Remote Sensing

1219 KiB

Open AccessArticle

An Unmanned Airship Thermal Infrared Remote Sensing System for Low-Altitude and High Spatial Resolution Monitoring of Urban Thermal Environments: Integration and an Experiment

by Peng Ren, Qinglin Meng, Yufeng Zhang, Lihua Zhao, Xu Yuan and Xiaoheng Feng

Remote Sens. 2015, 7(10), 14259-14275; https://doi.org/10.3390/rs71014259 - 27 Oct 2015

Cited by 27 | Viewed by 8383

Abstract

Satellite remote sensing data that lacks spatial resolution and timeliness is of limited ability to access urban thermal environment on a micro scale. This paper presents an unmanned airship low-altitude thermal infrared remote sensing system (UALTIRSS), which is composed of an unmanned airship, [...] Read more.

Satellite remote sensing data that lacks spatial resolution and timeliness is of limited ability to access urban thermal environment on a micro scale. This paper presents an unmanned airship low-altitude thermal infrared remote sensing system (UALTIRSS), which is composed of an unmanned airship, an onboard control and navigation subsystem, a task subsystem, a communication subsystem, and a ground-base station. Furthermore, an experimental method and an airborne-field experiment for collecting land surface temperature (LST) were designed and conducted. The LST pattern within 0.8-m spatial resolution and with root mean square error (RMSE) value of 2.63 °C was achieved and analyzed in the study region. Finally, the effects of surface types on the surrounding thermal environment were analyzed by LST profiles. Results show that the high thermal resolution imagery obtained from UALTIRSS can provide more detailed thermal information, which are conducive to classify fine urban material and assess surface urban heat island (SUHI). There is a significant positive correlation between the average LST of profiles and the percent impervious surface area (ISA%) with R² around 0.917. Overall, UALTIRSS and the retrieval method were proved to be low-cost and feasible for studying micro urban thermal environments. Full article

(This article belongs to the Special Issue The Application of Thermal Urban Remote Sensing to Understand and Monitor Urban Climates)

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3901 KiB

Open AccessArticle

The Potential of EnMAP and Sentinel-2 Data for Detecting Drought Stress Phenomena in Deciduous Forest Communities

by Sandra Dotzler, Joachim Hill, Henning Buddenbaum and Johannes Stoffels

Remote Sens. 2015, 7(10), 14227-14258; https://doi.org/10.3390/rs71014227 - 27 Oct 2015

Cited by 58 | Viewed by 11214

Abstract

Given the importance of forest ecosystems, the availability of reliable, spatially explicit information about the site-specific climate sensitivity of tree species is essential for implementing suitable adaptation strategies. In this study, airborne hyperspectral data were used to assess the response of deciduous species [...] Read more.

Given the importance of forest ecosystems, the availability of reliable, spatially explicit information about the site-specific climate sensitivity of tree species is essential for implementing suitable adaptation strategies. In this study, airborne hyperspectral data were used to assess the response of deciduous species (dominated by European beech and Sessile and Pedunculate oak) to water stress during a summery dry spell. After masking canopy gaps, shaded crown areas and non-deciduous species, potentially indicative spectral indices, the Photochemical Reflectance Index (PRI), Moisture Stress Index (MSI), Normalized Difference Water Index (NDWI), and Chlorophyll Index (CI), were analyzed with respect to available maps of site-specific soil moisture regimes. PRI provided an important indication of site-specific photosynthetic stress on leaf level in relation to limitations in soil water availability. The CI, MSI and NDWI revealed statistically significant differences in total chlorophyll and water concentration at the canopy level. However, after reducing the canopy effects by normalizing these indices with respect to the structure-sensitive simple ratio (SR) vegetation index, it was not yet possible to identify site-specific concentration differences in leaf level at this early stage of the drought. The selected indicators were also tested with simulated EnMAP and Sentinel-2 data (derived from the original airborne data set). While PRI proved to be useful also in the spatial resolution of EnMAP (GSD = 30 m), this was not the case with Sentinel-2, owing to the lack of adequate spectral bands; the remaining indicators (MSI, CI, SR) were also successfully produced with Sentinel-2 data at superior spatial resolution (GSD = 10 m). The study confirms the importance of using earth observation systems for supplementing traditional ecological site classification maps, particularly during dry spells and heat waves when ecological gradients are increasingly reflected in the spectral response at the tree crown level. It also underlined the importance of using Sentinel-2 and EnMAP in synergy, as soon as both systems become available. Full article

(This article belongs to the Special Issue The Environmental Mapping and Analysis Program (EnMAP) Mission: Preparing for Its Scientific Exploitation)

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1580 KiB

Open AccessArticle

Flood Hazard Mapping Combining Hydrodynamic Modeling and Multi Annual Remote Sensing data

by Laura Giustarini, Marco Chini, Renaud Hostache, Florian Pappenberger and Patrick Matgen

Remote Sens. 2015, 7(10), 14200-14226; https://doi.org/10.3390/rs71014200 - 27 Oct 2015

Cited by 52 | Viewed by 11911

Abstract

This paper explores a method to combine the time and space continuity of a large-scale inundation model with discontinuous satellite microwave observations, for high-resolution flood hazard mapping. The assumption behind this approach is that hydraulic variables computed from continuous spatially-distributed hydrodynamic modeling and [...] Read more.

This paper explores a method to combine the time and space continuity of a large-scale inundation model with discontinuous satellite microwave observations, for high-resolution flood hazard mapping. The assumption behind this approach is that hydraulic variables computed from continuous spatially-distributed hydrodynamic modeling and observed as discrete satellite-derived flood extents are correlated in time, so that probabilities can be transferred from the model series to the observations. A prerequisite is, therefore, the existence of a significant correlation between a modeled variable (i.e., flood extent or volume) and the synchronously-observed flood extent. If this is the case, the availability of model simulations over a long time period allows for a robust estimate of non-exceedance probabilities that can be attributed to corresponding synchronously-available satellite observations. The generated flood hazard map has a spatial resolution equal to that of the satellite images, which is higher than that of currently available large scale inundation models. The method was applied on the Severn River (UK), using the outputs of a global inundation model provided by the European Centre for Medium-range Weather Forecasts and a large collection of ENVISAT ASAR imagery. A comparison between the hazard map obtained with the proposed method and with a more traditional numerical modeling approach supports the hypothesis that combining model results and satellite observations could provide advantages for high-resolution flood hazard mapping, provided that a sufficient number of remote sensing images is available and that a time correlation is present between variables derived from a global model and obtained from satellite observations. Full article

(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)

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1190 KiB

Open AccessArticle

Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion

by Feng Zhao, Yiqing Guo, Yanbo Huang, Wout Verhoef, Christiaan Van der Tol, Bo Dai, Liangyun Liu, Huijie Zhao and Guang Liu

Remote Sens. 2015, 7(10), 14179-14199; https://doi.org/10.3390/rs71014179 - 27 Oct 2015

Cited by 11 | Viewed by 6241

Abstract

In this study, backward and forward fluorescence radiance within the emission spectrum of 640–850 nm were measured for leaves of soybean, cotton, peanut and wheat using a hyperspectral spectroradiometer coupled with an integration sphere. Fluorescence parameters of crop leaves were retrieved from the [...] Read more.

