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Remote Sens., Volume 7, Issue 12 (December 2015)

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Open AccessArticle
Toward High Altitude Airship Ground-Based Boresight Calibration of Hyperspectral Pushbroom Imaging Sensors
Remote Sens. 2015, 7(12), 17297-17311; https://doi.org/10.3390/rs71215883
Received: 28 July 2015 / Revised: 1 December 2015 / Accepted: 7 December 2015 / Published: 19 December 2015
Cited by 5 | Viewed by 2399 | PDF Full-text (6008 KB) | HTML Full-text | XML Full-text
Abstract
The complexity of the single linear hyperspectral pushbroom imaging based on a high altitude airship (HAA) without a three-axis stabilized platform is much more than that based on the spaceborne and airborne. Due to the effects of air pressure, temperature and airflow, the [...] Read more.
The complexity of the single linear hyperspectral pushbroom imaging based on a high altitude airship (HAA) without a three-axis stabilized platform is much more than that based on the spaceborne and airborne. Due to the effects of air pressure, temperature and airflow, the large pitch and roll angles tend to appear frequently that create pushbroom images highly characterized with severe geometric distortions. Thus, the in-flight calibration procedure is not appropriate to apply to the single linear pushbroom sensors on HAA having no three-axis stabilized platform. In order to address this problem, a new ground-based boresight calibration method is proposed. Firstly, a coordinate’s transformation model is developed for direct georeferencing (DG) of the linear imaging sensor, and then the linear error equation is derived from it by using the Taylor expansion formula. Secondly, the boresight misalignments are worked out by using iterative least squares method with few ground control points (GCPs) and ground-based side-scanning experiments. The proposed method is demonstrated by three sets of experiments: (i) the stability and reliability of the method is verified through simulation-based experiments; (ii) the boresight calibration is performed using ground-based experiments; and (iii) the validation is done by applying on the orthorectification of the real hyperspectral pushbroom images from a HAA Earth observation payload system developed by our research team—“LanTianHao”. The test results show that the proposed boresight calibration approach significantly improves the quality of georeferencing by reducing the geometric distortions caused by boresight misalignments to the minimum level. Full article
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Open AccessCorrection
Correction: Tilly, N. et al. Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass. Remote Sens. 2015, 7, 11449–11480
Remote Sens. 2015, 7(12), 17291-17296; https://doi.org/10.3390/rs71215878
Received: 10 December 2015 / Accepted: 15 December 2015 / Published: 19 December 2015
Cited by 1 | Viewed by 2037 | PDF Full-text (1277 KB) | HTML Full-text | XML Full-text
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle
Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity
Remote Sens. 2015, 7(12), 17272-17290; https://doi.org/10.3390/rs71215875
Received: 22 September 2015 / Revised: 1 December 2015 / Accepted: 10 December 2015 / Published: 18 December 2015
Cited by 2 | Viewed by 2436 | PDF Full-text (4666 KB) | HTML Full-text | XML Full-text
Abstract
Eddy-covariance carbon dioxide flux measurement is an established method to estimate primary productivity at the forest stand level (typically 10 ha). To validate eddy-covariance estimates, researchers rely on extensive time-series analysis and an assessment of flux contributions made by various ecosystem components at [...] Read more.
Eddy-covariance carbon dioxide flux measurement is an established method to estimate primary productivity at the forest stand level (typically 10 ha). To validate eddy-covariance estimates, researchers rely on extensive time-series analysis and an assessment of flux contributions made by various ecosystem components at spatial scales much finer than the eddy-covariance footprint. Scaling these contributions to the stand level requires a consideration of the heterogeneity in the canopy radiation field. This paper presents a stochastic ray tracing approach to predict the probabilities of light absorption from over a thousand hemispherical directions by thousands of individual scene elements. Once a look-up table of absorption probabilities is computed, dynamic illumination conditions can be simulated in a computationally realistic time, from which stand-level gross primary productivity can be obtained by integrating photosynthetic assimilation over the scene. We demonstrate the method by inverting a leaf-level photosynthesis model with eddy-covariance and meteorological data. Optimized leaf photosynthesis parameters and canopy structure were able to explain 75% of variation in eddy-covariance gross primary productivity estimates, and commonly used parameters, including photosynthetic capacity and quantum yield, fell within reported ranges. Remaining challenges are discussed including the need to address the distribution of radiation within shoots and needles. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle
Validation of the Suomi NPP VIIRS Ice Surface Temperature Environmental Data Record
Remote Sens. 2015, 7(12), 17258-17271; https://doi.org/10.3390/rs71215880
Received: 28 October 2015 / Revised: 3 December 2015 / Accepted: 11 December 2015 / Published: 18 December 2015
Cited by 2 | Viewed by 2396 | PDF Full-text (2422 KB) | HTML Full-text | XML Full-text
Abstract
Continuous monitoring of the surface temperature is critical to understanding and forecasting Arctic climate change; as surface temperature integrates changes in the surface energy budget. The sea-ice surface temperature (IST) has been measured with optical and thermal infrared sensors for many years. With [...] Read more.
Continuous monitoring of the surface temperature is critical to understanding and forecasting Arctic climate change; as surface temperature integrates changes in the surface energy budget. The sea-ice surface temperature (IST) has been measured with optical and thermal infrared sensors for many years. With the IST Environmental Data Record (EDR) available from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) and future Joint Polar Satellite System (JPSS) satellites; we can continue to monitor and investigate Arctic climate change. This work examines the quality of the VIIRS IST EDR. Validation is performed through comparisons with multiple datasets; including NASA IceBridge measurements; air temperature from Arctic drifting ice buoys; Moderate Resolution Imaging Spectroradiometer (MODIS) IST; MODIS IST simultaneous nadir overpass (SNO); and surface air temperature from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis. Results show biases of −0.34; −0.12; 0.16; −3.20; and −3.41 K compared to an aircraft-mounted downward-looking pyrometer; MODIS; MODIS SNO; drifting buoy; and NCEP/NCAR reanalysis; respectively; root-mean-square errors of 0.98; 1.02; 0.95; 4.89; and 6.94 K; and root-mean-square errors with the bias removed of 0.92; 1.01; 0.94; 3.70; and 6.04 K. Based on the IceBridge and MODIS results; the VIIRS IST uncertainty (RMSE) meets or exceeds the JPSS system requirement of 1.0 K. The product can therefore be considered useful for meteorological and climatological applications. Full article
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Open AccessArticle
An Effective Method for Snow-Cover Mapping of Dense Coniferous Forests in the Upper Heihe River Basin Using Landsat Operational Land Imager Data
Remote Sens. 2015, 7(12), 17246-17257; https://doi.org/10.3390/rs71215882
Received: 10 October 2015 / Revised: 4 December 2015 / Accepted: 11 December 2015 / Published: 18 December 2015
Cited by 6 | Viewed by 2145 | PDF Full-text (7678 KB) | HTML Full-text | XML Full-text
Abstract
The Normalized Difference Snow Index (NDSI) is an effective index for snow-cover mapping at large scales, but in forested regions the identification accuracy for snow using the NDSI is low because of forest cover effects. In this study, typical evergreen coniferous forest zones [...] Read more.
