Next Issue
Volume 13, July-1
Previous Issue
Volume 13, June-1

Remote Sens., Volume 13, Issue 12 (June-2 2021) – 192 articles

Cover Story (view full-size image): Alaska’s Yukon River is a prominent feature of the Arctic–Boreal landscape—providing transportation and ecosystem services to many communities. We present a first analysis of satellite-derived snow properties and their interaction with hydrologic processes along the Yukon River, including the spring flood pulse and river ice break up (RIB) timing. A suite of passive microwave satellite-derived snow metrics, including Main Melt Onset Date, Snowoff Date, and Snowmelt Duration from 1988 to 2016, are presented and validated using in situ observations and complementary satellite data. We found meaningful correspondence between areal quantiles of the satellite snow metrics and measured streamflow quantiles and RIB observations, demonstrating the snow metrics’ potential for the monitoring and forecasting of hydrologic events along the Yukon River. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
Order results
Result details
Select all
Export citation of selected articles as:
Article
An Ontology-Based Approach to Enable Data-Driven Research in the Field of NDT in Civil Engineering
Remote Sens. 2021, 13(12), 2426; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122426 - 21 Jun 2021
Viewed by 508
Abstract
Although measurement data from the civil engineering sector are an important basis for scientific analyses in the field of non-destructive testing (NDT), there is still no uniform representation of these data. An analysis of data sets across different test objects or test types [...] Read more.
Although measurement data from the civil engineering sector are an important basis for scientific analyses in the field of non-destructive testing (NDT), there is still no uniform representation of these data. An analysis of data sets across different test objects or test types is therefore associated with a high manual effort. Ontologies and the semantic web are technologies already used in numerous intelligent systems such as material cyberinfrastructures or research databases. This contribution demonstrates the application of these technologies to the case of the 1H nuclear magnetic resonance relaxometry, which is commonly used to characterize water content and porosity distribution in solids. The methodology implemented for this purpose was developed specifically to be applied to materials science (MS) tests. The aim of this paper is to analyze such a methodology from the perspective of data interoperability using ontologies. Three benefits are expected from this approach to the study of the implementation of interoperability in the NDT domain: First, expanding knowledge of how the intrinsic characteristics of the NDT domain determine the application of semantic technologies. Second, to determine which aspects of such an implementation can be improved and in what ways. Finally, the baselines of future research in the field of data integration for NDT are drawn. Full article
Show Figures

Graphical abstract

Technical Note
Attention Multi-Scale Network for Automatic Layer Extraction of Ice Radar Topological Sequences
Remote Sens. 2021, 13(12), 2425; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122425 - 21 Jun 2021
Viewed by 385
Abstract
Analyzing the surface and bedrock locations in radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume and how they may contribute to global climate change. However, the traditional handcrafted methods cannot quickly [...] Read more.
Analyzing the surface and bedrock locations in radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume and how they may contribute to global climate change. However, the traditional handcrafted methods cannot quickly provide quantitative, objective and reliable extraction of information from radargrams. Most traditional handcrafted methods, designed to detect ice-surface and ice-bed layers from ice sheet radargrams, require complex human involvement and are difficult to apply to large datasets, while deep learning methods can obtain better results in a generalized way. In this study, an end-to-end multi-scale attention network (MsANet) is proposed to realize the estimation and reconstruction of layers in sequences of ice sheet radar tomographic images. First, we use an improved 3D convolutional network, C3D-M, whose first full connection layer is replaced by a convolution unit to better maintain the spatial relativity of ice layer features, as the backbone. Then, an adjustable multi-scale module uses different scale filters to learn scale information to enhance the feature extraction capabilities of the network. Finally, an attention module extended to 3D space removes a redundant bottleneck unit to better fuse and refine ice layer features. Radar sequential images collected by the Center of Remote Sensing of Ice Sheets in 2014 are used as training and testing data. Compared with state-of-the-art deep learning methods, the MsANet shows a 10% reduction (2.14 pixels) on the measurement of average mean absolute column-wise error for detecting the ice-surface and ice-bottom layers, runs faster and uses approximately 12 million fewer parameters. Full article
(This article belongs to the Special Issue The Cryosphere Observations Based on Using Remote Sensing Techniques)
Show Figures

Graphical abstract

Communication
Integrating Ecological Assessments to Target Priority Restoration Areas: A Case Study in the Pearl River Delta Urban Agglomeration, China
Remote Sens. 2021, 13(12), 2424; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122424 - 21 Jun 2021
Viewed by 393
Abstract
The identification and management of ecological restoration areas play important roles in promoting sustainable urban development. However, current research lacks a scientific basis for the scope and scale of ecological restoration. Further, the absence of a framework to assess policy goals and public [...] Read more.
The identification and management of ecological restoration areas play important roles in promoting sustainable urban development. However, current research lacks a scientific basis for the scope and scale of ecological restoration. Further, the absence of a framework to assess policy goals and public preferences that leads to identification of ecological restoration areas across the science-policy interface is difficult, and the existing frameworks’ performance has little applicability. We proposed a transdisciplinary framework to combine ecological quality, ecological health, and ecosystem services as an assessment endpoint to identify priority restoration areas. Further, we classified the ecological restoration areas on a township scale by K-means. Based upon policy goals and public preferences of the Pearl River Delta urban agglomeration, we chose air quality, biodiversity, soil fragility, recreation quality, ecosystem vigor, landscape metrics, and the water supply ecosystem service as elements of the evaluation system. This study showed that priority restoration areas accounted for 10.8% of the urban agglomeration area and classified township, largely in the difference between natural and semi-natural ecosystems and the human environment. Policymakers can use this framework comprehensively and flexibly to identify and classify ecological restoration areas to achieve policy goals and fulfil public preferences. Full article
(This article belongs to the Section Ecological Remote Sensing)
Show Figures

Graphical abstract

Article
A Pansharpening Generative Adversarial Network with Multilevel Structure Enhancement and a Multistream Fusion Architecture
Remote Sens. 2021, 13(12), 2423; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122423 - 21 Jun 2021
Viewed by 386
Abstract
Deep learning has been widely used in various computer vision tasks. As a result, researchers have begun to explore the application of deep learning for pansharpening and have achieved remarkable results. However, most current pansharpening methods focus only on the mapping relationship between [...] Read more.
Deep learning has been widely used in various computer vision tasks. As a result, researchers have begun to explore the application of deep learning for pansharpening and have achieved remarkable results. However, most current pansharpening methods focus only on the mapping relationship between images and the lack overall structure enhancement, and do not fully and completely research optimization goals and fusion rules. Therefore, for these problems, we propose a pansharpening generative adversarial network with multilevel structure enhancement and a multistream fusion architecture. This method first uses multilevel gradient operators to obtain the structural information of the high-resolution panchromatic image. Then, it combines the spectral features with multilevel gradient information and inputs them into two subnetworks of the generator for fusion training. We design a comprehensive optimization goal for the generator, which not only minimizes the gap between the fused image and the real image but also considers the adversarial loss between the generator and the discriminator and the multilevel structure loss between the fused image and the panchromatic image. It is worth mentioning that we comprehensively consider the spectral information and the multilevel structure as the input of the discriminator, which makes it easier for the discriminator to distinguish real and fake images. Experiments show that our proposed method is superior to state-of-the-art methods in both the subjective visual and objective assessments of fused images, especially in road and building areas. Full article
(This article belongs to the Section Remote Sensing Image Processing)
Show Figures

