Remote Sensing

19 pages, 119585 KiB

Open AccessEditor’s ChoiceArticle

Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

by Anushree Badola, Santosh K. Panda, Dar A. Roberts, Christine F. Waigl, Uma S. Bhatt, Christopher W. Smith and Randi R. Jandt

Remote Sens. 2021, 13(9), 1693; https://doi.org/10.3390/rs13091693 - 27 Apr 2021

Cited by 15 | Viewed by 8752

Abstract

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land [...] Read more.

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest. Full article

(This article belongs to the Special Issue Imaging Spectroscopy of Forest Ecosystems)

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20 pages, 5274 KiB

Open AccessEditor’s ChoiceArticle

In-Season Interactions between Vine Vigor, Water Status and Wine Quality in Terrain-Based Management-Zones in a ‘Cabernet Sauvignon’ Vineyard

by Idan Bahat, Yishai Netzer, José M. Grünzweig, Victor Alchanatis, Aviva Peeters, Eitan Goldshtein, Noa Ohana-Levi, Alon Ben-Gal and Yafit Cohen

Remote Sens. 2021, 13(9), 1636; https://doi.org/10.3390/rs13091636 - 22 Apr 2021

Cited by 22 | Viewed by 6204

Abstract

Wine quality is the final outcome of the interactions within a vineyard between meteorological conditions, terrain and soil properties, plant physiology and numerous viticultural decisions, all of which are commonly summarized as the terroir effect. Associations between wine quality and a single soil [...] Read more.

Wine quality is the final outcome of the interactions within a vineyard between meteorological conditions, terrain and soil properties, plant physiology and numerous viticultural decisions, all of which are commonly summarized as the terroir effect. Associations between wine quality and a single soil or topographic factor are usually weak, but little information is available on the effect of terrain (elevation, aspect and slope) as a compound micro-terroir factor. We used the topographic wetness index (TWI) as a steady-state hydrologic and integrative measure to delineate management zones (MZs) within a vineyard and to study the interactions between vine vigor, water status and grape and wine quality. The study was conducted in a commercial 2.5-ha Vitis vinifera ‘Cabernet Sauvignon’ vineyard in Israel. Based on the TWI, the vineyard was divided into three MZs located along an elongate wadi that crosses the vineyard and bears water only in the rainy winter season. MZ1 was the most distant from the wadi and had low TWI values, MZ3 was closest to the wadi and had high TWI values. Remotely sensed crop water stress index (CWSI) was measured simultaneously with canopy cover (as determined by normalized difference vegetation index; NDVI) and with field measurements of midday stem water potential (Ψ_stem) and leaf area index (LAI) on several days during the growing seasons of 2017 and 2018. Vines in MZ1 had narrow trunk diameter and low LAI and canopy cover on most measurement days compared to the other two MZs. MZ1 vines also exhibited the highest water stress (highest CWSI and lowest Ψ_stem), lowest yield and highest wine quality. MZ3 vines showed higher LAI on most measurement days, lowest water deficit stress (Ψ_stem) during phenological stage I, highest yield and lowest wine quality. Yet, in stage III, MZ3 vines exhibited a similar water deficit stress (CWSI and Ψ_stem) as MZ2, suggesting that the relatively high vigor in MZ3 vines resulted in higher water deficit stress than expected towards the end of the season, possibly because of high water consumption over the course of the season. TWI and its classification into three MZs served as a reliable predictor for most of the attributes in the vineyard and for their dynamics within the season, and, thus, can be used as a key factor in delineation of MZs for irrigation. Yet, in-season remotely sensed monitoring is required to follow the vine dynamics to improve precision irrigation decisions. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Water Stress Detection and Irrigation Management)

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23 pages, 29385 KiB

Open AccessEditor’s ChoiceArticle

Leveraging River Network Topology and Regionalization to Expand SWOT-Derived River Discharge Time Series in the Mississippi River Basin

by Cassandra Nickles and Edward Beighley

Remote Sens. 2021, 13(8), 1590; https://doi.org/10.3390/rs13081590 - 20 Apr 2021

Cited by 3 | Viewed by 3378

Abstract

The upcoming Surface Water and Ocean Topography (SWOT) mission will measure rivers wider than 50–100 m using a 21-day orbit, providing river reach derived discharges that can inform applications like flood forecasting and large-scale hydrologic modelling. However, these discharges will not be uniform [...] Read more.

The upcoming Surface Water and Ocean Topography (SWOT) mission will measure rivers wider than 50–100 m using a 21-day orbit, providing river reach derived discharges that can inform applications like flood forecasting and large-scale hydrologic modelling. However, these discharges will not be uniform in time or coincident with those of neighboring reaches. It is often assumed discharge upstream and downstream of a river location are highly correlated in natural conditions and can be transferred using a scaling factor like the drainage area ratio between locations. Here, the applicability of the drainage area ratio method to integrate, in space and time, SWOT-derived discharges throughout the observable river network of the Mississippi River basin is assessed. In some cases, area ratios ranging from 0.01 to 100 can be used, but cumulative urban area and/or the number of dams/reservoirs between locations decrease the method’s applicability. Though the mean number of SWOT observations for a given reach increases by 83% and the number of peak events captured increases by 100%, expanded SWOT sampled time series distributions often underperform compared to the original SWOT sampled time series for significance tests and quantile results. Alternate expansion methods may be more viable for future work. Full article

(This article belongs to the Special Issue Remote Sensing of Coastal Waters, Land Use/Cover, Lakes, Rivers and Watersheds)

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25 pages, 15506 KiB

Open AccessEditor’s ChoiceArticle

Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing

by Shangharsha Thapa, Virginia E. Garcia Millan and Lars Eklundh

Remote Sens. 2021, 13(8), 1597; https://doi.org/10.3390/rs13081597 - 20 Apr 2021

Cited by 52 | Viewed by 10989

Abstract

The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different [...] Read more.

The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different aspects of forest phenology based on observations from these sensors and drawing comparatives from the time series of vegetation indices (VIs) still remains a challenge. Accordingly, this research explores the potential of near-surface sensors to track the temporal dynamics of phenology, cross-compare their results against satellite observations (MODIS, Sentinel-2), and validate satellite-derived phenology. A time series of Normalized Difference Vegetation Index (NDVI), Green Chromatic Coordinate (GCC), and Normalized Difference of Green & Red (VIgreen) indices were extracted from both near-surface and satellite sensor platforms. The regression analysis between time series of NDVI data from different sensors shows the high Pearson’s correlation coefficients (r > 0.75). Despite the good correlations, there was a remarkable offset and significant differences in slope during green-up and senescence periods. SRS showed the most distinctive NDVI profile and was different to other sensors. PhenoCam_GCC tracked green-up of the canopy better than the other indices, with a well-defined start, end, and peak of the season, and was most closely correlated (r > 0.93) with the satellites, while SRS-based VIgreen accounted for the least correlation (r = 0.58) against Sentinel-2. Phenophase transition dates were estimated and validated against visual inspection of the PhenoCam data. The Start of Spring (SOS) and End of Spring (EOS) could be predicted with an accuracy of <3 days with GCC, while these metrics from VIgreen and NDVI resulted in a slightly higher bias of (3–10) days. The observed agreement between UAV_NDVI vs. satellite_NDVI and PhenoCam_GCC vs. satellite_GCC suggests that it is feasible to use PhenoCams and UAVs for satellite data validation and upscaling. Thus, a combination of these near-surface vegetation metrics is promising for a holistic understanding of vegetation phenology from canopy perspective and could serve as a good foundation for analysing the interoperability of different sensors for vegetation dynamics and change analysis. Full article

(This article belongs to the Special Issue UAV Photogrammetry for Environmental Monitoring)

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24 pages, 6964 KiB

Open AccessEditor’s ChoiceArticle

Development of Novel Classification Algorithms for Detection of Floating Plastic Debris in Coastal Waterbodies Using Multispectral Sentinel-2 Remote Sensing Imagery

by Bidroha Basu, Srikanta Sannigrahi, Arunima Sarkar Basu and Francesco Pilla

Remote Sens. 2021, 13(8), 1598; https://doi.org/10.3390/rs13081598 - 20 Apr 2021

Cited by 55 | Viewed by 7626

Abstract

Plastic pollution poses a significant environmental threat to the existence and health of biodiversity and the marine ecosystem. The intrusion of plastic to the food chain is a massive concern for human health. Urbanisation, population growth, and tourism have been identified as major [...] Read more.

