellite radiative products, yet its accuracy needs comprehensive assessment. This study evaluates monthly surface net radiation generated from the Clouds and the Earth’s Radiant Energy System [...] Read more.
Surface net radiation plays an important role in land–atmosphere interactions. The net radiation can be retrieved from satellite radiative products, yet its accuracy needs comprehensive assessment. This study evaluates monthly surface net radiation generated from the Clouds and the Earth’s Radiant Energy System (CERES) and the Surface Radiation Budget project (SRB) products, respectively, with quality-controlled radiation data from 50 meteorological stations in China for the period from March 2000 to December 2007. Our results show that surface net radiation is generally overestimated for CERES (SRB), with a bias of 26.52 W/m2 (18.57 W/m2) and a root mean square error of 34.58 W/m2 (29.49 W/m2). Spatially, the satellite-retrieved monthly mean of surface net radiation has relatively small errors for both CERES and SRB at inland sites in south China. Substantial errors are found at northeastern sites for two datasets, in addition to coastal sites for CERES. Temporally, multi-year averaged monthly mean errors are large at sites in western China in spring and summer, and in northeastern China in spring and winter. The annual mean error fluctuates for SRB, but decreases for CERES between 2000 and 2007. For CERES, 56% of net radiation errors come from net shortwave (NSW) radiation and 44% from net longwave (NLW) radiation. The errors are attributable to environmental parameters including surface albedo, surface water vapor pressure, land surface temperature, normalized difference vegetation index (NDVI) of land surface proxy, and visibility for CERES. For SRB, 65% of the errors come from NSW and 35% from NLW radiation. The major influencing factors in a descending order are surface water vapor pressure, surface albedo, land surface temperature, NDVI, and visibility. Our findings offer an insight into error patterns in satellite-retrieved surface net radiation and should be valuable to improving retrieval accuracy of surface net radiation. Moreover, our study on radiation data of China provides a case example for worldwide validation. Full article
(This article belongs to the Special Issue Remote Sensing of Solar Surface Radiation)
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Open AccessArticle
Assessing the Impacts of Urbanization-Associated Land Use/Cover Change on Land Surface Temperature and Surface Moisture: A Case Study in the Midwestern United States
Remote Sens. 2015, 7(4), 4880-4898; https://doi.org/10.3390/rs70404880
Received: 7 February 2015 / Revised: 9 April 2015 / Accepted: 13 April 2015 / Published: 20 April 2015
Cited by 21 | Viewed by 3593 | PDF Full-text (11535 KB) | HTML Full-text | XML Full-text
Abstract
Urbanization-associated land use and land cover (LULC) changes lead to modifications of surface microclimatic and hydrological conditions, including the formation of urban heat islands and changes in surface runoff pattern. The goal of the paper is to investigate the changes of biophysical variables [...] Read more.
Urbanization-associated land use and land cover (LULC) changes lead to modifications of surface microclimatic and hydrological conditions, including the formation of urban heat islands and changes in surface runoff pattern. The goal of the paper is to investigate the changes of biophysical variables due to urbanization induced LULC changes in Indianapolis, USA, from 2001 to 2006. The biophysical parameters analyzed included Land Surface Temperature (LST), fractional vegetation cover, Normalized Difference Water Index (NDWI), impervious fractions evaporative fraction, and soil moisture. Land cover classification and changes and impervious fractions were obtained from the National Land Cover Database of 2001 and 2006. The Temperature-Vegetation Index (TVX) space was created to analyze how these satellite-derived biophysical parameters change during urbanization. The results showed that the general trend of pixel migration in response to the LULC changes was from the areas of low temperature, dense vegetation cover, and high surface moisture conditions to the areas of high temperature, sparse vegetation cover, and low surface moisture condition in the TVX space. Analyses of the T-soil moisture and T-NDWI spaces revealed similar changed patterns. The rate of change in LST, vegetation cover, and moisture varied with LULC type and percent imperviousness. Compared to conversion from cultivated to residential land, the change from forest to commercial land altered LST and moisture more intensively. Compared to the area changed from cultivated to residential, the area changed from forest to commercial altered 48% more in fractional vegetation cover, 71% more in LST, and 15% more in soil moisture Soil moisture and NDWI were both tested as measures of surface moisture in the urban areas. NDWI was proven to be a useful measure of vegetation liquid water and was more sensitive to the land cover changes comparing to soil moisture. From a change forest to commercial land, the mean soil moisture changed 17%, while the mean NDWI changed 90%. Full article
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Open AccessArticle
Hydrodynamic and Inundation Modeling of China’s Largest Freshwater Lake Aided by Remote Sensing Data
Remote Sens. 2015, 7(4), 4858-4879; https://doi.org/10.3390/rs70404858
Received: 19 December 2014 / Revised: 14 April 2015 / Accepted: 15 April 2015 / Published: 20 April 2015
Cited by 12 | Viewed by 2754 | PDF Full-text (46047 KB) | HTML Full-text | XML Full-text
Abstract
China’s largest freshwater lake, Poyang Lake, is characterized by rapid changes in its inundation area and hydrodynamics, so in this study, a hydrodynamic model of Poyang Lake was established to simulate these long-term changes. Inundation information was extracted from Moderate Resolution Imaging Spectroradiometer [...] Read more.