The Normalized Difference Snow Index (NDSI) is an effective index for snow-cover mapping at large scales, but in forested regions the identification accuracy for snow using the NDSI is low because of forest cover effects. In this study, typical evergreen coniferous forest zones on Qilian Mountain in the Upper Heihe River Basin (UHRB) were chosen as example regions. By analyzing the spectral signature of snow-covered and snow-free evergreen coniferous forests with Landsat Operational Land Imager (OLI) data, a novel spectral band ratio using near-infrared (NIR) and shortwave infrared (SWIR) bands, defined as (ρnir − ρswir)/(ρnir + ρswir), is proposed. Our research shows that this band ratio, named the normalized difference forest snow index (NDFSI), can be used to effectively distinguish snow-covered evergreen coniferous forests from snow-free evergreen coniferous forests in UHRB. Full article
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Open AccessArticle
Validation of S-NPP VIIRS Sea Surface Temperature Retrieved from NAVO
Remote Sens. 2015, 7(12), 17234-17245; https://doi.org/10.3390/rs71215881
Received: 15 October 2015 / Revised: 26 November 2015 / Accepted: 7 December 2015 / Published: 18 December 2015
Cited by 3 | Viewed by 2158 | PDF Full-text (2546 KB) | HTML Full-text | XML Full-text
Abstract
The validation of sea surface temperature (SST) retrieved from the new sensor Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) Satellite is essential for the interpretation, use, and improvement of the new generation SST product. In this study, [...] Read more.
The validation of sea surface temperature (SST) retrieved from the new sensor Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) Satellite is essential for the interpretation, use, and improvement of the new generation SST product. In this study, the magnitude and characteristics of uncertainties in S-NPP VIIRS SST produced by the Naval Oceanographic Office (NAVO) are investigated. The NAVO S-NPP VIIRS SST and eight types of quality-controlled in situ SST from the National Oceanic and Atmospheric Administration in situ Quality Monitor (iQuam) are condensed into a Taylor diagram. Considering these comparisons and their spatial coverage, the NAVO S-NPP VIIRS SST is then validated using collocated drifters measured SST via a three-way error analysis which also includes SST derived from Moderate Resolution Imaging Spectro-radiometer (MODIS) onboard AQUA. The analysis shows that the NAVO S-NPP VIIRS SST is of high accuracy, which lies between the drifters measured SST and AQUA MODIS SST. The histogram of NAVO S-NPP VIIRS SST root-mean-square error (RMSE) shows normality in the range of 0–0.6 °C with a median of ~0.31 °C. Global distribution of NAVO VIIRS SST shows pronounced warm biases up to 0.5 °C in the Southern Hemisphere at high latitudes with respect to the drifters measured SST, while near-zero biases are observed in AQUA MODIS. It means that these biases may be caused by the NAVO S-NPP VIIRS SST retrieval algorithm rather than the nature of the SST. The reasons and correction for this bias need to be further studied. Full article
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Open AccessArticle
Estimating Roof Solar Energy Potential in the Downtown Area Using a GPU-Accelerated Solar Radiation Model and Airborne LiDAR Data
Remote Sens. 2015, 7(12), 17212-17233; https://doi.org/10.3390/rs71215877
Received: 30 October 2015 / Revised: 3 December 2015 / Accepted: 8 December 2015 / Published: 18 December 2015
Cited by 12 | Viewed by 3338 | PDF Full-text (12279 KB) | HTML Full-text | XML Full-text
Abstract
Solar energy, as a clean and renewable resource is becoming increasingly important in the global context of climate change and energy crisis. Utilization of solar energy in urban areas is of great importance in urban energy planning, environmental conservation, and sustainable development. However, [...] Read more.
Solar energy, as a clean and renewable resource is becoming increasingly important in the global context of climate change and energy crisis. Utilization of solar energy in urban areas is of great importance in urban energy planning, environmental conservation, and sustainable development. However, available spaces for solar panel installation in cities are quite limited except for building roofs. Furthermore, complex urban 3D morphology greatly affects sunlit patterns on building roofs, especially in downtown areas, which makes the determination of roof solar energy potential a challenging task. The object of this study is to estimate the solar radiation on building roofs in an urban area in Shanghai, China, and select suitable spaces for installing solar panels that can effectively utilize solar energy. A Graphic Processing Unit (GPU)-based solar radiation model named SHORTWAVE-C simulating direct and non-direct solar radiation intensity was developed by adding the capability of considering cloud influence into the previous SHORTWAVE model. Airborne Light Detection and Ranging (LiDAR) data was used as the input of the SHORTWAVE-C model and to investigate the morphological characteristics of the study area. The results show that the SHORTWAVE-C model can accurately estimate the solar radiation intensity in a complex urban environment under cloudy conditions, and the GPU acceleration method can reduce the computation time by up to 46%. Two sites with different building densities and rooftop structures were selected to illustrate the influence of urban morphology on the solar radiation and solar illumination duration. Based on the findings, an object-based method was implemented to identify suitable places for rooftop solar panel installation that can fully utilize the solar energy potential. Our study provides useful strategic guidelines for the selection and assessment of roof solar energy potential for urban energy planning. Full article
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Open AccessArticle
Satellite and Ground Based Thermal Observation of the 2014 Effusive Eruption at Stromboli Volcano
Remote Sens. 2015, 7(12), 17190-17211; https://doi.org/10.3390/rs71215876
Received: 6 September 2015 / Revised: 3 December 2015 / Accepted: 8 December 2015 / Published: 18 December 2015
Cited by 24 | Viewed by 3197 | PDF Full-text (2008 KB) | HTML Full-text | XML Full-text
Abstract
As specifically designed platforms are still unavailable at this point in time, lava flows are usually monitored remotely with the use of meteorological satellites. Generally, meteorological satellites have a low spatial resolution, which leads to uncertain results. This paper presents the first long [...] Read more.
