Remote Sensing

6991 KiB

Open AccessArticle

Landsat-8 Operational Land Imager (OLI) Radiometric Performance On-Orbit

by Ron Morfitt, Julia Barsi, Raviv Levy, Brian Markham, Esad Micijevic, Lawrence Ong, Pat Scaramuzza and Kelly Vanderwerff

Remote Sens. 2015, 7(2), 2208-2237; https://doi.org/10.3390/rs70202208 - 17 Feb 2015

Cited by 147 | Viewed by 13450

Abstract

Expectations of the Operational Land Imager (OLI) radiometric performance onboard Landsat-8 have been met or exceeded. The calibration activities that occurred prior to launch provided calibration parameters that enabled ground processing to produce imagery that met most requirements when data were transmitted to [...] Read more.

Expectations of the Operational Land Imager (OLI) radiometric performance onboard Landsat-8 have been met or exceeded. The calibration activities that occurred prior to launch provided calibration parameters that enabled ground processing to produce imagery that met most requirements when data were transmitted to the ground. Since launch, calibration updates have improved the image quality even more, so that all requirements are met. These updates range from detector gain coefficients to reduce striping and banding to alignment parameters to improve the geometric accuracy. This paper concentrates on the on-orbit radiometric performance of the OLI, excepting the radiometric calibration performance. Topics discussed in this paper include: signal-to-noise ratios that are an order of magnitude higher than previous Landsat missions; radiometric uniformity that shows little residual banding and striping, and continues to improve; a dynamic range that limits saturation to extremely high radiance levels; extremely stable detectors; slight nonlinearity that is corrected in ground processing; detectors that are stable and 100% operable; and few image artifacts. Full article

(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)

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40902 KiB

Open AccessArticle

Inter-Comparison between VIIRS and MODIS Radiances and Ocean Color Data Products over the Chesapeake Bay

by Rong-Rong Li, Mark David Lewis, Richard W. Gould, Jr., Adam Lawson, Ruhul Amin, Sonia C. Gallegos and Sherwin Ladner

Remote Sens. 2015, 7(2), 2193-2207; https://doi.org/10.3390/rs70202193 - 17 Feb 2015

Cited by 7 | Viewed by 7059

Abstract

Since the October 2011 launch of the VIIRS (Visible Infrared Imaging Radiometer Suite) instrument, a number of inter-sensor comparisons between VIIRS and MODIS (Moderate Resolution Imaging Spectroradiometer) radiances have been reported. Most of these comparisons are between calibrated radiances and temperatures based on [...] Read more.

Since the October 2011 launch of the VIIRS (Visible Infrared Imaging Radiometer Suite) instrument, a number of inter-sensor comparisons between VIIRS and MODIS (Moderate Resolution Imaging Spectroradiometer) radiances have been reported. Most of these comparisons are between calibrated radiances and temperatures based on observations of the two sensors from simultaneous nadir overpasses (SNO). Few comparisons between the retrieved ocean color data products, such as chlorophyll concentration, from VIIRS and MODIS data have been reported. Retrievals from measured data at large solar zenith angles and large view zenith angles are excluded from these comparison studies. In this paper, we report the inter-sensor comparisons between VIIRS and MODIS data acquired over the Chesapeake Bay and nearby areas with relatively large differences in sensor view angles. The goal for this study is to check the consistency between MODIS and VIIRS ocean color data products in order to merge the products from the two sensors. We compare total radiances (Lt) at the top of atmosphere (TOA) and the ocean color (OC) data products derived with the automatic processing system (APS) from both VIIRS and MODIS data. APS was developed at the Naval Research Laboratory, Stennis Space Center (NRL/SSC). We have found that, although there are large differences between the measured radiances (Lt) of the two sensors when the sensor zenith angle differences are significant, the mean percent differences between the retrieved normalized water-leaving radiances are about 15%. The results show that the variation in satellite view zenith angles is not a main factor affecting the retrieval of ocean color data products, i.e., the atmospheric correction routine adequately removes the view-angle dependence. Full article

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3326 KiB

Open AccessArticle

Unsupervised Global Urban Area Mapping via Automatic Labeling from ASTER and PALSAR Satellite Images

by Yulin Duan, Xiaowei Shao, Yun Shi, Hiroyuki Miyazaki, Koki Iwao and Ryosuke Shibasaki

Remote Sens. 2015, 7(2), 2171-2192; https://doi.org/10.3390/rs70202171 - 16 Feb 2015

Cited by 14 | Viewed by 6567

Abstract

In this study, a novel unsupervised method for global urban area mapping is proposed. Different from traditional clustering-based unsupervised methods, in our approach a labeler is designed, which is able to automatically select training samples from satellite images by propagating common urban/non-urban knowledge [...] Read more.

In this study, a novel unsupervised method for global urban area mapping is proposed. Different from traditional clustering-based unsupervised methods, in our approach a labeler is designed, which is able to automatically select training samples from satellite images by propagating common urban/non-urban knowledge through the unlabeled data. Two kinds of satellite images, captured by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Phased Array L-band Synthetic Aperture Radar (PALSAR), are exploited here. In this method, spectral features are first extracted from the original dataset, followed by coarse prediction of urban/non-urban areas via weak classifiers. By developing an improved belief-propagation based clustering algorithm, a confidence map is obtained and training data are selected via weighted sampling. Finally, the urban area map is obtained by employing the Support Vector Machine (SVM) classifier. The proposed method can generate urban areamaps at a resolution of 15 m, while the same settings are used for all test cases. Experimental results involving 75 scenes from different climate zones show that our proposed method achieves an overall accuracy of 84.4% and a kappa coefficient of 0.628, which is competitive relative to the supervised SVM method. Full article

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11383 KiB

Open AccessArticle

Aerial Thermography for Energetic Modelling of Cities

by Gabriele Bitelli, Paolo Conte, Tamas Csoknyai, Francesca Franci, Valentina A. Girelli and Emanuele Mandanici

Remote Sens. 2015, 7(2), 2152-2170; https://doi.org/10.3390/rs70202152 - 16 Feb 2015

Cited by 32 | Viewed by 8045

Abstract

The rising attention to energy consumption problems is renewing interest in the applications of thermal remote sensing in urban areas. The research presented here aims to test a methodology to retrieve information about roof surface temperature by means of a high resolution orthomosaic [...] Read more.