China’s largest freshwater lake, Poyang Lake, is characterized by rapid changes in its inundation area and hydrodynamics, so in this study, a hydrodynamic model of Poyang Lake was established to simulate these long-term changes. Inundation information was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data and used to calibrate the wetting and drying parameter by assessing the accuracy of the simulated inundation area and its boundary. The bottom friction parameter was calibrated using current velocity measurements from Acoustic Doppler Current Profilers (ADCP). The results show the model is capable of predicting the inundation area dynamic through cross-validation with remotely sensed inundation data, and can reproduce the seasonal dynamics of the water level, and water discharge through a comparison with hydrological data. Based on the model results, the characteristics of the current velocities of the lake in the wet season and the dry season of the lake were explored, and the potential effect of the current dynamic on water quality patterns was discussed. The model is a promising basic tool for prediction and management of the water resource and water quality of Poyang Lake. Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
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Open AccessArticle
Comparing the Dry Season In-Situ Leaf Area Index (LAI) Derived from High-Resolution RapidEye Imagery with MODIS LAI in a Namibian Savanna
Remote Sens. 2015, 7(4), 4834-4857; https://doi.org/10.3390/rs70404834
Received: 28 January 2015 / Revised: 15 April 2015 / Accepted: 15 April 2015 / Published: 20 April 2015
Cited by 8 | Viewed by 2597 | PDF Full-text (1624 KB) | HTML Full-text | XML Full-text
Abstract
The Leaf Area Index (LAI) is one of the most frequently applied measures to characterize vegetation and its dynamics and functions with remote sensing. Satellite missions, such as NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) operationally produce global datasets of LAI. Due to their [...] Read more.
The Leaf Area Index (LAI) is one of the most frequently applied measures to characterize vegetation and its dynamics and functions with remote sensing. Satellite missions, such as NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) operationally produce global datasets of LAI. Due to their role as an input to large-scale modeling activities, evaluation and verification of such datasets are of high importance. In this context, savannas appear to be underrepresented with regards to their heterogeneous appearance (e.g., tree/grass-ratio, seasonality). Here, we aim to examine the LAI in a heterogeneous savanna ecosystem located in Namibia’s Owamboland during the dry season. Ground measurements of LAI are used to derive a high-resolution LAI model with RapidEye satellite data. This model is related to the corresponding MODIS LAI/FPAR (Fraction of Absorbed Photosynthetically Active Radiation) scene (MOD15A2) in order to evaluate its performance at the intended annual minimum during the dry season. Based on a field survey we first assessed vegetation patterns from species composition and elevation for 109 sites. Secondly, we measured in situ LAI to quantitatively estimate the available vegetation (mean = 0.28). Green LAI samples were then empirically modeled (LAImodel) with high resolution RapidEye imagery derived Difference Vegetation Index (DVI) using a linear regression (R2 = 0.71). As indicated by several measures of model performance, the comparison with MOD15A2 revealed moderate consistency mostly due to overestimation by the aggregated LAImodel. Model constraints aside, this study may point to important issues for MOD15A2 in savannas concerning the underlying MODIS Land Cover product (MCD12Q1) and a potential adjustment by means of the MODIS Burned Area product (MCD45A1). Full article
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Open AccessArticle
Analysis of the Urban Heat Island Effect in Shijiazhuang, China Using Satellite and Airborne Data
Remote Sens. 2015, 7(4), 4804-4833; https://doi.org/10.3390/rs70404804
Received: 5 November 2014 / Revised: 7 April 2015 / Accepted: 8 April 2015 / Published: 20 April 2015
Cited by 27 | Viewed by 3278 | PDF Full-text (4348 KB) | HTML Full-text | XML Full-text
Abstract
The urban heat island (UHI) effect resulting from rapid urbanization generally has a negative impact on urban residents. Shijiazhuang, the capital of Hebei Province in China, was selected to assess surface thermal patterns and its correlation with Land Cover Types (LCTs). This study [...] Read more.
The urban heat island (UHI) effect resulting from rapid urbanization generally has a negative impact on urban residents. Shijiazhuang, the capital of Hebei Province in China, was selected to assess surface thermal patterns and its correlation with Land Cover Types (LCTs). This study was conducted using Landsat TM images on the mesoscale level and airborne hyperspectral thermal images on the microscale level. Land surface temperature (LST) was retrieved from four scenes of Landsat TM data in the summer days to analyze the thermal spatial patterns and intensity of surface UHI (SUHI). Surface thermal characteristics were further examined by relating LST to percentage of imperious surface area (ISA%) and four remote sensing indices (RSIs), the Normalized Difference Vegetation Index (NDVI), Universal Pattern Decomposition method (VIUPD), Normalized Difference Built-up Index (NDBI) and Biophysical Composition Index (BCI). On the other hand, fives scenes of airborne TASI (Thermal Airborne Spectrographic Imager sensor) images were utilized to describe more detailed urban thermal characteristics of the downtown of Shijiazhuang city. Our results show that an obvious surface heat island effect existed in the study area during summer days, with a SUHI intensity of 2–4 °C. The analyses reveal that ISA% can provide an additional metric for the study of SUHI, yet its association with LST is not straightforward and this should a focus in future work. It was also found that two physically based indices, VIUPD and BCI, have the potential to account for the variation in urban LST. The results concerning on TASI indicate that diversity of impervious surfaces (rooftops, concrete, and mixed asphalt) contribute most to the SUHI, among all of the land cover features. Moreover, the effect of impervious surfaces on LST is complicated, and the composition and arrangement of land cover features may play an important role in determining the magnitude and intensity of SUHI. Overall, the analysis of urban thermal signatures at two spatial scales complement each other and the use of airborne imagery data with higher spatial resolution is helpful in revealing more details for understanding urban thermal environments. Full article
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Open AccessArticle
Polarimetric Calibration of CASMSAR P-Band Data Affected by Terrain Slopes Using a Dual-Band Data Fusion Technique
Remote Sens. 2015, 7(4), 4784-4803; https://doi.org/10.3390/rs70404784
Received: 22 December 2014 / Revised: 13 April 2015 / Accepted: 14 April 2015 / Published: 20 April 2015
Cited by 1 | Viewed by 2096 | PDF Full-text (37001 KB) | HTML Full-text | XML Full-text
Abstract
For airborne synthetic aperture radar (SAR) polarimetric calibration (PolCAL) based on distributed targets, it is important to ensure the removal of both the polarimetric distortion and terrain slope effect. This paper proposes a new technique for PolCAL in mountainous areas, without the use [...] Read more.