As specifically designed platforms are still unavailable at this point in time, lava flows are usually monitored remotely with the use of meteorological satellites. Generally, meteorological satellites have a low spatial resolution, which leads to uncertain results. This paper presents the first long term satellite monitoring of active lava flows on Stromboli volcano (August–November 2014) at high spatial resolution (160 m) and relatively high temporal resolution (~3 days). These data were retrieved by the small satellite Technology Experiment Carrier-1 (TET-1), which was developed and built by the German Aerospace Center (DLR). The satellite instrument is dedicated to high temperature event monitoring. The satellite observations were accompanied by field observations conducted by thermal cameras. These provided short time lava flow dynamics and validation for satellite data. TET-1 retrieved 27 datasets over Stromboli during its effusive activity. Using the radiant density approach, TET-1 data were used to calibrate the MODVOLC data and estimate the time averaged lava discharge rate. With a mean output rate of 0.87 m3/s during the three-month-long eruption, we estimate the total erupted volume to be 7.4 × 106 m3. Full article
(This article belongs to the Special Issue Volcano Remote Sensing)
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Open AccessArticle
A Normalized Urban Areas Composite Index (NUACI) Based on Combination of DMSP-OLS and MODIS for Mapping Impervious Surface Area
Remote Sens. 2015, 7(12), 17168-17189; https://doi.org/10.3390/rs71215863
Received: 14 August 2015 / Revised: 29 November 2015 / Accepted: 1 December 2015 / Published: 18 December 2015
Cited by 26 | Viewed by 2739 | PDF Full-text (11058 KB) | HTML Full-text | XML Full-text
Abstract
Mapping Impervious Surface Area (ISA) at regional and global scales has attracted increasing interest. DMSP-OLS nighttime light (NTL) data have proven to be successful for mapping urban land in large areas. However, the well-documented issues of pixel blooming and saturation limit the ability [...] Read more.
Mapping Impervious Surface Area (ISA) at regional and global scales has attracted increasing interest. DMSP-OLS nighttime light (NTL) data have proven to be successful for mapping urban land in large areas. However, the well-documented issues of pixel blooming and saturation limit the ability of DMSP-OLS data to provide accurate urban information. In this paper, a multi-source composition index is proposed to overcome the limitations of extracting urban land using only the NTL data. We combined three data sources (i.e., DMSP-OLS, MODSI EVI and NDWI) to generate a new index called the Normalized Urban Areas Composite Index (NUACI). This index aims to quickly map impervious surface area at regional and global scales. Experimental results indicate that NUACI has the ability to reduce the pixel saturation of NTL and eliminate the blooming effect. With the reference data derived from Landsat TM/ETM+, regression models based on normalized DMSP-OLS, Human Settlement Index (HSI), vegetation adjusted NTL urban index (VANUI), and NUACI are then established to estimate ISA. Our assessments reveal that the NUACI-based regression model yields the highest performance. The NUACI-based regression models were then used to map ISA for China for the years 2000, 2005 and 2010 (Free download link for the ISA products can be found at the end of this paper). Full article
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Open AccessArticle
A Regional Land Use Drought Index for Florida
Remote Sens. 2015, 7(12), 17149-17167; https://doi.org/10.3390/rs71215879
Received: 13 August 2015 / Revised: 4 December 2015 / Accepted: 6 December 2015 / Published: 18 December 2015
Cited by 5 | Viewed by 2488 | PDF Full-text (17019 KB) | HTML Full-text | XML Full-text
Abstract
Drought index is a useful tool to assess and respond to drought. However, current drought indices could not fully reveal land use effects and they have limitations in applications. Besides, El Niño Southern Oscillation (ENSO), strongly influences the climate of Florida. Hence, understanding [...] Read more.
Drought index is a useful tool to assess and respond to drought. However, current drought indices could not fully reveal land use effects and they have limitations in applications. Besides, El Niño Southern Oscillation (ENSO), strongly influences the climate of Florida. Hence, understanding ENSO patterns on a regional scale and developing a new land use drought index suitable for Florida are critical in agriculture and water resources planning and management. This paper presents a 32 km high resolution land use adapted drought index, which relies on five types of land uses (lake, urban, forest, wetland, and agriculture) in Florida. The land uses were obtained from National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) data from 1979 to 2002. The results showed that Bowen ratio responded to land use and could be used as an indicator to monitor drought events. Then, an innovative regional land use drought index was developed from the normalized Bowen ratio, which could reflect not only the level of severity during drought events resulting from land use effects, but also La Niña driven drought impacts. The proposed new index may help scientists answer the critical questions about drought effect on various land uses and potential feedbacks of changes in land use and land cover to climate. Full article
(This article belongs to the Special Issue Earth Observations for the Sustainable Development)
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Open AccessTechnical Note
Variations of Microwave Scattering Properties by Seasonal Freeze/Thaw Transition in the Permafrost Active Layer Observed by ALOS PALSAR Polarimetric Data
Remote Sens. 2015, 7(12), 17135-17148; https://doi.org/10.3390/rs71215874
Received: 7 September 2015 / Revised: 7 December 2015 / Accepted: 10 December 2015 / Published: 18 December 2015
Cited by 5 | Viewed by 1907 | PDF Full-text (3217 KB) | HTML Full-text | XML Full-text
Abstract
Radar backscattering properties can be extremely sensitive to the freeze/thaw states of the ground surface. This study aims to evaluate the changes of L-band microwave scattering characteristics between thawed and frozen conditions by using polarimetric scattering mechanism indicators. ALOS PALSAR polarimetric mode data [...] Read more.
Radar backscattering properties can be extremely sensitive to the freeze/thaw states of the ground surface. This study aims to evaluate the changes of L-band microwave scattering characteristics between thawed and frozen conditions by using polarimetric scattering mechanism indicators. ALOS PALSAR polarimetric mode data acquired in winter and spring seasons over Eastern Siberia are used in this study. Experimental results show that the actual scattering mechanisms and their seasonal variations over various forested and non-forested permafrost ecosystems can be successfully characterized by the polarimetric target decomposition parameters and the polarimetric coherences. In addition, fully polarimetric radar observations exhibit great potential for mapping land cover types and surficial features in the permafrost active layer. Particularly, the co-polarization coherences on the HV-polarization basis and circular-polarization basis were found to be very useful for discriminating different surficial geocryological characteristics in recently burnt forests and thermokarst regions. Full article
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Open AccessArticle
Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation
Remote Sens. 2015, 7(12), 17113-17134; https://doi.org/10.3390/rs71215872
Received: 9 October 2015 / Revised: 1 December 2015 / Accepted: 7 December 2015 / Published: 18 December 2015
Cited by 9 | Viewed by 2463 | PDF Full-text (4100 KB) | HTML Full-text | XML Full-text | Correction
Abstract
To reduce hydrological uncertainties in the regular monitoring of poorly gauged lakes and reservoirs, multi-dimensional remote sensing data have emerged as an excellent alternative. In this paper, we propose three methods to delineate the volume of such equipotential water bodies through a combination [...] Read more.