The rising attention to energy consumption problems is renewing interest in the applications of thermal remote sensing in urban areas. The research presented here aims to test a methodology to retrieve information about roof surface temperature by means of a high resolution orthomosaic of airborne thermal infrared images, based on a case study acquired over Bologna (Italy). The ultimate aim of such work is obtaining datasets useful to support, in a GIS environment, the decision makers in developing adequate strategies to reduce energy consumption and CO₂ emission. In the processing proposed, the computing of radiometric quantities related to the atmosphere was performed by the Modtran 5 radiative transfer code, while an object-oriented supervised classification was applied on a WorldView-2 multispectral image, together with a high-resolution digital surface model (DSM), to distinguish among the major roofing material types and to model the effects of the emissivity. The emissivity values were derived from literature data, except for some roofing materials, which were measured during ad hoc surveys, by means of a thermal camera and a contact probe. These preliminary results demonstrate the high sensitivity of the model to the variability of the surface emissivity and of the atmospheric parameters, especially transmittance and upwelling radiance. Full article

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Graphical abstract

73203 KiB

Open AccessArticle

Inundations in the Inner Niger Delta: Monitoring and Analysis Using MODIS and Global Precipitation Datasets

by Muriel Bergé-Nguyen and Jean-François Crétaux

Remote Sens. 2015, 7(2), 2127-2151; https://doi.org/10.3390/rs70202127 - 16 Feb 2015

Cited by 43 | Viewed by 9329

Abstract

A method of wetland mapping and flood survey based on satellite optical imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra instrument was used over the Inner Niger Delta (IND) from 2000–2013. It has allowed us to describe the phenomenon of inundations in [...] Read more.

A method of wetland mapping and flood survey based on satellite optical imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra instrument was used over the Inner Niger Delta (IND) from 2000–2013. It has allowed us to describe the phenomenon of inundations in the delta and to decompose the flooded areas in the IND into open water and mixture of water and dry land, and that aquatic vegetation is separated from bare soil and “dry” vegetation. An Empirical Orthogonal Function (EOF) analysis of the MODIS data and precipitation rates from a global gridded data set is carried out. Connections between flood sequence and precipitation patterns from the upstream part of the Niger and Bani river watersheds up to the IND are studied. We have shown that inter-annual variability of flood dominates over the IND and we have estimated that the surface extent of open water varies by a factor of four between dry and wet years. We finally observed an increase in vegetation over the 14 years of study and a slight decrease of open water. Full article

(This article belongs to the Special Issue Earth Observation for Water Resource Management in Africa)

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5702 KiB

Open AccessArticle

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

by Javier Hernandez, Gustavo A. Lobos, Iván Matus, Alejandro Del Pozo, Paola Silva and Mauricio Galleguillos

Remote Sens. 2015, 7(2), 2109-2126; https://doi.org/10.3390/rs70202109 - 16 Feb 2015

Cited by 60 | Viewed by 7869

Abstract

Plant breeding based on grain yield (GY) is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at [...] Read more.

Plant breeding based on grain yield (GY) is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at the range from 350 to 2500 nm to predict GY in a large panel (368 genotypes) of wheat (Triticum aestivum L.) through multivariate ridge regression models. Plants were treated under three water regimes in the Mediterranean conditions of central Chile: severe water stress (SWS, rain fed), mild water stress (MWS; one irrigation event around booting) and full irrigation (FI) with mean GYs of 1655, 4739, and 7967 kg∙ha⁻¹, respectively. Models developed from reflectance data during anthesis and grain filling under all water regimes explained between 77% and 91% of the GY variability, with the highest values in SWS condition. When individual models were used to predict yield in the rest of the trials assessed, models fitted during anthesis under MWS performed best. Combined models using data from different water regimes and each phenological stage were used to predict grain yield, and the coefficients of determination (R²) increased to 89.9% and 92.0% for anthesis and grain filling, respectively. The model generated during anthesis in MWS was the best at predicting yields when it was applied to other conditions. Comparisons against conventional reflectance indices were made, showing lower predictive abilities. It was concluded that a Ridge Regression Model using a data set based on spectral reflectance at anthesis or grain filling represents an effective method to predict grain yield in genotypes under different water regimes. Full article

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Graphical abstract

12530 KiB

Open AccessArticle

Comparative Analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD Sensor Data for Grassland Monitoring Applications

by Lei Wang, Ranran Yang, Qingjiu Tian, Yanjun Yang, Yang Zhou, Yuan Sun and Xiaofei Mi

Remote Sens. 2015, 7(2), 2089-2108; https://doi.org/10.3390/rs70202089 - 13 Feb 2015

Cited by 31 | Viewed by 10391

Abstract

The increasing number of Chinese sensor types used for terrestrial remote sensing has necessitated an additional effort to evaluate and standardize the data they acquire. In this study, we assessed the potential use of GF-1 WFV (Wild Field Camera), ZY-3 MUX (Multi-spectral camera), [...] Read more.

The increasing number of Chinese sensor types used for terrestrial remote sensing has necessitated an additional effort to evaluate and standardize the data they acquire. In this study, we assessed the potential use of GF-1 WFV (Wild Field Camera), ZY-3 MUX (Multi-spectral camera), and HJ-1 CCD (Charge Coupled Device) sensor data for grassland monitoring by comparing spectral field measurements, vegetation coverage, and the leaf area index (LAI) of grassland stands with reflectance in the red and near-infrared bands and the Normalized Difference Vegetation Index (NDVI). Based on spectral field measurements, the characteristic differences of spectral response functions of the sensors were analyzed. Based on simulations using the SAIL bidirectional canopy reflectance model coupled with the PROSPECT leaf optical properties model (PROSAIL), we investigated the effects of changes in the sensors’ zenith angle caused by side sway. The following conclusions were drawn. (1) Differences in the adjusted coefficients of determination (R²) exist when comparing correlations between the reflectances from the three sensor types in different bands. The values of R² are 0.556–0.893 and 0.819–0.850 for the infrared and red bands, respectively, and these data show a better correlation for the red band than for the infrared band. Fitted slope equations revealed inconsistencies in the data between the different sensor types. In the red band, GF-1 WFV and HJ-1 CCD data are the most consistent, but in the near-infrared band, GF-1 WFV and ZY-3 MUX data are the most consistent; (2) The correlation of NDVIs obtained from the different sensor types is high (R² between 0.758 and 0.852); however, the consistency is low in that the NDVI based on GF-1 WFV data is significantly higher than that based on ZY-3 MUX and HJ-1 CCD data. In contrast, the mean difference is small between the NDVIs based on ZY-3 MUX and HJ-1 CCD; (3) Correlation analysis between ground grass-coverage and measured LAI data shows that the three sensor types are better at estimating coverage than the LAI, and that the GF-1 WFV sensor gave the best performance; (4) Changes in the sensors’ zenith angle caused by side sway were proven to have greater impact on reflectance and NDVI than the spectral response function; (5) For LAI values of 0–3, the NDVI changes significantly with increasing LAI, and differences between the three sensor types are obvious. For LAI > 3.5, the NDVI appears to experience a saturated tendency, which greatly reduces the differences between the sensors. Full article

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Graphical abstract

15327 KiB

Open AccessArticle

Correlations between Urbanization and Vegetation Degradation across the World’s Metropolises Using DMSP/OLS Nighttime Light Data

by Yanxu Liu, Yanglin Wang, Jian Peng, Yueyue Du, Xianfeng Liu, Shuangshuang Li and Donghai Zhang

Remote Sens. 2015, 7(2), 2067-2088; https://doi.org/10.3390/rs70202067 - 12 Feb 2015

Cited by 145 | Viewed by 12202

Abstract

Changes in biodiversity owing to vegetation degradation resulting from widespread urbanization demands serious attention. However, the connection between vegetation degradation and urbanization appears to be complex and nonlinear, and deserves a series of long-term observations. On the basis of the Normalized Difference Vegetation [...] Read more.