For airborne synthetic aperture radar (SAR) polarimetric calibration (PolCAL) based on distributed targets, it is important to ensure the removal of both the polarimetric distortion and terrain slope effect. This paper proposes a new technique for PolCAL in mountainous areas, without the use of corner reflectors (CRs). The technique based on dual-band data fusion consists of two steps. First, the polarization orientation angle shift (POAS), as a priori asymmetry information, is derived from X-band interferometry and applied to P-band fully-polarimetric data. Second, the crosstalk and cross-polarization (cross-pol) channel imbalance are iteratively determined using the POAS after dual-band data fusion. The performance and feasibility of the technique was evaluated by CRs. It was demonstrated that the proposed technique is capable of deriving the distortion parameters and performs better than the methods presented in Quegan and Ainsworth et al. The signal-to-noise ratio (SNR) and pedestal height have been investigated in polarimetric signatures. The proposed technique is useful for PolCAL in mountainous areas and for monitoring systems without CRs in long-term operation. Full article
(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)
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Open AccessArticle
Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets
Remote Sens. 2015, 7(4), 4753-4783; https://doi.org/10.3390/rs70404753
Received: 12 March 2015 / Revised: 14 April 2015 / Accepted: 15 April 2015 / Published: 17 April 2015
Cited by 38 | Viewed by 4146 | PDF Full-text (2317 KB) | HTML Full-text | XML Full-text
Abstract
Providing accurate maps of mangroves, where the spatial scales of the mapped features correspond to the ecological structures and processes, as opposed to pixel sizes and mapping approaches, is a major challenge for remote sensing. This study developed and evaluated an object-based approach [...] Read more.
Providing accurate maps of mangroves, where the spatial scales of the mapped features correspond to the ecological structures and processes, as opposed to pixel sizes and mapping approaches, is a major challenge for remote sensing. This study developed and evaluated an object-based approach to understand what types of mangrove information can be mapped using different image datasets (Landsat TM, ALOS AVNIR-2, WorldView-2, and LiDAR). We compared and contrasted the ability of these images to map five levels of mangrove features, including vegetation boundary, mangrove stands, mangrove zonations, individual tree crowns, and species communities. We used the Moreton Bay site in Australia as the primary site to develop the classification rule sets and Karimunjawa Island in Indonesia to test the applicability of the rule sets. The results demonstrated the effectiveness of a conceptual hierarchical model for mapping specific mangrove features at discrete spatial scales. However, the rule sets developed in this study require modification to map similar mangrove features at different locations or when using image data acquired by different sensors. Across the hierarchical levels, smaller object sizes (i.e., tree crowns) required more complex classification rule sets. Incorporation of contextual information (e.g., distance and elevation) increased the overall mapping accuracy at the mangrove stand level (from 85% to 94%) and mangrove zonation level (from 53% to 59%). We found that higher image spatial resolution, larger object size, and fewer land-cover classes result in higher mapping accuracies. This study highlights the potential of selected images and mapping techniques to map mangrove features, and provides guidance for how to do this effectively through multi-scale mangrove composition mapping. Full article
(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)
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Open AccessArticle
A Quantitative Inspection on Spatio-Temporal Variation of Remote Sensing-Based Estimates of Land Surface Evapotranspiration in South Asia
Remote Sens. 2015, 7(4), 4726-4752; https://doi.org/10.3390/rs70404726
Received: 21 January 2015 / Revised: 25 March 2015 / Accepted: 13 April 2015 / Published: 17 April 2015
Cited by 5 | Viewed by 2974 | PDF Full-text (30530 KB) | HTML Full-text | XML Full-text
Abstract
Evapotranspiration (ET) plays a key role in water resource management. It is important to understand the ET spatio-temporal pattern of South Asia for understanding and anticipating serious water resource shortages. In this study, daily ET in 2008 was estimated over South Asia by [...] Read more.
Evapotranspiration (ET) plays a key role in water resource management. It is important to understand the ET spatio-temporal pattern of South Asia for understanding and anticipating serious water resource shortages. In this study, daily ET in 2008 was estimated over South Asia by using MODerate Resolution Imaging Spectroradiometer (MODIS) products combined with field observations and Global Land Data Assimilation System (GLDAS) product through Surface Energy Balance System (SEBS) model. Monthly ET data were calculated based on daily ET and evaluated by the GLDAS ET data. Good agreements were found between two datasets for winter months (October to February) with R2 from 0.5 to 0.7. Spatio-temporal analysis of ET was conducted. Ten specific sites with different land cover types at typical climate regions were selected to analyze the ET temporal change pattern, and the result indicated that the semi-arid or arid areas in the northwest had the lowest average daily ET (around 0.3 mm) with a big fluctuation in the monsoon season, while the sites in the Indo-Gangetic Plain and in southern India has bigger daily ET (more than 3 mm) due to a large water supplement. It is suggested that the monsoon climate has a large impact on ET spatio-temporal variation in the whole region. Full article
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
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Open AccessArticle
Vertical Height Errors in Digital Terrain Models Derived from Airborne Laser Scanner Data in a Boreal-Alpine Ecotone in Norway
Remote Sens. 2015, 7(4), 4702-4725; https://doi.org/10.3390/rs70404702
Received: 28 January 2015 / Revised: 6 March 2015 / Accepted: 8 April 2015 / Published: 17 April 2015
Cited by 8 | Viewed by 2235 | PDF Full-text (11355 KB) | HTML Full-text | XML Full-text
Abstract
It has been suggested that airborne laser scanning (ALS) could be used for operational monitoring of vegetation changes in the alpine tree line caused by climate change. Because the vegetation is low in such tree-less areas close to the alpine zone, the accuracy [...] Read more.