To reduce hydrological uncertainties in the regular monitoring of poorly gauged lakes and reservoirs, multi-dimensional remote sensing data have emerged as an excellent alternative. In this paper, we propose three methods to delineate the volume of such equipotential water bodies through a combination of altimetry (1D), Landsat (2D) and bathymetry (2D) data, namely an altimetry-bathymetry-volume method (ABV), a Landsat-bathymetry-volume method (LBV) and an altimetry-Landsat-volume-variation method (ALVV). The first two data products are further merged by a Kalman-filter-based state space model (SSM) to obtain a combined estimate (CSSME) time series and near future prediction. To validate our methods, we tested them on the well-measured Lake Mead and further applied them on the poorly gauged Aral Sea, which has inaccurate bathymetry and very limited ground observation data. We updated the lake bathymetry of the Aral Sea, which was more than half a century old. The resultant remote sensing products have a very good long-term agreement among each other. The Lake Mead volume estimations are very highly coherent with the ground observations for all cases (R2 > 0.96 and NRMSE < 2.1%), except for the forecast (R2 = 0.75 and NRMSE = 3.7%). Due to lack of in situ data for the Aral Sea, the estimated volumes are compared, and the entire Aral Sea LBV and ABV have R2 = 0.91 and NRMSE = 5.5%, and the forecast compared to CSSME has R2 = 0.60 and NRMSE = 2.4%. Full article
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Open AccessArticle
Radarsat-2 Backscattering for the Modeling of Biophysical Parameters of Regenerating Mangrove Forests
Remote Sens. 2015, 7(12), 17097-17112; https://doi.org/10.3390/rs71215873
Received: 31 August 2015 / Revised: 27 November 2015 / Accepted: 9 December 2015 / Published: 17 December 2015
Cited by 12 | Viewed by 2405 | PDF Full-text (5667 KB) | HTML Full-text | XML Full-text
Abstract
The aim of this study is to understand the relationship between radar backscattering (σ°, β° and γ) of a multi-polarized Radarsat-2 C-band image with the structural attributes of regenerating mangrove vegetation located at the mouth of the Amazon River. CBH (circumference at breast [...] Read more.
The aim of this study is to understand the relationship between radar backscattering (σ°, β° and γ) of a multi-polarized Radarsat-2 C-band image with the structural attributes of regenerating mangrove vegetation located at the mouth of the Amazon River. CBH (circumference at breast height), height and species data were collected to characterize vegetation structure and above-ground biomass (AGB) at 17 plots with a total of 3090 measured individuals. Significant relationships between the linear σ° in VH (vertical transmit, horizontal receive) cross-polarization produced r2 values of 0.63 for the average height, 0.53 for the DBH, 0.46 for the basal area (BA) and 0.52 for the AGB. Using co-polarized HH (horizontal transmit, horizontal receive) and VV (vertical transmit, vertical receive), r2 values increased to 0.81, 0.79, 0.67 and 0.79, respectively. Vegetation attribute maps of average canopy height, DBH and AGB were generated for the study area. We conclude that multi-polarized Radarsat-2 images were adequate for characterization of vegetation attributes in areas of mangrove regeneration. Full article
(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)
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Open AccessArticle
3D Maize Plant Reconstruction Based on Georeferenced Overlapping LiDAR Point Clouds
Remote Sens. 2015, 7(12), 17077-17096; https://doi.org/10.3390/rs71215870
Received: 14 October 2015 / Revised: 19 November 2015 / Accepted: 1 December 2015 / Published: 17 December 2015
Cited by 22 | Viewed by 3156 | PDF Full-text (10866 KB) | HTML Full-text | XML Full-text
Abstract
3D crop reconstruction with a high temporal resolution and by the use of non-destructive measuring technologies can support the automation of plant phenotyping processes. Thereby, the availability of such 3D data can give valuable information about the plant development and the interaction of [...] Read more.
3D crop reconstruction with a high temporal resolution and by the use of non-destructive measuring technologies can support the automation of plant phenotyping processes. Thereby, the availability of such 3D data can give valuable information about the plant development and the interaction of the plant genotype with the environment. This article presents a new methodology for georeferenced 3D reconstruction of maize plant structure. For this purpose a total station, an IMU, and several 2D LiDARs with different orientations were mounted on an autonomous vehicle. By the multistep methodology presented, based on the application of the ICP algorithm for point cloud fusion, it was possible to perform the georeferenced point clouds overlapping. The overlapping point cloud algorithm showed that the aerial points (corresponding mainly to plant parts) were reduced to 1.5%–9% of the total registered data. The remaining were redundant or ground points. Through the inclusion of different LiDAR point of views of the scene, a more realistic representation of the surrounding is obtained by the incorporation of new useful information but also of noise. The use of georeferenced 3D maize plant reconstruction at different growth stages, combined with the total station accuracy could be highly useful when performing precision agriculture at the crop plant level. Full article
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Open AccessArticle
Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data
Remote Sens. 2015, 7(12), 17051-17076; https://doi.org/10.3390/rs71215867
Received: 20 July 2015 / Revised: 4 December 2015 / Accepted: 8 December 2015 / Published: 17 December 2015
Cited by 9 | Viewed by 2518 | PDF Full-text (12333 KB) | HTML Full-text | XML Full-text
Abstract
The difficulty associated with the Lidar data change detection method is lack of data, which is mainly caused by occlusion or pulse absorption by the surface material, e.g., water. To address this challenge, we present a new strategy for detecting buildings that are [...] Read more.
The difficulty associated with the Lidar data change detection method is lack of data, which is mainly caused by occlusion or pulse absorption by the surface material, e.g., water. To address this challenge, we present a new strategy for detecting buildings that are “changed”, “unchanged”, or “unknown”, and quantifying the changes. The designation “unknown” is applied to locations where, due to lack of data in at least one of the epochs, it is not possible to reliably detect changes in the structure. The process starts with classified data sets in which buildings are extracted. Next, a point-to-plane surface difference map is generated by merging and comparing the two data sets. Context rules are applied to the difference map to distinguish between “changed”, “unchanged”, and “unknown”. Rules are defined to solve problems caused by the lack of data. Further, points labelled as “changed” are re-classified into changes to roofs, walls, dormers, cars, constructions above the roof line, and undefined objects. Next, all the classified changes are organized as changed building objects, and the geometric indices are calculated from their 3D minimum bounding boxes. Performance analysis showed that 80%–90% of real changes are found, of which approximately 50% are considered relevant. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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Open AccessArticle
Application of InSAR and Gravimetry for Land Subsidence Hazard Zoning in Aguascalientes, Mexico
Remote Sens. 2015, 7(12), 17035-17050; https://doi.org/10.3390/rs71215868
Received: 23 August 2015 / Revised: 27 November 2015 / Accepted: 7 December 2015 / Published: 17 December 2015
Cited by 11 | Viewed by 3156 | PDF Full-text (3158 KB) | HTML Full-text | XML Full-text
Abstract
In this work we present an application of InSAR and gravimetric surveys for risk management related to land subsidence and surface ground faulting generation. A subsidence velocity map derived from the 2007–2011 ALOS SAR imagery and a sediment thicknesses map obtained from the [...] Read more.