Figure 1

Article
A Case Study of the 3D Water Vapor Tomography Model Based on a Fast Voxel Traversal Algorithm for Ray Tracing
Remote Sens. 2021, 13(12), 2422; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122422 - 21 Jun 2021
Viewed by 401
Abstract
A fast voxel traversal algorithm for ray tracing was applied to build a 4 × 4 × 20 tomography model using the observation data of 11 ground-based Global Navigation Satellite System (GNSS) meteorology (GNSS/MET) stations in Hebei Province, China. The precipitation water vapor [...] Read more.
A fast voxel traversal algorithm for ray tracing was applied to build a 4 × 4 × 20 tomography model using the observation data of 11 ground-based Global Navigation Satellite System (GNSS) meteorology (GNSS/MET) stations in Hebei Province, China. The precipitation water vapor (PWV) observed at 05 a.m. (Universal Time Coordinated: UTC) on 10 December 2019, was used to reconstruct three-dimensional (3D) water vapor density fields over the test area. The tomographic results (GNSS_T) show that the water vapor density above this area is mainly below 25 g/m3 and is concentrated between the first to the fourth layers. The vertical distribution conforms to the exponential characteristics, while the horizontal distribution shows a decreasing trend from southwest to northeast. In addition, the results of the 0.25° grid dataset generated by the Global Forecast System (GFS) of the National Center for Environmental Forecasting (NCEP) (GFS_L) were interpolated to the height of the tomographic grid, which is in good agreement with the tomographic results. GFS_L is larger than GNSS_T on the first floor at the surface, with an average deviation of 0.19 g/m3. In contrast, GFS_L from the second floor to the top of the model is smaller than GNSS_T, with the average deviations distributed between −0.08 and −0.15 g/m3. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
Show Figures

Figure 1

Article
Impacts of Reservoir Water Level Fluctuation on Measuring Seasonal Seismic Travel Time Changes in the Binchuan Basin, Yunnan, China
Remote Sens. 2021, 13(12), 2421; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122421 - 21 Jun 2021
Viewed by 430
Abstract
An airgun source in a water reservoir has been developed in the past decade as a green active source that had been proven effective to derive short-term subsurface structural changes. However, seasonal water level fluctuation in the reservoir affects the airgun signal, and [...] Read more.
An airgun source in a water reservoir has been developed in the past decade as a green active source that had been proven effective to derive short-term subsurface structural changes. However, seasonal water level fluctuation in the reservoir affects the airgun signal, and thus whether the airgun signals can be used to derive robust seasonal variation in subsurface structure remains unclear. We use the airgun data observed in the Binchuan basin to estimate the seasonal variation of seismic travel time and compare the results with those derived from ambient noise data in the same frequency band. Our main observation is that seasonal change δt/t from airgun is negatively correlated to the variation of dominant frequency and water table fluctuation in the reservoir. One possible explanation is that water table fluctuation in the reservoir affects the dominant frequency of the airgun signal and causes significant phase shift. We also compute the travel time changes in P-wave from the empirical Green’s function after deconvolving the waveforms from a reference station that is 50 m from the airgun source. The dominant frequency after deconvolution still shows seasonal variation and correlates inversely to the travel time changes, suggesting that deconvolution cannot completely eliminate the source effect on travel time changes. We also use ambient noise cross-correlation to retrieve coda waves and then derive travel time changes in monthly stacked cross-correlations relative to a yearly average cross-correlation. We observe that seismic travel time increases to its local maximum in the end of August. The travel time changes lag behind the precipitation for about one month. We apply a poroelastic physical model to explain seismic travel time changes and find that a combined effect from precipitation and evaporation might induce the seasonal changes as shown in the ambient noise data. However, the pattern of travel time changes from the airgun differs from that from ambient noise, reflecting the strong effects of airgun source property changes. Therefore, we should be cautious to derive long-term subsurface structural variation from the airgun source and put more attention on stabilizing the dominant frequency of each excitation in the future experiments. Full article
(This article belongs to the Special Issue Advances in Seismic Interferometry)
Show Figures

Graphical abstract

Article
DLR Earth Sensing Imaging Spectrometer (DESIS) Level 1 Product Evaluation Using RadCalNet Measurements
Remote Sens. 2021, 13(12), 2420; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122420 - 21 Jun 2021
Viewed by 582
Abstract
The DLR Earth Sensing Imaging Spectrometer (DESIS) is the first hyperspectral imaging spectrometer installed in the Multi-User System for Earth Sensing (MUSES) on the International Space Station (ISS) for acquiring routine science grade images from orbit. It was launched on 29 June 2018 [...] Read more.
The DLR Earth Sensing Imaging Spectrometer (DESIS) is the first hyperspectral imaging spectrometer installed in the Multi-User System for Earth Sensing (MUSES) on the International Space Station (ISS) for acquiring routine science grade images from orbit. It was launched on 29 June 2018 and integrated into MUSES. DESIS measures energy in the spectral range of 400 to 1000 nm with high spatial and spectral resolution: 30 m and 2.55 nm, respectively. DESIS data should be sufficiently quantitative and accurate to use it for different applications and research. This work performs a radiometric evaluation of DESIS Level 1 product (Top of Atmosphere (TOA) reflectance) by comparing it with coincident Radiometric Calibration Network (RadCalNet) measurements at Railroad Valley Playa (RVUS), Gobabeb (GONA), and La Crau (LCFR). RVUS, GONA, and LCFR offer 4, 15, and 5 coincident datasets between DESIS and RadCalNet measurements, respectively. The results show an agreement between DESIS and RadCalNet TOA reflectance within ~5% for most spectral regions. However, there is an additional ~5% disagreement across the wavelengths affected by water vapor absorption and atmospheric scattering. Among the three RadCalNet sites, RVUS and GONA show a similar measurement disagreement with DESIS of ~5%, while LCFR differs by ~10%. Agreement between DESIS and RadCalNet measurements is variable across all three sites, likely due to surface type differences. DESIS and RadCalNet agreement show a precision of ~2.5%, 4%, and 7% at RVUS, GONA, and LCFR, respectively. RVUS and GONA, which have a similar surface type, sand, have a similar level of radiometric accuracy and precision, whereas LCFR, which consists of sparse vegetation, has lower accuracy and precision. The observed precision of DESIS Level 1 products from all the sites, especially LCFR, can be improved with a better Bidirectional Reflection Distribution Function (BRDF) characterization of the RadCalNet sites. Full article
Show Figures