Changes in biodiversity owing to vegetation degradation resulting from widespread urbanization demands serious attention. However, the connection between vegetation degradation and urbanization appears to be complex and nonlinear, and deserves a series of long-term observations. On the basis of the Normalized Difference Vegetation Index (NDVI) and the image’s digital number (DN) in nighttime stable light data (NTL), we delineated the spatiotemporal relations between urbanization and vegetation degradation of different metropolises by using a simplified NTL calibration method and Theil-Sen regression. The results showed clear and noticeable spatiotemporal differences. On spatial relations, rapidly urbanized cities were found to have a high probability of vegetation degradation, but in reality, not all of them experience sharp vegetation degradation. On temporal characteristics, the degradation degree was found to vary during different periods, which may depend on different stages of urbanization and climate history. These results verify that under the scenario of a vegetation restoration effort combined with increasing demand for a high-quality urban environment, the urbanization process will not necessarily result in vegetation degradation on a large scale. The positive effects of urban vegetation restoration should be emphasized since there has been an increase in demand for improved urban environmental quality. However, slight vegetation degradation is still observed when NDVI in an urbanized area is compared with NDVI in the outside buffer. It is worthwhile to pay attention to landscape sustainability and reduce the negative urbanization effects by urban landscape planning. Full article

(This article belongs to the Special Issue Earth Observations for the Sustainable Development)

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3180 KiB

Open AccessArticle

Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

by Péter Burai, Balázs Deák, Orsolya Valkó and Tamás Tomor

Remote Sens. 2015, 7(2), 2046-2066; https://doi.org/10.3390/rs70202046 - 12 Feb 2015

Cited by 108 | Viewed by 9531

Abstract

Alkali landscapes hold an extremely fine-scale mosaic of several vegetation types, thus it seems challenging to separate these classes by remote sensing. Our aim was to test the applicability of different image classification methods of hyperspectral data in this complex situation. To reach [...] Read more.

Alkali landscapes hold an extremely fine-scale mosaic of several vegetation types, thus it seems challenging to separate these classes by remote sensing. Our aim was to test the applicability of different image classification methods of hyperspectral data in this complex situation. To reach the highest classification accuracy, we tested traditional image classifiers (maximum likelihood classifier—MLC), machine learning algorithms (support vector machine—SVM, random forest—RF) and feature extraction (minimum noise fraction (MNF)-transformation) on training datasets of different sizes. Digital images were acquired from an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400–1000 nm), a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. For the classification, we established twenty vegetation classes based on the dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction) transformed dataset with various training sample sizes between 10 and 30 pixels. In order to select the optimal number of the transformed features, we applied SVM, RF and MLC classification to 2–15 MNF transformed bands. In the case of the original bands, SVM and RF classifiers provided high accuracy irrespective of the number of the training pixels. We found that SVM and RF produced the best accuracy when using the first nine MNF transformed bands; involving further features did not increase classification accuracy. SVM and RF provided high accuracies with the transformed bands, especially in the case of the aggregated groups. Even MLC provided high accuracy with 30 training pixels (80.78%), but the use of a smaller training dataset (10 training pixels) significantly reduced the accuracy of classification (52.56%). Our results suggest that in alkali landscapes, the application of SVM is a feasible solution, as it provided the highest accuracies compared to RF and MLC. SVM was not sensitive in the training sample size, which makes it an adequate tool when only a limited number of training pixels are available for some classes. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Habitat Quality Monitoring)

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18435 KiB

Open AccessArticle

Applying Terrestrial Laser Scanning for Soil Surface Roughness Assessment

by Milutin Milenković, Norbert Pfeifer and Philipp Glira

Remote Sens. 2015, 7(2), 2007-2045; https://doi.org/10.3390/rs70202007 - 11 Feb 2015

Cited by 51 | Viewed by 8831

Abstract

Terrestrial laser scanning can provide high-resolution, two-dimensional sampling of soil surface roughness. While previous studies demonstrated the usefulness of these roughness measurements in geophysical applications, questions about the number of required scans and their resolution were not investigated thoroughly. Here, we suggest a [...] Read more.

Terrestrial laser scanning can provide high-resolution, two-dimensional sampling of soil surface roughness. While previous studies demonstrated the usefulness of these roughness measurements in geophysical applications, questions about the number of required scans and their resolution were not investigated thoroughly. Here, we suggest a method to generate digital elevation models, while preserving the surface’s stochastic properties at high frequencies and additionally providing an estimate of their spatial resolution. We also study the impact of the number and positions of scans on roughness indices’ estimates. An experiment over a smooth and isotropic soil plot accompanies the analysis, where scanning results are compared to results from active triangulation. The roughness measurement conditions for ideal sampling are revisited and updated for diffraction-limited sampling valid for close-range laser scanning over smooth and isotropic soil roughness. Our results show that terrestrial laser scanning can be readily used for roughness assessment on scales larger than 5 cm, while for smaller scales, special processing is required to mitigate the effect of the laser beam footprint. Interestingly, classical roughness parametrization (correlation length, root mean square height (RMSh)) was not sensitive to these effects. Furthermore, comparing the classical roughness parametrization between one- and four-scan setups shows that the one-scan data can replace the four-scan setup with a relative loss of accuracy below 1% for ranges up to 3 m and incidence angles no larger than 50°, while two opposite scans can replace it over the whole plot. The incidence angle limit for the spectral slope is even stronger and is 40°. These findings are valid for scanning over smooth and isotropic soil roughness. Full article

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Open AccessArticle

Evaluation of the 2010 MODIS Collection 5.1 Land Cover Type Product over China

by Tian Zeng, Zengxiang Zhang, Xiaoli Zhao, Xiao Wang and Lijun Zuo

Remote Sens. 2015, 7(2), 1981-2006; https://doi.org/10.3390/rs70201981 - 11 Feb 2015

Cited by 26 | Viewed by 6799

Abstract

Although the MODIS Collection 5.1 Land Cover Type (MODIS v5.1 LCT) product is one of the most recent global land cover datasets and has the shortest updating cycle, evaluations regarding this collection have not been reported. Given the importance of evaluating global land [...] Read more.