In this study, backward and forward fluorescence radiance within the emission spectrum of 640–850 nm were measured for leaves of soybean, cotton, peanut and wheat using a hyperspectral spectroradiometer coupled with an integration sphere. Fluorescence parameters of crop leaves were retrieved from the leaf hyperspectral measurements by inverting the FluorMODleaf model, a leaf-level fluorescence model able to simulate chlorophyll fluorescence spectra for both sides of leaves. This model is based on the widely used and validated PROSPECT (leaf optical properties) model. Firstly, a sensitivity analysis of the FluorMODleaf model was performed to identify and quantify influential parameters to assist the strategy for the inversion. Implementation of the Extended Fourier Amplitude Sensitivity Test (EFAST) method showed that the leaf chlorophyll content and the fluorescence lifetimes of photosystem I (PSI) and photosystem II (PSII) were the most sensitive parameters among all eight inputs of the FluorMODleaf model. Based on results of sensitivity analysis, the FluorMODleaf model was inverted using the leaf fluorescence spectra measured from both sides of crop leaves. In order to achieve stable inversion results, the Bayesian inference theory was applied. The relative absorption cross section of PSI and PSII and the fluorescence lifetimes of PSI and PSII of the FluorMODleaf model were retrieved with the Bayesian inversion approach. Results showed that the coefficient of determination (R²) and root mean square error (RMSE) between the fluorescence signal reconstructed from the inverted fluorescence parameters and measured in the experiment were 0.96 and 3.14 × 10⁻⁶ W·m⁻²·sr⁻¹·nm⁻¹, respectively, for backward fluorescence, and 0.92 and 3.84 × 10⁻⁶ W·m⁻²·sr⁻¹·nm⁻¹ for forward fluorescence. Based on results, the inverted values of the fluorescence parameters were analyzed, and the potential of this method was investigated. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing for Crop Growth Monitoring)

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1935 KiB

Open AccessArticle

The Use of Stereoscopic Satellite Images to Map Rills and Ephemeral Gullies

by Federica Fiorucci, Francesca Ardizzone, Mauro Rossi and Dino Torri

Remote Sens. 2015, 7(10), 14151-14178; https://doi.org/10.3390/rs71014151 - 27 Oct 2015

Cited by 33 | Viewed by 8826

Abstract

Accurate mapping and measurement of erosion channels is necessary to accurately estimate the impact of channeled erosion in an area. Field surveys can provide optimal quantitative results, but they are only applicable to small areas. Recently, photogrammetric techniques have been applied to small [...] Read more.

Accurate mapping and measurement of erosion channels is necessary to accurately estimate the impact of channeled erosion in an area. Field surveys can provide optimal quantitative results, but they are only applicable to small areas. Recently, photogrammetric techniques have been applied to small format aerial photographs that were taken by UAVs. Few studies have applied photogrammetry for mapping and measuring single permanent gullies using very high resolution stereoscopic satellite images. We explore the use of such images to map rills and ephemeral gullies and to measure the length, width and depth of individual erosion channels to estimate the eroded volumes. The proposed methodology was applied to the Collazzone area of Central Italy. All of the channel characteristics were determined using GeoEye-1^® panchromatic stereoscopic satellite images of the 48-km² study area and a 3D floating cursor. We identified, mapped, and measured the lengths of 555 channel segments. The top width and depth could be measured in only a subset of the channel segments (the SMC subset). The SMC data were used to determine the coefficients of the power law relationship between the rill/gully volume and length (V = aL^b) and the uncertainties due to the channel depth measurements and the cross-sectional shape. The field data of the rill and gully volumes were within the estimated uncertainty. We defined a decision rule to distinguish rills from gullies on the basis of the segment length and applied the corresponding power law relationship that was derived from the SMC subset to estimate the eroded volume of the entire dataset. The erosion values that were calculated at different scales (0.680 Mg∙ha⁻¹at the catchment scale, 28.4 Mg∙ha⁻¹ on the parcels affected by erosion) are consistent with values found in the literature. Our results indicate that erosion at the catchment scale can be considered moderate, whereas the erosion at the field scale exceeds the tolerance limit, which is consistent with data that have been summarized and/or discussed by several authors. Full article

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8123 KiB

Open AccessArticle

Effective Generation and Update of a Building Map Database Through Automatic Building Change Detection from LiDAR Point Cloud Data

by Mohammad Awrangjeb

Remote Sens. 2015, 7(10), 14119-14150; https://doi.org/10.3390/rs71014119 - 27 Oct 2015

Cited by 32 | Viewed by 9060

Abstract

Periodic building change detection is important for many applications, including disaster management. Building map databases need to be updated based on detected changes so as to ensure their currency and usefulness. This paper first presents a graphical user interface (GUI) developed to support [...] Read more.

Periodic building change detection is important for many applications, including disaster management. Building map databases need to be updated based on detected changes so as to ensure their currency and usefulness. This paper first presents a graphical user interface (GUI) developed to support the creation of a building database from building footprints automatically extracted from LiDAR (light detection and ranging) point cloud data. An automatic building change detection technique by which buildings are automatically extracted from newly-available LiDAR point cloud data and compared to those within an existing building database is then presented. Buildings identified as totally new or demolished are directly added to the change detection output. However, for part-building demolition or extension, a connected component analysis algorithm is applied, and for each connected building component, the area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building-part. Using the developed GUI, a user can quickly examine each suggested change and indicate his/her decision to update the database, with a minimum number of mouse clicks. In experimental tests, the proposed change detection technique was found to produce almost no omission errors, and when compared to the number of reference building corners, it reduced the human interaction to 14% for initial building map generation and to 3% for map updating. Thus, the proposed approach can be exploited for enhanced automated building information updating within a topographic database. Full article

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

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1330 KiB

Open AccessArticle

Modeling Microwave Emission from Short Vegetation-Covered Surfaces

by Yanhui Xie, Jiancheng Shi, Yonghui Lei and Yunqing Li

Remote Sens. 2015, 7(10), 14099-14118; https://doi.org/10.3390/rs71014099 - 26 Oct 2015

Cited by 4 | Viewed by 5939

Abstract

Owing to the temporal and spatial variability of the emissivity spectra, problems remain in the interpretation and application of satellite passive microwave data over vegetation-covered surfaces. The commonly used microwave land emissivity model, developed by Weng et al. (2001) and implemented into the [...] Read more.