Figure 1

Article
A More Accurate Field-to-Field Method towards the Wind Retrieval of HY-2B Scatterometer
Remote Sens. 2021, 13(12), 2419; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122419 - 21 Jun 2021
Viewed by 424
Abstract
In this paper, we present a method for retrieving sea surface wind field (SSWF) from HaiYang-2B (HY-2B) scatterometer data. In contrast to the conventional algorithm, i.e., using a point-to-point (P2P) method based on geophysical model functions (GMF) to retrieve SSWF by spaceborne scatterometer, [...] Read more.
In this paper, we present a method for retrieving sea surface wind field (SSWF) from HaiYang-2B (HY-2B) scatterometer data. In contrast to the conventional algorithm, i.e., using a point-to-point (P2P) method based on geophysical model functions (GMF) to retrieve SSWF by spaceborne scatterometer, we introduce a more accurate field-to-field (F2F) retrieval method based on convolutional neural network (CNN). We fully consider the spatial correlation and continuity between adjacent observation points, and input the observation data of continuous wind field within a certain range into the neural network to construct the neural network model, and then synchronously obtain the wind field within the range. The wind field obtained by our retrieval method maintains its continuity and solves the problem of ambiguity removal in traditional wind direction retrieval methods. Comparing the retrieval results with the buoy data, the results show that the root mean square errors (RMSE) of wind direction and wind speed are less than 0.18 rad (10.31°) and 0.75 m/s, respectively. The retrieval accuracy is better than the L2B product of HY-2B. Full article
Show Figures

Figure 1

Article
A Spatial-Scale Evaluation of Soil Consolidation Concerning Land Subsidence and Integrated Mechanism Analysis at Macro-, and Micro-Scale: A Case Study in Chongming East Shoal Reclamation Area, Shanghai, China
Remote Sens. 2021, 13(12), 2418; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122418 - 21 Jun 2021
Viewed by 481
Abstract
Land reclamation has been increasingly employed in many coastal cities to resolve issues associated with land scarcity and natural hazards. Especially, land subsidence is a non-negligible environmental geological problem in reclamation areas, which is essentially caused by soil consolidation. However, spatial-scale evaluation on [...] Read more.
Land reclamation has been increasingly employed in many coastal cities to resolve issues associated with land scarcity and natural hazards. Especially, land subsidence is a non-negligible environmental geological problem in reclamation areas, which is essentially caused by soil consolidation. However, spatial-scale evaluation on the average degree of consolidation (ADC) of soil layers and the effects of soil consolidation on land subsidence have rarely been reported. This study aims to carry out the integrated analysis on soil consolidation and subsidence mechanism in Chongming East Shoal (CES) reclamation area, Shanghai, at spatial-, macro-, and micro-scale so that appropriate guides can be provided to resist the potential environmental hazards. The interferometric synthetic aperture radar (InSAR) technique was utilized to retrieve the settlement curves of the selected onshore (Ra) and offshore (Rb) areas. Then, the hyperbolic (HP) model and three-point modified exponential (TME) model were combined applied to predict the ultimate settlement and to determine the range of ADC rather than a single pattern. With two boreholes Ba and Bb set within Ra and Rb, conventional tests, MIP test, and SEM test were conducted on the collected undisturbed soil to clarify the geological features of exposed soil layers and the micro-scale pore and structure characteristics of representative compression layer. The preliminary results showed that the ADC in Rb (93.1–94.1%) was considerably higher than that in Ra (60.8–78.7%); the clay layer was distinguished as the representative compression layer; on micro-scale, the poor permeability conditions contributed to the low consolidation efficiency and slight subsidence in Rb, although there was more compression space. During urbanization, the offshore area may suffer from potential subsidence when it is subjected to an increasing ground load, which requires special attention. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Geological Engineering)
Show Figures

Figure 1

Article
IMG2nDSM: Height Estimation from Single Airborne RGB Images with Deep Learning
Remote Sens. 2021, 13(12), 2417; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122417 - 21 Jun 2021
Viewed by 602
Abstract
Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to [...] Read more.
Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to a normalized Digital Surface Model (nDSM) was proposed. The model was trained on aerial images whose corresponding DSM and Digital Terrain Models (DTM) were available and was then used to infer the nDSM of images with no elevation information. The model was evaluated with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses other state-of-the-art DL approaches by a large margin. Full article
Show Figures

Figure 1

Article
Potential Land Use Conflict Identification Based on Improved Multi-Objective Suitability Evaluation
Remote Sens. 2021, 13(12), 2416; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122416 - 20 Jun 2021
Viewed by 442
Abstract
Accurately identifying potential land use conflicts (LUCs) is critical for alleviating the ever-intensifying contradictions between humans and nature. The previous studies using the method of suitability analysis did not take full advantage of the current land use and multi-function characteristics of land resources. [...] Read more.
Accurately identifying potential land use conflicts (LUCs) is critical for alleviating the ever-intensifying contradictions between humans and nature. The previous studies using the method of suitability analysis did not take full advantage of the current land use and multi-function characteristics of land resources. In this study, an improved model of suitability analysis was realized. In order to explore the LUCs status, including the types, intensity and distribution, a multi-objective suitability evaluation model was constructed from the perspective of production-living-ecological functions. And it was applied to Hengkou District, a typical region of the Qin-Ba mountainous area in the central part of China. The results show that the suitability distribution of living- production-ecological functions vary widely from the center to the periphery with altitude in Hengkou District; 22.03% of the land is at a risk of land use conflict. Among them, the high potential conflict areas account for 55.32%, and the conflicts between production and ecological lands (L2P1E1, L3P1E1) are the largest, which are located at the fringe of the central urban and ecologically dominant area. Therefore, it is necessary to adopt effective strategies to achieve a balance between the differential demands of land use. This research could better reflect the true situation of land use in ecologically sensitive mountainous areas and would provide theoretical and methodological support for the identification and prevention of potential LUCs. Full article
Show Figures