Plastic pollution poses a significant environmental threat to the existence and health of biodiversity and the marine ecosystem. The intrusion of plastic to the food chain is a massive concern for human health. Urbanisation, population growth, and tourism have been identified as major contributors to the growing rate of plastic debris, particularly in waterbodies such as rivers, lakes, seas, and oceans. Over the past decade, many studies have focused on identifying the waterbodies near the coastal regions where a high level of accumulated plastics have been found. This research focused on using high-resolution Sentinel-2 satellite remote sensing images to detect floating plastic debris in coastal waterbodies. Accurate detection of plastic debris can help in deploying appropriate measures to reduce plastics in oceans. Two unsupervised (K-means and fuzzy c-means (FCM)) and two supervised (support vector regression (SVR) and semi-supervised fuzzy c-means (SFCM)) classification algorithms were developed to identify floating plastics. The unsupervised classification algorithms consider the remote sensing data as the sole input to develop the models, while the supervised classifications require in situ information on the presence/absence of floating plastics in selected Sentinel-2 grids for modelling. Data from Cyprus and Greece were considered to calibrate the supervised models and to estimate model efficiency. Out of available multiple bands of Sentinel-2 data, a combination of 6 bands of reflectance data (blue, green, red, red edge 2, near infrared, and short wave infrared 1) and two indices (NDVI and FDI) were selected to develop the models, as they were found to be most efficient for detecting floating plastics. The SVR-based supervised classification has an accuracy in the range of 96.9–98.4%, while that for SFCM and FCM clustering are between 35.7 and 64.3% and 69.8 and 82.2%, respectively, and for K-means, the range varies from 69.8 to 81.4%. It needs to be noted that the total number of grids with floating plastics in real-world data considered in this study is 59, which needs to be increased considerably to improve model performance. Training data from other parts of the world needs to be collected to investigate the performance of the classification algorithms at a global scale. Full article

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19 pages, 3361 KiB

Open AccessEditor’s ChoiceArticle

On the Geopolitics of Fire, Conflict and Land in the Kurdistan Region of Iraq

by Lina Eklund, Abdulhakim M. Abdi, Aiman Shahpurwala and Pinar Dinc

Remote Sens. 2021, 13(8), 1575; https://doi.org/10.3390/rs13081575 - 19 Apr 2021

Cited by 18 | Viewed by 8458

Abstract

There is limited understanding of the geopolitics of fire, conflict, and land, for example, how conflict and fire are related and how conflict impacts the biophysical environment. Since 2014, the natural environment in the Kurdistan Region of Iraq has been negatively affected by [...] Read more.

There is limited understanding of the geopolitics of fire, conflict, and land, for example, how conflict and fire are related and how conflict impacts the biophysical environment. Since 2014, the natural environment in the Kurdistan Region of Iraq has been negatively affected by recurrent conflict that coincided with a sharp increase in the number of reported fires. Against this background, this study explores the spatiotemporal aspects of conflict, fire, and land use and land cover in this region. We combine several satellite-derived products, including land use and land cover, active fire, and precipitation. We apply a partial correlation analysis to understand the relationship between fire, conflict, climate, and land use and land cover. Conflict events and fires have increased since 2014 and have followed a similar temporal pattern, and we show that certain conflicts were particular to certain land use and land cover contexts. For example, the conflict involving the Islamic State was concentrated in southern areas with bare soil/sparse vegetation, and the conflict involving Turkey largely took place in northern mountainous areas characterized by natural vegetation and rugged topography. This dichotomy indicates divergent effects of conflict on the land system. A surprising finding was that fire hotspots had a low positive correlation with the amplitude of distance to conflict while accounting for other variables such as land cover and climate. The high statistical significance of this relationship indicates nonlinearity and implies that a larger range of distances to conflict creates more space for the fires to spread in the surrounding landscape. At the same time, fire hotspots had a weaker but negative correlation to distance from conflict events, which is somewhat expected as areas farther away from conflict locations have lower exposure risk to fires. We discuss the implications of these findings within the geopolitical context of the region and acknowledge the limitations of the study. We conclude with a summary of the main findings and recommendations for future research. Full article

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

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26 pages, 4511 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs

by Yixin He, Daosen Zhai, Fanghui Huang, Dawei Wang, Xiao Tang and Ruonan Zhang

Remote Sens. 2021, 13(8), 1547; https://doi.org/10.3390/rs13081547 - 16 Apr 2021

Cited by 58 | Viewed by 4927

Abstract

In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks [...] Read more.

In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on the UAV due to the limited computation ability. To counter the problems above, we first model and analyze the transmission model and the security assurance model from the vehicle to the MEC server on UAV, and the task computation model of the local vehicle and the edge UAV. Then, the vehicle offloading problem is formulated as a multi-objective optimization problem by jointly considering the task offloading, the resource allocation, and the security assurance. For tackling this hard problem, we decouple the multi-objective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed scheme achieves significant performance superiority compared with other schemes in terms of the successful task processing ratio and the task processing delay. Full article

(This article belongs to the Special Issue Advances and Innovative Applications of Unmanned Aerial Vehicles)

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18 pages, 5280 KiB

Open AccessEditor’s ChoiceArticle

High-Resolution Aerial Detection of Marine Plastic Litter by Hyperspectral Sensing

by Marco Balsi, Monica Moroni, Valter Chiarabini and Giovanni Tanda

Remote Sens. 2021, 13(8), 1557; https://doi.org/10.3390/rs13081557 - 16 Apr 2021

Cited by 52 | Viewed by 6339

Abstract

An automatic custom-made procedure is developed to identify macroplastic debris loads in coastal and marine environment, through hyperspectral imaging from unmanned aerial vehicles (UAVs). Results obtained during a remote-sensing field campaign carried out in the seashore of Sassari (Sardinia, Italy) are presented. A [...] Read more.

An automatic custom-made procedure is developed to identify macroplastic debris loads in coastal and marine environment, through hyperspectral imaging from unmanned aerial vehicles (UAVs). Results obtained during a remote-sensing field campaign carried out in the seashore of Sassari (Sardinia, Italy) are presented. A push-broom-sensor-based spectral device, carried onboard a DJI Matrice 600 drone, was employed for the acquisition of spectral data in the range 900−1700 nm. The hyperspectral platform was realized by assembling commercial devices, whereas algorithms for mosaicking, post-flight georeferencing, and orthorectification of the acquired images were developed in-house. Generation of the hyperspectral cube was based on mosaicking visible-spectrum images acquired synchronously with the hyperspectral lines, by performing correlation-based registration and applying the same translations, rotations, and scale changes to the hyperspectral data. Plastics detection was based on statistically relevant feature selection and Linear Discriminant Analysis, trained on a manually labeled sample. The results obtained from the inspection of either the beach site or the sea water facing the beach clearly show the successful separate identification of polyethylene (PE) and polyethylene terephthalate (PET) objects through the post-processing data treatment based on the developed classifier algorithm. As a further implementation of the procedure described, direct real-time processing, by an embedded computer carried onboard the drone, permitted the immediate plastics identification (and visual inspection in synchronized images) during the UAV survey, as documented by short video sequences provided in this research paper. Full article

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21 pages, 2836 KiB

Open AccessEditor’s ChoiceArticle

High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data

by Yufeng Jiang, Li Zhang, Min Yan, Jianguo Qi, Tianmeng Fu, Shunxiang Fan and Bowei Chen

Remote Sens. 2021, 13(8), 1529; https://doi.org/10.3390/rs13081529 - 15 Apr 2021

Cited by 75 | Viewed by 6619

Abstract

Mangrove forests, as important ecological and economic resources, have suffered a loss in the area due to natural and human activities. Monitoring the distribution of and obtaining accurate information on mangrove species is necessary for ameliorating the damage and protecting and restoring mangrove [...] Read more.