In this work we present an application of InSAR and gravimetric surveys for risk management related to land subsidence and surface ground faulting generation. A subsidence velocity map derived from the 2007–2011 ALOS SAR imagery and a sediment thicknesses map obtained from the inversion of gravimetric data were integrated with a surface fault map to produce a subsidence hazard zoning in the city of Aguascalientes, Mexico. The resulting zoning is presented together with specific recommendations about geotechnical studies needed for further evaluation of surface faulting in these hazard zones. The derived zoning map consists in four zones including null hazard (stable terrain without subsidence), low hazard (areas prone to subsidence), medium hazard (zones with subsidence) and high hazard (zones with surface faulting). InSAR results displayed subsidence LOS velocities up to 10 cm/year and two subsidence areas unknown before this study. Gravimetric results revealed that the thicker sediment sequence is located toward north of Aguascalientes City reaching up to 600 m in thickness, which correspond to a high subsidence LOS velocity zone (up to 6 cm/year). Full article
(This article belongs to the Special Issue Earth Observations for Geohazards) Printed Edition available
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Open AccessArticle
Quality Assessment of Roof Planes Extracted from Height Data for Solar Energy Systems by the EAGLE Platform
Remote Sens. 2015, 7(12), 17016-17034; https://doi.org/10.3390/rs71215866
Received: 8 July 2015 / Revised: 30 November 2015 / Accepted: 7 December 2015 / Published: 17 December 2015
Cited by 6 | Viewed by 2287 | PDF Full-text (4934 KB) | HTML Full-text | XML Full-text
Abstract
Due to the increasing scarcity of fossil fuels and the upwards trend in energy costs over time, many countries—especially in Europe—have begun to modify their energy policies aiming to increase that percentage obtained from renewable energies. The EAGLE (FP7 program, European Commission) has [...] Read more.
Due to the increasing scarcity of fossil fuels and the upwards trend in energy costs over time, many countries—especially in Europe—have begun to modify their energy policies aiming to increase that percentage obtained from renewable energies. The EAGLE (FP7 program, European Commission) has developed a web-based platform to promote renewable energy systems (RES) in the public and private sectors, and to deliver a comprehensive information source for all interested users. In this paper, a comprehensive quality assessment of extracted roof planes suitable for solar energy installations (photovoltaic, solar thermal) from height data derived automatically from both LiDAR (Light Detection and Ranging) and aerial images will be presented. A shadow analysis is performed regarding the daily path of the sun including the shading effects of nearby objects (chimneys, dormers, vegetation, buildings, topography, etc.). A quality assessment was carried out for both LiDAR and aerial images of the same test sites in UK and Germany concerning building outline accuracy, extraction rate of roof planes and the accuracy of their geometric parameters (inclination and aspect angle, size). The benefit is an optimized system to extract roof planes for RES with a high level of detail, accuracy and flexibility (concerning different commonly available data sources) including an estimation of quality of the results which is important for individual house owners as well as for regional applications by governments or solar energy companies to judge their usefulness. Full article
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Open AccessFeature PaperEditorial
Preface: Remote Sensing in Flood Monitoring and Management
Remote Sens. 2015, 7(12), 17013-17015; https://doi.org/10.3390/rs71215871
Received: 14 December 2015 / Accepted: 15 December 2015 / Published: 16 December 2015
Cited by 9 | Viewed by 3110 | PDF Full-text (150 KB) | HTML Full-text | XML Full-text
Abstract
This Special Issue is a collection of papers studying the use of remote sensing data and methods for flood monitoring and management. The articles contributed span a wide range of topics and present novel processing techniques, review methods and discuss limitations, and also [...] Read more.
This Special Issue is a collection of papers studying the use of remote sensing data and methods for flood monitoring and management. The articles contributed span a wide range of topics and present novel processing techniques, review methods and discuss limitations, and also report on current capabilities and outline emerging needs. This preface provides a brief overview of the content. [...] Full article
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
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Open AccessArticle
Impact of Environmental Factors on the Spectral Characteristics of Lava Surfaces: Field Spectrometry of Basaltic Lava Flows on Tenerife, Canary Islands, Spain
Remote Sens. 2015, 7(12), 16986-17012; https://doi.org/10.3390/rs71215864
Received: 31 August 2015 / Revised: 1 December 2015 / Accepted: 9 December 2015 / Published: 16 December 2015
Cited by 4 | Viewed by 4045 | PDF Full-text (9072 KB) | HTML Full-text | XML Full-text
Abstract
We report on spectral reflectance measurements of basaltic lava flows on Tenerife Island, Spain. Lava flow surfaces of different ages, surface roughness and elevations were systematically measured using a field spectroradiometer operating in the range of 350–2500 nm. Surface roughness, oxidation and lichen [...] Read more.
We report on spectral reflectance measurements of basaltic lava flows on Tenerife Island, Spain. Lava flow surfaces of different ages, surface roughness and elevations were systematically measured using a field spectroradiometer operating in the range of 350–2500 nm. Surface roughness, oxidation and lichen coverage were documented at each measured site. Spectral properties vary with age and morphology of lava. Pre-historical lavas with no biological coverage show a prominent increase in spectral reflectance in the 400–760 nm range and a decrease in the 2140–2210 nm range. Pāhoehoe surfaces have higher reflectance values than ʻaʻā ones and attain a maximum reflectance at wavelengths < 760 nm. Lichen-covered lavas are characterized by multiple lichen-related absorption and reflection features. We demonstrate that oxidation and lichen growth are two major factors controlling spectra of Tenerife lava surfaces and, therefore, propose an oxidation index and a lichen index to quantify surface alterations of lava flows: (1) the oxidation index is based on the increase of the slope of the spectral profile from blue to red as the field-observed oxidation level strengthens; and (2) the lichen index is based on the spectral reflectance in the 1660–1725 nm range, which proves to be highly correlated with lichen coverage documented in the field. The two spectral indices are applied to Landsat ETM+ and Hyperion imagery of the study area for mapping oxidation and lichen coverage on lava surfaces, respectively. Hyperion is shown to be capable of discriminating different volcanic surfaces, i.e., tephra vs. lava and oxidized lava vs. lichen-covered lava. Our study highlights the value of field spectroscopic measurements to aid interpretation of lava flow characterization using satellite images and of the effects of environmental factors on lava surface evolution over time, and, therefore, has the potential to contribute to the mapping as well as dating of lava surfaces. Full article
(This article belongs to the Special Issue Volcano Remote Sensing)
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Open AccessArticle
A New Approach to the Generation of Orthoimages of Cultural Heritage Objects—Integrating TLS and Image Data
Remote Sens. 2015, 7(12), 16963-16985; https://doi.org/10.3390/rs71215869
Received: 30 June 2015 / Revised: 20 November 2015 / Accepted: 9 December 2015 / Published: 16 December 2015
Cited by 10 | Viewed by 2530 | PDF Full-text (10494 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper discusses the issue of automation of orthoimage generation based on Terrestrial Laser Scanning (TLS) data and digital images. The following two problems are discussed: automatic generation of projection planes based on TLS data, and automatic orientation of digital images in relation [...] Read more.
