Remote Sensing

3882 KiB

Open AccessArticle

Assessment of GPM and TRMM Multi-Satellite Precipitation Products in Streamflow Simulations in a Data-Sparse Mountainous Watershed in Myanmar

by Fei Yuan, Limin Zhang, Khin Wah Wah Win, Liliang Ren, Chongxu Zhao, Yonghua Zhu, Shanhu Jiang and Yi Liu

Remote Sens. 2017, 9(3), 302; https://doi.org/10.3390/rs9030302 - 22 Mar 2017

Cited by 130 | Viewed by 9502

Abstract

Satellite precipitation products from the Global Precipitation Measurement (GPM) mission and its predecessor the Tropical Rainfall Measuring Mission (TRMM) are a critical data source for hydrological applications in ungauged basins. This study conducted an initial and early evaluation of the performance of the [...] Read more.

Satellite precipitation products from the Global Precipitation Measurement (GPM) mission and its predecessor the Tropical Rainfall Measuring Mission (TRMM) are a critical data source for hydrological applications in ungauged basins. This study conducted an initial and early evaluation of the performance of the Integrated Multi-satellite Retrievals for GPM (IMERG) final run and the TRMM Multi-satellite Precipitation Analysis 3B42V7 precipitation products, and their feasibility in streamflow simulations in the Chindwin River basin, Myanmar, from April 2014 to December 2015 was also assessed. Results show that, although IMERG and 3B42V7 can potentially capture the spatiotemporal patterns of historical precipitation, the two products contain considerable errors. Compared with 3B42V7, no significant improvements were found in IMERG. Moreover, 3B42V7 outperformed IMERG at daily and monthly scales and in heavy rain detections at four out of five gauges. The large errors in IMERG and 3B42V7 distinctly propagated to streamflow simulations via the Xinanjiang hydrological model, with a significant underestimation of total runoff and high flows. The bias correction of the satellite precipitation effectively improved the streamflow simulations. The 3B42V7-based streamflow simulations performed better than the gauge-based simulations. In general, IMERG and 3B42V7 are feasible for use in streamflow simulations in the study area, although 3B42V7 is better suited than IMERG. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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4877 KiB

Open AccessArticle

Evaluation of Satellite Retrievals of Chlorophyll-a in the Arabian Gulf

by Noora Al-Naimi, Dionysios E. Raitsos, Radhouan Ben-Hamadou and Yousria Soliman

Remote Sens. 2017, 9(3), 301; https://doi.org/10.3390/rs9030301 - 22 Mar 2017

Cited by 44 | Viewed by 8347

Abstract

The Arabian Gulf is a highly turbid, shallow sedimentary basin whose coastal areas have been classified as optically complex Case II waters (where ocean colour sensors have been proved to be unreliable). Yet, there is no such study assessing the performance and quality [...] Read more.

The Arabian Gulf is a highly turbid, shallow sedimentary basin whose coastal areas have been classified as optically complex Case II waters (where ocean colour sensors have been proved to be unreliable). Yet, there is no such study assessing the performance and quality of satellite ocean-colour datasets in relation to ground truth data in the Gulf. Here, using a unique set of in situ Chlorophyll-a measurements (Chl-a; an index of phytoplankton biomass), collected from 24 locations in four transects in the central Gulf over six recent research cruises (2015–2016), we evaluated the performance of VIIRS and other merged satellite datasets, for the first time in the region. A highly significant relationship was found (r = 0.795, p < 0.001), though a clear overestimation in satellite-derived Chl-a concentrations is evident. Regardless of this constant overestimation, the remotely sensed Chl-a observations illustrated adequately the seasonal cycles. Due to the optically complex environment, the first optical depth was calculated to be on average 6–10 m depth, and thus the satellite signal is not capturing the deep chlorophyll maximum (DCM at ~25 m). Overall, the ocean colour sensors’ performance was comparable to other Case II waters in other regions, supporting the use of satellite ocean colour in the Gulf. Yet, the development of a regional-tuned algorithm is needed to account for the unique environmental conditions of the Gulf, and ultimately provide a better estimation of surface Chl-a in the region. Full article

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22860 KiB

Open AccessFeature PaperArticle

Elevation Change and Improved Velocity Retrieval Using Orthorectified Optical Satellite Data from Different Orbits

by Bas Altena and Andreas Kääb

Remote Sens. 2017, 9(3), 300; https://doi.org/10.3390/rs9030300 - 22 Mar 2017

Cited by 27 | Viewed by 10182

Abstract

Optical satellite products are available at different processing levels. Of these products, terrain corrected (i.e., orthorectified) products are the ones mostly used for glacier displacement estimation. For terrain correction, a digital elevation model (DEM) is used that typically stems from various data sources [...] Read more.

Optical satellite products are available at different processing levels. Of these products, terrain corrected (i.e., orthorectified) products are the ones mostly used for glacier displacement estimation. For terrain correction, a digital elevation model (DEM) is used that typically stems from various data sources with variable qualities, from dispersed time instances, or with different spatial resolutions. Consequently, terrain representation used for orthorectifying satellite images is often in disagreement with reality at image acquisition. Normally, the lateral orthoprojection offsets resulting from vertical DEM errors are taken into account in the geolocation error budget of the corrected images, or may even be neglected. The largest offsets of this type are often found over glaciers, as these may show strong elevation changes over time and thus large elevation errors in the reference DEM with respect to image acquisition. The detection and correction of such orthorectification offsets is further complicated by ice flow which adds a second offset component to the displacement vectors between orthorectified data. Vice versa, measurement of glacier flow is complicated by the inherent superposition of ice movement vectors and orthorectification offset vectors. In this study, we try to estimate these orthorectification offsets in the presence of terrain movement and translate them to elevation biases in the reference surface. We demonstrate our method using three different sites which include very dynamic glaciers. For the Oriental Glacier, an outlet of the Southern Patagonian icefield, Landsat 7 and 8 data from different orbits enabled the identification of trends related to elevation change. For the Aletsch Glacier, Swiss Alps, we assess the terrain offsets of both Landsat 8 and Sentinel-2A: a superior DEM appears to be used for Landsat in comparison to Sentinel-2, however a systematic bias is observed in the snow covered areas. Lastly, we demonstrate our methodology in a pipeline structure; displacement estimates for the Helheim-glacier, in Greenland, are mapped and corrected for orthorectification offsets between data from different orbits, which enables a twice as dense a temporal resolution of velocity data, as compared to the standard method of measuring velocities from repeat-orbit data only. In addition, we introduce and implement a novel matching method which uses image triplets. By formulating the three image displacements as a convolution, a geometric constraint can be exploited. Such a constraint enhances the reliability of the displacement estimations. Furthermore the implementation is simple and computationally swift. Full article

(This article belongs to the Special Issue Remote Sensing of Glaciers)

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10479 KiB

Open AccessArticle

First Results from Sentinel-1A InSAR over Australia: Application to the Perth Basin

by Amy L. Parker, Mick S. Filmer and Will E. Featherstone

Remote Sens. 2017, 9(3), 299; https://doi.org/10.3390/rs9030299 - 22 Mar 2017

Cited by 33 | Viewed by 8538

Abstract

Past ground-based geodetic measurements in the Perth Basin, Australia, record small-magnitude subsidence (up to 7 mm/y), but are limited to discrete points or traverses across parts of the metropolitan area. Here, we investigate deformation over a much larger region by performing the first [...] Read more.