Mangrove forests, as important ecological and economic resources, have suffered a loss in the area due to natural and human activities. Monitoring the distribution of and obtaining accurate information on mangrove species is necessary for ameliorating the damage and protecting and restoring mangrove forests. In this study, we compared the performance of UAV Rikola hyperspectral images, WorldView-2 (WV-2) satellite-based multispectral images, and a fusion of data from both in the classification of mangrove species. We first used recursive feature elimination‒random forest (RFE-RF) to select the vegetation’s spectral and texture feature variables, and then implemented random forest (RF) and support vector machine (SVM) algorithms as classifiers. The results showed that the accuracy of the combined data was higher than that of UAV and WV-2 data; the vegetation index features of UAV hyperspectral data and texture index of WV-2 data played dominant roles; the overall accuracy of the RF algorithm was 95.89% with a Kappa coefficient of 0.95, which is more accurate and efficient than SVM. The use of combined data and RF methods for the classification of mangrove species could be useful in biomass estimation and breeding cultivation. Full article

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17 pages, 11723 KiB

Open AccessEditor’s ChoiceArticle

Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine

by Jiwei Li, David E. Knapp, Mitchell Lyons, Chris Roelfsema, Stuart Phinn, Steven R. Schill and Gregory P. Asner

Remote Sens. 2021, 13(8), 1469; https://doi.org/10.3390/rs13081469 - 10 Apr 2021

Cited by 70 | Viewed by 18796

Abstract

Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative to conventional shipborne measurements, offering high spatial resolution combined with extensive coverage. We developed [...] Read more.

Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative to conventional shipborne measurements, offering high spatial resolution combined with extensive coverage. We developed an automated bathymetry mapping approach based on the Sentinel-2 surface reflectance dataset in Google Earth Engine. We created a new method for generating a clean-water mosaic and a tailored automatic bathymetric estimation algorithm. We then evaluated the performance of the models at six globally diverse sites (Heron Island, Australia; West Coast of Hawaiʻi Island, Hawaiʻi; Saona Island, Dominican Republic; Punta Cana, Dominican Republic; St. Croix, United States Virgin Islands; and The Grenadines) using 113,520 field bathymetry sampling points. Our approach derived accurate bathymetry maps in shallow waters, with Root Mean Square Error (RMSE) values ranging from 1.2 to 1.9 m. This automatic, efficient, and robust method was applied to map shallow water bathymetry at the global scale, especially in areas which have high biodiversity (i.e., coral reefs). Full article

(This article belongs to the Section Ocean Remote Sensing)

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15 pages, 10271 KiB

Open AccessEditor’s ChoiceArticle

The Potential Role of News Media to Construct a Machine Learning Based Damage Mapping Framework

by Genki Okada, Luis Moya, Erick Mas and Shunichi Koshimura

Remote Sens. 2021, 13(7), 1401; https://doi.org/10.3390/rs13071401 - 5 Apr 2021

Cited by 9 | Viewed by 6322

Abstract

When flooding occurs, Synthetic Aperture Radar (SAR) imagery is often used to identify flood extent and the affected buildings for two reasons: (i) for early disaster response, such as rescue operations, and (ii) for flood risk analysis. Furthermore, the application of machine learning [...] Read more.

When flooding occurs, Synthetic Aperture Radar (SAR) imagery is often used to identify flood extent and the affected buildings for two reasons: (i) for early disaster response, such as rescue operations, and (ii) for flood risk analysis. Furthermore, the application of machine learning has been valuable for the identification of damaged buildings. However, the performance of machine learning depends on the number and quality of training data, which is scarce in the aftermath of a large scale disaster. To address this issue, we propose the use of fragmentary but reliable news media photographs at the time of a disaster and use them to detect the whole extent of the flooded buildings. As an experimental test, the flood occurred in the town of Mabi, Japan, in 2018 is used. Five hand-engineered features were extracted from SAR images acquired before and after the disaster. The training data were collected based on news photos. The date release of the photographs were considered to assess the potential role of news information as a source of training data. Then, a discriminant function was calibrated using the training data and the support vector machine method. We found that news information taken within 24 h of a disaster can classify flooded and nonflooded buildings with about 80% accuracy. The results were also compared with a standard unsupervised learning method and confirmed that training data generated from news media photographs improves the accuracy obtained from unsupervised classification methods. We also provide a discussion on the potential role of news media as a source of reliable information to be used as training data and other activities associated to early disaster response. Full article

(This article belongs to the Special Issue Improving Disaster Damage and Loss Assessments by Modeling and Remote Sensing Techniques)

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25 pages, 2625 KiB

Open AccessEditor’s ChoiceArticle

Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal

by Ruben Fernandez-Beltran, Tina Baidar, Jian Kang and Filiberto Pla

Remote Sens. 2021, 13(7), 1391; https://doi.org/10.3390/rs13071391 - 4 Apr 2021

Cited by 67 | Viewed by 11093

Abstract

Crop yield estimation is a major issue of crop monitoring which remains particularly challenging in developing countries due to the problem of timely and adequate data availability. Whereas traditional agricultural systems mainly rely on scarce ground-survey data, freely available multi-temporal and multi-spectral remote [...] Read more.

Crop yield estimation is a major issue of crop monitoring which remains particularly challenging in developing countries due to the problem of timely and adequate data availability. Whereas traditional agricultural systems mainly rely on scarce ground-survey data, freely available multi-temporal and multi-spectral remote sensing images are excellent tools to support these vulnerable systems by accurately monitoring and estimating crop yields before harvest. In this context, we introduce the use of Sentinel-2 (S2) imagery, with a medium spatial, spectral and temporal resolutions, to estimate rice crop yields in Nepal as a case study. Firstly, we build a new large-scale rice crop database (RicePAL) composed by multi-temporal S2 and climate/soil data from the Terai districts of Nepal. Secondly, we propose a novel 3D Convolutional Neural Network (CNN) adapted to these intrinsic data constraints for the accurate rice crop yield estimation. Thirdly, we study the effect of considering different temporal, climate and soil data configurations in terms of the performance achieved by the proposed approach and several state-of-the-art regression and CNN-based yield estimation methods. The extensive experiments conducted in this work demonstrate the suitability of the proposed CNN-based framework for rice crop yield estimation in the developing country of Nepal using S2 data. Full article

(This article belongs to the Special Issue Machine Learning for Remote Sensing Image/Signal Processing)

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20 pages, 8658 KiB

Open AccessEditor’s ChoiceArticle

Flood Monitoring in Rural Areas of the Pearl River Basin (China) Using Sentinel-1 SAR

by Junliang Qiu, Bowen Cao, Edward Park, Xiankun Yang, Wenxin Zhang and Paolo Tarolli

Remote Sens. 2021, 13(7), 1384; https://doi.org/10.3390/rs13071384 - 3 Apr 2021

Cited by 51 | Viewed by 9424

Abstract

Flood hazards result in enormous casualties and huge economic losses every year in the Pearl River Basin (PRB), China. It is, therefore, crucial to monitor floods in PRB for a better understanding of the flooding patterns and characteristics of the PRB. Previous studies, [...] Read more.