Owing to the temporal and spatial variability of the emissivity spectra, problems remain in the interpretation and application of satellite passive microwave data over vegetation-covered surfaces. The commonly used microwave land emissivity model, developed by Weng et al. (2001) and implemented into the community radiative transfer model (CRTM), treats vegetation-covered surfaces as a three-layer medium. This simplification comes at the cost of accuracy. In this study, to reduce bias in the modeling of microwave emissions from short vegetation-covered surfaces, two modifications are made. First, vegetation was considered as a multilayered medium including leaves and stems to simulate volumetric absorption and scattering. The results suggest that the calculated brightness temperatures well agree with field experiments under different incidence angles for low soil moisture and sparse crop cover. On the other hand, large errors from the measurements are found for high soil moisture content and dense crop cover. Second, the advanced integral equation model (AIEM) was also used to improve the simulation of reflectivity from rough soil surfaces. Comparisons with field experimental data show that the determination coefficient between the calculated and measured brightness temperatures significantly increased and the root-mean-square errors remarkably decreased. The average improvement using the proposed approach is about 80% and 59% in accuracy for the vertical and horizontal polarization, respectively. Full article

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2034 KiB

Open AccessArticle

The Impact of Sunlight Conditions on the Consistency of Vegetation Indices in Croplands—Effective Usage of Vegetation Indices from Continuous Ground-Based Spectral Measurements

by Mitsunori Ishihara, Yoshio Inoue, Keisuke Ono, Mariko Shimizu and Shoji Matsuura

Remote Sens. 2015, 7(10), 14079-14098; https://doi.org/10.3390/rs71014079 - 26 Oct 2015

Cited by 45 | Viewed by 10934

Abstract

A ground-based network of spectral observations is useful for ecosystem monitoring and validation of satellite data. However, these observations contain inherent uncertainties due to the change of sunlight conditions. This study investigated the impact of changing solar zenith angles and diffuse/direct light conditions [...] Read more.

A ground-based network of spectral observations is useful for ecosystem monitoring and validation of satellite data. However, these observations contain inherent uncertainties due to the change of sunlight conditions. This study investigated the impact of changing solar zenith angles and diffuse/direct light conditions on the consistency of vegetation indices (normalized difference vegetation index (NDVI) and green-red vegetation index (GRVI)) derived from ground-based spectral measurements in three different types of cropland (paddy field, upland field, cultivated grassland) in Japan. In general, the vegetation indices decreased with decreasing solar zenith angle. This response was affected significantly by the growth stage and diffuse/direct light conditions. The decreasing response of the NDVI to the decreasing solar zenith angle was high during the middle growth stage (0.4 < NDVI < 0.8). On the other hand, a similar response of the GRVI was evident except in the early growth stage (GRVI < 0). The response of vegetation indices to the solar zenith angle was evident under clear sky conditions but almost negligible under cloudy sky conditions. At large solar zenith angles, neither the NDVI nor the GRVI were affected by diffuse/direct light conditions in any growth stage. These experimental results were supported well by the results of simulations based on a physically-based canopy reflectance model (PROSAIL). Systematic selection of the data from continuous diurnal spectral measurements in consideration of the solar light conditions would be effective for accurate and consistent assessment of the canopy structure and functioning. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing for Crop Growth Monitoring)

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2232 KiB

Open AccessArticle

A Natural-Rule-Based-Connection (NRBC) Method for River Network Extraction from High-Resolution Imagery

by Chuiqing Zeng, Stephen Bird, James J. Luce and Jinfei Wang

Remote Sens. 2015, 7(10), 14055-14078; https://doi.org/10.3390/rs71014055 - 26 Oct 2015

Cited by 19 | Viewed by 9692

Abstract

This study proposed a natural-rule-based-connection (NRBC) method to connect river segments after water body detection from remotely sensed imagery. A complete river network is important for many hydrological applications. While water body detection methods using remote sensing are well-developed, less attention has been [...] Read more.

This study proposed a natural-rule-based-connection (NRBC) method to connect river segments after water body detection from remotely sensed imagery. A complete river network is important for many hydrological applications. While water body detection methods using remote sensing are well-developed, less attention has been paid to connect discontinuous river segments and form a complete river network. This study designed an automated NRBC method to extract a complete river network by connecting river segments at polygon level. With the assistance of an image pyramid, neighbouring river segments are connected based on four criteria: gap width (Tg), river direction consistency (Tθ), river width consistency (Tw), and minimum river segment length (Tl). The sensitivity of these four criteria were tested, analyzed, and proper criteria values were suggested using image scenes from two diverse river cases. The comparison of NRBC and the alternative morphological method demonstrated NRBC’s advantage of natural rule based selective connection. We refined a river centerline extraction method and show how it outperformed three other existing centerline extraction methods on the test sites. The extracted river polygons and centerlines have a multitude of end uses including rapidly mapping flood extents, monitoring surface water supply, and the provision of validation data for simulation models required for water quantity, quality and aquatic biota assessments. The code for the NRBC is available on GitHub. Full article

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

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1321 KiB

Open AccessArticle

Responses of Natural Vegetation to Different Stages of Extreme Drought during 2009–2010 in Southwestern China

by Xiang Zhao, Hong Wei, Shunlin Liang, Tao Zhou, Bin He, Bijian Tang and Donghai Wu

Remote Sens. 2015, 7(10), 14039-14054; https://doi.org/10.3390/rs71014039 - 26 Oct 2015

Cited by 43 | Viewed by 6993

Abstract

An extreme drought event is usually a long-term process with different stages. Although it is well known that extreme droughts that have occurred frequently in recent years can substantially affect vegetation growth, few studies have revealed the characteristics of vegetation responses for different [...] Read more.

An extreme drought event is usually a long-term process with different stages. Although it is well known that extreme droughts that have occurred frequently in recent years can substantially affect vegetation growth, few studies have revealed the characteristics of vegetation responses for different stages of an extreme drought event. Especially, studies should address when the vegetation growth was disturbed and how it recovered through an extreme drought event. In this study, we used the Normalized Difference Vegetation Index (NDVI) and Palmer Drought Severity Index (PDSI) to evaluate the response of vegetation to different stages of a severe drought event during 2009–2010 throughout Southwestern China. The PDSI time series indicated that the drought can be divided into three stages, including an initial stage represented by moderate drought (S1), a middle stage represented by continual severe drought (S2), and a final recovery stage (S3). The results revealed that the drought during the initial stage inhibited the growth of grassland and woody savanna, however, forest growth did not decrease during the first stage of droughts, and there was even a trend towards higher NDVI values. The continual severe drought in the middle stage inhibited growth for all vegetation types, and the woody savanna was affected most severely. In the final stage, all vegetation types underwent recovery, including the grassland that had endured the most severe drought. This study provides observational evidence and reveals that the responses of forest to the extreme drought are different from grassland and woody savanna in the different drought stages. Full article

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9946 KiB

Open AccessArticle

Multi-Image and Multi-Sensor Change Detection for Long-Term Monitoring of Arid Environments With Landsat Series

by Emanuele Mandanici and Gabriele Bitelli

Remote Sens. 2015, 7(10), 14019-14038; https://doi.org/10.3390/rs71014019 - 26 Oct 2015

Cited by 18 | Viewed by 5552

Abstract

An automated procedure has been proposed to monitor by multispectral satellite imagery the cultivation expansion between 1987 and 2013 in the arid environment of the Fayyum Oasis (Egypt), which is subject to land reclamation. A change detection procedure was applied to the four [...] Read more.