Past ground-based geodetic measurements in the Perth Basin, Australia, record small-magnitude subsidence (up to 7 mm/y), but are limited to discrete points or traverses across parts of the metropolitan area. Here, we investigate deformation over a much larger region by performing the first application of Sentinel-1A InSAR data to Australia. The duration of the study is short (0.7 y), as dictated by the availability of Sentinel-1A data. Despite this limited observation period, verification of Sentinel-1A with continuous GPS and independent TerraSAR-X provides new insights into the deformation field of the Perth Basin. The displacements recorded by each satellite are in agreement, identifying broad (>5 km wide) areas of subsidence at rates up to 15 mm/y. Subsidence at rates greater than 20 mm/y over smaller regions (∼2 km wide) is coincident with wetland areas, where displacements are temporally correlated with changes in groundwater levels in the unconfined aquifer. Longer InSAR time series are required to determine whether these measured displacements are representative of long-term deformation or (more likely) seasonal variations. However, the agreement between datasets demonstrates the ability of Sentinel-1A to detect small-magnitude deformation over different spatial scales (from 2 km–10 s of km) in the Perth Basin. We suggest that, even over short time periods, these data are useful as a reconnaissance tool to identify regions for subsequent targeted studies, particularly given the large swath size of radar acquisitions, which facilitates analysis of a broader portion of the deformation field than ground-based methods or single scenes of TerraSAR-X. Full article

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Open AccessArticle

A Sparse SAR Imaging Method Based on Multiple Measurement Vectors Model

by Dongyang Ao, Rui Wang, Cheng Hu and Yuanhao Li

Remote Sens. 2017, 9(3), 297; https://doi.org/10.3390/rs9030297 - 22 Mar 2017

Cited by 29 | Viewed by 5980

Abstract

In recent decades, compressive sensing (CS) is a popular theory for studying the inverse problem, and has been widely used in synthetic aperture radar (SAR) image processing. However, the computation complexity of CS-based methods limits its wide applications in SAR imaging. In this [...] Read more.

In recent decades, compressive sensing (CS) is a popular theory for studying the inverse problem, and has been widely used in synthetic aperture radar (SAR) image processing. However, the computation complexity of CS-based methods limits its wide applications in SAR imaging. In this paper, we propose a novel sparse SAR imaging method using the Multiple Measurement Vectors model to reduce the computation cost and enhance the imaging result. Based on using the structure information and the matched filter processing, the new CS-SAR imaging method can be applied to high-quality and high-resolution imaging under sub-Nyquist rate sampling with the advantages of saving the computational cost substantially both in time and memory. The results of simulations and real SAR data experiments suggest that the proposed method can realize SAR imaging effectively and efficiently. Full article

(This article belongs to the Special Issue Radar Systems for the Societal Challenges)

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17264 KiB

Open AccessArticle

Convolutional Recurrent Neural Networks for
Hyperspectral Data Classification

by Hao Wu and Saurabh Prasad

Remote Sens. 2017, 9(3), 298; https://doi.org/10.3390/rs9030298 - 21 Mar 2017

Cited by 233 | Viewed by 16372

Abstract

Deep neural networks, such as convolutional neural networks (CNN) and stacked autoencoders, have recently been successfully used to extract deep features for hyperspectral data classification. Recurrent neural networks (RNN) are another type of neural networks, which are widely used for sequence analysis because [...] Read more.

Deep neural networks, such as convolutional neural networks (CNN) and stacked autoencoders, have recently been successfully used to extract deep features for hyperspectral data classification. Recurrent neural networks (RNN) are another type of neural networks, which are widely used for sequence analysis because they are constructed to extract contextual information from sequences by modeling the dependencies between different time steps. In this paper, we study the ability of RNN for hyperspectral data classification by extracting the contextual information from the data. Specifically, hyperspectral data are treated as spectral sequences, and an RNN is used to model the dependencies between different spectral bands. In addition, we propose to use a convolutional recurrent neural network (CRNN) to learn more discriminative features for hyperspectral data classification. In CRNN, a few convolutional layers are first learned to extract middle-level and locally-invariant features from the input data, and the following recurrent layers are then employed to further extract spectrally-contextual information from the features generated by the convolutional layers. Experimental results on real hyperspectral datasets show that our method provides better classification performance compared to traditional methods and other state-of-the-art deep learning methods for hyperspectral data classification. Full article

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4893 KiB

Open AccessArticle

A 30+ Year AVHRR Land Surface Reflectance Climate Data Record and Its Application to Wheat Yield Monitoring

by Belen Franch, Eric F. Vermote, Jean-Claude Roger, Emilie Murphy, Inbal Becker-Reshef, Chris Justice, Martin Claverie, Jyoteshwar Nagol, Ivan Csiszar, Dave Meyer, Frederic Baret, Edward Masuoka, Robert Wolfe and Sadashiva Devadiga

Remote Sens. 2017, 9(3), 296; https://doi.org/10.3390/rs9030296 - 21 Mar 2017

Cited by 59 | Viewed by 9137

Abstract

The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980s to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land [...] Read more.

The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980s to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR surface reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geolocation, improvement of cloud masking, and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream leaf area index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by Becker-Reshef et al. (2010) and Franch et al. (2015) are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980s, the results have errors equivalent to those derived from MODIS. Full article

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Open AccessArticle

Changes in Landscape Greenness and Climatic Factors over 25 Years (1989–2013) in the USA

by Maliha S. Nash, James Wickham, Jay Christensen and Timothy Wade

Remote Sens. 2017, 9(3), 295; https://doi.org/10.3390/rs9030295 - 21 Mar 2017

Cited by 15 | Viewed by 4687

Abstract

Monitoring and quantifying changes in vegetation cover over large areas using remote sensing can be achieved using the Normalized Difference Vegetation Index (NDVI), an indicator of greenness. However, distinguishing gradual shifts in NDVI (e.g., climate related-changes) versus direct and rapid changes (e.g., fire, [...] Read more.