Graphical abstract

Article
Investigating the Temporal and Spatial Dynamics of Human Development Index: A Comparative Study on Countries and Regions in the Eastern Hemisphere from the Perspective of Evolution
Remote Sens. 2021, 13(12), 2415; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122415 - 20 Jun 2021
Viewed by 450
Abstract
The Human Development Index (HDI) is a prevailing indicator to present the status and trend of sustainability of nations, hereby offers a valuable measurement on the Sustainable Development Goals (SDGs). Revealing the dynamics of the HDI of the Eastern Hemisphere countries is vital [...] Read more.
The Human Development Index (HDI) is a prevailing indicator to present the status and trend of sustainability of nations, hereby offers a valuable measurement on the Sustainable Development Goals (SDGs). Revealing the dynamics of the HDI of the Eastern Hemisphere countries is vital for measurement and evaluation of the human development process and revealing the spatial disparities and evolutionary characteristics of human development. However, the statistical data-based HDI, which is currently widely applied, has defects in terms of data availability and inconsistent statistical caliber. To tackle such an existing gap, we applied nighttime lights (NTL) data to reconstruct new HDI indicators named HDINTL and quantify the HDINTL at multispatial scales of Eastern Hemisphere countries during 1992–2013. Results showed that South Central Asia countries had the smallest discrepancies in HDINTL, while the largest was found in North Africa. The national-level HDINTL values in the Eastern Hemisphere ranged between 0.138 and 0.947 during 1992–2013. At the subnational scale, the distribution pattern of HDINTL was spatially clustered based on the results of spatial autocorrelation analysis. The evolutionary trajectory of subnational level HDINTL exhibited a decreasing and then increasing trend along the northwest to the southeast direction of Eastern Hemisphere. At the pixel scale, 93.52% of the grids showed an increasing trend in HDINTL, especially in the urban agglomerations of China and India. These results are essential for the ever-improvement of policy making to reduce HDI’s regional disparity and promote the continuous development of humankind’s living qualities. This study offers an improved HDI accounting method. It expects to extend the channel of HDI application, e.g., potential integration with environmental, physical, and socioeconomic data where the NTL data could present as well. Full article
Show Figures

Graphical abstract

Article
A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm
Remote Sens. 2021, 13(12), 2414; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122414 - 20 Jun 2021
Viewed by 542
Abstract
Evapotranspiration (ET) is a vital part of the hydrological cycle and the water–energy balance. To explore the characteristics of five typical remote sensing evapotranspiration datasets and provide guidance for algorithm development, we used reconstructed evapotranspiration (Recon) data based on ground and GRACE satellite [...] Read more.
Evapotranspiration (ET) is a vital part of the hydrological cycle and the water–energy balance. To explore the characteristics of five typical remote sensing evapotranspiration datasets and provide guidance for algorithm development, we used reconstructed evapotranspiration (Recon) data based on ground and GRACE satellite observations as a benchmark and evaluated five remote sensing datasets for 592 watersheds across the continental United States. The Global Land Evaporation Amsterdam Model (GLEAM) dataset (with bias and RMSE values of 23.18 mm/year and 106.10 mm/year, respectively), process-based land surface evapotranspiration/heat flux (P-LSH) dataset (bias = 22.94 mm/year and RMSE = 114.44 mm/year) and the Penman–Monteith–Leuning (PML) algorithm generated ET dataset (bias = −17.73 mm/year and RMSE = 108.97 mm/year) showed the better performance on a yearly scale, followed by the model tree ensemble (MTE) dataset (bias = 99.45 mm/year and RMSE = 141.32 mm/year) and the moderate-resolution imaging spectroradiometer (MODIS) dataset (bias = −106.71 mm/year and RMSE = 158.90 mm/year). The P-LSH dataset outperformed the other four ET datasets on a seasonal scale, especially from March to August. Both PML and MTE showed better overall accuracy and could accurately capture the spatial variability of evapotranspiration in arid regions. The P-LSH and GLEAM products were consistent with the Recon data in middle-value section. MODIS and MTE had larger bias and RMSE values on a yearly scale, whereby the MODIS and MTE datasets tended to underestimate and overestimate ET values in all the sections, respectively. In the future, the aim should be to reduce bias in the MODIS and MTE algorithms and further improve seasonality of the ET estimation in the GLEAM algorithm, while the estimation accuracy of the P-LSH and MODIS algorithms should be improved in arid regions. Our analysis suggests that combining artificial intelligence algorithms or data-driven algorithms and physical process algorithms will further improve the accuracy of ET estimation algorithms and the quality of ET datasets, as well as enhancing their capacity to be applied in different climate regions. Full article
(This article belongs to the Section Environmental Remote Sensing)
Show Figures

Graphical abstract

Article
Preliminary Significant Wave Height Retrieval from Interferometric Imaging Radar Altimeter Aboard the Chinese Tiangong-2 Space Laboratory
Remote Sens. 2021, 13(12), 2413; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122413 - 20 Jun 2021
Viewed by 459
Abstract
The interferometric imaging radar altimeter (InIRA) aboard the Chinese Tiangong-2 space laboratory is the first spaceborne imaging radar working at low incidence angles. This study focuses on the retrieval of significant wave heights (SWHs) from InIRA data. The retrieved SWHs can be used [...] Read more.
The interferometric imaging radar altimeter (InIRA) aboard the Chinese Tiangong-2 space laboratory is the first spaceborne imaging radar working at low incidence angles. This study focuses on the retrieval of significant wave heights (SWHs) from InIRA data. The retrieved SWHs can be used for correcting the sea state bias of InIRA-derived sea surface heights and can supplement SWH products from other spaceborne sensors. First, we analyzed tilt, range bunching and velocity bunching wave modulations at low incidence angles, and we found clear dependencies between the SWH and two defined factors, range and azimuth integration, for ocean waves in the range and azimuth directions, respectively. These dependencies were further confirmed using InIRA measurements and collocated WaveWatch III (WW3) data. Then, an empirical orthogonal SWH model using the range and azimuth integration factors as model inputs was proposed. The model was segmented by the incidence angle, and the model coefficients were estimated by fitting the collocation at each incidence angle bin. Finally, the SWHs were retrieved from InIRA data using the proposed model. The retrievals were validated using both WW3 and altimeter (JASON2, JASON3, SARAL, and HY2A) SWHs. The validation with WW3 data shows a root mean square error (RMSE) of 0.43 m, while the average RMSE with all traditional altimeter data is 0.48 m. This indicates that the InIRA can be used to measure SWHs. Full article
Show Figures