Although the MODIS Collection 5.1 Land Cover Type (MODIS v5.1 LCT) product is one of the most recent global land cover datasets and has the shortest updating cycle, evaluations regarding this collection have not been reported. Given the importance of evaluating global land cover data for producers and potential users, the 2010 MODIS v5.1 LCT product IGBP (International Geosphere-Biosphere Programme) layer was evaluated based on two grid maps at scales of 100-m and 500-m,which were derived by rasterizing the 2010 data from the national land use/cover database of China (NLUD-C). This comparison was conducted based on a new legend consisting of nine classes constructed based on the definitions of classes in the IGBP and NLUD-C legends. The overall accuracies of the aggregated classification data were 64.62% and 66.42% at the sub-pixel and pixel scales, respectively. These accuracies differed significantly in different regions. Specifically, high-quality data were obtained more easily for regions with a single land cover type, such as Xinjiang province and the northeast plain of China. The lowest accuracies were obtained for the middle of China, including Ningxia, Shaanxi, Chongqing, Yunnan and Guizhou. At the sub-pixel scale, relatively high producer and user accuracies were obtained for cropland, grass and barren regions; the highest producer accuracy was obtained for forests, and the highest user accuracy was obtained for water bodies. Shrublands and wetlands were associated with low producer and user accuracies at the sub-pixel and pixel scales, of less than 10%. Based on dominant-type reference data, the errors were classified as mixed-pixel errors and labeling errors. Labeling errors primarily originated from misclassification between grassland and barren lands. Mixed pixel errors increased as the pixel diversity increased and as the percentage of dominant-type sub-pixels decreased. Overall, mixed pixels were sources of error for most land cover types other than grassland and barren lands; whereas labeling errors were more prevalent than mixed pixel errors when considering all of the land cover data over China, due to the large amount of misclassification between the pure pixels of grassland and barren lands. Next, the accuracy of cropland/natural vegetation mosaics was assessed based on the qualitative (a mosaic of croplands, forests, shrublands, and grasslands) and quantitative (no single component composes more than 60% of the landscape) parts in the definition, which resulted in accuracies of 91.43% and less than 19.26%, respectively. These results are summarized with their implications for the development of the next generation of MCD12Q1 data and with suggestions for potential users of MCD12Q1 v5.1. Full article

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35913 KiB

Open AccessArticle

Pre- and Post-Launch Spatial Quality of the Landsat 8 Thermal Infrared Sensor

by Brian N. Wenny, Dennis Helder, Jungseok Hong, Larry Leigh, Kurtis J. Thome and Dennis Reuter

Remote Sens. 2015, 7(2), 1962-1980; https://doi.org/10.3390/rs70201962 - 11 Feb 2015

Cited by 31 | Viewed by 9697

Abstract

The Thermal Infrared Sensor (TIRS) for the Landsat 8 platform was designed and built at NASA Goddard Space Flight Center (GSFC). TIRS data will extend the data record for thermal observations from the heritage Landsat sensors, dating back to the launch of Landsat [...] Read more.

The Thermal Infrared Sensor (TIRS) for the Landsat 8 platform was designed and built at NASA Goddard Space Flight Center (GSFC). TIRS data will extend the data record for thermal observations from the heritage Landsat sensors, dating back to the launch of Landsat 4 in 1982. The two-band (10.9 and 12.0 μm) pushbroom sensor with a 185 km-wide swath uses a staggered arrangement of quantum well infrared photodetector (QWIPs) arrays. The required spatial resolution is 100 m for TIRS, with the assessment of crop moisture and water resources being science drivers for that resolution. The evaluation of spatial resolution typically relies on a straight knife-edge technique to determine the spatial edge response of a detector system, and such an approach was implemented for TIRS. Flexibility in the ground calibration equipment used for TIRS thermal-vacuum chamber testing also made possible an alternate strategy that implemented a circular target moved in precise sub-pixel increments across the detectors to derive the edge response. On-orbit, coastline targets were developed to evaluate the spatial response performance. Multiple targets were identified that produced similar results to one another. Even though there may be a slight bias in the point spread function (PSF)/modulation transfer function (MTF) estimates towards poorer performance using this approach, it does have the ability to track relative changes for monitoring long-term instrument status. The results for both pre- and post-launch response analysis show general good agreement and consistency with edge slope along-track values of 0.53 and 0.58 pre- and post-launch and across-track values 0f 0.59 and 0.55 pre- and post-launch. Full article

(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)

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2517 KiB

Open AccessArticle

Regional Equivalent Water Thickness Modeling from Remote Sensing across a Tree Cover/LAI Gradient in Mediterranean Forests of Northern Tunisia

by Hedia Chakroun, Florent Mouillot and Abdelaziz Hamdi

Remote Sens. 2015, 7(2), 1937-1961; https://doi.org/10.3390/rs70201937 - 10 Feb 2015

Cited by 15 | Viewed by 7024

Abstract

The performance of vegetation indexes derived from moderate resolution imaging spectroradiometer (MODIS) sensors is explored for drought monitoring in the forests of Northern Tunisia; representing a transition zone between the Mediterranean Sea and the Sahara Desert. We investigated the suitability of biomass and [...] Read more.

The performance of vegetation indexes derived from moderate resolution imaging spectroradiometer (MODIS) sensors is explored for drought monitoring in the forests of Northern Tunisia; representing a transition zone between the Mediterranean Sea and the Sahara Desert. We investigated the suitability of biomass and moisture vegetation indexes for vegetation water content expressed by the equivalent water thickness (EWT) in a Mediterranean forest ecosystem with contrasted water budgets and desiccation rates. We proposed a revised EWT at canopy level (EWT_CAN) based on weekly field measurements of fuel moisture in seven species during the 2010 dry period; considering the mixture of plant functional types for water use (trees; shrubs and herbaceous layers) and a varying vegetation cover. MODIS vegetation indexes computed and smoothed over the dry season were highly correlated with the EWT_CAN. The performances of moisture indexes (Normalized Difference Infrared Index (NDII6 and NDII7); and Global Moisture Vegetation Index (GVMI6 and GVMI7)) were comparable; whereas; for biomass vegetation indexes; Normalized Difference Vegetation Index (NDVI); Modified Soil Adjusted Vegetation Index (MSAVI) and Adjusted Normalized Difference Vegetation Index (ANDVI) performed better than Enhanced Vegetation Index (EVI) and Soil Adjusted Vegetation Index (SAVI). We also identified the effect of Leaf Area Index (LAI) on EWT_CAN monitoring at the regional scale under the tree cover/LAI gradient of the region from relatively dense to open forest. Statistical analysis revealed a significant decreasing linear relationship; indicating that for LAI less than two; the greater the LAI; the less responsive are the vegetation indexes to changes in EWT_CAN; whereas for higher LAI; its influence becomes less significant and was not considered in the inversion models based on vegetation indexes. The EWT_CAN time-course from LAI-adapted inversion models; based on significantly-related vegetation indexes to EWT_CAN; showed close profiles resulting from the inversion models using NDVI; ANDVI; MSAVI and NDII6 applied during the dry season. The developed EWT_CAN model from MODIS vegetation indexes for the study region was finally tested for its ability to capture the topo-climatic effects on the seasonal and the spatial patterns of desiccation/rewetting for keystone periods of Mediterranean vegetation functioning. Implications for further use in scientific developments or management are discussed. Full article

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Open AccessArticle

Shiftable Leading Point Method for High Accuracy Registration of Airborne and Terrestrial LiDAR Data

by Liang Cheng, Lihua Tong, Yang Wu, Yanming Chen and Manchun Li

Remote Sens. 2015, 7(2), 1915-1936; https://doi.org/10.3390/rs70201915 - 9 Feb 2015

Cited by 17 | Viewed by 7245

Abstract

A new automated approach to the high-accuracy registration of airborne and terrestrial LiDAR data is proposed, which has three primary steps. Firstly, airborne and terrestrial LiDAR data are used to extract building corners, known as airborne corners and terrestrial corners, respectively. Secondly, an [...] Read more.