It has been suggested that airborne laser scanning (ALS) could be used for operational monitoring of vegetation changes in the alpine tree line caused by climate change. Because the vegetation is low in such tree-less areas close to the alpine zone, the accuracy of the digital terrain model (DTM) becomes crucial for early detection of, e.g., pioneer trees representing an ongoing tree migration given that the height of the vegetation may be on the same order of magnitude as the DTM uncertainty. The goal of this study was to assess and exemplify the vertical height errors of DTMs derived from ALS data under varying flying altitudes and pulse repetition frequencies (PRF). Important effects in the analysis were local terrain form, terrain surface, ground vegetation height, and terrain slope, because they may be correlated with recruitment patterns of pioneer trees. Based on 426 ground control points collected in a boreal-alpine ecotone, a standard deviation of 0.07–0.08 m was found for the lowest flying altitudes and lowest PRFs. For the highest PRF the standard deviation was 0.13 m. There were statistically significant mean errors for the different terrain forms and ground vegetation heights (−0.11 to 0.13 m). Full article
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Open AccessArticle
Building Deformation Assessment by Means of Persistent Scatterer Interferometry Analysis on a Landslide-Affected Area: The Volterra (Italy) Case Study
Remote Sens. 2015, 7(4), 4678-4701; https://doi.org/10.3390/rs70404678
Received: 19 January 2015 / Revised: 27 March 2015 / Accepted: 8 April 2015 / Published: 17 April 2015
Cited by 28 | Viewed by 2556 | PDF Full-text (3067 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, space-borne InSAR (interferometric synthetic aperture radar) techniques have shown their capabilities to provide precise measurements of Earth surface displacements for monitoring natural processes. Landslides threaten human lives and structures, especially in urbanized areas, where the density of elements at risk [...] Read more.
In recent years, space-borne InSAR (interferometric synthetic aperture radar) techniques have shown their capabilities to provide precise measurements of Earth surface displacements for monitoring natural processes. Landslides threaten human lives and structures, especially in urbanized areas, where the density of elements at risk sensitive to ground movements is high. The methodology described in this paper aims at detecting terrain motions and building deformations at the local scale, by means of satellite radar data combined with in situ validation campaigns. The proposed approach consists of deriving maximum settlement directions of the investigated buildings from displacement data revealed by radar measurements and then in the cross-comparison of these values with background geological data, constructive features and on-field evidence. This validation permits better understanding whether or not the detected movements correspond to visible and effective damages to buildings. The method has been applied to the southwestern sector of Volterra (Tuscany region, Italy), which is a landslide-affected and partially urbanized area, through the use of COSMO-SkyMed satellite images as input data. Moreover, we discuss issues and possible misinterpretations when dealing with PSI (Persistent Scatterer Interferometry) data referring to single manufactures and the consequent difficulty of attributing the motion rate to ground displacements, rather than to structural failures. Full article
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Open AccessArticle
Optimal Combination of Classification Algorithms and Feature Ranking Methods for Object-Based Classification of Submeter Resolution Z/I-Imaging DMC Imagery
Remote Sens. 2015, 7(4), 4651-4677; https://doi.org/10.3390/rs70404651
Received: 11 February 2015 / Revised: 28 March 2015 / Accepted: 8 April 2015 / Published: 17 April 2015
Cited by 18 | Viewed by 2090 | PDF Full-text (10183 KB) | HTML Full-text | XML Full-text
Abstract
Object-based image analysis allows several different features to be calculated for the resulting objects. However, a large number of features means longer computing times and might even result in a loss of classification accuracy. In this study, we use four feature ranking methods [...] Read more.
Object-based image analysis allows several different features to be calculated for the resulting objects. However, a large number of features means longer computing times and might even result in a loss of classification accuracy. In this study, we use four feature ranking methods (maximum correlation, average correlation, Jeffries–Matusita distance and mean decrease in the Gini index) and five classification algorithms (linear discriminant analysis, naive Bayes, weighted k-nearest neighbors, support vector machines and random forest). The objective is to discover the optimal algorithm and feature subset to maximize accuracy when classifying a set of 1,076,937 objects, produced by the prior segmentation of a 0.45-m resolution multispectral image, with 356 features calculated on each object. The study area is both large (9070 ha) and diverse, which increases the possibility to generalize the results. The mean decrease in the Gini index was found to be the feature ranking method that provided highest accuracy for all of the classification algorithms. In addition, support vector machines and random forest obtained the highest accuracy in the classification, both using their default parameters. This is a useful result that could be taken into account in the processing of high-resolution images in large and diverse areas to obtain a land cover classification. Full article
Open AccessArticle
Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties
Remote Sens. 2015, 7(4), 4626-4650; https://doi.org/10.3390/rs70404626
Received: 30 October 2014 / Revised: 5 April 2015 / Accepted: 8 April 2015 / Published: 17 April 2015
Cited by 13 | Viewed by 2348 | PDF Full-text (1312 KB) | HTML Full-text | XML Full-text
Abstract
This study analyzed the vertical distribution of gravimetric water content (GWC), relative water content (RWC), and equivalent water thickness (EWT) in winter wheat during heading and early ripening stages, and evaluated the position of leaf number at which Vegetation Indexes (VIs) can best [...] Read more.
This study analyzed the vertical distribution of gravimetric water content (GWC), relative water content (RWC), and equivalent water thickness (EWT) in winter wheat during heading and early ripening stages, and evaluated the position of leaf number at which Vegetation Indexes (VIs) can best retrieve canopy water-related properties of winter wheat. Results demonstrated that the vertical distribution of these properties followed a near-bell-shaped curve with the highest values at the intermediate leaf position. GWC of the top three or four leaves during the heading stage and the top two or three leaves during the early ripening stage can represent the GWC of the whole canopy, but the RWC and EWT of the whole canopy should be calculated based on the top four leaves. At leaf level, the analysis demonstrated strong relationships between EWT and VIs for the top leaf layer, but for GWCD, GWCF, and RWC, the strongest relationships with VIs were found in the intermediate leaf layers. At canopy level, VIs provided the most accurate estimation of GWCfor the top three or four leaves. Water absorption-based VIs could estimate canopy EWT of winter wheat for the top four leaves, but the suitable bands sensitive to water absorptions should be carefully selected for the studied species. Full article
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Open AccessArticle
Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection
Remote Sens. 2015, 7(4), 4604-4625; https://doi.org/10.3390/rs70404604
Received: 19 December 2014 / Revised: 4 April 2015 / Accepted: 8 April 2015 / Published: 16 April 2015
Cited by 21 | Viewed by 2604 | PDF Full-text (13766 KB) | HTML Full-text | XML Full-text
Abstract
Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the [...] Read more.
Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval. If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model’s potential to retrieve LAI is limited by the availability of a priori information on RT model parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types. Full article
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Open AccessArticle
Analysis of Geometric Primitives in Quantitative Structure Models of Tree Stems
Remote Sens. 2015, 7(4), 4581-4603; https://doi.org/10.3390/rs70404581
Received: 3 February 2015 / Revised: 13 March 2015 / Accepted: 3 April 2015 / Published: 16 April 2015
Cited by 19 | Viewed by 2832 | PDF Full-text (3424 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
One way to model a tree is to use a collection of geometric primitives to represent the surface and topology of the stem and branches of a tree. The circular cylinder is often used as the geometric primitive, but it is not the [...] Read more.
One way to model a tree is to use a collection of geometric primitives to represent the surface and topology of the stem and branches of a tree. The circular cylinder is often used as the geometric primitive, but it is not the only possible choice. We investigate various geometric primitives and modelling schemes, discuss their properties and give practical estimates for expected modelling errors associated with the primitives. We find that the circular cylinder is the most robust primitive in the sense of a well-bounded volumetric modelling error, even with noise and gaps in the data. Its use does not cause errors significantly larger than those with more complex primitives, while the latter are much more sensitive to data quality. However, in some cases, a hybrid approach with more complex primitives for the stem is useful. Full article
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Open AccessArticle
Potential of Space-Borne Hyperspectral Data for Biomass Quantification in an Arid Environment: Advantages and Limitations
Remote Sens. 2015, 7(4), 4565-4580; https://doi.org/10.3390/rs70404565
Received: 24 February 2015 / Revised: 23 March 2015 / Accepted: 8 April 2015 / Published: 15 April 2015
Cited by 6 | Viewed by 2949 | PDF Full-text (32217 KB) | HTML Full-text | XML Full-text
Abstract
In spite of considerable efforts to monitor global vegetation, biomass quantification in drylands is still a major challenge due to low spectral resolution and considerable background effects. Hence, this study examines the potential of the space-borne hyperspectral Hyperion sensor compared to the multispectral [...] Read more.
In spite of considerable efforts to monitor global vegetation, biomass quantification in drylands is still a major challenge due to low spectral resolution and considerable background effects. Hence, this study examines the potential of the space-borne hyperspectral Hyperion sensor compared to the multispectral Landsat OLI sensor in predicting dwarf shrub biomass in an arid region characterized by challenging conditions for satellite-based analysis: The Eastern Pamirs of Tajikistan. We calculated vegetation indices for all available wavelengths of both sensors, correlated these indices with field-mapped biomass while considering the multiple comparison problem, and assessed the predictive performance of single-variable linear models constructed with data from each of the sensors. Results showed an increased performance of the hyperspectral sensor and the particular suitability of indices capturing the short-wave infrared spectral region in dwarf shrub biomass prediction. Performance was considerably poorer in the area with less vegetation cover. Furthermore, spatial transferability of vegetation indices was not feasible in this region, underlining the importance of repeated model building. This study indicates that upcoming space-borne hyperspectral sensors increase the performance of biomass prediction in the world’s arid environments. Full article
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Open AccessArticle
Geo-Positioning Accuracy Using Multiple-Satellite Images: IKONOS, QuickBird, and KOMPSAT-2 Stereo Images
Remote Sens. 2015, 7(4), 4549-4564; https://doi.org/10.3390/rs70404549
Received: 29 January 2015 / Revised: 30 March 2015 / Accepted: 3 April 2015 / Published: 15 April 2015
Cited by 13 | Viewed by 3354 | PDF Full-text (30947 KB) | HTML Full-text | XML Full-text
Abstract
This paper investigates the positioning accuracy of image pairs achieved by integrating images from multiple satellites. High-resolution satellite images from IKONOS, QuickBird, and KOMPSAT-2 for Daejeon, Korea were combined to produce pairs of stereo images. From single-satellite stereo pairs to multiple-satellite image pairs, [...] Read more.
This paper investigates the positioning accuracy of image pairs achieved by integrating images from multiple satellites. High-resolution satellite images from IKONOS, QuickBird, and KOMPSAT-2 for Daejeon, Korea were combined to produce pairs of stereo images. From single-satellite stereo pairs to multiple-satellite image pairs, all available combinations were analyzed via a rational function model (RFM). The positioning accuracy of multiple-satellite pairs was compared to a typical single-satellite stereo pair. The results show that dual-satellite integration can be an effective alternative to single-satellite stereo imagery for horizontal position mapping, but is less accurate for vertical mapping. The integration of additional higher-resolution images can improve the overall accuracy of the existing two images, but, conversely, may result in lower accuracy when very weak convergence or bisector elevation (BIE) angles occur. This highlights that the use of higher resolution images may not ensure improved accuracy, as it can result in very weak geometry. The findings confirm that multiple-satellite images can replace or enhance typical stereo pairs, but also suggest the need for careful verification, including consideration of various geometric elements and image resolution. This paper reveals the potential, limitations, and important considerations for mapping applications using images from multiple satellites. Full article
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Open AccessArticle
A Comparison of Two Approaches for Estimating the Wheat Nitrogen Nutrition Index Using Remote Sensing
Remote Sens. 2015, 7(4), 4527-4548; https://doi.org/10.3390/rs70404527
Received: 14 November 2014 / Revised: 2 April 2015 / Accepted: 8 April 2015 / Published: 15 April 2015
Cited by 13 | Viewed by 2033 | PDF Full-text (6832 KB) | HTML Full-text | XML Full-text
Abstract
Remote predictions of the nitrogen nutrition index (NNI) are useful for precise nitrogen (N) management in the field. Several studies have recommended two methods for estimating the NNI, which are classified as mechanistic and semi-empirical methods in this study. However, [...] Read more.