An automated procedure has been proposed to monitor by multispectral satellite imagery the cultivation expansion between 1987 and 2013 in the arid environment of the Fayyum Oasis (Egypt), which is subject to land reclamation. A change detection procedure was applied to the four years investigated (1987, 1998, 2003 and 2013). This long-term analysis is based on images from the Landsat series, adopting a classification strategy relying on vegetation index computations. In particular: (a) the consequences of the radiometric differences of three Landsat sensors on the vegetation index values were analyzed using data simulated by a hyperspectral Hyperion image; (b) the problems resulting from harvesting cycles were minimized using five images per year, after a preliminary analysis on the effects deriving from the number of processed images; (c) an accuracy assessment was carried out on the 2003 and 2013 maps using high resolution images for a portion of the investigated area, with an estimated overall accuracy of 91% for the change detection. The method is implemented in a batch procedure and can be applied to other similar environmental contexts, supporting analyses for sustainable development and exploitation of soil and water resources. Full article

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3874 KiB

Open AccessArticle

A Spectral Unmixing Model for the Integration of Multi-Sensor Imagery: A Tool to Generate Consistent Time Series Data

by Georgia Doxani, Zina Mitraka, Ferran Gascon, Philippe Goryl and Bojan R. Bojkov

Remote Sens. 2015, 7(10), 14000-14018; https://doi.org/10.3390/rs71014000 - 23 Oct 2015

Cited by 12 | Viewed by 7785

Abstract

The Sentinel missions have been designed to support the operational services of the Copernicus program, ensuring long-term availability of data for a wide range of spectral, spatial and temporal resolutions. In particular, Sentinel-2 (S-2) data with improved high spatial resolution and higher revisit [...] Read more.

The Sentinel missions have been designed to support the operational services of the Copernicus program, ensuring long-term availability of data for a wide range of spectral, spatial and temporal resolutions. In particular, Sentinel-2 (S-2) data with improved high spatial resolution and higher revisit frequency (five days with the pair of satellites in operation) will play a fundamental role in recording land cover types and monitoring land cover changes at regular intervals. Nevertheless, cloud coverage usually hinders the time series availability and consequently the continuous land surface monitoring. In an attempt to alleviate this limitation, the synergistic use of instruments with different features is investigated, aiming at the future synergy of the S-2 MultiSpectral Instrument (MSI) and Sentinel-3 (S-3) Ocean and Land Colour Instrument (OLCI). To that end, an unmixing model is proposed with the intention of integrating the benefits of the two Sentinel missions, when both in orbit, in one composite image. The main goal is to fill the data gaps in the S-2 record, based on the more frequent information of the S-3 time series. The proposed fusion model has been applied on MODIS (MOD09GA L2G) and SPOT4 (Take 5) data and the experimental results have demonstrated that the approach has high potential. However, the different acquisition characteristics of the sensors, i.e. illumination and viewing geometry, should be taken into consideration and bidirectional effects correction has to be performed in order to reduce noise in the reflectance time series. Full article

(This article belongs to the Special Issue Lessons Learned from the SPOT4 (Take5): Experiment in Preparation for Sentinel-2)

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1618 KiB

Open AccessArticle

Landsat-Based Long-Term Monitoring of Total Suspended Matter Concentration Pattern Change in the Wet Season for Dongting Lake, China

by Zhubin Zheng, Yunmei Li, Yulong Guo, Yifan Xu, Ge Liu and Chenggong Du

Remote Sens. 2015, 7(10), 13975-13999; https://doi.org/10.3390/rs71013975 - 23 Oct 2015

Cited by 106 | Viewed by 9075

Abstract

Assessing the impacts of environmental change and anthropogenic activities on the historical and current total suspended matter (TSM) pattern in Dongting Lake, China, is a large challenge. We addressed this challenge by using more than three decades of Landsat data. Based on in [...] Read more.

Assessing the impacts of environmental change and anthropogenic activities on the historical and current total suspended matter (TSM) pattern in Dongting Lake, China, is a large challenge. We addressed this challenge by using more than three decades of Landsat data. Based on in situ measurements, we developed an algorithm based on the near-infrared (NIR) band to estimate TSM in Dongting Lake. The algorithm was applied to Landsat images to derive TSM distribution maps from 1978 to 2013 in the wet season, revealing significant inter-annual and spatial variability. The relationship of TSM to water level, precipitation, and wind speed was analyzed, and we found that: (1) sand mining areas usually coincide with regions that have high TSM levels in Dongting Lake; (2) water level and seven-day precipitation were both important to TSM variation, but no significant relationship was found between TSM and wind speed or other meteorological data; (3) the increased level of sand mining in response to rapid economic growth has deeply influenced the TSM pattern since 2000 due to the resuspension of sediment; and (4) TSM variation might be associated with policy changes regarding the management of sand mining; it might also be affected by lower water levels caused by the impoundment of the Three Gorges Dam since 2000. Full article

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

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1695 KiB

Open AccessArticle

Semantic Decomposition and Reconstruction of Compound Buildings with Symmetric Roofs from LiDAR Data and Aerial Imagery

by Hongtao Wang, Wuming Zhang, Yiming Chen, Mei Chen and Kai Yan

Remote Sens. 2015, 7(10), 13945-13974; https://doi.org/10.3390/rs71013945 - 23 Oct 2015

Cited by 33 | Viewed by 7546

Abstract

3D building models are important for many applications related to human activities in urban environments. However, due to the high complexity of the building structures, it is still difficult to automatically reconstruct building models with accurate geometric description and semantic information. To simplify [...] Read more.

3D building models are important for many applications related to human activities in urban environments. However, due to the high complexity of the building structures, it is still difficult to automatically reconstruct building models with accurate geometric description and semantic information. To simplify this problem, this article proposes a novel approach to automatically decompose the compound buildings with symmetric roofs into semantic primitives by exploiting local symmetry contained in the building structure. In this approach, the proposed decomposition allows the overlapping of neighbor primitives and each decomposed primitive can be represented as a parametric form, which simplify the complexity of the building reconstruction and facilitate the integration of LiDAR data and aerial imagery into a parameters optimization process. The proposed method starts by extracting isolated building regions from the LiDAR point clouds. Next, point clouds belonging to each compound building are segmented into planar patches to construct an attributed graph, and then the local symmetries contained in the attributed graph are exploited to automatically decompose the compound buildings into different semantic primitives. In the final step, 2D image features are extracted depending on the initial 3D primitives generated from LiDAR data, and then the compound building is reconstructed using constraints from LiDAR data and aerial imagery by a nonlinear least squares optimization. The proposed method is applied to two datasets with different point densities to show that the complexity of building reconstruction can be reduced considerably by decomposing the compound buildings into semantic primitives. The experimental results also demonstrate that the traditional model driven methods can be further extended to the automated reconstruction of compound buildings by using the proposed semantic decomposition method. Full article

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1678 KiB

Open AccessArticle

Hierarchical Registration Method for Airborne and Vehicle LiDAR Point Cloud

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

Remote Sens. 2015, 7(10), 13921-13944; https://doi.org/10.3390/rs71013921 - 23 Oct 2015

Cited by 22 | Viewed by 7565

Abstract

A new hierarchical method for the automatic registration of airborne and vehicle light detection and ranging (LiDAR) data is proposed, using three-dimensional (3D) road networks and 3D building contours. Firstly, 3D road networks are extracted from airborne LiDAR data and then registered with [...] Read more.