Flood hazards result in enormous casualties and huge economic losses every year in the Pearl River Basin (PRB), China. It is, therefore, crucial to monitor floods in PRB for a better understanding of the flooding patterns and characteristics of the PRB. Previous studies, which utilized hydrological data were not successful in identifying flooding patterns in the rural and remote regions in PRB. Such regions are the key supplier of agricultural products and water resources for the entire PRB. Thus, an analysis of the impacts of floods could provide a useful tool to support mitigation strategies. Using 66 Sentinel-1 images, this study employed Otsu’s method to investigate floods and explore flood patterns across the PRB from 2017 to 2020. The results indicated that floods are mainly located in the central West River Basin (WRB), middle reaches of the North River (NR) and middle reaches of the East River (ER). WRB is more prone to flood hazards. In 2017, 94.0% flood-impacted croplands were located in WRB; 95.0% of inundated croplands (~9480 hectares) were also in WRB. The most vulnerable areas to flooding are sections of the Yijiang, Luoqingjiang, Qianjiang, and Xunjiang tributaries and the lower reaches of Liujiang. Our results highlight the severity of flood hazards in a rural region of the PRB and emphasize the need for policy overhaul to enhance flood control in rural regions in the PRB to ensure food safety. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Natural Disasters and Human Impacts in Asian Rivers)

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16 pages, 2564 KiB

Open AccessEditor’s ChoiceArticle

Responses of Summer Upwelling to Recent Climate Changes in the Taiwan Strait

by Caiyun Zhang

Remote Sens. 2021, 13(7), 1386; https://doi.org/10.3390/rs13071386 - 3 Apr 2021

Cited by 6 | Viewed by 3815

Abstract

The response of a summer upwelling system to recent climate change in the Taiwan Strait has been investigated using a time series of sea surface temperature and wind data over the period 1982–2019. Our results revealed that summer upwelling intensities of the Taiwan [...] Read more.

The response of a summer upwelling system to recent climate change in the Taiwan Strait has been investigated using a time series of sea surface temperature and wind data over the period 1982–2019. Our results revealed that summer upwelling intensities of the Taiwan Strait decreased with a nonlinear fluctuation over the past four decades. The average upwelling intensity after 2000 was 35% lower than that before 2000. The long-term changes in upwelling intensities show strong correlations with offshore Ekman transport, which experienced a decreasing trend after 2000. Unlike the delay effect of canonical ENSO events on changes in summer upwelling, ENSO Modoki events had a significant negative influence on upwelling intensity. Strong El Niño Modoki events were not favorable for the development of upwelling. This study also suggested that decreased upwelling could not slow down the warming rate of the sea surface temperature and would probably cause the decline of chlorophyll a in the coastal upwelling system of the Taiwan Strait. These results will contribute to a better understanding of the dynamic process of summer upwelling in the Taiwan Strait, and provide a sound scientific basis for evaluating future trends in coastal upwelling and their potential ecological effects. Full article

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20 pages, 7820 KiB

Open AccessEditor’s ChoiceArticle

Sea Ice Thickness Estimation Based on Regression Neural Networks Using L-Band Microwave Radiometry Data from the FSSCat Mission

by Christoph Herbert, Joan Francesc Munoz-Martin, David Llaveria, Miriam Pablos and Adriano Camps

Remote Sens. 2021, 13(7), 1366; https://doi.org/10.3390/rs13071366 - 2 Apr 2021

Cited by 14 | Viewed by 4793

Abstract

Several methods have been developed to provide polar maps of sea ice thickness (SIT) from L-band brightness temperature (T

_{B}

) and altimetry data. Current process-based inversion methods to yield SIT fail to address the complex surface characteristics because sea ice is subject [...] Read more.

Several methods have been developed to provide polar maps of sea ice thickness (SIT) from L-band brightness temperature (T

_{B}

) and altimetry data. Current process-based inversion methods to yield SIT fail to address the complex surface characteristics because sea ice is subject to strong seasonal dynamics and ice-physical properties are often non-linearly related. Neural networks can be trained to find hidden links among large datasets and often perform better on convoluted problems for which traditional approaches miss out important relationships between the observations. The FSSCat mission launched on 3 September 2020, carries the Flexible Microwave Payload-2 (FMPL-2), which contains the first Reflected Global Navigation Satellite System (GNSS-R) and L-band radiometer on board a CubeSat—designed to provide T

_{B}

data on global coverage for soil moisture retrieval, and sea ice applications. This work investigates a predictive regression neural network approach with the goal to infer SIT using FMPL-2 T

_{B}

and ancillary data (sea ice concentration, surface temperature, and sea ice freeboard). Two models—covering thin ice up to 0.6 m and full-range thickness—were separately trained on Arctic data in a two-month period from mid-October to the beginning of December 2020, while using ground truth data derived from the Soil Moisture and Ocean Salinity (SMOS) and Cryosat-2 missions. The thin ice and the full-range models resulted in a mean absolute error of 6.5 cm and 23 cm, respectively. Both of the models allowed for one to produce weekly composites of Arctic maps, and monthly composites of Antarctic SIT were predicted based on the Arctic full-range model. This work presents the first results of the FSSCat mission over the polar regions. It reveals the benefits of neural networks for sea ice retrievals and demonstrates that moderate-cost CubeSat missions can provide valuable data for applications in Earth observation. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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9 pages, 633 KiB

Open AccessEditor’s ChoiceCommunication

The Road to Operationalization of Effective Tropical Forest Monitoring Systems

by Carlos Portillo-Quintero, Jose L. Hernández-Stefanoni, Gabriela Reyes-Palomeque and Mukti R. Subedi

Remote Sens. 2021, 13(7), 1370; https://doi.org/10.3390/rs13071370 - 2 Apr 2021

Cited by 11 | Viewed by 3474

Abstract

The urgency to preserve tropical forest remnants has encouraged the development of remote sensing tools and techniques to monitor diverse forest attributes for management and conservation. State-of-the-art methodologies for mapping and tracking these attributes usually achieve accuracies greater than 0.8 for forest cover [...] Read more.

The urgency to preserve tropical forest remnants has encouraged the development of remote sensing tools and techniques to monitor diverse forest attributes for management and conservation. State-of-the-art methodologies for mapping and tracking these attributes usually achieve accuracies greater than 0.8 for forest cover monitoring; r-square values of ~0.5–0.7 for plant diversity, vegetation structure, and plant functional trait mapping, and overall accuracies of ~0.8 for categorical maps of forest attributes. Nonetheless, existing operational tropical forest monitoring systems only track single attributes at national to global scales. For the design and implementation of effective and integrated tropical forest monitoring systems, we recommend the integration of multiple data sources and techniques for monitoring structural, functional, and compositional attributes. We also recommend its decentralized implementation for adjusting methods to local climatic and ecological characteristics and for proper end-user engagement. The operationalization of the system should be based on all open-source computing platforms, leveraging international support in research and development and ensuring direct and constant user engagement. We recommend continuing the efforts to address these multiple challenges for effective monitoring. Full article

(This article belongs to the Special Issue Novel Approaches in Tropical Forests Mapping and Monitoring – Time for Operationalization)

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42 pages, 4383 KiB

Open AccessEditor’s ChoiceReview

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

by Mercedes Vélez-Nicolás, Santiago García-López, Luis Barbero, Verónica Ruiz-Ortiz and Ángel Sánchez-Bellón

Remote Sens. 2021, 13(7), 1359; https://doi.org/10.3390/rs13071359 - 1 Apr 2021

Cited by 76 | Viewed by 18479

Abstract

In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring [...] Read more.