Monitoring and quantifying changes in vegetation cover over large areas using remote sensing can be achieved using the Normalized Difference Vegetation Index (NDVI), an indicator of greenness. However, distinguishing gradual shifts in NDVI (e.g., climate related-changes) versus direct and rapid changes (e.g., fire, land development) is challenging as changes can be confounded by time-dependent patterns, and variation associated with climatic factors. In the present study, we leveraged a method that we previously developed for a pilot study to address these confounding factors by evaluating NDVI change using autoregression techniques that compare results from univariate (NDVI vs. time) and multivariate analyses (NDVI vs. time and climatic factors) for 7,660,636 1 km × 1 km pixels comprising the 48 contiguous states of the USA, over a 25-year period (1989–2013). NDVI changed significantly for 48% of the nation over the 25-year period in the univariate analyses where most significant trends (85%) indicated an increase in greenness over time. By including climatic factors in the multivariate analyses of NDVI over time, the detection of significant NDVI trends increased to 53% (an increase of 5%). Comparisons of univariate and multivariate analyses for each pixel showed that less than 4% of the pixels had a significant NDVI trend attributable to gradual climatic changes while the remainder of pixels with a significant NDVI trend indicated that changes were due to direct factors. While most NDVI changes were attributable to direct factors like wildfires, drought or flooding of agriculture, and tree mortality associated with insect infestation, these conditions may be indirectly influenced by changes in climatic factors. Full article

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Open AccessArticle

Evaluating Water Controls on Vegetation Growth in the Semi-Arid Sahel Using Field and Earth Observation Data

by Abdulhakim M. Abdi, Niklas Boke-Olén, David E. Tenenbaum, Torbern Tagesson, Bernard Cappelaere and Jonas Ardö

Remote Sens. 2017, 9(3), 294; https://doi.org/10.3390/rs9030294 - 21 Mar 2017

Cited by 18 | Viewed by 7709

Abstract

Water loss is a crucial factor for vegetation in the semi-arid Sahel region of Africa. Global satellite-driven estimates of plant CO₂ uptake (gross primary productivity, GPP) have been found to not accurately account for Sahelian conditions, particularly the impact of canopy water [...] Read more.

Water loss is a crucial factor for vegetation in the semi-arid Sahel region of Africa. Global satellite-driven estimates of plant CO₂ uptake (gross primary productivity, GPP) have been found to not accurately account for Sahelian conditions, particularly the impact of canopy water stress. Here, we identify the main biophysical limitations that induce canopy water stress in Sahelian vegetation and evaluate the relationships between field data and Earth observation-derived spectral products for up-scaling GPP. We find that plant-available water and vapor pressure deficit together control the GPP of Sahelian vegetation through their impact on the greening and browning phases. Our results show that a multiple linear regression (MLR) GPP model that combines the enhanced vegetation index, land surface temperature, and the short-wave infrared reflectance (Band 7, 2105–2155 nm) of the moderate-resolution imaging spectroradiometer satellite sensor was able to explain between 88% and 96% of the variability of eddy covariance flux tower GPP at three Sahelian sites (overall = 89%). The MLR GPP model presented here is potentially scalable at a relatively high spatial and temporal resolution. Given the scarcity of field data on CO₂ fluxes in the Sahel, this scalability is important due to the low number of flux towers in the region. Full article

(This article belongs to the Special Issue Ecophysiological Remote Sensing)

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5578 KiB

Open AccessArticle

Monitoring of Wheat Growth Status and Mapping of Wheat Yield’s within-Field Spatial Variations Using Color Images Acquired from UAV-camera System

by Mengmeng Du and Noboru Noguchi

Remote Sens. 2017, 9(3), 289; https://doi.org/10.3390/rs9030289 - 21 Mar 2017

Cited by 177 | Viewed by 12490

Abstract

Applications of remote sensing using unmanned aerial vehicle (UAV) in agriculture has proved to be an effective and efficient way of obtaining field information. In this study, we validated the feasibility of utilizing multi-temporal color images acquired from a low altitude UAV-camera system [...] Read more.

Applications of remote sensing using unmanned aerial vehicle (UAV) in agriculture has proved to be an effective and efficient way of obtaining field information. In this study, we validated the feasibility of utilizing multi-temporal color images acquired from a low altitude UAV-camera system to monitor real-time wheat growth status and to map within-field spatial variations of wheat yield for smallholder wheat growers, which could serve as references for site-specific operations. Firstly, eight orthomosaic images covering a small winter wheat field were generated to monitor wheat growth status from heading stage to ripening stage in Hokkaido, Japan. Multi-temporal orthomosaic images indicated straightforward sense of canopy color changes and spatial variations of tiller densities. Besides, the last two orthomosaic images taken from about two weeks prior to harvesting also notified the occurrence of lodging by visual inspection, which could be used to generate navigation maps guiding drivers or autonomous harvesting vehicles to adjust operation speed according to specific lodging situations for less harvesting loss. Subsequently orthomosaic images were geo-referenced so that further study on stepwise regression analysis among nine wheat yield samples and five color vegetation indices (CVI) could be conducted, which showed that wheat yield correlated with four accumulative CVIs of visible-band difference vegetation index (VDVI), normalized green-blue difference index (NGBDI), green-red ratio index (GRRI), and excess green vegetation index (ExG), with the coefficient of determination and RMSE as 0.94 and 0.02, respectively. The average value of sampled wheat yield was 8.6 t/ha. The regression model was also validated by using leave-one-out cross validation (LOOCV) method, of which root-mean-square error of predication (RMSEP) was 0.06. Finally, based on the stepwise regression model, a map of estimated wheat yield was generated, so that within-field spatial variations of wheat yield, which was usually seen as general information on soil fertility, water potential, tiller density, etc., could be better understood for applications of site-specific or variable-rate operations. Average yield of the studied field was also calculated according to the map of wheat yield as 7.2 t/ha. Full article

(This article belongs to the Special Issue Remote Sensing of Smallholder Subsistence Agriculture Using Satellites and UAVs)

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31125 KiB

Open AccessArticle

Multi-Stack Persistent Scatterer Interferometry Analysis in Wider Athens, Greece

by Ioannis Papoutsis, Charalampos Kontoes and Demitrios Paradissis

Remote Sens. 2017, 9(3), 276; https://doi.org/10.3390/rs9030276 - 21 Mar 2017

Cited by 11 | Viewed by 6228

Abstract

The wider Athens metropolitan area serves as an interesting setting for conducting geodetic studies. On the one hand, it has a complex regional geotectonic characteristic with several active and blind faults, one of which gave the deadly

M_{w}

5.9 Athens earthquake on [...] Read more.

The wider Athens metropolitan area serves as an interesting setting for conducting geodetic studies. On the one hand, it has a complex regional geotectonic characteristic with several active and blind faults, one of which gave the deadly

M_{w}

5.9 Athens earthquake on September 1999. On the other hand, the Greek capital is heavily urbanized, and construction activities have been taking place in the last few decades to address the city’s needs for advanced infrastructures. This work focuses on estimating ground velocities for the wider Athens area in a period spanning two decades, with an extended spatial coverage, increased spatial sampling of the measurements and at high precision. The aim is to deliver to the community a reference geodetic database containing consistent and robust velocity estimates to support further studies for modeling and multi-hazard assessment. The analysis employs advanced persistent scatterer interferometry methods, covering Athens with both ascending and descending ERS-1, ERS-2 and Envisat Synthetic Aperture Radar data, forming six independent interferometric stacks. A methodology is developed and applied to exploit track diversity for decomposing the actual surface velocity field to its vertical and horizontal components and coping with the post-processing of the multi-track big data. Results of the time series analysis reveal that a large area containing the Kifisia municipality experienced non-linear motion; while it had been subsiding in the period 1992–1995 (−12 mm/year), the same area has been uplifting since 2005 (+4 mm/year). This behavior is speculated to have its origin on the regional water extraction activities, which when halted, led to a physical restoration phase of the municipality. In addition, a zoom in the area inflicted by the 1999 earthquake shows that there were zones of counter-force horizontal movement prior to the event. Further analysis is suggested to investigate the source and tectonic implications of this observation. Full article