A new hierarchical method for the automatic registration of airborne and vehicle light detection and ranging (LiDAR) data is proposed, using three-dimensional (3D) road networks and 3D building contours. Firstly, 3D road networks are extracted from airborne LiDAR data and then registered with vehicle trajectory lines. During the registration of airborne road networks and vehicle trajectory lines, a network matching rate is introduced for the determination of reliable transformation matrix. Then, the RIMM (reversed iterative mathematic morphological) method and a height value accumulation method are employed to extract 3D building contours from airborne and vehicle LiDAR data, respectively. The Rodriguez matrix and collinearity equation are used for the determination of conjugate building contours. Based on this, a rule is defined to determine reliable conjugate contours, which are finally used for the fine registration of airborne and vehicle LiDAR data. The experiments show that the coarse registration method with 3D road networks can contribute to a reliable initial registration result, and the fine registration using 3D building contours obtains a final registration result with high reliability and geometric accuracy. Full article

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

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1048 KiB

Open AccessArticle

Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure

by Jonathan P. Dandois, Marc Olano and Erle C. Ellis

Remote Sens. 2015, 7(10), 13895-13920; https://doi.org/10.3390/rs71013895 - 23 Oct 2015

Cited by 344 | Viewed by 21216

Abstract

Ecological remote sensing is being transformed by three-dimensional (3D), multispectral measurements of forest canopies by unmanned aerial vehicles (UAV) and computer vision structure from motion (SFM) algorithms. Yet applications of this technology have out-paced understanding of the relationship between collection method and data [...] Read more.

Ecological remote sensing is being transformed by three-dimensional (3D), multispectral measurements of forest canopies by unmanned aerial vehicles (UAV) and computer vision structure from motion (SFM) algorithms. Yet applications of this technology have out-paced understanding of the relationship between collection method and data quality. Here, UAV-SFM remote sensing was used to produce 3D multispectral point clouds of Temperate Deciduous forests at different levels of UAV altitude, image overlap, weather, and image processing. Error in canopy height estimates was explained by the alignment of the canopy height model to the digital terrain model (R² = 0.81) due to differences in lighting and image overlap. Accounting for this, no significant differences were observed in height error at different levels of lighting, altitude, and side overlap. Overall, accurate estimates of canopy height compared to field measurements (R² = 0.86, RMSE = 3.6 m) and LIDAR (R² = 0.99, RMSE = 3.0 m) were obtained under optimal conditions of clear lighting and high image overlap (>80%). Variation in point cloud quality appeared related to the behavior of SFM ‘image features’. Future research should consider the role of image features as the fundamental unit of SFM remote sensing, akin to the pixel of optical imaging and the laser pulse of LIDAR. Full article

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Open AccessArticle

Integration of Concentration-Area Fractal Modeling and Spectral Angle Mapper for Ferric Iron Alteration Mapping and Uranium Exploration in the Xiemisitan Area, NW China

by Jun-Ting Qiu, Chuan Zhang and Xiao Hu

Remote Sens. 2015, 7(10), 13878-13894; https://doi.org/10.3390/rs71013878 - 22 Oct 2015

Cited by 21 | Viewed by 7193

Abstract

The high-grade uranium deposits in the Xiemisitan area, northwestern China, are genetically associated with the faulting of felsic volcanic or sub-volcanic rocks. Ferric iron alteration indicates that oxidizing hydrothermal fluids percolated through the rocks. In this study, we measured the gamma-ray intensities of [...] Read more.

The high-grade uranium deposits in the Xiemisitan area, northwestern China, are genetically associated with the faulting of felsic volcanic or sub-volcanic rocks. Ferric iron alteration indicates that oxidizing hydrothermal fluids percolated through the rocks. In this study, we measured the gamma-ray intensities of rocks in the Xiemisitan area and we propose a hybrid method for the mapping of ferric iron alteration using concentration-area fractal modeling and spectral angle mapper. The method enables ferric iron alteration to be distinguished from potash-feldspar granitic rocks. The mapping results were integrated with structural data to assist with exploration for uranium in the study area. Using this approach, six prospective areas of mineralization were proposed. Of these areas, two anomalies with high gamma-ray intensities of 104 and 650 Uγ were identified and verified by field inspection. These observations suggest that Enhanced Thematic Mapper Plus images are a valuable tool that can improve the efficiency of uranium exploration. Full article

(This article belongs to the Special Issue Remote Sensing in Geology)

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669 KiB

Open AccessCommunication

Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees—Experiences from Laboratory Test

by Samuli Junttila, Sanna Kaasalainen, Mikko Vastaranta, Teemu Hakala, Olli Nevalainen and Markus Holopainen

Remote Sens. 2015, 7(10), 13863-13877; https://doi.org/10.3390/rs71013863 - 22 Oct 2015

Cited by 24 | Viewed by 6704

Abstract

Global warming is posing a threat to the health and condition of forests as the amount and length of biotic and abiotic disturbances increase. Most methods for detecting disturbances and measuring forest health are based on multi- and hyperspectral imaging. We conducted a [...] Read more.

Global warming is posing a threat to the health and condition of forests as the amount and length of biotic and abiotic disturbances increase. Most methods for detecting disturbances and measuring forest health are based on multi- and hyperspectral imaging. We conducted a test with spruce and pine trees using a hyperspectral Lidar instrument in a laboratory to determine the capability of combined range and reflectance measurements to investigate forest health. A simple drought treatment was conducted by leaving the harvested trees outdoors without a water supply for 12 days. The results showed statistically significant variation in reflectance after the drought treatment for both species. However, the changes differed between the species, indicating that drought-induced alterations in spectral characteristics may be species-dependent. Based on our results, hyperspectral Lidar has the potential to detect drought in spruce and pine trees. Full article

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

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Open AccessArticle

Single- and Multi-Date Crop Identification Using PROBA-V 100 and 300 m S1 Products on Zlatia Test Site, Bulgaria

by Eugenia Roumenina, Clement Atzberger, Vassil Vassilev, Petar Dimitrov, Ilina Kamenova, Martin Banov, Lachezar Filchev and Georgi Jelev

Remote Sens. 2015, 7(10), 13843-13862; https://doi.org/10.3390/rs71013843 - 22 Oct 2015

Cited by 23 | Viewed by 7797

Abstract

The monitoring of crops is of vital importance for food and environmental security in a global and European context. The main goal of this study was to assess the crop mapping performance provided by the 100 m spatial resolution of PROBA-V compared to [...] Read more.