Figure 1

Article
Measurements of Rainfall Rate, Drop Size Distribution, and Variability at Middle and Higher Latitudes: Application to the Combined DPR-GMI Algorithm
Remote Sens. 2021, 13(12), 2412; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122412 - 20 Jun 2021
Viewed by 500
Abstract
The Global Precipitation Measurement mission is a major U.S.–Japan joint mission to understand the physics of the Earth’s global precipitation as a key component of its weather, climate, and hydrological systems. The core satellite carries a dual-precipitation radar and an advanced microwave imager [...] Read more.
The Global Precipitation Measurement mission is a major U.S.–Japan joint mission to understand the physics of the Earth’s global precipitation as a key component of its weather, climate, and hydrological systems. The core satellite carries a dual-precipitation radar and an advanced microwave imager which provide measurements to retrieve the drop size distribution (DSD) and rain rates using a Combined Radar-Radiometer Algorithm (CORRA). Our objective is to validate key assumptions and parameterizations in CORRA and enable improved estimation of precipitation products, especially in the middle-to-higher latitudes in both hemispheres. The DSD parameters and statistical relationships between DSD parameters and radar measurements are a central part of the rainfall retrieval algorithm, which is complicated by regimes where DSD measurements are abysmally sparse (over the open ocean). In view of this, we have assembled optical disdrometer datasets gathered by research vessels, ground stations, and aircrafts to simulate radar observables and validate the scattering lookup tables used in CORRA. The joint use of all DSD datasets spans a large range of drop concentrations and characteristic drop diameters. The scaling normalization of DSDs defines an intercept parameter NW, which normalizes the concentrations, and a scaling diameter Dm, which compresses or stretches the diameter coordinate axis. A major finding of this study is that a single relationship between NW and Dm, on average, unifies all datasets included, from stratocumulus to heavier rainfall regimes. A comparison with the NW–Dm relation used as a constraint in versions 6 and 7 of CORRA highlights the scope for improvement of rainfall retrievals for small drops (Dm < 1 mm) and large drops (Dm > 2 mm). The normalized specific attenuation–reflectivity relationships used in the combined algorithm are also found to match well the equivalent relationships derived using DSDs from the three datasets, suggesting that the currently assumed lookup tables are not a major source of uncertainty in the combined algorithm rainfall estimates. Full article
(This article belongs to the Special Issue Remote Sensing of Precipitation at the Mid- to High-Latitudes)
Show Figures

Figure 1

Technical Note
Detection of Changes in Arable Chernozemic Soil Health Based on Landsat TM Archive Data
Remote Sens. 2021, 13(12), 2411; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122411 - 19 Jun 2021
Viewed by 485
Abstract
When soils are used for a long period of time as arable land, their properties change. This can lead to soil degradation and loss of fertility, as well as other important soil biosphere functions. Obtaining data on the trends in arable soil conditions [...] Read more.
When soils are used for a long period of time as arable land, their properties change. This can lead to soil degradation and loss of fertility, as well as other important soil biosphere functions. Obtaining data on the trends in arable soil conditions over large areas using traditional field survey methods is expensive and time-consuming. Currently, there are large archives of satellite data that can be used to monitor the status of arable soils. The analysis of changes in the color of the surface of arable chernozem soils of the Belgorod region, for the period from 1985 to the present, has been carried out based on the analysis of Landsat TM5 satellite data and information about the spectral reflectance of the soils of the region. It is found that, on most parts of arable lands of the region, the color of the soil surface has not changed significantly since 1985. Color changes were revealed on 11% of the analyzed area. The greatest changes are connected with the humus content and moisture content of soils. The three most probable reasons for the change of humus content in an arable horizon of soils are as follows: the dehumidification of soils during plowing; the reduction of the humus content due to water erosion; and the increase in humus content due to changes in the land-use system of the region in recent years. The change in soil moisture regime has mainly been found in arable lands in river valleys, most likely conditioned by the natural evolution of soils. Trends of increasing soil moisture are prevalent. The revealed regularities testify to the high stability of arable soils in the region during the last few decades. Full article
Show Figures

Graphical abstract

Article
Spatial Patterns in Actual Evapotranspiration Climatologies for Europe
Remote Sens. 2021, 13(12), 2410; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122410 - 19 Jun 2021
Viewed by 568
Abstract
Spatial patterns in long-term average evapotranspiration (ET) represent a unique source of information for evaluating the spatial pattern performance of distributed hydrological models on a river basin to continental scale. This kind of model evaluation is getting increased attention, acknowledging the shortcomings of [...] Read more.
Spatial patterns in long-term average evapotranspiration (ET) represent a unique source of information for evaluating the spatial pattern performance of distributed hydrological models on a river basin to continental scale. This kind of model evaluation is getting increased attention, acknowledging the shortcomings of traditional aggregated or timeseries-based evaluations. A variety of satellite remote sensing (RS)-based ET estimates exist, covering a range of methods and resolutions. There is, therefore, a need to evaluate these estimates, not only in terms of temporal performance and similarity, but also in terms of long-term spatial patterns. The current study evaluates four RS-ET estimates at moderate resolution with respect to spatial patterns in comparison to two alternative continental-scale gridded ET estimates (water-balance ET and Budyko). To increase comparability, an empirical correction factor between clear sky and all-weather ET, based on eddy covariance data, is derived, which could be suitable for simple corrections of clear sky estimates. Three RS-ET estimates (MODIS16, TSEB and PT-JPL) and the Budyko method generally display similar spatial patterns both across the European domain (mean SPAEF = 0.41, range 0.25–0.61) and within river basins (mean SPAEF range 0.19–0.38), although the pattern similarity within river basins varies significantly across basins. In contrast, the WB-ET and PML_V2 produced very different spatial patterns. The similarity between different methods ranging over different combinations of water, energy, vegetation and land surface temperature constraints suggests that robust spatial patterns of ET can be achieved by combining several methods. Full article
Show Figures