Remote predictions of the nitrogen nutrition index (NNI) are useful for precise nitrogen (N) management in the field. Several studies have recommended two methods for estimating the NNI, which are classified as mechanistic and semi-empirical methods in this study. However, no studies have been conducted to thoroughly analyze and compare these two methods. Using winter wheat as an example, this study compared the performances of these two methods for estimating the NNI to determine which method is more suitable for practical use. Field measurements were conducted to determine the above ground biomass, N concentration and canopy spectra during different wheat growth stages in 2012. Nearly 120 samples of data were collected and divided into different calibration and validation datasets (containing data from single or multi-growth stages). Based on the above datasets, the performances of the two NNI estimation methods were compared, and the influences of phenology on the methods were analyzed. All models that used the mechanistic method with different calibration datasets performed well when validated by validation datasets containing single growth or multi-growth stage data. The validation results had R2 values between 0.82 and 0.94, root mean square error (RMSE) values between 0.05 and 0.17, and RMSE% values between 5.10% and 14.41%. Phenology had no effect on this type of NNI estimation method. However, the semi-empirical method was influenced by phenology. The performances of the models established using this method were determined by the type of data used for calibration. Thus, the mechanistic method is recommended as a better method for estimating the NNI. By combining proper N management strategies, it can be used for precise N management. Full article
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Open AccessArticle
Assessing Field Spectroscopy Metadata Quality
Remote Sens. 2015, 7(4), 4499-4526; https://doi.org/10.3390/rs70404499
Received: 31 January 2015 / Revised: 23 March 2015 / Accepted: 3 April 2015 / Published: 15 April 2015
Cited by 4 | Viewed by 2365 | PDF Full-text (14386 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents the proposed criteria for measuring the quality and completeness of field spectroscopy metadata in a spectral archive. Definitions for metadata quality and completeness for field spectroscopy datasets are introduced. Unique methods for measuring quality and completeness of metadata to meet [...] Read more.
This paper presents the proposed criteria for measuring the quality and completeness of field spectroscopy metadata in a spectral archive. Definitions for metadata quality and completeness for field spectroscopy datasets are introduced. Unique methods for measuring quality and completeness of metadata to meet the requirements of field spectroscopy datasets are presented. Field spectroscopy metadata quality can be defined in terms of (but is not limited to) logical consistency, lineage, semantic and syntactic error rates, compliance with a quality standard, quality assurance by a recognized authority, and reputational authority of the data owners/data creators. Two spectral libraries are examined as case studies of operationalized metadata policies, and the degree to which they are aligned with the needs of field spectroscopy scientists. The case studies reveal that the metadata in publicly available spectral datasets are underperforming on the quality and completeness measures. This paper is part two in a series examining the issues central to a metadata standard for field spectroscopy datasets. Full article
(This article belongs to the Special Issue Field Spectroscopy and Radiometry)
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Open AccessArticle
Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data
Remote Sens. 2015, 7(4), 4473-4498; https://doi.org/10.3390/rs70404473
Received: 14 December 2014 / Revised: 15 March 2015 / Accepted: 15 March 2015 / Published: 15 April 2015
Cited by 11 | Viewed by 3034 | PDF Full-text (6960 KB) | HTML Full-text | XML Full-text
Abstract
Satellite remote sensing provides global observations of the Earth’s surface and provides useful information for monitoring smoke plumes emitted from forest fires. The aim of this study is to automatically separate smoke plumes from the background by analyzing the MODIS data. An identification [...] Read more.
Satellite remote sensing provides global observations of the Earth’s surface and provides useful information for monitoring smoke plumes emitted from forest fires. The aim of this study is to automatically separate smoke plumes from the background by analyzing the MODIS data. An identification algorithm was improved based on the spectral analysis among the smoke, cloud and underlying surface. In order to get satisfactory results, a multi-threshold method is used for extracting training sample sets to train back-propagation neural network (BPNN) classification for merging the smoke detection algorithm. The MODIS data from three forest fires were used to develop the algorithm and get parameter values. These fires occurred in (i) China on 16 October 2004, (ii) Northeast Asia on 29 April 2009 and (iii) Russia on 29 July 2010 in different seasons. Then, the data from four other fires were used to validate the algorithm. Results indicated that the algorithm captured both thick smoke and thin dispersed smoke over land, as well as the mixed pixels of smoke over the ocean. These results could provide valuable information concerning forest fire location, fire spreading and so on. Full article
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Open AccessArticle
L-Band SAR Backscatter Related to Forest Cover, Height and Aboveground Biomass at Multiple Spatial Scales across Denmark
Remote Sens. 2015, 7(4), 4442-4472; https://doi.org/10.3390/rs70404442
Received: 8 February 2015 / Revised: 23 March 2015 / Accepted: 3 April 2015 / Published: 14 April 2015
Cited by 19 | Viewed by 3218 | PDF Full-text (8049 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Mapping forest aboveground biomass (AGB) using satellite data is an important task, particularly for reporting of carbon stocks and changes under climate change legislation. It is known that AGB can be mapped using synthetic aperture radar (SAR), but relationships between AGB and radar [...] Read more.