In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring possibilities, UAS are contributing to the acquisition of large volumes of data on water bodies, submerged parameters and their interactions in different hydrological contexts and in inaccessible or hazardous locations. This paper provides a comprehensive review of 122 works on the applications of UASs in surface water and groundwater research with a purpose-oriented approach. Concretely, the review addresses: (i) the current applications of UAS in surface and groundwater studies, (ii) the type of platforms and sensors mainly used in these tasks, (iii) types of products generated from UAS-borne data, (iv) the associated advantages and limitations, and (v) knowledge gaps and future prospects of UASs application in hydrology. The first aim of this review is to serve as a reference or introductory document for all researchers and water managers who are interested in embracing this novel technology. The second aim is to unify in a single document all the possibilities, potential approaches and results obtained by different authors through the implementation of UASs. Full article

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

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16 pages, 120896 KiB

Open AccessEditor’s ChoiceArticle

Photogrammetry Using UAV-Mounted GNSS RTK: Georeferencing Strategies without GCPs

by Martin Štroner, Rudolf Urban, Jan Seidl, Tomáš Reindl and Josef Brouček

Remote Sens. 2021, 13(7), 1336; https://doi.org/10.3390/rs13071336 - 31 Mar 2021

Cited by 115 | Viewed by 23988

Abstract

Georeferencing using ground control points (GCPs) is the most common strategy in photogrammetry modeling using unmanned aerial vehicle (UAV)-acquired imagery. With the increased availability of UAVs with onboard global navigation satellite system–real-time kinematic (GNSS RTK), georeferencing without GCPs is becoming a promising alternative. [...] Read more.

Georeferencing using ground control points (GCPs) is the most common strategy in photogrammetry modeling using unmanned aerial vehicle (UAV)-acquired imagery. With the increased availability of UAVs with onboard global navigation satellite system–real-time kinematic (GNSS RTK), georeferencing without GCPs is becoming a promising alternative. However, systematic elevation error remains a problem with this technique. We aimed to analyze the reasons for this systematic error and propose strategies for its elimination. Multiple flights differing in the flight altitude and image acquisition axis were performed at two real-world sites. A flight height of 100 m with a vertical (nadiral) image acquisition axis was considered primary, supplemented with flight altitudes of 75 m and 125 m with a vertical image acquisition axis and two flights at 100 m with oblique image acquisition axes (30° and 15°). Each of these flights was performed twice to produce a full double grid. Models were reconstructed from individual flights and their combinations. The elevation error from individual flights or even combinations yielded systematic elevation errors of up to several decimeters. This error was linearly dependent on the deviation of the focal length from the reference value. A combination of two flights at the same altitude (with nadiral and oblique image acquisition) was capable of reducing the systematic elevation error to less than 0.03 m. This study is the first to demonstrate the linear dependence between the systematic elevation error of the models based only on the onboard GNSS RTK data and the deviation in the determined internal orientation parameters (focal length). In addition, we have shown that a combination of two flights with different image acquisition axes can eliminate this systematic error even in real-world conditions and that georeferencing without GCPs is, therefore, a feasible alternative to the use of GCPs. Full article

(This article belongs to the Special Issue UAV Photogrammetry and Remote Sensing)

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57 pages, 12701 KiB

Open AccessEditor’s ChoiceReview

Trends in Satellite Earth Observation for Permafrost Related Analyses—A Review

by Marius Philipp, Andreas Dietz, Sebastian Buchelt and Claudia Kuenzer

Remote Sens. 2021, 13(6), 1217; https://doi.org/10.3390/rs13061217 - 23 Mar 2021

Cited by 38 | Viewed by 13125

Abstract

Climate change and associated Arctic amplification cause a degradation of permafrost which in turn has major implications for the environment. The potential turnover of frozen ground from a carbon sink to a carbon source, eroding coastlines, landslides, amplified surface deformation and endangerment of [...] Read more.

Climate change and associated Arctic amplification cause a degradation of permafrost which in turn has major implications for the environment. The potential turnover of frozen ground from a carbon sink to a carbon source, eroding coastlines, landslides, amplified surface deformation and endangerment of human infrastructure are some of the consequences connected with thawing permafrost. Satellite remote sensing is hereby a powerful tool to identify and monitor these features and processes on a spatially explicit, cheap, operational, long-term basis and up to circum-Arctic scale. By filtering after a selection of relevant keywords, a total of 325 articles from 30 international journals published during the last two decades were analyzed based on study location, spatio-temporal resolution of applied remote sensing data, platform, sensor combination and studied environmental focus for a comprehensive overview of past achievements, current efforts, together with future challenges and opportunities. The temporal development of publication frequency, utilized platforms/sensors and the addressed environmental topic is thereby highlighted. The total number of publications more than doubled since 2015. Distinct geographical study hot spots were revealed, while at the same time large portions of the continuous permafrost zone are still only sparsely covered by satellite remote sensing investigations. Moreover, studies related to Arctic greenhouse gas emissions in the context of permafrost degradation appear heavily underrepresented. New tools (e.g., Google Earth Engine (GEE)), methodologies (e.g., deep learning or data fusion etc.) and satellite data (e.g., the Methane Remote Sensing LiDAR Mission (Merlin) and the Sentinel-fleet) will thereby enable future studies to further investigate the distribution of permafrost, its thermal state and its implications on the environment such as thermokarst features and greenhouse gas emission rates on increasingly larger spatial and temporal scales. Full article

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25 pages, 1465 KiB

Open AccessEditor’s ChoiceReview

A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges

by Nadia Delavarpour, Cengiz Koparan, John Nowatzki, Sreekala Bajwa and Xin Sun

Remote Sens. 2021, 13(6), 1204; https://doi.org/10.3390/rs13061204 - 22 Mar 2021

Cited by 259 | Viewed by 22895

Abstract

The incorporation of advanced technologies into Unmanned Aerial Vehicles (UAVs) platforms have enabled many practical applications in Precision Agriculture (PA) over the past decade. These PA tools offer capabilities that increase agricultural productivity and inputs’ efficiency and minimize operational costs simultaneously. However, these [...] Read more.

The incorporation of advanced technologies into Unmanned Aerial Vehicles (UAVs) platforms have enabled many practical applications in Precision Agriculture (PA) over the past decade. These PA tools offer capabilities that increase agricultural productivity and inputs’ efficiency and minimize operational costs simultaneously. However, these platforms also have some constraints that limit the application of UAVs in agricultural operations. The constraints include limitations in providing imagery of adequate spatial and temporal resolutions, dependency on weather conditions, and geometric and radiometric correction requirements. In this paper, a practical guide on technical characterizations of common types of UAVs used in PA is presented. This paper helps select the most suitable UAVs and on-board sensors for different agricultural operations by considering all the possible constraints. Over a hundred research studies were reviewed on UAVs applications in PA and practical challenges in monitoring and mapping field crops. We concluded by providing suggestions and future directions to overcome challenges in optimizing operational proficiency. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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21 pages, 17937 KiB

Open AccessEditor’s ChoiceArticle

The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities

by Matthias Schramm, Edzer Pebesma, Milutin Milenković, Luca Foresta, Jeroen Dries, Alexander Jacob, Wolfgang Wagner, Matthias Mohr, Markus Neteler, Miha Kadunc, Tomasz Miksa, Pieter Kempeneers, Jan Verbesselt, Bernhard Gößwein, Claudio Navacchi, Stefaan Lippens and Johannes Reiche

Remote Sens. 2021, 13(6), 1125; https://doi.org/10.3390/rs13061125 - 16 Mar 2021

Cited by 55 | Viewed by 11307

Abstract

At present, accessing and processing Earth Observation (EO) data on different cloud platforms requires users to exercise distinct communication strategies as each backend platform is designed differently. The openEO API (Application Programming Interface) standardises EO-related contracts between local clients (R, Python, and JavaScript) [...] Read more.