Figure 1

Article
Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery
Remote Sens. 2021, 13(12), 2409; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122409 - 19 Jun 2021
Viewed by 620
Abstract
The monitoring of impervious surfaces in urban areas using remote sensing with fine spatial and temporal resolutions is crucial for monitoring urban development and environmental changes in urban areas. Spatiotemporal super-resolution mapping (STSRM) fuses fine-spatial-coarse-temporal remote sensing data with coarse-spatial-fine-temporal data, allowing for [...] Read more.
The monitoring of impervious surfaces in urban areas using remote sensing with fine spatial and temporal resolutions is crucial for monitoring urban development and environmental changes in urban areas. Spatiotemporal super-resolution mapping (STSRM) fuses fine-spatial-coarse-temporal remote sensing data with coarse-spatial-fine-temporal data, allowing for urban impervious surface mapping at both fine-spatial and fine-temporal resolutions. The STSRM involves two main steps: unmixing the coarse-spatial-fine-temporal remote sensing data to class fraction images, and downscaling the fraction images to sub-pixel land cover maps. Yet, challenges exist in each step when applying STSRM in mapping impervious surfaces. First, the impervious surfaces have high spectral variability (i.e., high intra-class and low inter-class variability), which impacts the accurate extraction of sub-pixel scale impervious surface fractions. Second, downscaling the fraction images to sub-pixel land cover maps is an ill-posed problem and would bring great uncertainty and error in the predictions. This paper proposed a new Spatiotemporal Continuous Impervious Surface Mapping (STCISM) method to deal with these challenges in fusing Landsat and Google Earth imagery. The STCISM used the Multiple Endmember Spectral Mixture Analysis and the Fisher Discriminant Analysis to minimize the within-class variability and maximize the between-class variability to reduce the spectral unmixing uncertainty. In addition, the STCISM adopted a new temporal consistency check model to incorporate temporal contextual information to reduce the uncertainty in the time-series impervious surface prediction maps. Unlike the traditional temporal consistency check model that assumed the impervious-to-pervious conversion is unlikely to happen, the new model allowed the bidirectional conversions between pervious and impervious surfaces. The temporal consistency check was used as a post-procession method to correct the errors in the prediction maps. The proposed STCISM method was used to predict time-series impervious surface maps at 5 m resolution of Google Earth image at the Landsat frequency. The results showed that the proposed STCISM outperformed the STSRM model without using the temporal consistency check and the STSRM model using the temporal consistency check based on the unidirectional pervious-to-impervious surface conversion rule. Full article
Show Figures

Figure 1

Review
Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review
Remote Sens. 2021, 13(12), 2408; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122408 - 19 Jun 2021
Viewed by 599
Abstract
The leaf area index (LAI) is an essential input parameter for quantitatively studying the energy and mass balance in soil-vegetation-atmosphere transfer systems. As an active remote sensing technology, light detection and ranging (LiDAR) provides a new method to describe forest canopy LAI. This [...] Read more.
The leaf area index (LAI) is an essential input parameter for quantitatively studying the energy and mass balance in soil-vegetation-atmosphere transfer systems. As an active remote sensing technology, light detection and ranging (LiDAR) provides a new method to describe forest canopy LAI. This paper reviewed the primary LAI retrieval methods using point cloud data (PCD) obtained by discrete airborne LiDAR scanner (DALS), its validation scheme, and its limitations. There are two types of LAI retrieval methods based on DALS PCD, i.e., the empirical regression and the gap fraction (GF) model. In the empirical model, tree height-related variables, LiDAR penetration indexes (LPIs), and canopy cover are the most widely used proxy variables. The height-related proxies are used most frequently; however, the LPIs proved the most efficient proxy. The GF model based on the Beer-Lambert law has been proven useful to estimate LAI; however, the suitability of LPIs is site-, tree species-, and LiDAR system-dependent. In the local validation in previous studies, poor scalability of both empirical and GF models in time, space, and across different DALS systems was observed, which means that field measurements are still needed to calibrate both types of models. The method to correct the impact from the clumping effect and woody material using DALS PCD and the saturation effect for both empirical and GF models still needs further exploration. Of most importance, further work is desired to emphasize assessing the transferability of published methods to new geographic contexts, different DALS sensors, and survey characteristics, based on figuring out the influence of each factor on the LAI retrieval process using DALS PCD. In addition, from a methodological perspective, taking advantage of DALS PCD in characterizing the 3D structure of the canopy, making full use of the ability of machine learning methods in the fusion of multisource data, developing a spatiotemporal scalable model of canopy structure parameters including LAI, and using multisource and heterogeneous data are promising areas of research. Full article
Show Figures

Figure 1

Article
The Extraction of Street Curbs from Mobile Laser Scanning Data in Urban Areas
Remote Sens. 2021, 13(12), 2407; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122407 - 19 Jun 2021
Viewed by 540
Abstract
The demand for mobile laser scanning in urban areas has grown in recent years. Mobile-based light detection and ranging (LiDAR) technology can be used to collect high-precision digital information on city roads and building façades. However, due to the small size of curbs, [...] Read more.
The demand for mobile laser scanning in urban areas has grown in recent years. Mobile-based light detection and ranging (LiDAR) technology can be used to collect high-precision digital information on city roads and building façades. However, due to the small size of curbs, the information that can be used for curb detection is limited. Moreover, occlusion may cause the extraction method unable to correctly capture the curb area. This paper presents the development of an algorithm for extracting street curbs from mobile-based LiDAR point cloud data to support city managers in street deformation monitoring and urban street reconstruction. The proposed method extracts curbs in three complex scenarios: vegetation covering the curbs, curved street curbs, and occlusion curbs by vehicles, pedestrians. This paper combined both spatial information and geometric information, using the spatial attributes of the road boundary. It can adapt to different heights and different road boundary structures. Analyses of real study sites show the rationality and applicability of this method for obtaining accurate results in curb-based street extraction from mobile-based LiDAR data. The overall performance of road curb extraction is fully discussed, and the results are shown to be promising. Both the completeness and correctness of the extracted left and right road edges are greater than 98%. Full article
Show Figures

Graphical abstract

Article
Object-Oriented Building Contour Optimization Methodology for Image Classification Results via Generalized Gradient Vector Flow Snake Model
Remote Sens. 2021, 13(12), 2406; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122406 - 19 Jun 2021
Viewed by 490
Abstract
Building boundary optimization is an essential post-process step for building extraction (by image classification). However, current boundary optimization methods through smoothing or line fitting principles are unable to optimize complex buildings. In response to this limitation, this paper proposes an object-oriented building contour [...] Read more.
Building boundary optimization is an essential post-process step for building extraction (by image classification). However, current boundary optimization methods through smoothing or line fitting principles are unable to optimize complex buildings. In response to this limitation, this paper proposes an object-oriented building contour optimization method via an improved generalized gradient vector flow (GGVF) snake model and based on the initial building contour results obtained by a classification method. First, to reduce interference from the adjacent non-building object, each building object is clipped via their extended minimum bounding rectangles (MBR). Second, an adaptive threshold Canny edge detection is applied to each building image to detect the edges, and the progressive probabilistic Hough transform (PPHT) is applied to the edge result to extract the line segments. For those cases with missing or wrong line segments in some edges, a hierarchical line segments reconstruction method is designed to obtain complete contour constraint segments. Third, accurate contour constraint segments for the GGVF snake model are designed to quickly find the target contour. With the help of the initial contour and constraint edge map for GGVF, a GGVF force field computation is executed, and the related optimization principle can be applied to complex buildings. Experimental results validate the robustness and effectiveness of the proposed method, whose contour optimization has higher accuracy and comprehensive value compared with that of the reference methods. This method can be used for effective post-processing to strengthen the accuracy of building extraction results. Full article
(This article belongs to the Special Issue A Review of Computer Vision for Remote Sensing Imagery)
Show Figures