The monitoring of crops is of vital importance for food and environmental security in a global and European context. The main goal of this study was to assess the crop mapping performance provided by the 100 m spatial resolution of PROBA-V compared to coarser resolution data (e.g., PROBA-V at 300 m) for a 2250 km²test site in Bulgaria. The focus was on winter and summer crop mapping with three to five classes. For classification, single- and multi-date spectral data were used as well as NDVI time series. Our results demonstrate that crop identification using 100 m PROBA-V data performed significantly better in all experiments compared to the PROBA-V 300 m data. PROBA-V multispectral imagery, acquired in spring (March) was the most appropriate for winter crop identification, while satellite data acquired in summer (July) was superior for summer crop identification. The classification accuracy from PROBA-V 100 m compared to PROBA-V 300 m was improved by 5.8% to 14.8% depending on crop type. Stacked multi-date satellite images with three to four images gave overall classification accuracies of 74%–77% (PROBA-V 100 m data) and 66%–70% (PROBA-V 300 m data) with four classes (wheat, rapeseed, maize, and sunflower). This demonstrates that three to four image acquisitions, well distributed over the growing season, capture most of the spectral and temporal variability in our test site. Regarding the PROBA-V NDVI time series, useful results were only obtained if crops were grouped into two broader crop type classes (summer and winter crops). Mapping accuracies decreased significantly when mapping more classes. Again, a positive impact of the increased spatial resolution was noted. Together, the findings demonstrate the positive effect of the 100 m resolution PROBA-V data compared to the 300 m for crop mapping. This has important implications for future data provision and strengthens the arguments for a second generation of this mission originally designed solely as a “gap-filler mission”. Full article

(This article belongs to the Special Issue Opportunities and Challenges for Medium Resolution (hecta- to kilometric) Earth Observation of Land Surfaces in the Advent of European Proba-V and Sentinel-3 Missions)

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Open AccessCorrection

Correction: Hammer, J., et al. Short-Term Forecasting of Surface Solar Irradiance Based on Meteosat-SEVIRI Data Using a Nighttime Cloud Index. Remote Sens. 2015, 7, 9070–9090

by Annette Hammer, Jan Kühnert, Kailash Weinreich and Elke Lorenz

Remote Sens. 2015, 7(10), 13842; https://doi.org/10.3390/rs71013842 - 21 Oct 2015

Viewed by 3560

Abstract

Due to an oversight by the authors, the following correction is necessary in this publication [1].[...] Full article

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Open AccessArticle

Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data

by Ram C. Sharma, Ryutaro Tateishi, Keitarou Hara and Luong Viet Nguyen

Remote Sens. 2015, 7(10), 13807-13841; https://doi.org/10.3390/rs71013807 - 21 Oct 2015

Cited by 53 | Viewed by 9058

Abstract

Monitoring of water cover and shorelines at a global scale is essential for better understanding climate change consequences and modern human disturbances. The level and turbidity of the surface water, and the background objects in which they interact with, vary significantly at a [...] Read more.

Monitoring of water cover and shorelines at a global scale is essential for better understanding climate change consequences and modern human disturbances. The level and turbidity of the surface water, and the background objects in which they interact with, vary significantly at a global scale. The existing water indices applicable to detection and extraction of water cover at local and regional scales cannot work efficiently everywhere in the globe. In this research, a new water index called Superfine Water Index (SWI) was developed for robust detection and discrimination of the surface water at a global scale using MODIS based multispectral data. The SWI was designed in such a way that it provides high contrast between the water and non-water areas. Achieving high contrast is vital for discriminating the surface water mixed with a variety of objects. The sensitivity analysis of the SWI demonstrated its high sensitivity to the surface water compared to the existing water indices. One single-layered global mosaic of a 90-percentile SWI image was used as a master image for global water cover mapping by reducing the large volume of MODIS data available between 2012 and 2014 globally. The random walker algorithm was applied in the SWI image with the support of reference training data for the extraction and mapping of water cover. This research produced an up-to-date global water cover map of the year 2013. The performance of a new map was evaluated with a number of case studies and compared with existing maps. The supremacy of the SWI over the existing water indices, and high performance of the SWI based water map confirmed the reliability of the new water mapping methodology developed. We expect that this methodology can contribute to seasonal and annual change analysis of the global water cover as well. Full article

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Open AccessArticle

Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

by Xavier Monteys, Paul Harris, Silvia Caloca and Conor Cahalane

Remote Sens. 2015, 7(10), 13782-13806; https://doi.org/10.3390/rs71013782 - 21 Oct 2015

Cited by 80 | Viewed by 10350

Abstract

The coastal shallow water zone can be a challenging and costly environment in which to acquire bathymetry and other oceanographic data using traditional survey methods. Much of the coastal shallow water zone worldwide remains unmapped using recent techniques and is, therefore, poorly understood. [...] Read more.

The coastal shallow water zone can be a challenging and costly environment in which to acquire bathymetry and other oceanographic data using traditional survey methods. Much of the coastal shallow water zone worldwide remains unmapped using recent techniques and is, therefore, poorly understood. Optical satellite imagery is proving to be a useful tool in predicting water depth in coastal zones, particularly in conjunction with other standard datasets, though its quality and accuracy remains largely unconstrained. A common challenge in any prediction study is to choose a small but representative group of predictors, one of which can be determined as the best. In this respect, exploratory analyses are used to guide the make-up of this group, where we choose to compare a basic non-spatial model versus four spatial alternatives, each catering for a variety of spatial effects. Using one instance of RapidEye satellite imagery, we show that all four spatial models show better adjustments than the non-spatial model in the water depth predictions, with the best predictor yielding a correlation coefficient of actual versus predicted at 0.985. All five predictors also factor in the influence of bottom type in explaining water depth variation. However, the prediction ranges are too large to be used in high accuracy bathymetry products such as navigation charts; nevertheless, they are considered beneficial in a variety of other applications in sensitive disciplines such as environmental monitoring, seabed mapping, or coastal zone management. Full article

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Open AccessArticle

Evaluation of a Backpack-Mounted 3D Mobile Scanning System

by Helge A. Lauterbach, Dorit Borrmann, Robin Heß, Daniel Eck, Klaus Schilling and Andreas Nüchter

Remote Sens. 2015, 7(10), 13753-13781; https://doi.org/10.3390/rs71013753 - 21 Oct 2015

Cited by 66 | Viewed by 9059

Abstract

Recently, several backpack-mounted systems, also known as personal laser scanning systems, have been developed. They consist of laser scanners or cameras that are carried by a human operator to acquire measurements of the environment while walking. These systems were first designed to overcome [...] Read more.

Recently, several backpack-mounted systems, also known as personal laser scanning systems, have been developed. They consist of laser scanners or cameras that are carried by a human operator to acquire measurements of the environment while walking. These systems were first designed to overcome the challenges of mapping indoor environments with doors and stairs. While the human operator inherently has the ability to open doors and to climb stairs, the flexible movements introduce irregularities of the trajectory to the system. To compete with other mapping systems, the accuracy of these systems has to be evaluated. In this paper, we present an extensive evaluation of our backpack mobile mapping system in indoor environments. It is shown that the system can deal with the normal human walking motion, but has problems with irregular jittering. Moreover, we demonstrate the applicability of the backpack in a suitable urban scenario. Full article

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

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Open AccessArticle

Spatio-Temporal Changes in Vegetation Activity and Its Driving Factors during the Growing Season in China from 1982 to 2011

by Bo Qu, Wenbin Zhu, Shaofeng Jia and Aifeng Lv

Remote Sens. 2015, 7(10), 13729-13752; https://doi.org/10.3390/rs71013729 - 21 Oct 2015

Cited by 50 | Viewed by 7345

Abstract

Using National Oceanographic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Climatic Research Unit (CRU) climate datasets, we analyzed interannual trends in the growing-season Normalized Difference Vegetation Index (NDVI) in China from 1982 to 2011, as well as the effects of climatic [...] Read more.