Mapping forest aboveground biomass (AGB) using satellite data is an important task, particularly for reporting of carbon stocks and changes under climate change legislation. It is known that AGB can be mapped using synthetic aperture radar (SAR), but relationships between AGB and radar backscatter may be confounded by variations in biophysical forest structure (density, height or cover fraction) and differences in the resolution of satellite and ground data. Here, we attempt to quantify the effect of these factors by relating L-band ALOS PALSAR HV backscatter and unique country-wide LiDAR-derived maps of vegetation penetrability, height and AGB over Denmark at different spatial scales (50 m to 500 m). Trends in the relations indicate that, first, AGB retrieval accuracy from SAR improves most in mapping at 100-m scale instead of 50 m, and improvements are negligible beyond 250 m. Relative errors (bias and root mean squared error) decrease particularly for high AGB values (\(>\)110 Mg ha\(^{-1}\)) at coarse scales, and hence, coarse-scale mapping (\(\ge\)150 m) may be most suited for areas with high AGB. Second, SAR backscatter and a LiDAR-derived measure of fractional forest cover were found to have a strong linear relation (R\(^2\) = 0.79 at 250-m scale). In areas of high fractional forest cover, there is a slight decline in backscatter as AGB increases, indicating signal attenuation. The two results demonstrate that accounting for spatial scale and variations in forest structure, such as cover fraction, will greatly benefit establishing adequate plot-sizes for SAR calibration and the accuracy of derived AGB maps. Full article
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Open AccessArticle
Advancing of Land Surface Temperature Retrieval Using Extreme Learning Machine and Spatio-Temporal Adaptive Data Fusion Algorithm
Remote Sens. 2015, 7(4), 4424-4441; https://doi.org/10.3390/rs70404424
Received: 3 November 2014 / Revised: 7 April 2015 / Accepted: 8 April 2015 / Published: 14 April 2015
Cited by 14 | Viewed by 3374 | PDF Full-text (3007 KB) | HTML Full-text | XML Full-text
Abstract
As a critical variable to characterize the biophysical processes in ecological environment, and as a key indicator in the surface energy balance, evapotranspiration and urban heat islands, Land Surface Temperature (LST) retrieved from Thermal Infra-Red (TIR) images at both high temporal and spatial [...] Read more.
As a critical variable to characterize the biophysical processes in ecological environment, and as a key indicator in the surface energy balance, evapotranspiration and urban heat islands, Land Surface Temperature (LST) retrieved from Thermal Infra-Red (TIR) images at both high temporal and spatial resolution is in urgent need. However, due to the limitations of the existing satellite sensors, there is no earth observation which can obtain TIR at detailed spatial- and temporal-resolution simultaneously. Thus, several attempts of image fusion by blending the TIR data from high temporal resolution sensor with data from high spatial resolution sensor have been studied. This paper presents a novel data fusion method by integrating image fusion and spatio-temporal fusion techniques, for deriving LST datasets at 30 m spatial resolution from daily MODIS image and Landsat ETM+ images. The Landsat ETM+ TIR data were firstly enhanced based on extreme learning machine (ELM) algorithm using neural network regression model, from 60 m to 30 m resolution. Then, the MODIS LST and enhanced Landsat ETM+ TIR data were fused by Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT) in order to derive high resolution synthetic data. The synthetic images were evaluated for both testing and simulated satellite images. The average difference (AD) and absolute average difference (AAD) are smaller than 1.7 K, where the correlation coefficient (CC) and root-mean-square error (RMSE) are 0.755 and 1.824, respectively, showing that the proposed method enhances the spatial resolution of the predicted LST images and preserves the spectral information at the same time. Full article
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
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Open AccessArticle
Drought Variability and Land Degradation in Semiarid Regions: Assessment Using Remote Sensing Data and Drought Indices (1982–2011)
Remote Sens. 2015, 7(4), 4391-4423; https://doi.org/10.3390/rs70404391
Received: 25 December 2014 / Revised: 26 March 2015 / Accepted: 1 April 2015 / Published: 14 April 2015
Cited by 33 | Viewed by 4203 | PDF Full-text (21160 KB) | HTML Full-text | XML Full-text
Abstract
We analyzed potential land degradation processes in semiarid regions worldwide using long time series of remote sensing images and the Normalized Difference Vegetation Index (NDVI) for the period 1981 to 2011. The objectives of the study were to identify semiarid regions showing a [...] Read more.
We analyzed potential land degradation processes in semiarid regions worldwide using long time series of remote sensing images and the Normalized Difference Vegetation Index (NDVI) for the period 1981 to 2011. The objectives of the study were to identify semiarid regions showing a marked decrease in potential vegetation activity, indicative of the occurrence of land degradation processes, and to assess the possible influence of the observed drought trends quantified using the Standardized Precipitation Evapotranspiration Index (SPEI). We found that the NDVI values recorded during the period of maximum vegetation activity (NDVImax) predominantly showed a positive evolution in the majority of the semiarid regions assessed, but NDVImax was highly correlated with drought variability, and the trends of drought events influenced trends in NDVImax at the global scale. The semiarid regions that showed most increase in NDVImax (the Sahel, northern Australia, South Africa) were characterized by a clear positive trend in the SPEI values, indicative of conditions of greater humidity and lesser drought conditions. While changes in drought severity may be an important driver of NDVI trends and land degradation processes in semiarid regions worldwide, drought did not apparently explain some of the observed changes in NDVImax. This reflects the complexity of vegetation activity processes in the world’s semiarid regions, and the difficulty of defining a universal response to drought in these regions, where a number of factors (natural and anthropogenic) may also affect on land degradation. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
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Open AccessArticle
A Practical Split-Window Algorithm for Retrieving Land Surface Temperature from Landsat-8 Data and a Case Study of an Urban Area in China
Remote Sens. 2015, 7(4), 4371-4390; https://doi.org/10.3390/rs70404371
Received: 4 January 2015 / Revised: 26 February 2015 / Accepted: 30 March 2015 / Published: 14 April 2015
Cited by 13 | Viewed by 3365 | PDF Full-text (27583 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a practical split-window algorithm (SWA) for retrieving land surface temperature (LST) from Landsat-8 Thermal Infrared Sensor (TIRS) data. This SWA has a universal applicability and a set of parameters that can be applied when retrieving LSTs year-round. The atmospheric transmittance [...] Read more.