Figure 1

Article
Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature
Remote Sens. 2021, 13(12), 2405; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122405 - 19 Jun 2021
Viewed by 386
Abstract
Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival. Full article
Show Figures

Figure 1

Article
Crop Nitrogen Retrieval Methods for Simulated Sentinel-2 Data Using In-Field Spectrometer Data
Remote Sens. 2021, 13(12), 2404; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122404 - 19 Jun 2021
Viewed by 443
Abstract
Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring [...] Read more.
Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) for plant-N-related traits was assessed on a diverse real-world dataset including multiple crops, field sites and years. The plant N traits included the mass-based N measure, N concentration in the biomass (Nconc), and an area-based N measure approximating the plant N uptake (NUP). Spectral indices such as normalized ratio indices (NRIs) performed well, but the RFR and GPR methods outperformed the NRIs. Key spectral bands for each trait were identified using the RFR variable importance measure and the Gaussian processes regression band analysis tool (GPR-BAT), highlighting the importance of the short-wave infrared (SWIR) region for estimation of plant Nconc—and to a lesser extent the NUP. The red edge (RE) region was also important. The GPR-BAT showed that five bands were sufficient for plant N trait and leaf area index (LAI) estimation and that a surplus of bands effectively reduced prediction performance. A global sensitivity analysis (GSA) was performed on all traits simultaneously, showing the dominance of the LAI in the mixed remote sensing signal. To delineate the plant-N-related traits from this signal, regional and/or national data collection campaigns producing large crop spectral libraries (CSL) are needed. An improved database will likely enable the mapping of N at the agro-ecosystem level or for use in precision farming by farmers in the future. Full article
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)
Show Figures

Graphical abstract

Article
Evaluation of the J-OFURO3 Sea Surface Net Radiation and Inconsistency Correction
Remote Sens. 2021, 13(12), 2403; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122403 - 19 Jun 2021
Viewed by 366
Abstract
A new satellite-based product containing daily sea surface net radiation (Rn) values at a spatial resolution of 0.25° from 1988 to 2013, named the Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations, version 3 (J-OFURO3), was recently generated and released. In this letter, the performance of the J-OFURO3 sea-surface Rn product was fully evaluated by using observations from 55 global moored buoy sites. The overall accuracy was satisfactory, with root-mean-square difference (RMSD) of 24.05 and 10.76 Wm−2 at daily and monthly scales, respectively. However, an inconsistency issue was found in the long-term variations in the J-OFURO3 sea-surface Rn values in approximately 2000; this inconsistency may be due to the replacement of the input dataset. To address this issue, a simple but effective inconsistency correction method was developed and conducted in this study. The analysis results demonstrated that the variations in the corrected J-OFURO3 sea-surface Rn data were more reasonable and that its daily validation accuracy was significantly improved by decreasing the bias from 4.67 to 0.27 Wm−2 before the year 2000. Thereby, it is suggested that the inconsistency correction method should be applied before using the J-OFURO3 sea-surface Rn data. However, the data users still should be cautious about another discontinuity issues caused by the quality of the input dataset itself. Full article
(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)
Show Figures

Figure 1

Technical Note
New Gridded Product for the Total Columnar Atmospheric Water Vapor over Ocean Surface Constructed from Microwave Radiometer Satellite Data
Remote Sens. 2021, 13(12), 2402; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122402 - 19 Jun 2021
Viewed by 388
Abstract
Based on the optimal interpolation (OI) algorithm, a daily fusion product of high-resolution global ocean columnar atmospheric water vapor with a resolution of 0.25° was generated in this study from multisource remote sensing observations. The product covers the period from 2003 to 2018, [...] Read more.
Based on the optimal interpolation (OI) algorithm, a daily fusion product of high-resolution global ocean columnar atmospheric water vapor with a resolution of 0.25° was generated in this study from multisource remote sensing observations. The product covers the period from 2003 to 2018, and the data represent a fusion of microwave radiometer observations, including those from the Special Sensor Microwave Imager Sounder (SSMIS), WindSat, Advanced Microwave Scanning Radiometer for Earth Observing System sensor (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), and HY-2A microwave radiometer (MR). The accuracy of this water vapor fusion product was validated using radiosonde water vapor observations. The comparative results show that the overall mean deviation (Bias) is smaller than 0.6 mm; the root mean square error (RMSE) and standard deviation (SD) are better than 3 mm, and the mean absolute deviation (MAD) and correlation coefficient (R) are better than 2 mm and 0.98, respectively. Full article
(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)
Show Figures

Graphical abstract

Article
Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: Lessons Learned from a Case Study in the Brazilian Atlantic Forest
Remote Sens. 2021, 13(12), 2401; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122401 - 19 Jun 2021
Viewed by 691
Abstract
Traditional forest restoration (FR) monitoring methods employ spreadsheets and photos taken at the ground level. Since remotely piloted aircraft (RPA) generate a panoramic high resolution and georeferenced view of the entire area of interest, this technology has high potential to improve the traditional [...] Read more.
Traditional forest restoration (FR) monitoring methods employ spreadsheets and photos taken at the ground level. Since remotely piloted aircraft (RPA) generate a panoramic high resolution and georeferenced view of the entire area of interest, this technology has high potential to improve the traditional FR monitoring methods. This study evaluates how low-cost RPA data may contribute to FR monitoring of the Brazilian Atlantic Forest by the automatic remote measurement of Tree Density, Tree Height, Vegetation Cover (area covered by trees), and Grass Infestation. The point cloud data was processed to map the Tree Density, Tree Height, and Vegetation Cover parameters. The orthomosaic was used for a Random Forest classification that considered trees and grasses as a single land cover class. The Grass Infestation parameter was mapped by the difference between this land cover class (which considered trees and grasses) and the Vegetation Cover results (obtained by the point cloud data processing). Tree Density, Vegetation Cover, and Grass Infestation parameters presented F_scores of 0.92, 0.85, and 0.64, respectively. Tree Height accuracy was indicated by the Error Percentage considering the traditional fieldwork and the RPA results. The Error Percentage was equal to 0.13 and was considered accurate because it estimated a 13% shorter height for trees that averaged 1.93 m tall. Thus, this study showed that the FR structural parameters were accurately measured by the low-cost RPA, a technology that contributes to FR monitoring. Despite accurately measuring the structural parameters, this study reinforced the challenge of measuring the Biodiversity parameter via remote sensing because the classification of tree species was not possible. After all, the Brazilian Atlantic Forest is a biodiversity hotspot, and thus different species have similar spectral responses in the visible spectrum and similar geometric forms. Therefore, until improved automatic classification methods become available for tree species, traditional fieldwork remains necessary for a complete FR monitoring diagnostic. Full article
Show Figures