A new automated approach to the high-accuracy registration of airborne and terrestrial LiDAR data is proposed, which has three primary steps. Firstly, airborne and terrestrial LiDAR data are used to extract building corners, known as airborne corners and terrestrial corners, respectively. Secondly, an initial matching relationship between the terrestrial corners and airborne corners is automatically derived using a matching technique based on maximum matching corner pairs with minimum errors (MTMM). Finally, a set of leading points are generated from matched airborne corners, and a shiftable leading point method is proposed. The key feature of this approach is the implementation of the concept of shiftable leading points in the final step. Since the geometric accuracy of terrestrial LiDAR data is much better than that of airborne LiDAR data, leading points corresponding to anomalous airborne corners could be modified for the improvement of the geometric accuracy of registration. The experiment demonstrates that the proposed approach can advance the geometric accuracy of two-platform LiDAR data registration effectively. Full article

(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)

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4554 KiB

Open AccessArticle

Forest Canopy LAI and Vertical FAVD Profile Inversion from Airborne Full-Waveform LiDAR Data Based on a Radiative Transfer Model

by Han Ma, Jinling Song and Jindi Wang

Remote Sens. 2015, 7(2), 1897-1914; https://doi.org/10.3390/rs70201897 - 9 Feb 2015

Cited by 24 | Viewed by 7951

Abstract

Forest canopy leaf area index (LAI) is a critical variable for the modeling of climates and ecosystems over both regional and global scales. This paper proposes a physically based method to retrieve LAI and foliage area volume density (FAVD) profile directly from full-waveform [...] Read more.

Forest canopy leaf area index (LAI) is a critical variable for the modeling of climates and ecosystems over both regional and global scales. This paper proposes a physically based method to retrieve LAI and foliage area volume density (FAVD) profile directly from full-waveform Light Detection And Ranging (LiDAR) data using a radiative transfer (RT) model. First, a physical interaction model between LiDAR and a forest scene was built on the basis of radiative transfer theories. Next, FAVD profile of each laser shot of full-waveform LiDAR was inverted using the physical model. In addition, the missing LiDAR data, caused by high-density forest and LiDAR system limitations, were filled in based on the inverted FAVD and the ancillary CHM data. Finally, LAI of the study area was retrieved from the inverted FAVD at a 10-m resolution. CHM derived LAI based on the Beer-Lambert law was compared with the LAI derived from full-waveform data. Also, we compared the results with the field measured LAI. The values of correlation coefficient r and RMSE of the estimated LAI were 0.73 and 0.67, respectively. The results indicate that full-waveform LiDAR data is a reliable data source and represent a useful tool for retrieving forest LAI. Full article

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Open AccessArticle

Terrestrial Laser Scanning as an Effective Tool to Retrieve Tree Level Height, Crown Width, and Stem Diameter

by Shruthi Srinivasan, Sorin C. Popescu, Marian Eriksson, Ryan D. Sheridan and Nian-Wei Ku

Remote Sens. 2015, 7(2), 1877-1896; https://doi.org/10.3390/rs70201877 - 9 Feb 2015

Cited by 137 | Viewed by 16163

Abstract

Accurate measures of forest structural parameters are essential to forest inventory and growth models, managing wildfires, and modeling of carbon cycle. Terrestrial laser scanning (TLS) fills the gap between tree scale manual measurements and large scale airborne LiDAR measurements by providing accurate below [...] Read more.

Accurate measures of forest structural parameters are essential to forest inventory and growth models, managing wildfires, and modeling of carbon cycle. Terrestrial laser scanning (TLS) fills the gap between tree scale manual measurements and large scale airborne LiDAR measurements by providing accurate below crown information through non-destructive methods. This study developed innovative methods to extract individual tree height, diameter at breast height (DBH), and crown width of trees in East Texas. Further, the influence of scan settings, such as leaf-on/leaf-off seasons, tree distance from the scanner, and processing choices, on the accuracy of deriving tree measurements were also investigated. DBH was retrieved by cylinder fitting at different height bins. Individual trees were extracted from the TLS point cloud to determine tree heights and crown widths. The R-squared value ranged from 0.91 to 0.97 when field measured DBH was validated against TLS derived DBH using different methods. An accuracy of 92% (RMSE = 1.51 m) was obtained for predicting tree heights. The R-squared value was 0.84 and RMSE was 1.08 m when TLS derived crown widths were validated using field measured crown widths. Examples of underestimations of field measured forest structural parameters due to tree shadowing have also been discussed in this study. The results from this study will benefit foresters and remote sensing studies from airborne and spaceborne platforms, for map upscaling or calibration purposes, for aboveground biomass estimation, and prudent decision making by the forest management. Full article

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Open AccessArticle

DMSP-OLS Radiance Calibrated Nighttime Lights Time Series with Intercalibration

by Feng-Chi Hsu, Kimberly E. Baugh, Tilottama Ghosh, Mikhail Zhizhin and Christopher D. Elvidge

Remote Sens. 2015, 7(2), 1855-1876; https://doi.org/10.3390/rs70201855 - 9 Feb 2015

Cited by 165 | Viewed by 16368

Abstract

The Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) stable lights products are made using operational OLS data collected at high gain settings, resulting in sensor saturation on brightly lit areas, such as city centers. This has been a paramount shortcoming of the DMSP-OLS [...] Read more.

The Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) stable lights products are made using operational OLS data collected at high gain settings, resulting in sensor saturation on brightly lit areas, such as city centers. This has been a paramount shortcoming of the DMSP-OLS stable lights time series. This study outlines a methodology that greatly expands the dynamic range of the OLS data using observations made at different fixed-gain settings, and by incorporating the areas not affected by saturation from the stable lights product. The radiances for the fixed-gain data are computed based on each OLS sensor’s pre-flight calibration. The result is a product known as the OLS radiance calibrated nighttime lights. A total of eight global datasets have been produced, representing years from 1996 to 2010. To further facilitate the usefulness of these data for time-series analyses, corrections have been made to counter the sensitivity differences of the sensors, and coefficients are provided to adjust the datasets to allow inter-comparison. Full article

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

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Open AccessArticle

Moving Target Detection Based on the Spreading Characteristics of SAR Interferograms in the Magnitude-Phase Plane

by Gui Gao, Gongtao Shi, Lei Yang and Shilin Zhou

Remote Sens. 2015, 7(2), 1836-1854; https://doi.org/10.3390/rs70201836 - 9 Feb 2015

Cited by 15 | Viewed by 7475

Abstract

We propose a constant false alarm rate (CFAR) algorithm for moving target detection in synthetic aperture radar (SAR) images based on the spreading characteristics of interferograms on the magnitude-phase (M-P) plane. This method is based on the observation that, in practice, both moving [...] Read more.