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Open AccessArticle

Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over China Using In Situ Data

by Yayong Sun, Shifeng Huang, Jianwei Ma, Jiren Li, Xiaotao Li, Hui Wang, Sheng Chen and Wenbin Zang

Remote Sens. 2017, 9(3), 292; https://doi.org/10.3390/rs9030292 - 20 Mar 2017

Cited by 27 | Viewed by 6703

Abstract

The Soil Moisture Active Passive (SMAP) satellite makes coincident global measurements of soil moisture using an L-band radar instrument and an L-band radiometer. It is crucial to evaluate the errors in the newest L-band SMAP satellite-derived soil moisture products, before they are routinely [...] Read more.

The Soil Moisture Active Passive (SMAP) satellite makes coincident global measurements of soil moisture using an L-band radar instrument and an L-band radiometer. It is crucial to evaluate the errors in the newest L-band SMAP satellite-derived soil moisture products, before they are routinely used in scientific research and applications. This study represents the first evaluation of the SMAP radiometer soil moisture product over China. In this paper, a preliminary evaluation was performed using sparse in situ measurements from 655 China Meteorological Administration (CMA) monitoring stations between 1 April 2015 and 31 August 2016. The SMAP radiometer-derived soil moisture product was evaluated against two schemes of original soil moisture and the soil moisture anomaly in different geographical zones and land cover types. Four performance metrics, i.e., bias, root mean square error (RMSE), unbiased root mean square error (ubRMSE), and the correlation coefficient (R), were used in the accuracy evaluation. The results indicated that the SMAP radiometer-derived soil moisture product agreed relatively well with the in situ measurements, with ubRMSE values of 0.058 cm³·cm⁻³ and 0.039 cm³·cm⁻³ based on original data and anomaly data, respectively. The values of the SMAP radiometer-based soil moisture product were overestimated in wet areas, especially in the Southwest China, South China, Southeast China, East China, and Central China zones. The accuracies over croplands and in Northeast China were the worst. Soil moisture, surface roughness, and vegetation are crucial factors contributing to the error in the soil moisture product. Moreover, radio frequency interference contributes to the overestimation over the northern portion of the East China zone. This study provides guidelines for the application of the SMAP-derived soil moisture product in China and acts as a reference for improving the retrieval algorithm. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)

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0 pages, 2346 KiB

Open AccessArticle

Limited Effects of Water Absorption on Reducing the Accuracy of Leaf Nitrogen Estimation

by Jun Wang, Jing M. Chen, Weimin Ju, Feng Qiu, Qian Zhang, Meihong Fang and Fenge Chen

Remote Sens. 2017, 9(3), 291; https://doi.org/10.3390/rs9030291 - 19 Mar 2017

Cited by 16 | Viewed by 6364

Abstract

Nitrogen is an essential nutrient in many terrestrial ecosystems because it affects vegetation’s primary production. Due to the variety of nitrogen-containing substances and the differences in their composition across species, statistical approaches are now dominant in remote sensing retrieval of leaf nitrogen content. [...] Read more.

Nitrogen is an essential nutrient in many terrestrial ecosystems because it affects vegetation’s primary production. Due to the variety of nitrogen-containing substances and the differences in their composition across species, statistical approaches are now dominant in remote sensing retrieval of leaf nitrogen content. Many studies remove spectral regions characterized by strong water absorptions before retrieving nitrogen content, because water is believed to mask the absorption features of nitrogen. The objectives of this study are to discuss the necessity of this practice and to explore how water absorption affects leaf nitrogen estimation. Spectral measurements and chemical analyses for Maize, Sawtooth Oak, and Sweetgum leaves were carried out in 2014. The leaf optical properties model PROSPECT5 was used to eliminate the influences of water on the measured reflectance spectra. The inversion accuracy of PROPECT5 for chlorophyll, carotenoid, water, and dry matter of Maize was also discussed. Measured, simulated, and water-removed spectra were used to: (1) find the optimal nitrogen-related spectral index; and (2) regress with the area-based leaf nitrogen concentration (LNC) using the partial least square regression technique (PLSR). Two types of spectral indices were selected in this study: Normalized Difference Spectral Index (NDSI) and Ratio Spectral Index (RSI). Additionally, first-order derivative forms of measured, simulated, and water-removed spectra were devised to search for the optimal spectral indices. Finally, species-specific optimal indices and cross-species optimal indices, as well as their root mean square errors (RMSE) and coefficients of determination (R²), were obtained. The Ending Top Percentile (ETP), an indicator of the performance of cross-species optimal indices, was also calculated. PLSR was combined with leave-one-out cross validation (LOOCV) for each species. The predicted root mean square errors (RMSEP) and predicted R² were finally calculated. The results showed that chlorophyll, carotenoid, and water contents could be estimated with R² of 0.75, 0.59, and 0.69, respectively, which were acceptable for fresh leaves. The dry matter was retrieved with a relatively lower accuracy because of the fixed absorption coefficients adopted by PROSPECT5. The performances of species-specific optimal indices using water-free spectra were comparable to or worse than the corresponding indices derived with measured or simulated spectra. Compared with measured spectra, ETP did not change much after the effects of water were removed, and the R² between cross-species optimal spectral indices and area-based LNC for Sawtooth Oak and Sweetgum decreased while it remained almost the same for Maize, suggesting that the water-removed cross-species optimal indices were inferior to the corresponding optimal indices found without water removal. ETP was larger than 30% for all spectra, demonstrating the non-existence of common optimal NDSI or RSI for the three species. After water removal, the accuracy of PLSR for Sawtooth Oak and Sweetgum decreased and increased negligibly for Maize. The results suggest that water absorption has limited effects on reducing the accuracy of leaf nitrogen estimation. On the contrary, the accuracy may decrease due to the loss of spectral information caused by the removal of water-sensitive spectral regions. Full article

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15511 KiB

Open AccessArticle

Reproducibility and Practical Adoption of GEOBIA with Open-Source Software in Docker Containers

by Christian Knoth and Daniel Nüst

Remote Sens. 2017, 9(3), 290; https://doi.org/10.3390/rs9030290 - 18 Mar 2017

Cited by 21 | Viewed by 9509

Abstract

Geographic Object-Based Image Analysis (GEOBIA) mostly uses proprietary software,
but the interest in Free and Open-Source Software (FOSS) for GEOBIA is growing. This interest stems not only from cost savings, but also from benefits concerning reproducibility and collaboration. Technical challenges hamper practical reproducibility, [...] Read more.