At present, accessing and processing Earth Observation (EO) data on different cloud platforms requires users to exercise distinct communication strategies as each backend platform is designed differently. The openEO API (Application Programming Interface) standardises EO-related contracts between local clients (R, Python, and JavaScript) and cloud service providers regarding data access and processing, simplifying their direct comparability. Independent of the providers’ data storage system, the API mimics the functionalities of a virtual EO raster data cube. This article introduces the communication strategy and aspects of the data cube model applied by the openEO API. Two test cases show the potential and current limitations of processing similar workflows on different cloud platforms and a comparison of the result of a locally running workflow and its openEO-dependent cloud equivalent. The outcomes demonstrate the flexibility of the openEO API in enabling complex scientific analysis of EO data collections on cloud platforms in a homogenised way. Full article

(This article belongs to the Special Issue Big Earth Observation Data: From Cloud Technologies to Insights and Foresight)

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28 pages, 86897 KiB

Open AccessEditor’s ChoiceArticle

Hydrocarbon Pollution Detection and Mapping Based on the Combination of Various Hyperspectral Imaging Processing Tools

by Véronique Achard, Pierre-Yves Foucher and Dominique Dubucq

Remote Sens. 2021, 13(5), 1020; https://doi.org/10.3390/rs13051020 - 8 Mar 2021

Cited by 22 | Viewed by 5527

Abstract

Oil extraction and transportation may lead to small or large scale accidental spills, whether at sea or on land. Detecting these spills is a major problem that can be addressed by means of hyperspectral images and specific processing methods. In this work, several [...] Read more.

Oil extraction and transportation may lead to small or large scale accidental spills, whether at sea or on land. Detecting these spills is a major problem that can be addressed by means of hyperspectral images and specific processing methods. In this work, several cases of onshore oil spills are studied. First, a controlled experiment was carried out: four boxes containing soil or sand mixed with crude oil or gasoil were deployed on the ONERA site near Fauga, France, and were overflown by HySpex hyperspectral cameras. Owing to this controlled experiment, different detection strategies were developed and tested, with a particular focus on the most automated methods requiring the least supervision. The methods developed were then applied to two very different cases: mapping of the shoreline contaminated due to the explosion of the Deepwater Horizon (DWH) platform based on AVIRIS images (AVIRIS: Airborne Visible/InfraRed Imaging Spectrometer), and detection of a tar pit on a former oil exploration site. The detection strategy depends on the type of oil, light or heavy, recently or formerly spilled, and on the substrate. In the first case (controlled experiment), the proposed methods included spectral index calculations, anomaly detection and spectral unmixing. In the case of DWH, spectral indices were computed and the unmixing method was tested. Finally, to detect the tar pit, a strategy based on anomaly detection and spectral indices was applied. In all the cases studied, the proposed methods were successful in detecting and mapping the oil pollution. Full article

(This article belongs to the Special Issue Monitoring Soil Contamination by Remote Sensors)

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21 pages, 2648 KiB

Open AccessEditor’s ChoiceArticle

Traditional vs. Machine-Learning Methods for Forecasting Sandy Shoreline Evolution Using Historic Satellite-Derived Shorelines

by Floris Calkoen, Arjen Luijendijk, Cristian Rodriguez Rivero, Etienne Kras and Fedor Baart

Remote Sens. 2021, 13(5), 934; https://doi.org/10.3390/rs13050934 - 3 Mar 2021

Cited by 35 | Viewed by 6414

Abstract

Forecasting shoreline evolution for sandy coasts is important for sustainable coastal management, given the present-day increasing anthropogenic pressures and a changing future climate. Here, we evaluate eight different time-series forecasting methods for predicting future shorelines derived from historic satellite-derived shorelines. Analyzing more than [...] Read more.

Forecasting shoreline evolution for sandy coasts is important for sustainable coastal management, given the present-day increasing anthropogenic pressures and a changing future climate. Here, we evaluate eight different time-series forecasting methods for predicting future shorelines derived from historic satellite-derived shorelines. Analyzing more than 37,000 transects around the globe, we find that traditional forecast methods altogether with some of the evaluated probabilistic Machine Learning (ML) time-series forecast algorithms, outperform Ordinary Least Squares (OLS) predictions for the majority of the sites. When forecasting seven years ahead, we find that these algorithms generate better predictions than OLS for 54% of the transect sites, producing forecasts with, on average, 29% smaller Mean Squared Error (MSE). Importantly, this advantage is shown to exist over all considered forecast horizons, i.e., from 1 up to 11 years. Although the ML algorithms do not produce significantly better predictions than traditional time-series forecast methods, some proved to be significantly more efficient in terms of computation time. We further provide insight in how these ML algorithms can be improved so that they can be expected to outperform not only OLS regression, but also the traditional time-series forecast methods. These forecasting algorithms can be used by coastal engineers, managers, and scientists to generate future shoreline prediction at a global level and derive conclusions thereof. Full article

(This article belongs to the Special Issue How the Combination of Satellite Remote Sensing with Artificial Intelligence Can Solve Coastal Issues)

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16 pages, 29954 KiB

Open AccessEditor’s ChoiceArticle

Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning

by Fuding Xie, Quanshan Gao, Cui Jin and Fengxia Zhao

Remote Sens. 2021, 13(5), 930; https://doi.org/10.3390/rs13050930 - 2 Mar 2021

Cited by 55 | Viewed by 5439

Abstract

Deep learning-based hyperspectral image (HSI) classification has attracted more and more attention because of its excellent classification ability. Generally, the outstanding performance of these methods mainly depends on a large number of labeled samples. Therefore, it still remains an ongoing challenge how to [...] Read more.

Deep learning-based hyperspectral image (HSI) classification has attracted more and more attention because of its excellent classification ability. Generally, the outstanding performance of these methods mainly depends on a large number of labeled samples. Therefore, it still remains an ongoing challenge how to integrate spatial structure information into these frameworks to classify the HSI with limited training samples. In this study, an effective spectral-spatial HSI classification scheme is proposed based on superpixel pooling convolutional neural network with transfer learning (SP-CNN). The suggested method includes three stages. The first part consists of convolution and pooling operation, which is a down-sampling process to extract the main spectral features of an HSI. The second part is composed of up-sampling and superpixel (homogeneous regions with adaptive shape and size) pooling to explore the spatial structure information of an HSI. Finally, the hyperspectral data with each superpixel as a basic input rather than a pixel are fed to fully connected neural network. In this method, the spectral and spatial information is effectively fused by using superpixel pooling technique. The use of popular transfer learning technology in the proposed classification framework significantly improves the training efficiency of SP-CNN. To evaluate the effectiveness of the SP-CNN, extensive experiments were conducted on three common real HSI datasets acquired from different sensors. With 30 labeled pixels per class, the overall classification accuracy provided by this method on three benchmarks all exceeded 93%, which was at least 4.55% higher than that of several state-of-the-art approaches. Experimental and comparative results prove that the proposed algorithm can effectively classify the HSI with limited training labels. Full article

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29 pages, 19956 KiB

Open AccessEditor’s ChoiceArticle

Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

by Theodora Lendzioch, Jakub Langhammer, Lukáš Vlček and Robert Minařík

Remote Sens. 2021, 13(5), 907; https://doi.org/10.3390/rs13050907 - 28 Feb 2021

Cited by 24 | Viewed by 7061

Abstract

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data [...] Read more.

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R² = 0.12) to high (R² = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data. Full article

(This article belongs to the Special Issue UAV Based Vegetation Parameter Retrieval)

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18 pages, 7393 KiB

Open AccessEditor’s ChoiceArticle

Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery

by Sergii Skakun, Natacha I. Kalecinski, Meredith G. L. Brown, David M. Johnson, Eric F. Vermote, Jean-Claude Roger and Belen Franch

Remote Sens. 2021, 13(5), 872; https://doi.org/10.3390/rs13050872 - 26 Feb 2021

Cited by 69 | Viewed by 9070

Abstract

Crop yield monitoring is an important component in agricultural assessment. Multi-spectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for [...] Read more.