We propose a constant false alarm rate (CFAR) algorithm for moving target detection in synthetic aperture radar (SAR) images based on the spreading characteristics of interferograms on the magnitude-phase (M-P) plane. This method is based on the observation that, in practice, both moving and stationary targets along with clutter are located at different regions in the M-P plane, and hence reasonable partitions of the M-P plane can help in detecting moving targets. To ensure efficient CFAR detection and to resolve the effect of factors that influence detection results, the proposed algorithm is divided into three distinct stages: coarse detection, fine detection, and post-processing. First, to accurately describe the statistical behavior of clutter, a global censoring strategy, called coarse detection, is introduced to adaptively eliminate the influences of the moving and stationary target points from the given data. Then, to acquire fine detection results, a novel CFAR detector is developed on the basis of the fits of a known theoretical M-P joint probability density function (PDF) against the two-dimensional (2-D) histogram of the censored clutter. The joint PDF’s projected contour line that satisfies the desirable probability of false alarm (PFA) corresponds to the required threshold of detection in the M-P plane. Finally, two filters, the magnitude and phase filters, are applied to reduce the false alarms generated from the previous procedures. The effectiveness of the proposed algorithm is validated through experimental results obtained from a two-channel SAR complex image. Full article

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Open AccessReview

Comparison of Spatiotemporal Fusion Models: A Review

by Bin Chen, Bo Huang and Bing Xu

Remote Sens. 2015, 7(2), 1798-1835; https://doi.org/10.3390/rs70201798 - 5 Feb 2015

Cited by 174 | Viewed by 12524

Abstract

Simultaneously capturing spatial and temporal dynamics is always a challenge for the remote sensing community. Spatiotemporal fusion has gained wide interest in various applications for its superiority in integrating both fine spatial resolution and frequent temporal coverage. Though many advances have been made [...] Read more.

Simultaneously capturing spatial and temporal dynamics is always a challenge for the remote sensing community. Spatiotemporal fusion has gained wide interest in various applications for its superiority in integrating both fine spatial resolution and frequent temporal coverage. Though many advances have been made in spatiotemporal fusion model development and applications in the past decade, a unified comparison among existing fusion models is still limited. In this research, we classify the models into three categories: transformation-based, reconstruction-based, and learning-based models. The objective of this study is to (i) compare four fusion models (STARFM, ESTARFM, ISTAFM, and SPSTFM) under a one Landsat-MODIS (L-M) pair prediction mode and two L-M pair prediction mode using time-series datasets from the Coleambally irrigation area and Poyang Lake wetland; (ii) quantitatively assess prediction accuracy considering spatiotemporal comparability, landscape heterogeneity, and model parameter selection; and (iii) discuss the advantages and disadvantages of the three categories of spatiotemporal fusion models. Full article

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Open AccessArticle

Improvements of a COMS Land Surface Temperature Retrieval Algorithm Based on the Temperature Lapse Rate and Water Vapor/Aerosol Effect

by A-Ra Cho, Youn-Young Choi and Myoung-Seok Suh

Remote Sens. 2015, 7(2), 1777-1797; https://doi.org/10.3390/rs70201777 - 5 Feb 2015

Cited by 4 | Viewed by 5811

Abstract

The National Meteorological Satellite Center in Korea retrieves land surface temperature (LST) by applying the split-window LST algorithm (CSW_v1.0) to Communication, Ocean, and Meteorological Satellite (COMS) data. Considerable errors were detected under conditions of high water vapor content or temperature lapse rates during [...] Read more.

The National Meteorological Satellite Center in Korea retrieves land surface temperature (LST) by applying the split-window LST algorithm (CSW_v1.0) to Communication, Ocean, and Meteorological Satellite (COMS) data. Considerable errors were detected under conditions of high water vapor content or temperature lapse rates during validation with Moderate Resolution Imaging Spectroradiometer (MODIS) LST because of the too simplified LST algorithm. In this study, six types of LST retrieval equations (CSW_v2.0) were developed to upgrade the CSW_v1.0. These methods were developed by classifying “dry,” “normal,” and “wet” cases for day and night and considering the relative sizes of brightness temperature difference (BTD) values. Similar to CSW_v1.0, the LST retrieved by CSW_v2.0 had a correlation coefficient of 0.99 with the prescribed LST and a slightly larger bias of −0.03 K from 0.00K; the root mean square error (RMSE) improved from 1.41 K to 1.39 K. In general, CSW_v2.0 improved the retrieval accuracy compared to CSW_v1.0, especially when the lapse rate was high (mid-day and dawn) and the water vapor content was high. The spatial distributions of LST retrieved by CSW_v2.0 were found to be similar to the MODIS LST independently of the season, day/night, and geographic locations. The validation using one year’s MODIS LST data showed that CSW_v2.0 improved the retrieval accuracy of LST in terms of correlations (from 0.988 to 0.989), bias (from −1.009 K to 0.292 K), and RMSEs (from 2.613 K to 2.237 K). Full article

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Open AccessArticle

Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique

by Carolien Toté, Domingos Patricio, Hendrik Boogaard, Raymond Van der Wijngaart, Elena Tarnavsky and Chris Funk

Remote Sens. 2015, 7(2), 1758-1776; https://doi.org/10.3390/rs70201758 - 5 Feb 2015

Cited by 292 | Viewed by 18784

Abstract

Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts [...] Read more.

Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day) gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT) v2.0, Famine Early Warning System NETwork (FEWS NET) Rainfall Estimate (RFE) v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)) are compared to independent gauge data (2001–2012). This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems. Full article

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Open AccessArticle

Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV)

by Darren Turner, Arko Lucieer and Steven M. De Jong

Remote Sens. 2015, 7(2), 1736-1757; https://doi.org/10.3390/rs70201736 - 5 Feb 2015

Cited by 352 | Viewed by 19205

Abstract

In this study, we used an Unmanned Aerial Vehicle (UAV) to collect a time series of high-resolution images over four years at seven epochs to assess landslide dynamics. Structure from Motion (SfM) was applied to create Digital Surface Models (DSMs) of the landslide [...] Read more.

In this study, we used an Unmanned Aerial Vehicle (UAV) to collect a time series of high-resolution images over four years at seven epochs to assess landslide dynamics. Structure from Motion (SfM) was applied to create Digital Surface Models (DSMs) of the landslide surface with an accuracy of 4–5 cm in the horizontal and 3–4 cm in the vertical direction. The accuracy of the co-registration of subsequent DSMs was checked and corrected based on comparing non-active areas of the landslide, which minimized alignment errors to a mean of 0.07 m. Variables such as landslide area and the leading edge slope were measured and temporal patterns were discovered. Volumetric changes of particular areas of the landslide were measured over the time series. Surface movement of the landslide was tracked and quantified with the COSI-Corr image correlation algorithm but without ground validation. Historical aerial photographs were used to create a baseline DSM, and the total displacement of the landslide was found to be approximately 6630 m³. This study has demonstrated a robust and repeatable algorithm that allows a landslide’s dynamics to be mapped and monitored with a UAV over a relatively long time series. Full article

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Open AccessLetter

Dynamics of Urbanization Levels in China from 1992 to 2012: Perspective from DMSP/OLS Nighttime Light Data

by Bin Gao, Qingxu Huang, Chunyang He and Qun Ma

Remote Sens. 2015, 7(2), 1721-1735; https://doi.org/10.3390/rs70201721 - 5 Feb 2015

Cited by 113 | Viewed by 11763

Abstract

The authenticity and reliability of urbanization levels measured by different indicators in China have not reached a consensus, which may impede our understanding of the process of urbanization and its impacts on the environment. The objective of this study was to describe a [...] Read more.