Geographic Object-Based Image Analysis (GEOBIA) mostly uses proprietary software,
but the interest in Free and Open-Source Software (FOSS) for GEOBIA is growing. This interest stems not only from cost savings, but also from benefits concerning reproducibility and collaboration. Technical challenges hamper practical reproducibility, especially when multiple software packages are required to conduct an analysis. In this study, we use containerization to package a GEOBIA workflow in a well-defined FOSS environment. We explore the approach using two software stacks to perform an exemplary analysis detecting destruction of buildings in bi-temporal images of a conflict area. The analysis combines feature extraction techniques with segmentation and object-based analysis to detect changes using automatically-defined local reference values and to distinguish disappeared buildings from non-target structures. The resulting workflow is published as FOSS comprising both the model and data in a ready to use Docker image and a user interface for interaction with the containerized workflow. The presented solution advances GEOBIA in the following aspects: higher transparency of methodology; easier reuse and adaption of workflows; better transferability between operating systems; complete description of the software environment; and easy application of workflows by image analysis experts and non-experts. As a result, it promotes not only the reproducibility of GEOBIA, but also its practical adoption. Full article

(This article belongs to the Special Issue Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning)

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12206 KiB

Open AccessArticle

Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests

by Huan Ni, Xiangguo Lin and Jixian Zhang

Remote Sens. 2017, 9(3), 288; https://doi.org/10.3390/rs9030288 - 18 Mar 2017

Cited by 94 | Viewed by 10960

Abstract

This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF) based feature selection and classification, and (iv) post-processing. [...] Read more.

This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF) based feature selection and classification, and (iv) post-processing. First, a step-wise point cloud segmentation method is proposed to extract three kinds of segments, including planar, smooth and rough surfaces. Second, a segment, rather than an individual point, is taken as the basic processing unit to extract features. Third, RF is employed to select features and classify these segments. Finally, semantic rules are employed to optimize the classification result. Three datasets provided by Open Topography are utilized to test the proposed method. Experiments show that our method achieves a superior classification result with an overall classification accuracy larger than 91.17%, and kappa coefficient larger than 83.79%. Full article

(This article belongs to the Special Issue Remote Sensing for 3D Urban Morphology)

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7306 KiB

Open AccessArticle

Exploring Digital Surface Models from Nine Different Sensors for Forest Monitoring and Change Detection

by Jiaojiao Tian, Thomas Schneider, Christoph Straub, Florian Kugler and Peter Reinartz

Remote Sens. 2017, 9(3), 287; https://doi.org/10.3390/rs9030287 - 18 Mar 2017

Cited by 31 | Viewed by 7442

Abstract

Digital surface models (DSMs) derived from spaceborne and airborne sensors enable the monitoring of the vertical structures for forests in large areas. Nevertheless, due to the lack of an objective performance assessment for this task, it is difficult to select the most appropriate [...] Read more.

Digital surface models (DSMs) derived from spaceborne and airborne sensors enable the monitoring of the vertical structures for forests in large areas. Nevertheless, due to the lack of an objective performance assessment for this task, it is difficult to select the most appropriate data source for DSM generation. In order to fill this gap, this paper performs change detection analysis including forest decrease and tree growth. The accuracy of the DSMs is evaluated by comparison with measured tree heights from inventory plots (field data). In addition, the DSMs are compared with LiDAR data to perform a pixel-wise quality assessment. DSMs from four different satellite stereo sensors (ALOS/PRISM, Cartosat-1, RapidEye and WorldView-2), one satellite InSAR sensor (TanDEM-X), two aerial stereo camera systems (HRSC and UltraCam) and two airborne laser scanning datasets with different point densities are adopted for the comparison. The case study is a complex central European temperate forest close to Traunstein in Bavaria, Germany. As a major experimental result, the quality of the DSM is found to be robust to variations in image resolution, especially when the forest density is high. The forest decrease results confirm that besides aerial photogrammetry data, very high resolution satellite data, such as WorldView-2, can deliver results with comparable quality as the ones derived from LiDAR, followed by TanDEM-X and Cartosat DSMs. The quality of the DSMs derived from ALOS and Rapid-Eye data is lower, but the main changes are still correctly highlighted. Moreover, the vertical tree growth and their relationship with tree height are analyzed. The major tree height in the study site is between 15 and 30 m and the periodic annual increments (PAIs) are in the range of 0.30–0.50 m. Full article

(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)

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Open AccessArticle

Synergistic Use of Nighttime Satellite Data, Electric Utility Infrastructure, and Ambient Population to Improve Power Outage Detections in Urban Areas

by Tony A. Cole, David W. Wanik, Andrew L. Molthan, Miguel O. Román and Robert E. Griffin

Remote Sens. 2017, 9(3), 286; https://doi.org/10.3390/rs9030286 - 17 Mar 2017

Cited by 69 | Viewed by 12681

Abstract

Natural and anthropogenic hazards are frequently responsible for disaster events, leading to damaged physical infrastructure, which can result in loss of electrical power for affected locations. Remotely-sensed, nighttime satellite imagery from the Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) [...] Read more.

Natural and anthropogenic hazards are frequently responsible for disaster events, leading to damaged physical infrastructure, which can result in loss of electrical power for affected locations. Remotely-sensed, nighttime satellite imagery from the Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) can monitor power outages in disaster-affected areas through the identification of missing city lights. When combined with locally-relevant geospatial information, these observations can be used to estimate power outages, defined as geographic locations requiring manual intervention to restore power. In this study, we produced a power outage product based on Suomi-NPP VIIRS DNB observations to estimate power outages following Hurricane Sandy in 2012. This product, combined with known power outage data and ambient population estimates, was then used to predict power outages in a layered, feedforward neural network model. We believe this is the first attempt to synergistically combine such data sources to quantitatively estimate power outages. The VIIRS DNB power outage product was able to identify initial loss of light following Hurricane Sandy, as well as the gradual restoration of electrical power. The neural network model predicted power outages with reasonable spatial accuracy, achieving Pearson coefficients (r) between 0.48 and 0.58 across all folds. Our results show promise for producing a continental United States (CONUS)- or global-scale power outage monitoring network using satellite imagery and locally-relevant geospatial data. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)

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9408 KiB

Open AccessArticle

Automatic Object-Oriented, Spectral-Spatial Feature Extraction Driven by Tobler’s First Law of Geography for Very High Resolution Aerial Imagery Classification

by Zhiyong Lv, Penglin Zhang and Jón Atli Benediktsson

Remote Sens. 2017, 9(3), 285; https://doi.org/10.3390/rs9030285 - 17 Mar 2017

Cited by 39 | Viewed by 9707

Abstract

Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, [...] Read more.

Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification. Full article

(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)

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3092 KiB

Open AccessArticle

Supervised Sub-Pixel Mapping for Change Detection from Remotely Sensed Images with Different Resolutions

by Ke Wu, Qian Du, Yi Wang and Yetao Yang

Remote Sens. 2017, 9(3), 284; https://doi.org/10.3390/rs9030284 - 17 Mar 2017

Cited by 38 | Viewed by 7733

Abstract

Due to the relatively low temporal resolutions of high spatial resolution (HR) remotely sensed images, land-cover change detection (LCCD) may have to use multi-temporal images with different resolutions. The low spatial resolution (LR) images often have high temporal repetition rates, but they contain [...] Read more.

Due to the relatively low temporal resolutions of high spatial resolution (HR) remotely sensed images, land-cover change detection (LCCD) may have to use multi-temporal images with different resolutions. The low spatial resolution (LR) images often have high temporal repetition rates, but they contain a large number of mixed pixels, which may seriously limit their capability in change detection. Soft classification (SC) can produce the proportional fractions of land-covers, on which sub-pixel mapping (SPM) can construct fine resolution land-cover maps to reduce the low-spatial-resolution-problem to some extent. Thus, in this paper, sub-pixel land-cover change detection with the use of different resolution images (SLCCD_DR) is addressed based on SC and SPM. Previously, endmember combinations within pixels are ignored in the LR image, which may result in flawed fractional differences. Meanwhile, the information of a known HR land-cover map is insignificantly treated in the SPM models, which leads to a reluctant SLCCD_DR result. In order to overcome these issues, a novel approach based on a back propagation neural network (BPNN) with different resolution images (BPNN_DR) is proposed in this paper. Firstly, endmember variability per pixel is considered during the SC process to ensure the high accuracy of the derived proportional fractional difference image. After that, the BPNN-based SPM model is constructed by a complete supervised framework. It takes full advantage of the prior known HR image, whether it predates or postdates the LR image, to train the BPNN, so that a sub-pixel change detection map is generated effectively. The proposed BPNN_DR is compared with four state-of-the-art methods at different scale factors. The experimental results using both synthetic data and real images demonstrated that it can outperform with a more detailed change detection map being produced. Full article

(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)

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4254 KiB

Open AccessArticle

Analyzing Glacier Surface Motion Using LiDAR Data

by Jennifer W. Telling, Craig Glennie, Andrew G. Fountain and David C. Finnegan

Remote Sens. 2017, 9(3), 283; https://doi.org/10.3390/rs9030283 - 17 Mar 2017

Cited by 22 | Viewed by 7946

Abstract

Understanding glacier motion is key to understanding how glaciers are growing, shrinking, and responding to changing environmental conditions. In situ observations are often difficult to collect and offer an analysis of glacier surface motion only at a few discrete points. Using light detection [...] Read more.

Understanding glacier motion is key to understanding how glaciers are growing, shrinking, and responding to changing environmental conditions. In situ observations are often difficult to collect and offer an analysis of glacier surface motion only at a few discrete points. Using light detection and ranging (LiDAR) data collected from surveys over six glaciers in Greenland and Antarctica, particle image velocimetry (PIV) was applied to temporally-spaced point clouds to detect and measure surface motion. The type and distribution of surface features, surface roughness, and spatial and temporal resolution of the data were all found to be important factors, which limited the use of PIV to four of the original six glaciers. The PIV results were found to be in good agreement with other, widely accepted, measurement techniques, including manual tracking and GPS, and offered a comprehensive distribution of velocity data points across glacier surfaces. For three glaciers in Taylor Valley, Antarctica, average velocities ranged from 0.8–2.1 m/year. For one glacier in Greenland, the average velocity was 22.1 m/day (8067 m/year). Full article

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5806 KiB

Open AccessArticle

Geometric Refinement of ALS-Data Derived Building Models Using Monoscopic Aerial Images

by Małgorzata Jarząbek-Rychard and Hans-Gerd Maas

Remote Sens. 2017, 9(3), 282; https://doi.org/10.3390/rs9030282 - 16 Mar 2017

Cited by 10 | Viewed by 5991

Abstract

Airborne laser scanning (ALS) has proven to be a strong basis for 3D building reconstruction. While ALS data allows for a highly automated processing workflow, a major drawback is often in the point spacing. As a consequence, the precision of roof plane and [...] Read more.

Airborne laser scanning (ALS) has proven to be a strong basis for 3D building reconstruction. While ALS data allows for a highly automated processing workflow, a major drawback is often in the point spacing. As a consequence, the precision of roof plane and ridge line parameters is usually significantly better than the precision of gutter lines. To cope with this problem, the paper presents an approach for geometric refinement of building models reconstructed from ALS data using monoscopic aerial images. The core idea of the proposed modeling method is to obtain refined roof edges by intersecting roof planes accurately and reliably extracted from 3D point clouds with viewing planes assigned with building edges detected in a high resolution aerial image. In order to minimize ambiguities that may arise during the integration of modeling cues, the ALS data is used as the master providing initial information about building shape and topology. We evaluate the performance of our algorithm by comparing the results of 3D reconstruction executed using only laser scanning data and reconstruction enhanced by image information. The assessment performed within a framework of the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark shows an increase in the final quality indicator up to 8.7%. Full article

(This article belongs to the Special Issue Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning)

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21646 KiB

Open AccessArticle

Multi-Feature Registration of Point Clouds

by Tzu-Yi Chuang and Jen-Jer Jaw

Remote Sens. 2017, 9(3), 281; https://doi.org/10.3390/rs9030281 - 16 Mar 2017

Cited by 12 | Viewed by 6780

Abstract

Light detection and ranging (LiDAR) has become a mainstream technique for rapid acquisition of 3-D geometry. Current LiDAR platforms can be mainly categorized into spaceborne LiDAR system (SLS), airborne LiDAR system (ALS), mobile LiDAR system (MLS), and terrestrial LiDAR system (TLS). Point cloud [...] Read more.

Light detection and ranging (LiDAR) has become a mainstream technique for rapid acquisition of 3-D geometry. Current LiDAR platforms can be mainly categorized into spaceborne LiDAR system (SLS), airborne LiDAR system (ALS), mobile LiDAR system (MLS), and terrestrial LiDAR system (TLS). Point cloud registration between different scans of the same platform or different platforms is essential for establishing a complete scene description and improving geometric consistency. The discrepancies in data characteristics should be manipulated properly for precise transformation estimation. This paper proposes a multi-feature registration scheme suitable for utilizing point, line, and plane features extracted from raw point clouds to realize the registrations of scans acquired within the same LIDAR system or across the different platforms. By exploiting the full geometric strength of the features, different features are used exclusively or combined with others. The uncertainty of feature observations is also considered within the proposed method, in which the registration of multiple scans can be simultaneously achieved. The simulated test with an ideal geometry and data simplification was performed to assess the contribution of different features towards point cloud registration in a very essential fashion. On the other hand, three real cases of registration between LIDAR scans from single platform and between those acquired by different platforms were demonstrated to validate the effectiveness of the proposed method. In light of the experimental results, it was found that the proposed model with simultaneous and weighted adjustment rendered satisfactory registration results and showed that not only features inherited in the scene can be more exploited to increase the robustness and reliability for transformation estimation, but also the weak geometry of poorly overlapping scans can be better treated than utilizing only one single type of feature. The registration errors of multiple scans in all tests were all less than point interval or positional error, whichever dominating, of the LiDAR data. Full article

(This article belongs to the Special Issue Airborne Laser Scanning)

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10178 KiB

Open AccessArticle

A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery

by Fang Xu, Jinghong Liu, Mingchao Sun, Dongdong Zeng and Xuan Wang

Remote Sens. 2017, 9(3), 280; https://doi.org/10.3390/rs9030280 - 16 Mar 2017

Cited by 37 | Viewed by 7143

Abstract

Maritime target detection from optical remote sensing images plays an important role in related military and civil applications and its weakness lies in its compromised performance under complex uncertain conditions. In this paper, a novel hierarchical ship detection method is proposed to overcome [...] Read more.