Crop yield monitoring is an important component in agricultural assessment. Multi-spectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 μm), red-edge (0.726 μm), and near-infrared (NIR − 0.865 μm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R²) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R² was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use/Cover Changes Using Very High Resolution Satellite Data)

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28 pages, 94494 KiB

Open AccessEditor’s ChoiceArticle

Landsat and Sentinel-2 Based Burned Area Mapping Tools in Google Earth Engine

by Ekhi Roteta, Aitor Bastarrika, Magí Franquesa and Emilio Chuvieco

Remote Sens. 2021, 13(4), 816; https://doi.org/10.3390/rs13040816 - 23 Feb 2021

Cited by 54 | Viewed by 12168

Abstract

Four burned area tools were implemented in Google Earth Engine (GEE), to obtain regular processes related to burned area (BA) mapping, using medium spatial resolution sensors (Landsat and Sentinel-2). The four tools are (i) the BA Cartography tool for supervised burned area over [...] Read more.

Four burned area tools were implemented in Google Earth Engine (GEE), to obtain regular processes related to burned area (BA) mapping, using medium spatial resolution sensors (Landsat and Sentinel-2). The four tools are (i) the BA Cartography tool for supervised burned area over the user-selected extent and period, (ii) two tools implementing a BA stratified random sampling to select the scenes and dates for validation, and (iii) the BA Reference Perimeter tool to obtain highly accurate BA maps that focus on validating coarser BA products. Burned Area Mapping Tools (BAMTs) go beyond the previously implemented Burned Area Mapping Software (BAMS) because of GEE parallel processing capabilities and preloaded geospatial datasets. BAMT also allows temporal image composites to be exploited in order to obtain BA maps over a larger extent and longer temporal periods. The tools consist of four scripts executable from the GEE Code Editor. The tools’ performance was discussed in two case studies: in the 2019/2020 fire season in Southeast Australia, where the BA cartography detected more than 50,000 km², using Landsat data with commission and omission errors below 12% when compared to Sentinel-2 imagery; and in the 2018 summer wildfires in Canada, where it was found that around 16,000 km² had burned. Full article

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

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14 pages, 3004 KiB

Open AccessEditor’s ChoiceArticle

Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale

by Zahra Sharifnezhad, Hamid Norouzi, Satya Prakash, Reginald Blake and Reza Khanbilvardi

Remote Sens. 2021, 13(4), 817; https://doi.org/10.3390/rs13040817 - 23 Feb 2021

Cited by 6 | Viewed by 3743

Abstract

Satellite-borne passive microwave radiometers provide brightness temperature (TB) measurements in a large spectral range which includes a number of frequency channels and generally two polarizations: horizontal and vertical. These TBs are widely used to retrieve several atmospheric and surface variables and parameters such [...] Read more.

Satellite-borne passive microwave radiometers provide brightness temperature (TB) measurements in a large spectral range which includes a number of frequency channels and generally two polarizations: horizontal and vertical. These TBs are widely used to retrieve several atmospheric and surface variables and parameters such as precipitation, soil moisture, water vapor, air temperature profile, and land surface emissivity. Since TBs are measured at different microwave frequencies with various instruments and at various incidence angles, spatial resolutions, and radiometric characteristics, a mere direct integration of them from different microwave sensors would not necessarily provide consistency. However, when appropriately harmonized, they can provide a complete dataset to estimate the diurnal cycle. This study first constructs the diurnal cycle of land TBs using the non-sun-synchronous Global Precipitation Measurement (GPM) Microwave Imager (GMI) observations by utilizing a cubic spline fit. The acquisition times of GMI vary from day to day and, therefore, the shape (amplitude and phase) of the diurnal cycle for each month is obtained by merging several days of measurements. This diurnal pattern is used as a point of reference when intercalibrated TBs from other passive microwave sensors with daily fixed acquisition times (e.g., Special Sensor Microwave Imager/Sounder, and Advanced Microwave Scanning Radiometer 2) are used to modify and tune the monthly diurnal cycle to daily diurnal cycle at a global scale. Since the GMI does not cover polar regions, the proposed method estimates a consistent diurnal cycle of land TBs at global scale. Results show that the shape and peak of the constructed TB diurnal cycle is approximately similar to the diurnal cycle of land surface temperature. The diurnal brightness temperature range for different land cover types has also been explored using the derived diurnal cycle of TBs. In general, a large diurnal TB range of more than 15 K has been observed for the grassland, shrubland, and tundra land cover types, whereas it is less than 5K over forests. Furthermore, seasonal variations in the diurnal TB range for different land cover types show a more consistent result over the Southern Hemisphere than over the Northern Hemisphere. The calibrated TB diurnal cycle may then be used to consistently estimate the diurnal cycle of land surface emissivity. Moreover, since changes in land surface emissivity are related to moisture change and freeze–thaw (FT) transitions in high-latitude regions, the results of this study enhance temporal detection of FT state, particularly during the transition times when multiple FT changes may occur within a day. Full article

(This article belongs to the Special Issue Remote Sensing Data Fusion as a Strategy to Add Value to Earth Observation Data)

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23 pages, 4682 KiB

Open AccessEditor’s ChoiceArticle

Crop Biomass Mapping Based on Ecosystem Modeling at Regional Scale Using High Resolution Sentinel-2 Data

by Liming He, Rong Wang, Georgy Mostovoy, Jane Liu, Jing M. Chen, Jiali Shang, Jiangui Liu, Heather McNairn and Jarrett Powers

Remote Sens. 2021, 13(4), 806; https://doi.org/10.3390/rs13040806 - 22 Feb 2021

Cited by 18 | Viewed by 5137

Abstract

We evaluate the potential of using a process-based ecosystem model (BEPS) for crop biomass mapping at 20 m resolution over the research site in Manitoba, western Canada driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 spectral reflectance throughout the entire [...] Read more.

We evaluate the potential of using a process-based ecosystem model (BEPS) for crop biomass mapping at 20 m resolution over the research site in Manitoba, western Canada driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 spectral reflectance throughout the entire growing season. We find that overall, the BEPS-simulated crop gross primary production (GPP), net primary production (NPP), and LAI time-series can explain 82%, 83%, and 85%, respectively, of the variation in the above-ground biomass (AGB) for six selected annual crops, while an application of individual crop LAI explains only 50% of the variation in AGB. The linear relationships between the AGB and these three indicators (GPP, NPP and LAI time-series) are rather high for the six crops, while the slopes of the regression models vary for individual crop type, indicating the need for calibration of key photosynthetic parameters and carbon allocation coefficients. This study demonstrates that accumulated GPP and NPP derived from an ecosystem model, driven by Sentinel-2 LAI data and abiotic data, can be effectively used for crop AGB mapping; the temporal information from LAI is also effective in AGB mapping for some crop types. Full article

(This article belongs to the Special Issue Recent Advances for Crop Mapping and Monitoring Using Remote Sensing Data)

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27 pages, 33548 KiB

Open AccessEditor’s ChoiceArticle

Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin

by Shilun Zhou, Wanchang Zhang, Shuhang Wang, Bo Zhang and Qiang Xu

Remote Sens. 2021, 13(4), 684; https://doi.org/10.3390/rs13040684 - 13 Feb 2021

Cited by 24 | Viewed by 4845

Abstract

Information about the growth, productivity, and distribution of vegetation, which are highly relied on and sensitive to natural and anthropogenic factors, is essential for agricultural production management and eco-environmental sustainability in the Amur River Basin (ARB). In this paper, the spatial–temporal trends of [...] Read more.