This paper discusses the issue of automation of orthoimage generation based on Terrestrial Laser Scanning (TLS) data and digital images. The following two problems are discussed: automatic generation of projection planes based on TLS data, and automatic orientation of digital images in relation to TLS data. The majority of popular software applications use manual definitions of projection planes. However, the authors propose an original software tool to address the first issue, which defines important planes based on a TLS point cloud utilizing different algorithms (RANdom SAmple Consensus–RANSAC, Hough transform, “region growing”). To address the second task, the authors present a series of algorithms for automated digital image orientation in relation to a point cloud. This is important in cases where scans and images are acquired from different places and at different times. The algorithms utilize Scale Invariant Feature Transform(SIFT) operators in order to find points that correspond in reflectance intensity between coloure images (Red Green Blue—RGB) and orthoimages, based on TLS data. The paper also presents a verification method using SIFT and Speeded-Up Robust Features (SURF) operators. The research results in an original tool and applied Computer Vision(CV) algorithms that improve the process of orthoimage generation. Full article
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Open AccessArticle
Ability of the Photochemical Reflectance Index to Track Light Use Efficiency for a Sub-Tropical Planted Coniferous Forest
Remote Sens. 2015, 7(12), 16938-16962; https://doi.org/10.3390/rs71215860
Received: 27 September 2015 / Revised: 19 November 2015 / Accepted: 7 December 2015 / Published: 15 December 2015
Cited by 9 | Viewed by 2740 | PDF Full-text (10139 KB) | HTML Full-text | XML Full-text
Abstract
Light use efficiency (LUE) models are widely used to estimate gross primary productivity (GPP), a dominant component of the terrestrial carbon cycle. Their outputs are very sensitive to LUE. Proper determination of this parameter is a prerequisite for LUE models to simulate GPP [...] Read more.
Light use efficiency (LUE) models are widely used to estimate gross primary productivity (GPP), a dominant component of the terrestrial carbon cycle. Their outputs are very sensitive to LUE. Proper determination of this parameter is a prerequisite for LUE models to simulate GPP at regional and global scales. This study was devoted to investigating the ability of the photochemical reflectance index (PRI) to track LUE variations for a sub-tropical planted coniferous forest in southern China using tower-based PRI and GPP measurements over the period from day 101 to 275 in 2013. Both half-hourly PRI and LUE exhibited detectable diurnal and seasonal variations, and decreased with increases of vapor pressure deficit (VPD), air temperature (Ta), and photosynthetically active radiation (PAR). Generally, PRI is able to capture diurnal and seasonal changes in LUE. However, correlations of PRI with LUE varied dramatically throughout the growing season. The correlation was the strongest (R2 = 0.6427, p < 0.001) in July and the poorest in May. Over the entire growing season, PRI relates better to LUE under clear or partially cloudy skies (clearness index, CI > 0.3) with moderate to high VPD (>20 hPa) and high temperatures (>31 C). Overall, we found that PRI is most sensitive to variations in LUE under stressed conditions, and the sensitivity decreases as the growing conditions become favorable when atmosphere water vapor, temperature and soil moisture are near the optimum conditions. Full article
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
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Open AccessArticle
Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images
Remote Sens. 2015, 7(12), 16917-16937; https://doi.org/10.3390/rs71215861
Received: 16 October 2015 / Revised: 28 November 2015 / Accepted: 10 December 2015 / Published: 15 December 2015
Cited by 35 | Viewed by 2943 | PDF Full-text (8328 KB) | HTML Full-text | XML Full-text
Abstract
Urban tree species mapping is an important prerequisite to understanding the value of urban vegetation in ecological services. In this study, we explored the potential of bi-temporal WorldView-2 (WV2, acquired on 14 September 2012) and WorldView-3 images (WV3, acquired on 18 October 2014) [...] Read more.
Urban tree species mapping is an important prerequisite to understanding the value of urban vegetation in ecological services. In this study, we explored the potential of bi-temporal WorldView-2 (WV2, acquired on 14 September 2012) and WorldView-3 images (WV3, acquired on 18 October 2014) for identifying five dominant urban tree species with the object-based Support Vector Machine (SVM) and Random Forest (RF) methods. Two study areas in Beijing, China, Capital Normal University (CNU) and Beijing Normal University (BNU), representing the typical urban environment, were evaluated. Three classification schemes—classification based solely on WV2; WV3; and bi-temporal WV2 and WV3 images—were examined. Our study showed that the single-date image did not produce satisfying classification results as both producer and user accuracies of tree species were relatively low (44.7%–82.5%), whereas those derived from bi-temporal images were on average 10.7% higher. In addition, the overall accuracy increased substantially (9.7%–20.2% for the CNU area and 4.7%–12% for BNU). A thorough analysis concluded that near-infrared 2, red-edge and green bands are always more important than the other bands to classification, and spectral features always contribute more than textural features. Our results also showed that the scattered distribution of trees and a more complex surrounding environment reduced classification accuracy. Comparisons between SVM and RF classifiers suggested that SVM is more effective for urban tree species classification as it outperforms RF when working with a smaller amount and imbalanced distribution of samples. Full article
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Open AccessErratum
Erratum: Kromer, R.A.; et al. A 4D Filtering and Calibration Technique for Small-Scale Point Cloud Change Detection with a Terrestrial Laser Scanner. Remote Sensing 2015, 7, 13029–13052
Remote Sens. 2015, 7(12), 16915-16916; https://doi.org/10.3390/rs71215865
Received: 23 November 2015 / Revised: 30 November 2015 / Accepted: 11 December 2015 / Published: 15 December 2015
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Abstract
The Remote Sensing Editorial Office wishes to make the following erratum to this paper [1]. [...] Full article
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Open AccessArticle
The Effect of Epidermal Structures on Leaf Spectral Signatures of Ice Plants (Aizoaceae)
Remote Sens. 2015, 7(12), 16901-16914; https://doi.org/10.3390/rs71215862
Received: 1 October 2015 / Revised: 26 November 2015 / Accepted: 6 December 2015 / Published: 15 December 2015
Cited by 9 | Viewed by 2762 | PDF Full-text (2305 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Epidermal structures (ES) of leaves are known to affect the functional properties and spectral responses. Spectral studies focused mostly on the effect of hairs or wax layers only. We studied a wider range of different ES and their impact on spectral properties. Additionally, [...] Read more.