The authenticity and reliability of urbanization levels measured by different indicators in China have not reached a consensus, which may impede our understanding of the process of urbanization and its impacts on the environment. The objective of this study was to describe a reliable method of estimating urbanization level based on the Operational Line-scan System (OLS) on the Defense Meteorological Satellite Program (DMSP) nighttime light data and to analyze the dynamics of urbanization levels in China from 1992 to 2012. We calculated the comprehensive urbanization level at the national, provincial, and county scales using a compounded night light index (CNLI) and compared the change rate of CNLI with those of the other two conventional urbanization level indicators, proportion of the nonagricultural population and proportion of built-up area. Our results showed that CNLI derived from the DMSP/OLS data set provided a relatively reliable and accurate measure of the comprehensive urbanization level in China. During the last two decades, China has experienced continued and rapid urbanization with large regional variations. The CNLI increased 3.12 times, from 1.72 × 10⁻³ to 7.09 × 10⁻³. The annual increases of CNLI in eastern provinces were much faster than those in western provinces. In addition, we found that the rates of change in these three indicators were consistent for most provinces with the exception of the four municipalities (Beijing, Tianjin, Shanghai, and Chongqing) and a few eastern coastal provinces (Jiangsu, Zhejiang, Fujian, and Guangdong). Because the imbalance among population growth, urban expansion and socioeconomic development may affect cities’ sustainable development, we should pay more attention to these regions with large disparities between different indicators. Full article

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

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Open AccessArticle

Mapping Spatial Distribution of Larch Plantations from Multi-Seasonal Landsat-8 OLI Imagery and Multi-Scale Textures Using Random Forests

by Tian Gao, Jiaojun Zhu, Xiao Zheng, Guiduo Shang, Liyan Huang and Shangrong Wu

Remote Sens. 2015, 7(2), 1702-1720; https://doi.org/10.3390/rs70201702 - 5 Feb 2015

Cited by 47 | Viewed by 7441

Abstract

The knowledge about spatial distribution of plantation forests is critical for forest management, monitoring programs and functional assessment. This study demonstrates the potential of multi-seasonal (spring, summer, autumn and winter) Landsat-8 Operational Land Imager imageries with random forests (RF) modeling to map larch [...] Read more.

The knowledge about spatial distribution of plantation forests is critical for forest management, monitoring programs and functional assessment. This study demonstrates the potential of multi-seasonal (spring, summer, autumn and winter) Landsat-8 Operational Land Imager imageries with random forests (RF) modeling to map larch plantations (LP) in a typical plantation forest landscape in North China. The spectral bands and two types of textures were applied for creating 675 input variables of RF. An accuracy of 92.7% for LP, with a Kappa coefficient of 0.834, was attained using the RF model. A RF-based importance assessment reveals that the spectral bands and bivariate textural features calculated by pseudo-cross variogram (PC) strongly promoted forest class-separability, whereas the univariate textural features influenced weakly. A feature selection strategy eliminated 93% of variables, and then a subset of the 47 most essential variables was generated. In this subset, PC texture derived from summer and winter appeared the most frequently, suggesting that this variability in growing peak season and non-growing season can effectively enhance forest class-separability. A RF classifier applied to the subset led to 91.9% accuracy for LP, with a Kappa coefficient of 0.829. This study provides an insight into approaches for discriminating plantation forests with phenological behaviors. Full article

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Open AccessArticle

Discrete Anisotropic Radiative Transfer (DART 5) for Modeling Airborne and Satellite Spectroradiometer and LIDAR Acquisitions of Natural and Urban Landscapes

by Jean-Philippe Gastellu-Etchegorry, Tiangang Yin, Nicolas Lauret, Thomas Cajgfinger, Tristan Gregoire, Eloi Grau, Jean-Baptiste Feret, Maïlys Lopes, Jordan Guilleux, Gérard Dedieu, Zbyněk Malenovský, Bruce Douglas Cook, Douglas Morton, Jeremy Rubio, Sylvie Durrieu, Gregory Cazanave, Emmanuel Martin and Thomas Ristorcelli

Remote Sens. 2015, 7(2), 1667-1701; https://doi.org/10.3390/rs70201667 - 5 Feb 2015

Cited by 256 | Viewed by 20085

Abstract

Satellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a [...] Read more.

Satellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It has been developed since 1992. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification. It is freely distributed for research and teaching activities. This paper presents DART physical bases and its latest functionality for simulating imaging spectroscopy of natural and urban landscapes with atmosphere, including the perspective projection of airborne acquisitions and LIght Detection And Ranging (LIDAR) waveform and photon counting signals. Full article

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Open AccessArticle

An Improved Unmixing-Based Fusion Method: Potential Application to Remote Monitoring of Inland Waters

by Yulong Guo, Yunmei Li, Li Zhu, Ge Liu, Shuai Wang and Chenggong Du

Remote Sens. 2015, 7(2), 1640-1666; https://doi.org/10.3390/rs70201640 - 5 Feb 2015

Cited by 9 | Viewed by 5814

Abstract

Although remote sensing technology has been widely used to monitor inland water bodies; the lack of suitable data with high spatial and spectral resolution has severely obstructed its practical development. The objective of this study is to improve the unmixing-based fusion (UBF) method [...] Read more.

Although remote sensing technology has been widely used to monitor inland water bodies; the lack of suitable data with high spatial and spectral resolution has severely obstructed its practical development. The objective of this study is to improve the unmixing-based fusion (UBF) method to produce fused images that maintain both spectral and spatial information from the original images. Images from Environmental Satellite 1 (HJ1) and Medium Resolution Imaging Spectrometer (MERIS) were used in this study to validate the method. An improved UBF (IUBF) algorithm is established by selecting a proper HJ1-CCD image band for each MERIS band and thereafter applying an unsupervised classification method in each sliding window. Viewing in the visual sense—the radiance and the spectrum—the results show that the improved method effectively yields images with the spatial resolution of the HJ1-CCD image and the spectrum resolution of the MERIS image. When validated using two datasets; the ERGAS index (Relative Dimensionless Global Error) indicates that IUBF is more robust than UBF. Finally, the fused data were applied to evaluate the chlorophyll a concentrations (C_chla) in Taihu Lake. The result shows that the C_chla map obtained by IUBF fusion captures more detailed information than that of MERIS. Full article

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Open AccessArticle

Assessing Habitat Quality of Forest-Corridors through NDVI Analysis in Dry Tropical Forests of South India: Implications for Conservation

by Paramesha Mallegowda, Ganesan Rengaian, Jayalakshmi Krishnan and Madhura Niphadkar

Remote Sens. 2015, 7(2), 1619-1639; https://doi.org/10.3390/rs70201619 - 4 Feb 2015

Cited by 29 | Viewed by 13672

Abstract

Most wildlife habitats and migratory routes are extremely threatened due to increasing demands on forestland and forest resources by burgeoning human population. Corridor landscape in Biligiri Rangaswamy Temple Tiger Reserve (BRT) is one among them, subjected to various anthropogenic pressures. Human habitation, intensive [...] Read more.