Maritime target detection from optical remote sensing images plays an important role in related military and civil applications and its weakness lies in its compromised performance under complex uncertain conditions. In this paper, a novel hierarchical ship detection method is proposed to overcome this issue. In the ship detection stage, based on Entropy information, we construct a combined saliency model with self-adaptive weights to prescreen ship candidates from across the entire maritime domain. To characterize ship targets and further reduce the false alarms, we introduce a novel and practical descriptor based on gradient features, and this descriptor is robust against clutter introduced by heavy clouds, islands, ship wakes as well as variation in target size. Furthermore, the proposed method is effective for not only color images but also gray images. The experimental results obtained using real optical remote sensing images have demonstrated that the locations and the number of ships can be determined accurately and that the false alarm rate is greatly decreased. A comprehensive comparison is performed between the proposed method and the state-of-the-art methods, which shows that the proposed method achieves higher accuracy and outperforms all the competing methods. Furthermore, the proposed method is robust under various backgrounds of maritime images and has great potential for providing more accurate target detection in engineering applications. Full article

(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)

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25156 KiB

Open AccessArticle

Calibrating Satellite-Based Indices of Burn Severity from UAV-Derived Metrics of a Burned Boreal Forest in NWT, Canada

by Robert H. Fraser, Jurjen Van der Sluijs and Ronald J. Hall

Remote Sens. 2017, 9(3), 279; https://doi.org/10.3390/rs9030279 - 16 Mar 2017

Cited by 75 | Viewed by 9678

Abstract

Wildfires are a dominant disturbance to boreal forests, and in North America, they typically cause widespread tree mortality. Forest fire burn severity is often measured at a plot scale using the Composite Burn Index (CBI), which was originally developed as a means of [...] Read more.

Wildfires are a dominant disturbance to boreal forests, and in North America, they typically cause widespread tree mortality. Forest fire burn severity is often measured at a plot scale using the Composite Burn Index (CBI), which was originally developed as a means of assigning severity levels to the Normalized Burn Ratio (NBR) computed from Landsat satellite imagery. Our study investigated the potential to map biophysical indicators of burn severity (residual green vegetation and charred organic surface) at very high (3 cm) resolution, using color orthomosaics and vegetation height models derived from UAV-based photographic surveys and Structure from Motion methods. These indicators were scaled to 30 m resolution Landsat pixel footprints and compared to the post-burn NBR (post-NBR) and differenced NBR (dNBR) ratios computed from pre- and post-fire Landsat imagery. The post-NBR showed the strongest relationship to both the fraction of charred surface (exponential R² = 0.79) and the fraction of green crown vegetation above 5 m (exponential R² = 0.81), while the dNBR was more closely related to the total green vegetation fraction (exponential R² = 0.69). Additionally, the UAV green fraction and Landsat indices could individually explain more than 50% of the variance in the overall CBI measured in 39 plots. These results provide a proof-of-concept for using low-cost UAV photogrammetric mapping to quantify key measures of boreal burn severity at landscape scales, which could be used to calibrate and assign a biophysical meaning to Landsat spectral indices for mapping severity at regional scales. Full article

(This article belongs to the Special Issue New Sensors, Multi-Sensor Integration, Large Volumes: New Opportunities and Challenges in Forest Fire Research)

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7571 KiB

Open AccessArticle

UAV-Based Oblique Photogrammetry for Outdoor Data Acquisition and Offsite Visual Inspection of Transmission Line

by San Jiang, Wanshou Jiang, Wei Huang and Liang Yang

Remote Sens. 2017, 9(3), 278; https://doi.org/10.3390/rs9030278 - 16 Mar 2017

Cited by 114 | Viewed by 11960

Abstract

Regular inspection of transmission lines is an essential work, which has been implemented by either labor intensive or very expensive approaches. 3D reconstruction could be an alternative solution to satisfy the need for accurate and low cost inspection. This paper exploits the use [...] Read more.

Regular inspection of transmission lines is an essential work, which has been implemented by either labor intensive or very expensive approaches. 3D reconstruction could be an alternative solution to satisfy the need for accurate and low cost inspection. This paper exploits the use of an unmanned aerial vehicle (UAV) for outdoor data acquisition and conducts accuracy assessment tests to explore potential usage for offsite inspection of transmission lines. Firstly, an oblique photogrammetric system, integrating with a cheap double-camera imaging system, an onboard dual-frequency GNSS (Global Navigation Satellite System) receiver and a ground master GNSS station in fixed position, is designed to acquire images with ground resolutions better than 3 cm. Secondly, an image orientation method, considering oblique imaging geometry of the dual-camera system, is applied to detect enough tie-points to construct stable image connection in both along-track and across-track directions. To achieve the best geo-referencing accuracy and evaluate model measurement precision, signalized ground control points (GCPs) and model key points have been surveyed. Finally, accuracy assessment tests, including absolute orientation precision and relative model precision, have been conducted with different GCP configurations. Experiments show that images captured by the designed photogrammetric system contain enough information of power pylons from different viewpoints. Quantitative assessment demonstrates that, with fewer GCPs for image orientation, the absolute and relative accuracies of image orientation and model measurement are better than 0.3 and 0.2 m, respectively. For regular inspection of transmission lines, the proposed solution can to some extent be an alternative method with competitive accuracy, lower operational complexity and considerable gains in economic cost. Full article

(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)

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Open AccessArticle

A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas

by Martin Weinmann, Michael Weinmann, Clément Mallet and Mathieu Brédif

Remote Sens. 2017, 9(3), 277; https://doi.org/10.3390/rs9030277 - 16 Mar 2017

Cited by 69 | Viewed by 12166

Abstract

In this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task [...] Read more.