Graphical abstract

Article
Mapping and Evaluating Human Pressure Changes in the Qilian Mountains
Remote Sens. 2021, 13(12), 2400; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122400 - 19 Jun 2021
Viewed by 392
Abstract
Human activities have dramatically changed ecosystems. As an irreplaceable ecological barrier in western China, the Qilian Mountains (QLM) provide various ecosystem services for humans. To evaluate the changes in the intensity of human activities in the QLM and their impact on the ecosystem, [...] Read more.
Human activities have dramatically changed ecosystems. As an irreplaceable ecological barrier in western China, the Qilian Mountains (QLM) provide various ecosystem services for humans. To evaluate the changes in the intensity of human activities in the QLM and their impact on the ecosystem, the human footprint (HF) method was used to conduct a spatial dataset of human activity intensity. In our study, the NDVI was used to characterize the growth of vegetation, and six categories of human pressures were employed to create the HF map in the QLM for 2000–2015 at a 1-km scale. The results showed that the mean NDVI during the growing season showed a significant increasing trend over the entire QLM in the period 2000–2015, while the NDVI showed a significant declining trend of more than 70% concentrated in Qinghai. Human pressure throughout the QLM occurred at a low level during 2000–2015, being greater in the eastern region than the western region, while the Qinghai area had greater human pressure than the Gansu area. Due to the improvement in traffic facilities, tourism, overgrazing, and other illegal activities, grasslands, shrublands, forests, wetlands, and bare land were the vegetation types most affected by human activities (in decreasing order). As the core area of the QLM, the Qilian Mountains National Nature Reserve (NR) has effectively reduced the impact of human activities. However, due to the existence of many ecological historical debts caused by unreasonable management in the past, the national park established in 2017 is facing great challenges to achieve its goals. These data and results will provide reference and guidance for future protection and restoration of the QLM ecosystem. Full article
(This article belongs to the Section Environmental Remote Sensing)
Show Figures

Figure 1

Article
HORUS: Multispectral and Multiangle CubeSat Mission Targeting Sub-Kilometer Remote Sensing Applications
Remote Sens. 2021, 13(12), 2399; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122399 - 19 Jun 2021
Viewed by 435
Abstract
This paper presents the HORUS mission, aimed at multispectral and multiangle (nadir and off-nadir) planetary optical observation, using Commercial Off-The-Shelf (COTS) instruments on-board a 6-Unit CubeSat. The collected data are characterized by a sub-kilometer resolution, useful for different applications for environmental monitoring, atmospheric [...] Read more.
This paper presents the HORUS mission, aimed at multispectral and multiangle (nadir and off-nadir) planetary optical observation, using Commercial Off-The-Shelf (COTS) instruments on-board a 6-Unit CubeSat. The collected data are characterized by a sub-kilometer resolution, useful for different applications for environmental monitoring, atmospheric characterization, and ocean studies. Latest advancements in electro-optical instrumentation permit to consider an optimized instrument able to fit in a small volume, in principle without significant reduction in the achievable performances with respect to typical large-spacecraft implementations. CubeSat-based platforms ensure high flexibility, with fast and simple components’ integration, and may be used as stand-alone system or in synergy with larger missions, for example to improve revisit time. The mission rationale, its main objectives and scientific background, including the combination of off-nadir potential continuous multiangle coverage in a full perspective and related observation bands are provided. The observation system conceptual design and its installation on-board a 6U CubeSat bus, together with the spacecraft subsystems are discussed, assessing the feasibility of the mission and its suitability as a building block for a multiplatform distributed system. Full article
Show Figures

Figure 1

Article
Fast Unsupervised Multi-Scale Characterization of Urban Landscapes Based on Earth Observation Data
Remote Sens. 2021, 13(12), 2398; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122398 - 19 Jun 2021
Viewed by 409
Abstract
Most remote sensing studies of urban areas focus on a single scale, using supervised methodologies and very few analyses focus on the “neighborhood” scale. The lack of multi-scale analysis, together with the scarcity of training and validation datasets in many countries lead us [...] Read more.
Most remote sensing studies of urban areas focus on a single scale, using supervised methodologies and very few analyses focus on the “neighborhood” scale. The lack of multi-scale analysis, together with the scarcity of training and validation datasets in many countries lead us to propose a single fast unsupervised method for the characterization of urban areas. With the FOTOTEX algorithm, this paper introduces a texture-based method to characterize urban areas at three nested scales: macro-scale (urban footprint), meso-scale (“neighbourhoods”) and micro-scale (objects). FOTOTEX combines a Fast Fourier Transform and a Principal Component Analysis to convert texture into frequency signal. Several parameters were tested over Sentinel-2 and Pleiades imagery on Bouake and Brasilia. Results showed that a single Sentinel-2 image better assesses the urban footprint than the global products. Pleiades images allowed discriminating neighbourhoods and urban objects using texture, which is correlated with metrics such as building density, built-up and vegetation proportions. The best configurations for each scale of analysis were determined and recommendations provided to users. The open FOTOTEX algorithm demonstrated a strong potential to characterize the three nested scales of urban areas, especially when training and validation data are scarce, and computing resources limited. Full article
Show Figures

Figure 1

Article
Application of RGB Images Obtained by UAV in Coffee Farming
Remote Sens. 2021, 13(12), 2397; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122397 - 19 Jun 2021
Viewed by 474
Abstract
The objective of this study was to evaluate the potential of the practical application of unmanned aerial vehicles and RGB vegetation indices (VIs) in the monitoring of a coffee crop. The study was conducted in an experimental coffee field over a 12-month period. [...] Read more.
The objective of this study was to evaluate the potential of the practical application of unmanned aerial vehicles and RGB vegetation indices (VIs) in the monitoring of a coffee crop. The study was conducted in an experimental coffee field over a 12-month period. An RGB digital camera coupled to a UAV was used. Nine VIs were evaluated in this study. These VIs were subjected to a Pearson correlation analysis with the leaf area index (LAI), and subsequently, the VIs with higher R2 values were selected. The LAI was estimated by plant height and crown diameter values obtained by imaging, which were correlated with these values measured in the field. Among the VIs evaluated, MPRI (0.31) and GLI (0.41) presented greater correlation with LAI; however, the correlation was weak. Thematic maps of VIs in the evaluated period showed variability present in the crop. The evolution of weeds in the planting rows was noticeable with both VIs, which can help managers to make the decision to start crop management, thus saving resources. The results show that the use of low-cost UAVs and RGB cameras has potential for monitoring the coffee production cycle, providing producers with information in a more accurate, quick and simple way. Full article
(This article belongs to the Special Issue Remote Sensing for Smart Agriculture Management)
Show Figures

Graphical abstract

Previous Issue
Next Issue
Back to TopTop