Most wildlife habitats and migratory routes are extremely threatened due to increasing demands on forestland and forest resources by burgeoning human population. Corridor landscape in Biligiri Rangaswamy Temple Tiger Reserve (BRT) is one among them, subjected to various anthropogenic pressures. Human habitation, intensive farming, coffee plantations, ill-planned infrastructure developments and rapid spreading of invasive plant species Lantana camara, pose a serious threat to wildlife habitat and their migration. Aim of this work is to create detailed NDVI based land change maps and to use them to identify time-series trends in greening and browning in forest corridors in the study area and to identify the drivers that are influencing the observed changes. Over the four decades in BRT, NDVI increased in the core area of the forest and reduced in the fringe areas. The change analysis between 1973 and 2014 shows significant changes; browning due to anthropogenic activities as well as natural processes and greening due to Lantana spread. This indicates that the change processes are complex, involving multiple driving factors, such as socio-economic changes, high population growth, historical forest management practices and policies. Our study suggests that the use of updated and accurate change detection maps will be useful in taking appropriate site specific action-oriented conservation decisions to restore and manage the degraded critical wildlife corridors in human-dominated landscape. Full article

(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)

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Open AccessArticle

Airborne LiDAR for the Detection of Archaeological Vegetation Marks Using Biomass as a Proxy

by David Stott, Doreen S. Boyd, Anthony Beck and Anthony G. Cohn

Remote Sens. 2015, 7(2), 1594-1618; https://doi.org/10.3390/rs70201594 - 3 Feb 2015

Cited by 16 | Viewed by 11389

Abstract

In arable landscapes, the airborne detection of archaeological features is often reliant on using the properties of the vegetation cover as a proxy for sub-surface features in the soil. Under the right conditions, the formation of vegetation marks allows archaeologists to identify and [...] Read more.

In arable landscapes, the airborne detection of archaeological features is often reliant on using the properties of the vegetation cover as a proxy for sub-surface features in the soil. Under the right conditions, the formation of vegetation marks allows archaeologists to identify and interpret archaeological features. Using airborne Laser Scanning, based on the principles of Light Detection and Ranging (LiDAR) to detect these marks is challenging, particularly given the difficulties of resolving subtle changes in a low and homogeneous crop with these sensors. In this paper, an experimental approach is adopted to explore how these marks could be detected as variations in canopy biomass using both range and full waveform LiDAR data. Although some detection was achieved using metrics of the full waveform data, it is the novel multi-temporal method of using discrete return data to detect and characterise archaeological vegetation marks that is offered for further consideration. This method was demonstrated to be applicable over a range of capture conditions, including soils deemed as difficult (i.e., clays and other heavy soils), and should increase the certainty of detection when employed in the increasingly multi-sensor approaches to heritage prospection and management. Full article

(This article belongs to the Special Issue New Perspectives of Remote Sensing for Archaeology)

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Open AccessArticle

Automation Aspects for the Georeferencing of Photogrammetric Aerial Image Archives in Forested Scenes

by Kimmo Nurminen, Paula Litkey, Eija Honkavaara, Mikko Vastaranta, Markus Holopainen, Päivi Lyytikäinen-Saarenmaa, Tuula Kantola and Minna Lyytikäinen

Remote Sens. 2015, 7(2), 1565-1593; https://doi.org/10.3390/rs70201565 - 2 Feb 2015

Cited by 20 | Viewed by 8524

Abstract

Photogrammetric aerial film image archives are scanned into digital form in many countries. These data sets offer an interesting source of information for scientists from different disciplines. The objective of this investigation was to contribute to the automation of a generation of 3D [...] Read more.

Photogrammetric aerial film image archives are scanned into digital form in many countries. These data sets offer an interesting source of information for scientists from different disciplines. The objective of this investigation was to contribute to the automation of a generation of 3D environmental model time series when using small-scale airborne image archives, especially in forested scenes. Furthermore, we investigated the usability of dense digital surface models (DSMs) generated using these data sets as well as the uncertainty propagation of the DSMs. A key element in the automation is georeferencing. It is obvious that for images captured years apart, it is essential to find ground reference locations that have changed as little as possible. We studied a 68-year-long aerial image time series in a Finnish Karelian forestland. The quality of candidate ground locations was evaluated by comparing digital DSMs created from the images to an airborne laser scanning (ALS)-originated reference DSM. The quality statistics of DSMs were consistent with the expectations; the estimated median root mean squared error for height varied between 0.3 and 2 m, indicating a photogrammetric modelling error of 0.1‰ with respect to flying height for data sets collected since the 1980s, and 0.2‰ for older data sets. The results show that of the studied land cover classes, “peatland without trees” changed the least over time and is one of the most promising candidates to serve as a location for automatic ground control measurement. Our results also highlight some potential challenges in the process as well as possible solutions. Our results indicate that using modern photogrammetric techniques, it is possible to reconstruct 3D environmental model time series using photogrammetric image archives in a highly automated way. Full article

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Open AccessArticle

Land Subsidence over Oilfields in the Yellow River Delta

by Peng Liu, Qingquan Li, Zhenhong Li, Trevor Hoey, Yanxiong Liu and Chisheng Wang

Remote Sens. 2015, 7(2), 1540-1564; https://doi.org/10.3390/rs70201540 - 2 Feb 2015

Cited by 35 | Viewed by 10815

Abstract

Subsidence in river deltas is a complex process that has both natural and human causes. Increasing human activities like aquaculture and petroleum extraction are affecting the Yellow River delta, and one consequence is subsidence. The purpose of this study is to measure the [...] Read more.

Subsidence in river deltas is a complex process that has both natural and human causes. Increasing human activities like aquaculture and petroleum extraction are affecting the Yellow River delta, and one consequence is subsidence. The purpose of this study is to measure the surface displacements in the Yellow River delta region and to investigate the corresponding subsidence source. In this paper, the Stanford Method for Persistent Scatterers (StaMPS) package was employed to process Envisat ASAR images collected between 2007 and 2010. Consistent results between two descending tracks show subsidence with a mean rate up to 30 mm/yr in the radar line of sight direction in Gudao Town (oilfield), Gudong oilfield and Xianhe Town of the delta, each of which is within the delta, and also show that subsidence is not uniform across the delta. Field investigation shows a connection between areas of non-uniform subsidence and of petroleum extraction. In a 9 km² area of the Gudao Oilfield, a poroelastic disk reservoir model is used to model the InSAR derived displacements. In general, good fits between InSAR observations and modeled displacements are seen. The subsidence observed in the vicinity of the oilfield is thus suggested to be caused by fluid extraction. Full article

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Remote Sens., Volume 7, Issue 2 (February 2015) – 48 articles , Pages 1181-2237

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