Epidermal structures (ES) of leaves are known to affect the functional properties and spectral responses. Spectral studies focused mostly on the effect of hairs or wax layers only. We studied a wider range of different ES and their impact on spectral properties. Additionally, we identified spectral regions that allow distinguishing different ES. We used a field spectrometer to measure ex situ leaf spectral responses from 350 nm–2500 nm. A spectral library for 25 species of the succulent family Aizoaceae was assembled. Five functional types were defined based on ES: flat epidermal cell surface, convex to papillary epidermal cell surface, bladder cells, hairs and wax cover. We tested the separability of ES using partial least squares discriminant analysis (PLS-DA) based on the spectral data. Subsequently, variable importance (VIP) was calculated to identify spectral regions relevant for discriminating our functional types (classes). Classification performance was high, with a kappa value of 0.9 indicating well-separable spectral classes. VIP calculations identified six spectral regions of increased importance for the classification. We confirmed and extended previous findings regarding the visible-near-infrared spectral region. Our experiments also confirmed that epidermal leaf traits can be classified due to clearly distinguishable spectral signatures across species and genera within the Aizoaceae. Full article
(This article belongs to the Special Issue Field Spectroscopy and Radiometry)
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Open AccessArticle
Accuracy and Precision of Habitat Structural Complexity Metrics Derived from Underwater Photogrammetry
Remote Sens. 2015, 7(12), 16883-16900; https://doi.org/10.3390/rs71215859
Received: 20 September 2015 / Revised: 27 November 2015 / Accepted: 7 December 2015 / Published: 15 December 2015
Cited by 40 | Viewed by 3710 | PDF Full-text (2483 KB) | HTML Full-text | XML Full-text
Abstract
In tropical reef ecosystems corals are the key habitat builders providing most ecosystem structure, which influences coral reef biodiversity and resilience. Remote sensing applications have progressed significantly and photogrammetry together with application of structure from motion software is emerging as a leading technique [...] Read more.
In tropical reef ecosystems corals are the key habitat builders providing most ecosystem structure, which influences coral reef biodiversity and resilience. Remote sensing applications have progressed significantly and photogrammetry together with application of structure from motion software is emerging as a leading technique to create three-dimensional (3D) models of corals and reefs from which biophysical properties of structural complexity can be quantified. This enables the addressing of a range of important marine research questions, such as what the role of habitat complexity is in driving key ecological processes (i.e., foraging). Yet, it is essential to assess the accuracy and precision of photogrammetric measurements to support their application in mapping, monitoring and quantifying coral reef form and structure. This study evaluated the precision (by repeated modeling) and accuracy (by comparison with laser reference models) of geometry and structural complexity metrics derived from photogrammetric 3D models of marine benthic habitat at two ecologically relevant spatial extents; individual coral colonies of a range of common morphologies and patches of reef area of 100s of square metres. Surface rugosity measurements were generally precise across all morphologies and spatial extents with average differences in the geometry of replicate models of 1–6 mm for coral colonies and 25 mm for the reef area. Precision decreased with complexity of the coral morphology, with metrics for small massive corals being the most precise (1% coefficient of variation (CV) in surface rugosity) and metrics for bottlebrush corals being the least precise (10% CV in surface rugosity). There was no indication however that precision was related to complexity for the patch-scale modelling. The 3D geometry of coral models differed by only 1–3 mm from laser reference models. However, high spatial variation in these differences around the model led to a consistent underestimation of surface rugosity values for all morphs of between 8% and 37%. This study highlights the utility of several off-the-shelf photogrammetry tools for the measurement of structural complexity across a range of scales relevant to ecologist and managers. It also provides important information on the accuracy and precision of these systems which should allow for their targeted use by non-experts in computer vision within these contexts. Full article
(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)
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Open AccessArticle
Autonomous Chemical Vapour Detection by Micro UAV
Remote Sens. 2015, 7(12), 16865-16882; https://doi.org/10.3390/rs71215858
Received: 11 August 2015 / Revised: 29 October 2015 / Accepted: 7 December 2015 / Published: 11 December 2015
Cited by 4 | Viewed by 2530 | PDF Full-text (3578 KB) | HTML Full-text | XML Full-text
Abstract
The ability to remotely detect and map chemical vapour clouds in open air environments is a topic of significant interest to both defence and civilian communities. In this study, we integrate a prototype miniature colorimetric chemical sensor developed for methyl salicylate (MeS), as [...] Read more.
The ability to remotely detect and map chemical vapour clouds in open air environments is a topic of significant interest to both defence and civilian communities. In this study, we integrate a prototype miniature colorimetric chemical sensor developed for methyl salicylate (MeS), as a model chemical vapour, into a micro unmanned aerial vehicle (UAV), and perform flights through a raised MeS vapour cloud. Our results show that that the system is capable of detecting MeS vapours at low ppm concentration in real-time flight and rapidly sending this information to users by on-board telemetry. Further, the results also indicate that the sensor is capable of distinguishing “clean” air from “dirty”, multiple times per flight, allowing us to look towards autonomous cloud mapping and source localization applications. Further development will focus on a broader range of integrated sensors, increased autonomy of detection and improved engineering of the system. Full article
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Open AccessArticle
On the Role of Land Surface Temperature as Proxy of Soil Moisture Status for Drought Monitoring in Europe
Remote Sens. 2015, 7(12), 16849-16864; https://doi.org/10.3390/rs71215857
Received: 16 October 2015 / Revised: 20 November 2015 / Accepted: 6 December 2015 / Published: 11 December 2015
Cited by 6 | Viewed by 2563 | PDF Full-text (4049 KB) | HTML Full-text | XML Full-text
Abstract
Remotely sensed Land Surface Temperature (LST) represents a valuable source of data for a simple modelling of the dynamic of soil moisture (SM) over large areas. In this paper we evaluated the capability of LST monthly anomalies, derived from the MOD11C3 standard product, [...] Read more.