Using National Oceanographic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Climatic Research Unit (CRU) climate datasets, we analyzed interannual trends in the growing-season Normalized Difference Vegetation Index (NDVI) in China from 1982 to 2011, as well as the effects of climatic variables and human activities on vegetation variation. Growing-season (period between the onset and end of plant growth) NDVI significantly increased (p < 0.01) on a national scale and showed positive trends in 52.76% of the study area. A multiple regression model was used to investigate the response of vegetation to climatic factors during recent and previous time intervals. The interactions between growing-season NDVI and climatic variables were more complex than expected, and a lag existed between climatic factors and their effects on NDVI. The regression residuals were used to show that over 6% of the study area experienced significantly human-induced vegetation variations (p < 0.05). These regions were mostly located in densely populated, reclaimed agriculture, afforestation, and conservation areas. Similar conclusions were drawn based on land-use change over the study period. Full article

(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)

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Open AccessArticle

Evaluation of Polarized Remote Sensing of Aerosol Optical Thickness Retrieval over China

by Hao Chen, Tianhai Cheng, Xingfa Gu, Zhengqiang Li and Yu Wu

Remote Sens. 2015, 7(10), 13711-13728; https://doi.org/10.3390/rs71013711 - 20 Oct 2015

Cited by 13 | Viewed by 5872

Abstract

The monitoring capability of a polarized instrument (POLDER) under high aerosol loading conditions over China is investigated. The aerosol optical thickness (AOT), which infers the aerosol burden, is used to measure the satellite monitoring capabilities. AOT products retrieved from POLDER on low aerosol [...] Read more.

The monitoring capability of a polarized instrument (POLDER) under high aerosol loading conditions over China is investigated. The aerosol optical thickness (AOT), which infers the aerosol burden, is used to measure the satellite monitoring capabilities. AOT products retrieved from POLDER on low aerosol loading days, and products from a radiometric instrument (MODIS) on high and low aerosol loading days, are presented for comparison. Our study reveals that for high aerosol days, the monitoring capability of the polarized instrument is lower than that of the traditional instrument. The accuracy of matched POLDER fine-AOTs is lower than that of MODIS-matched AOTs. On low aerosol loading days, the performance of the polarized instrument is good when monitoring the aerosol optical thickness. Further analysis reveals that for the high aerosol loading days, the mean relative errors of matched POLDER fine AOTs and MODIS AOTs with respect to AERONET measurements are 44% and 16%, respectively. For the low aerosol loading days, the mean relative errors of POLDER and MODIS measurements with respect to AERONET measurements are 41% and 40%, respectively. During high aerosol days, POLDER-retrieved fine-AOTs reveal a poor accuracy with only 14% of matches falling within the error range, which is nearly one fourth of the MODIS regression results (51.59%). For the low aerosol loading days, the POLDER regression results are good. Approximately 62% of the POLDER measurements fall within the expected error range ±(0.05 + 15%) compared with the AERONET observed values. Full article

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Open AccessArticle

Reconstructing Turbidity in a Glacially Influenced Lake Using the Landsat TM and ETM+ Surface Reflectance Climate Data Record Archive, Lake Clark, Alaska

by Carson A. Baughman, Benjamin M. Jones, Krista K. Bartz, Daniel B. Young and Christian E. Zimmerman

Remote Sens. 2015, 7(10), 13692-13710; https://doi.org/10.3390/rs71013692 - 20 Oct 2015

Cited by 36 | Viewed by 8218

Abstract

Lake Clark is an important nursery lake for sockeye salmon (Oncorhynchus nerka) in the headwaters of Bristol Bay, Alaska, the most productive wild salmon fishery in the world. Reductions in water clarity within Alaska lake systems as a result of increased [...] Read more.

Lake Clark is an important nursery lake for sockeye salmon (Oncorhynchus nerka) in the headwaters of Bristol Bay, Alaska, the most productive wild salmon fishery in the world. Reductions in water clarity within Alaska lake systems as a result of increased glacial runoff have been shown to reduce salmon production via reduced abundance of zooplankton and macroinvertebrates. In this study, we reconstruct long-term, lake-wide water clarity for Lake Clark using the Landsat TM and ETM+ surface reflectance products (1985–2014) and in situ water clarity data collected between 2009 and 2013. Analysis of a Landsat scene acquired in 2009, coincident with in situ measurements in the lake, and uncertainty analysis with four scenes acquired within two weeks of field data collection showed that Band 3 surface reflectance was the best indicator of turbidity (r² = 0.55, RMSE << 0.01). We then processed 151 (98 partial- and 53 whole-lake) Landsat scenes using this relation and detected no significant long-term trend in mean turbidity for Lake Clark between 1991 and 2014. We did, however, detect interannual variation that exhibited a non-significant (r²= 0.20) but positive correlation (r= 0.20) with regional mean summer air temperature and found the month of May exhibited a significant positive trend (r² = 0.68, p = 0.02) in turbidity between 2000 and 2014. This study demonstrates the utility of hindcasting turbidity in a glacially influenced lake using the Landsat surface reflectance products. It may also help land and resource managers reconstruct turbidity records for lakes that lack in situ monitoring, and may be useful in predicting future water clarity conditions based on projected climate scenarios. Full article

(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)

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Open AccessArticle

Monitoring the Variation in Ice-Cover Characteristics of the Slave River, Canada Using RADARSAT-2 Data—A Case Study

by Thuan Chu, Apurba Das and Karl-Erich Lindenschmidt

Remote Sens. 2015, 7(10), 13664-13691; https://doi.org/10.3390/rs71013664 - 20 Oct 2015

Cited by 27 | Viewed by 9615

Abstract

The winter regime of river-ice covers in high northern latitude regions is often a determining factor in the management of water resources, conservation of aquatic ecosystems and preservation of traditional and cultural lifestyles of local peoples. As ground-based monitoring of river-ice regimes in [...] Read more.