In this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a binary point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D points, whereby the labels are defined as “tree points” and “other points”. The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the “tree points”. This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains

10.13

M labeled 3D points among which

17.6

% are labeled as “tree points”. The derived results clearly reveal a semantic classification of high accuracy (up to

90.77

%) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than

2.5

h). Full article

(This article belongs to the Special Issue Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning)

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Open AccessArticle

Glacier Mass Loss during the 1960s and 1970s in the Ak-Shirak Range (Kyrgyzstan) from Multiple Stereoscopic Corona and Hexagon Imagery

by Franz Goerlich, Tobias Bolch, Kriti Mukherjee and Tino Pieczonka

Remote Sens. 2017, 9(3), 275; https://doi.org/10.3390/rs9030275 - 16 Mar 2017

Cited by 28 | Viewed by 8349

Abstract

Comprehensive research on glacier changes in the Tian Shan is available for the current decade; however, there is limited information about glacier investigations of previous decades and especially before the mid 1970s. The earliest stereo images from the Corona missions were acquired in [...] Read more.

Comprehensive research on glacier changes in the Tian Shan is available for the current decade; however, there is limited information about glacier investigations of previous decades and especially before the mid 1970s. The earliest stereo images from the Corona missions were acquired in the 1960s but existing studies dealing with these images focus on single glaciers or small areas only. We developed a workflow to generate digital terrain models (DTMs) and orthophotos from 1964 Corona KH-4 for an entire mountain range (Ak-Shirak) located in the Central Tian Shan. From these DTMs and orthoimages, we calculated geodetic mass balances and length changes in comparison to 1973 and 1980 Hexagon KH-9 data. We found mass budgets between −0.4 ± 0.1 m·w.e.a⁻¹ (1964–1980) and −0.9 ± 0.4 m·w.e.a⁻¹ (1973–1980) for the whole region and individual glaciers. The length changes, on the other hand, vary heterogeneously between +624 ± 18 m (+39.0 ± 1.1 m·a⁻¹) and −923 ± 18 m (−57.7 ± 1.1 m·a⁻¹) for 1964–1980. An automation of the processing line can successively lead to region-wide Corona data processing allowing the analysis and interpretation of glacier changes on a larger scale and supporting a refinement of glacier modelling. Full article

(This article belongs to the Special Issue Remote Sensing of Glaciers)

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550 KiB

Open AccessObituary

In Memoriam: Gunter Menz

by Frank Thonfeld, Andreas Rienow, Olena Dubovyk, Ayman Abdel-Hamid, Agatha Akpeokhai, Esther Amler, Georg Bareth, Amit Kumar Basukala, Morton Canty, Manfred Denich, Tomasz Dobrzeniecki, Jessica Ferner, Hendrik Flügel, Gohar Ghazaryan, Ellen Götz, Valerie Graw, Klaus Greve, Reginald Guuroh, Sascha Heinemann, Tobias Henning, Konrad Hentze, Jens L. Hollberg, Fridah Kirimi, Sophie Kocherscheidt, Bärbel Konermann-Krüger, Di Liu, Javier Muro, Carsten Oldenburg, Annette Ortwein, Ruben Piroska, Fabián Santos, Jürgen Schellberg, Johannes Schultz, Ulrich Schurr, Fabian Selg, Kilian Staar, Stefanie Steinbach, Adrian Strauch, Jörg Szarzynski, Andreas Tewes, Kuria Bartholomew Thiong’o, Anna Vetter, Lars Wirkus and Uwe Rascher Show full author list Hide full author list

Remote Sens. 2017, 9(3), 274; https://doi.org/10.3390/rs9030274 - 16 Mar 2017

Viewed by 8565

Abstract

Prof. Dr. Gunter Menz passed away on 9 August 2016 following a dramatic accident.[...] Full article

4475 KiB

Open AccessArticle

Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST

by Weijing Chen, Huanfeng Shen, Chunlin Huang and Xin Li

Remote Sens. 2017, 9(3), 273; https://doi.org/10.3390/rs9030273 - 15 Mar 2017

Cited by 13 | Viewed by 5420

Abstract

Uncertainties in model parameters can easily result in systematic differences between model states and observations, which significantly affect the accuracy of soil moisture estimation in data assimilation systems. In this research, a soil moisture assimilation scheme is developed to jointly assimilate AMSR-E (Advanced [...] Read more.

Uncertainties in model parameters can easily result in systematic differences between model states and observations, which significantly affect the accuracy of soil moisture estimation in data assimilation systems. In this research, a soil moisture assimilation scheme is developed to jointly assimilate AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System) brightness temperature (TB) and MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) products, which also corrects model bias by simultaneously updating model states and parameters with a dual ensemble Kalman filter (DEnKS). Common Land Model (CoLM) and a Radiative Transfer Model (RTM) are adopted as model and observation operator, respectively. The assimilation experiment was conducted in Naqu on the Tibet Plateau from 31 May to 27 September 2011. The updated soil temperature at surface obtained by assimilating MODIS LST serving as inputs of RTM is to reduce the differences between the simulated and observed TB, then AMSR-E TB is assimilated to update soil moisture and model parameters. Compared with in situ measurements, the accuracy of soil moisture estimation derived from the assimilation experiment has been tremendously improved at a variety of scales. The updated parameters effectively reduce the states bias of CoLM. The results demonstrate the potential of assimilating AMSR-E TB and MODIS LST to improve the estimation of soil moisture and related parameters. Furthermore, this study indicates that the developed scheme is an effective way to retrieve downscaled soil moisture when assimilating the coarse-scale microwave TB. Full article

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Open AccessArticle

Mapping of Vegetation Using Multi-Temporal Downscaled Satellite Images of a Reclaimed Area in Saemangeum, Republic of Korea

by Mu-Sup Beon, Ki Hwan Cho, Hyun Ok Kim, Hyun-Kyung Oh and Jong-Chul Jeong

Remote Sens. 2017, 9(3), 272; https://doi.org/10.3390/rs9030272 - 15 Mar 2017

Cited by 5 | Viewed by 6599

Abstract

The aim of this study is to adapt and evaluate the effectiveness of a multi-temporal downscaled images technique for classifying the typical vegetation types of a reclaimed area. The areas reclaimed from estuarine tidal flats show high spatial heterogeneity in soil salinity conditions. [...] Read more.

The aim of this study is to adapt and evaluate the effectiveness of a multi-temporal downscaled images technique for classifying the typical vegetation types of a reclaimed area. The areas reclaimed from estuarine tidal flats show high spatial heterogeneity in soil salinity conditions. There are three typical vegetation types for which the distribution is restricted by the soil conditions. A halophyte-dominated vegetation is located in a high saline area, grass vegetation is found in a mid- or low saline area, and reed/small-reed vegetation is situated in a low saline area. Multi-temporal satellite images were used to classify the vegetation types. Landsat images were downscaled to take into account spatial heterogeneity using cokriging. A random forest classifier was used for the classification, with downscaled Landsat and RapidEye images. Classification with RapidEye images alone demonstrated a lower level of accuracy than when combined with multi-temporal downscaled images. The results demonstrate the usefulness of a downscaling technique for mapping. This approach can provide a framework which is able to maintain low costs whilst producing richer images for the monitoring of a large and heterogeneous ecosystem. Full article

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