Remotely sensed Land Surface Temperature (LST) represents a valuable source of data for a simple modelling of the dynamic of soil moisture (SM) over large areas. In this paper we evaluated the capability of LST monthly anomalies, derived from the MOD11C3 standard product, to capture the SM dynamic as modelled over Europe by means of an ensemble of three land surface models. The direct use of LST as proxy of SM outperformed other LST-derived quantities, such as surface-to-air temperature gradient and day-night temperature variations, returning significant correlation values over the whole domain. LST performed better over Southern Europe compared to the Northern part of the domain, with the best results over areas characterized by water-limited conditions and moderate stress. Additionally, the analysis of the contingency matrix shows that the LST model is skillful in capturing extreme dry SM events, and it also has a good overall capability to correctly detect the dry events in 66% of the cases, with an average probability of false alarm of about 30%. Overall, the use of LST anomalies seems a promising starting point for a reliable modelling of the SM dynamic with a minimum amount of information. Even if the adopted approach is simple, the results are encouraging for a practical use of LST in an operational drought monitoring system over the study area. Full article
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Open AccessArticle
Radiometric Block Adjustment for Multi-Strip Airborne Waveform Lidar Data
Remote Sens. 2015, 7(12), 16831-16848; https://doi.org/10.3390/rs71215856
Received: 31 July 2015 / Revised: 16 November 2015 / Accepted: 7 December 2015 / Published: 11 December 2015
Cited by 2 | Viewed by 2118 | PDF Full-text (12490 KB) | HTML Full-text | XML Full-text
Abstract
The airborne lidar system has been shown to be an effective and reliable method for spatial data collection. Lidar records the coordinates of point and intensity, dependent on range, incident angle, reflectivity of object, atmospheric condition, and several external factors. To fully utilize [...] Read more.
The airborne lidar system has been shown to be an effective and reliable method for spatial data collection. Lidar records the coordinates of point and intensity, dependent on range, incident angle, reflectivity of object, atmospheric condition, and several external factors. To fully utilize the intensity of a lidar system, several researchers have proposed correction models from lidar equations. The radiometric correction models are divided into physically-oriented models and data-oriented models. The lidar acquisition often contains multiple flight lines, and the radiation energy of each flight line can be calibrated independently by calibration coefficient. However, the calibrated radiances in the overlapped area have slightly different measurements. These parameters should be implicitly taken into account if calibrating radiances back to reflectance using known calibration targets. This study used a single-strip physically-oriented model to obtain a backscattering coefficient and a data-oriented model to obtain corrected intensity. We then selected homogeneous tie regions in the overlapped areas, and the differences between strips were compensated by gain and offset parameters in multi-strip radiometric block adjustment. The results were evaluated by the radiometric differences. Nine strips were acquired by Rigel Q680i system, and the experimental results showed that the delta intensity and delta backscattering coefficient of tie regions were improved up to 60% after multi-strip block adjustment. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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Open AccessArticle
A Comparison of the Performance of Bias-Corrected RSMs and RFMs for the Geo-Positioning of High-Resolution Satellite Stereo Imagery
Remote Sens. 2015, 7(12), 16815-16830; https://doi.org/10.3390/rs71215855
Received: 9 October 2015 / Revised: 19 November 2015 / Accepted: 5 December 2015 / Published: 10 December 2015
Cited by 3 | Viewed by 1727 | PDF Full-text (2397 KB) | HTML Full-text | XML Full-text
Abstract
High-resolution stereo satellite imagery is widely used in environmental monitoring, topographic mapping, and urban three-dimensional (3D) reconstruction. However, a critical issue in these applications using high-resolution stereo satellite imagery is to improve the accuracy of point geo-positioning. This paper presents a framework for [...] Read more.
High-resolution stereo satellite imagery is widely used in environmental monitoring, topographic mapping, and urban three-dimensional (3D) reconstruction. However, a critical issue in these applications using high-resolution stereo satellite imagery is to improve the accuracy of point geo-positioning. This paper presents a framework for comparison of the performance of the three-dimensional (3D) geo-positioning of the bias-corrected Rigorous Sensor Models (RSMs) and rational function models (RFMs) with respect to the high-resolution QuickBird stereo images in three spaces (i.e., orbital space, image space and object space). The compared models include a bias-corrected RSM in the orbital space, a bias-corrected RSM and RFM in the image space, and a bias-corrected RSM and RFM in the object space. In the comparison, the RSMs and RFMs use the vendor-provided orbit data and Rational Polynomial Coefficients (RPCs), respectively. The experimental results indicated that, (1) these five bias-corrected models can provide a sub-pixel geo-positioning accuracy. With the zero-order polynomial correction model in the orbital space and a minimum of three Ground Control Points (GCPs), the accuracy based on RPCs better than 0.8 m in horizontal direction and 1.3 m in vertical direction. With an increase in the number of GCPs, or in the order of correction models, the regenerated orbital parameters achieve a slight improved positioning accuracy of 0.5 m in horizontal direction and 0.8 m in vertical direction with 25 GCPs, which indicates that the low-order correction model in the orbital space can accurately model the effects of ephemeris and attitude errors; (2) the performances of bias-corrected RSM and RFM in image space are rather similar. However, the bias-corrected RSM and RFM in image space achieve a better accuracy than the bias-corrected RSM and RFM in object space, with the same configuration of GCPs. Full article
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Open AccessArticle
Improving Estimation of Evapotranspiration under Water-Limited Conditions Based on SEBS and MODIS Data in Arid Regions
Remote Sens. 2015, 7(12), 16795-16814; https://doi.org/10.3390/rs71215854
Received: 28 September 2015 / Revised: 30 November 2015 / Accepted: 7 December 2015 / Published: 10 December 2015
Cited by 18 | Viewed by 2162 | PDF Full-text (8607 KB) | HTML Full-text | XML Full-text
Abstract
This study proposes a method for improving the estimation of surface turbulent fluxes in surface energy balance system (SEBS) model under water stress conditions using MODIS data. The normalized difference water index (NDWI) as an indicator of water stress is integrated into SEBS. [...] Read more.
This study proposes a method for improving the estimation of surface turbulent fluxes in surface energy balance system (SEBS) model under water stress conditions using MODIS data. The normalized difference water index (NDWI) as an indicator of water stress is integrated into SEBS. To investigate the feasibility of the new approach, the desert-oasis region in the middle reaches of the Heihe River Basin (HRB) is selected as the study area. The proposed model is calibrated with meteorological and flux data over 2008–2011 at the Yingke station and is verified with data from 16 stations of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project in 2012. The results show that soil moisture significantly affects evapotranspiration (ET) under water stress conditions in the study area. Adding the NDWI in SEBS can significantly improve the estimations of surface turbulent fluxes in water-limited regions, especially for spare vegetation cover area. The daily ET maps generated by the new model also show improvements in drylands with low ET values. This study demonstrates that integrating the NDWI into SEBS as an indicator of water stress is an effective way to improve the assessment of the regional ET in semi-arid and arid regions. Full article
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