This paper proposes a practical split-window algorithm (SWA) for retrieving land surface temperature (LST) from Landsat-8 Thermal Infrared Sensor (TIRS) data. This SWA has a universal applicability and a set of parameters that can be applied when retrieving LSTs year-round. The atmospheric transmittance and the land surface emissivity (LSE), the essential SWA input parameters, of the Landsat-8 TIRS data are determined in this paper. We also analysed the error sensitivity of these SWA input parameters. The accuracy evaluation of the proposed SWA in this paper was conducted using the software MODTRAN 4.0. The root mean square error (RMSE) of the simulated LST using the mid-latitude summer atmospheric profile is 0.51 K, improving on the result of 0.93 K from Rozenstein (2014). Among the 90 simulated data points, the maximum absolute error is 0.99 °C, and the minimum absolute error is 0.02 °C. Under the Tropical model and 1976 US standard atmospheric conditions, the RMSE of the LST errors are 0.70 K and 0.63 K, respectively. The accuracy results indicate that the SWA provides an LST retrieval method that features not only high accuracy but also a certain universality. Additionally, the SWA was applied to retrieve the LST of an urban area using two Landsat-8 images. The SWA presented in this paper should promote the application of Landsat-8 data in the study of environmental evolution. Full article
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
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Open AccessArticle
Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory
Remote Sens. 2015, 7(4), 4343-4370; https://doi.org/10.3390/rs70404343
Received: 26 February 2015 / Revised: 30 March 2015 / Accepted: 8 April 2015 / Published: 13 April 2015
Cited by 35 | Viewed by 2999 | PDF Full-text (49581 KB) | HTML Full-text | XML Full-text
Abstract
Surface models provide key knowledge of the 3-d structure of forests. Aerial stereo imagery acquired during routine mapping campaigns covering the whole of Switzerland (41,285 km2), offers a potential data source to calculate digital surface models (DSMs). We present an automated [...] Read more.
Surface models provide key knowledge of the 3-d structure of forests. Aerial stereo imagery acquired during routine mapping campaigns covering the whole of Switzerland (41,285 km2), offers a potential data source to calculate digital surface models (DSMs). We present an automated workflow to generate a nationwide DSM with a resolution of 1 × 1 m based on photogrammetric image matching. A canopy height model (CHM) is derived in combination with an existing digital terrain model (DTM). ADS40/ADS80 summer images from 2007 to 2012 were used for stereo matching, with ground sample distances (GSD) of 0.25 m in lowlands and 0.5 m in high mountain areas. Two different image matching strategies for DSM calculation were applied: one optimized for single features such as trees and for abrupt changes in elevation such as steep rocks, and another optimized for homogeneous areas such as meadows or glaciers. The country was divided into 165,500 blocks, which were matched independently using an automated workflow. The completeness of successfully matched points was high, 97.9%. To test the accuracy of the derived DSM, two reference data sets were used: (1) topographic survey points (n = 198) and (2) stereo measurements (n = 195,784) within the framework of the Swiss National Forest Inventory (NFI), in order to distinguish various land cover types. An overall median accuracy of 0.04 m with a normalized median absolute deviation (NMAD) of 0.32 m was found using the topographic survey points. The agreement between the stereo measurements and the values of the DSM revealed acceptable NMAD values between 1.76 and 3.94 m for forested areas. A good correlation (Pearson’s r = 0.83) was found between terrestrially measured tree height (n = 3109) and the height derived from the CHM. Optimized image matching strategies, an automatic workflow and acceptable computation time mean that the presented approach is suitable for operational usage at the nationwide extent. The CHM will be used to reduce estimation errors of different forest characteristics in the Swiss NFI and has high potential for change detection assessments, since an aerial stereo imagery update is available every six years. Full article
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Open AccessArticle
Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm
Remote Sens. 2015, 7(4), 4318-4342; https://doi.org/10.3390/rs70404318
Received: 13 November 2014 / Revised: 12 March 2015 / Accepted: 3 April 2015 / Published: 13 April 2015
Cited by 34 | Viewed by 3114 | PDF Full-text (3013 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) image processing and multi-image segmentation; (2) feature [...] Read more.
This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) image processing and multi-image segmentation; (2) feature optimization; and (3) detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87) demonstrates higher classification performance than the stand-alone OOIA (0.75) method for detecting landslides. The area under curve (AUC) value was also higher than that of the simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes. Full article
(This article belongs to the Special Issue Remote Sensing in Geology)
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Open AccessArticle
Reviewing ALOS PALSAR Backscatter Observations for Stem Volume Retrieval in Swedish Forest
Remote Sens. 2015, 7(4), 4290-4317; https://doi.org/10.3390/rs70404290
Received: 4 February 2015 / Revised: 25 March 2015 / Accepted: 1 April 2015 / Published: 13 April 2015
Cited by 16 | Viewed by 3059 | PDF Full-text (1946 KB) | HTML Full-text | XML Full-text
Abstract
Between 2006 and 2011, the Advanced Land Observing Satellite (ALOS) Phased Array L-type Synthetic Aperture Radar (PALSAR) instrument acquired multi-temporal datasets under several environmental conditions and multiple configurations of look angle and polarization. The extensive archive of SAR backscatter observations over the forest [...] Read more.
Between 2006 and 2011, the Advanced Land Observing Satellite (ALOS) Phased Array L-type Synthetic Aperture Radar (PALSAR) instrument acquired multi-temporal datasets under several environmental conditions and multiple configurations of look angle and polarization. The extensive archive of SAR backscatter observations over the forest test sites of Krycklan (boreal) and Remningstorp (hemi-boreal), Sweden, was used to assess the retrieval of stem volume at stand level. The retrieval was based on the inversion of a simple Water Cloud Model with gaps; single estimates of stem volume are then combined to obtain the final multi-temporal estimate. The model matched the relationship between the SAR backscatter and the stem volume under all configurations. The retrieval relative Root Mean Square Error (RMSE) differed depending upon environmental conditions, polarization and look angle. Stem volume was best retrieved in Krycklan using only HV-polarized data acquired under unfrozen conditions with a look angle of 34.3° (relative RMSE: 44.0%). In Remningstorp, the smallest error was obtained using only HH-polarized data acquired under predominantly frozen conditions with a look angle of 34.3° (relative RMSE: 35.1%). The relative RMSE was below 30% for stands >20 ha, suggesting high accuracy of ALOS PALSAR estimates of stem volumes aggregated at moderate resolution. Full article
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