The winter regime of river-ice covers in high northern latitude regions is often a determining factor in the management of water resources, conservation of aquatic ecosystems and preservation of traditional and cultural lifestyles of local peoples. As ground-based monitoring of river-ice regimes in high northern latitudes is expensive and restricted to a few locations due to limited accessibility to most places along rivers from shorelines, remote sensing techniques are a suitable approach for monitoring. This study developed a RADARSAT-2 based method to monitor the spatio-temporal variation of ice covers, as well as ice types during the freeze-up period, along the main channel of the Slave River Delta in the Northwest Territories of Canada. The spatio-temporal variation of ice covers along the river was analyzed using the backscatter-based coefficient of variation (CV) in the 2013–2014 and 2014–2015 winters. As a consequence of weather and flow conditions, the ice cover in the 2013–2014 winter had the higher variation than the 2014–2015 winter, particularly in the potential areas of flooded/cracked ice covers. The river sections near active channels (e.g., Middle Channel and Nagle Channel), Big Eddy, and Great Slave Lake also yielded higher intra-annual variation of ice cover characteristics during the winters. With the inclusion of backscatter and texture analysis from RADARSAT-2 data, four water and ice cover classes consisting of open water, thermal ice, juxtaposed ice, and consolidated ice, were discriminated in the images acquired between November and March in both the studied winters. In addition to river geomorphology and climatic conditions such as river width, sinuosity or air temperature, the fluctuation of water flows during the winter has a significant impact on the variation of ice cover as well as the formation of different ice types in the Slave River. The RADARSAT-2 based monitoring algorithm can also be applied to other river systems in high latitude ecosystems to annually monitor their river-ice variation and formation during the freeze-up and ice cover progression period. Full article

(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)

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Open AccessArticle

Vegetation Dynamics and Associated Driving Forces in Eastern China during 1999–2008

by Jian Peng, You Li, Lu Tian, Yanxu Liu and Yanglin Wang

Remote Sens. 2015, 7(10), 13641-13663; https://doi.org/10.3390/rs71013641 - 20 Oct 2015

Cited by 48 | Viewed by 6313

Abstract

Vegetation is one of the most important components of the terrestrial ecosystem and, thus, monitoring the spatial and temporal dynamics of vegetation has become the key to exploring the basic process of the terrestrial ecosystem. Vegetation change studies have focused on the relationship [...] Read more.

Vegetation is one of the most important components of the terrestrial ecosystem and, thus, monitoring the spatial and temporal dynamics of vegetation has become the key to exploring the basic process of the terrestrial ecosystem. Vegetation change studies have focused on the relationship between climatic factors and vegetation dynamics. However, correlations among the climatic factors always disturb the results. In addition, the impact of anthropogenic activities on vegetation dynamics was indeterminate. Here, vegetation dynamics in 14 provinces in Eastern China over a 10-year period was quantified to determine the driving mechanisms relating to climate and anthropogenic factors using partial correlation analysis. The results showed that from 1999 to 2008, the vegetation density increased in the whole, with spatial variations. The vegetation improvement was concentrated in the Yangtze River Delta, with the vegetation degradation concentrated in the other developed areas, such as Beijing-Tianjin-Hebei Region and the Pearl River Delta. The annual NDVI changes were mainly driven by temperature in Northeast China and the Pearl River Delta, and by precipitation in the Bohai Rim; while in the Yangtze River Delta, the driving forces of temperature and precipitation almost equaled each other. Furthermore, the impact of anthropogenic activities on vegetation dynamics had accumulative effects in the time series, and had a phase effect on the vegetation change trend. Full article

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Open AccessArticle

Semi-Empirical Calibration of the Integral Equation Model for Co-Polarized L-Band Backscattering

by Nicolas Baghdadi, Mehrez Zribi, Simonetta Paloscia, Niko E. C. Verhoest, Hans Lievens, Frederic Baup and Francesco Mattia

Remote Sens. 2015, 7(10), 13626-13640; https://doi.org/10.3390/rs71013626 - 20 Oct 2015

Cited by 47 | Viewed by 6681

Abstract

The objective of this paper is to extend the semi-empirical calibration of the backscattering Integral Equation Model (IEM) initially proposed for Synthetic Aperture Radar (SAR) data at C- and X-bands to SAR data at L-band. A large dataset of radar signal and in [...] Read more.

The objective of this paper is to extend the semi-empirical calibration of the backscattering Integral Equation Model (IEM) initially proposed for Synthetic Aperture Radar (SAR) data at C- and X-bands to SAR data at L-band. A large dataset of radar signal and in situ measurements (soil moisture and surface roughness) over bare soil surfaces were used. This dataset was collected over numerous agricultural study sites in France, Luxembourg, Belgium, Germany and Italy using various SAR sensors (AIRSAR, SIR-C, JERS-1, PALSAR-1, ESAR). Results showed slightly better simulations with exponential autocorrelation function than with Gaussian function and with HH than with VV. Using the exponential autocorrelation function, the mean difference between experimental data and Integral Equation Model (IEM) simulations is +0.4 dB in HH and −1.2 dB in VV with a Root Mean Square Error (RMSE) about 3.5 dB. In order to improve the modeling results of the IEM for a better use in the inversion of SAR data, a semi-empirical calibration of the IEM was performed at L-band in replacing the correlation length derived from field experiments by a fitting parameter. Better agreement was observed between the backscattering coefficient provided by the SAR and that simulated by the calibrated version of the IEM (RMSE about 2.2 dB). Full article

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Open AccessArticle

Estimating the Augmented Reflectance Ratio of the Ocean Surface When Whitecaps Appear

by Zhantang Xu, Wen Zhou, Zhaohua Sun, Yuezhong Yang, Junfang Lin, Guifen Wang, Wenxi Cao and Qian Yang

Remote Sens. 2015, 7(10), 13606-13625; https://doi.org/10.3390/rs71013606 - 20 Oct 2015

Cited by 6 | Viewed by 4974

Abstract

The presence of foam influences the accuracy of satellite-derived water-leaving radiance. A model has been developed to estimate the augmented reflectance ratio (A(λ,U)) due to differences in the fraction of whitecap coverage (w) on the ocean surface. A(λ,U) can be calculated from the [...] Read more.

The presence of foam influences the accuracy of satellite-derived water-leaving radiance. A model has been developed to estimate the augmented reflectance ratio (A(λ,U)) due to differences in the fraction of whitecap coverage (w) on the ocean surface. A(λ,U) can be calculated from the product of w and ρ(λ,U), where ρ(λ,U) is the augmented ratio of the reflectance of background water (R_b(λ)) caused by the presence of whitecaps. Our results showed that the average A(400~700,U) in the visible region was approximately 1.3% at U = 9 m∙s⁻¹, 2.2% at U = 10 m∙s⁻¹, 4.4% at U = 12 m∙s⁻¹, 7.4% at U = 14 m∙s⁻¹, 19% at U = 19 m∙s⁻¹ and 37.9% at U = 24 m∙s⁻¹, making it is necessary to consider the augmented reflectance ratio for remote sensing applications. By estimating remote sensing augmented reflectance using A(λ,U), it was found that the result was in good agreement with previous studies conducted in other areas with U from 9 to 12 m∙s⁻¹. Since R_b(λ) is temporally and spatially variable, our model considered the variation of R_b(λ), whereas existing models have assumed that R_b(λ) is constant. Therefore, the proposed model is more suitable for estimating the augmented reflectance ratio due to whitecaps. Full article

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Remote Sens., Volume 7, Issue 10 (October 2015) – 76 articles , Pages 12588-14275

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