Information about the growth, productivity, and distribution of vegetation, which are highly relied on and sensitive to natural and anthropogenic factors, is essential for agricultural production management and eco-environmental sustainability in the Amur River Basin (ARB). In this paper, the spatial–temporal trends of vegetation dynamics were analyzed at the pixel scale in the ARB for the period of 1982–2013 using remotely sensed data of long-term leaf area index (LAI), fractional vegetation cover (FVC), and terrestrial gross primary productivity (GPP). The spatial autocorrelation characteristics of the vegetation indexes were further explored with global and local Moran’s I techniques. The spatial–temporal relationships between vegetation and climatic factors, land use/cover types and hydrological variables in the ARB were determined using a geographical and temporal weighted regression (GTWR) model based on the observed meteorological data, remotely sensed vegetation information, while the simulated hydrological variables were determined with the soil and water assessment tool (SWAT) model. The results suggest that the variation in area-average annual FVC was significant with an increase rate of 0.0004/year, and LAI, FVC, and GPP all exhibited strong spatial heterogeneity trends in the ARB. For LAI and FVC, the most significant changes in local spatial autocorrelation were recognized over the Sanjiang Plain, and the low–low agglomeration in the Sanjiang Plain decreased continuously. The GTWR model results indicate that natural and anthropogenic factors jointly took effect and interacted with each other to affect the vegetated regime of the region. The decrease in the impact of precipitation to vegetation growth over the Songnen Plain was determined as having started around 1991, which was most likely attributed to dramatic changes in water use styles induced by local land use changes, and corresponded to the negative correlation between pasture areas and vegetation indexes during the same period. The analysis results presented in this paper can provide vital information to decision-makers for use in managing vegetation resources. Full article

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28 pages, 2827 KiB

Open AccessEditor’s ChoiceReview

Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

by Caiwang Zheng, Amr Abd-Elrahman and Vance Whitaker

Remote Sens. 2021, 13(3), 531; https://doi.org/10.3390/rs13030531 - 2 Feb 2021

Cited by 76 | Viewed by 14132

Abstract

Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping [...] Read more.

Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming. Full article

(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)

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25 pages, 6023 KiB

Open AccessEditor’s ChoiceArticle

Spatiotemporal Characteristics and Trend Analysis of Two Evapotranspiration-Based Drought Products and Their Mechanisms in Sub-Saharan Africa

by Isaac Kwesi Nooni, Daniel Fiifi T. Hagan, Guojie Wang, Waheed Ullah, Shijie Li, Jiao Lu, Asher Samuel Bhatti, Xiao Shi, Dan Lou, Nana Agyemang Prempeh, Kenny T. C. Lim Kam Sian, Mawuli Dzakpasu, Solomon Obiri Yeboah Amankwah and Chenxia Zhu

Remote Sens. 2021, 13(3), 533; https://doi.org/10.3390/rs13030533 - 2 Feb 2021

Cited by 14 | Viewed by 4165

Abstract

Drought severity still remains a serious concern across Sub-Saharan Africa (SSA) due to its destructive impact on multiple sectors of society. In this study, the interannual variability and trends in the changes of the self-calibrating Palmer Drought Severity Index (scPDSI) based on the [...] Read more.

Drought severity still remains a serious concern across Sub-Saharan Africa (SSA) due to its destructive impact on multiple sectors of society. In this study, the interannual variability and trends in the changes of the self-calibrating Palmer Drought Severity Index (scPDSI) based on the Penman–Monteith (scPDSI_PM) and Thornthwaite (scPDSI_TH) methods for measuring potential evapotranspiration (PET), precipitation (P), normalized difference vegetation index (NDVI), and sea surface temperature (SST) anomalies were investigated through statistical analysis of modeled and remote sensing data. It was shown that scPDSI_PM and scPDSI_TH differed in the representation of drought characteristics over SSA. The regional trend magnitudes of scPDSI in SSA were 0.69 (scPDSI_PM) and 0.2 mm/decade (scPDSI_TH), with a difference in values attributed to the choice of PET measuring method used. The scPDSI and remotely sensed-based anomalies of P and NDVI showed wetting and drying trends over the period 1980–2012 with coefficients of trend magnitudes of 0.12 mm/decade (0.002 mm/decade). The trend analysis showed increased drought events in the semi-arid and arid regions of SSA over the same period. A correlation analysis revealed a strong relationship between the choice of PET measuring method and both P and NDVI anomalies for monsoon and pre-monsoon seasons. The correlation analysis of the choice of PET measuring method with SST anomalies indicated significant positive and negative relationships. This study has demonstrated the applicability of multiple data sources for drought assessment and provides useful information for regional drought predictability and mitigation strategies. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Tropical Meteorology and Climatology)

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19 pages, 5818 KiB

Open AccessEditor’s ChoiceArticle

Complex Principal Component Analysis of Antarctic Ice Sheet Mass Balance

by Jingang Zhan, Hongling Shi, Yong Wang and Yixin Yao

Remote Sens. 2021, 13(3), 480; https://doi.org/10.3390/rs13030480 - 29 Jan 2021

Cited by 7 | Viewed by 3177

Abstract

Ice sheet changes of the Antarctic are the result of interactions among the ocean, atmosphere, and ice sheet. Studying the ice sheet mass variations helps us to understand the possible reasons for these changes. We used 164 months of Gravity Recovery and Climate [...] Read more.

Ice sheet changes of the Antarctic are the result of interactions among the ocean, atmosphere, and ice sheet. Studying the ice sheet mass variations helps us to understand the possible reasons for these changes. We used 164 months of Gravity Recovery and Climate Experiment (GRACE) satellite time-varying solutions to study the principal components (PCs) of the Antarctic ice sheet mass change and their time-frequency variation. This assessment was based on complex principal component analysis (CPCA) and the wavelet amplitude-period spectrum (WAPS) method to study the PCs and their time-frequency information. The CPCA results revealed the PCs that affect the ice sheet balance, and the wavelet analysis exposed the time-frequency variation of the quasi-periodic signal in each component. The results show that the first PC, which has a linear term and low-frequency signals with periods greater than five years, dominates the variation trend of ice sheet in the Antarctic. The ratio of its variance to the total variance shows that the first PC explains 83.73% of the mass change in the ice sheet. Similar low-frequency signals are also found in the meridional wind at 700 hPa in the South Pacific and the sea surface temperature anomaly (SSTA) in the equatorial Pacific, with the correlation between the low-frequency periodic signal of SSTA in the equatorial Pacific and the first PC of the ice sheet mass change in Antarctica found to be 0.73. The phase signals in the mass change of West Antarctica indicate the upstream propagation of mass loss information over time from the ocean–ice interface to the southward upslope, which mainly reflects ocean-driven factors such as enhanced ice–ocean interaction and the intrusion of warm saline water into the cavities under ice shelves associated with ice sheets which sit on retrograde slopes. Meanwhile, the phase signals in the mass change of East Antarctica indicate the downstream propagation of mass increase information from the South Pole toward Dronning Maud Land, which mainly reflects atmospheric factors such as precipitation accumulation. Full article

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17 pages, 2834 KiB

Open AccessEditor’s ChoiceReview

Imaging Spectroscopy for Conservation Applications

by Megan Seeley and Gregory P. Asner

Remote Sens. 2021, 13(2), 292; https://doi.org/10.3390/rs13020292 - 15 Jan 2021

Cited by 13 | Viewed by 7258

Abstract

As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, [...] Read more.

As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers. Full article

(This article belongs to the Section Environmental Remote Sensing)

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28 pages, 14253 KiB

Open AccessEditor’s ChoiceArticle

A Comparison of Machine Learning Approaches to Improve Free Topography Data for Flood Modelling

by Michael Meadows and Matthew Wilson

Remote Sens. 2021, 13(2), 275; https://doi.org/10.3390/rs13020275 - 14 Jan 2021

Cited by 26 | Viewed by 9891

Abstract

Given the high financial and institutional cost of collecting and processing accurate topography data, many large-scale flood hazard assessments continue to rely instead on freely-available global Digital Elevation Models, despite the significant vertical biases known to affect them. To predict (and thereby reduce) [...] Read more.

Given the high financial and institutional cost of collecting and processing accurate topography data, many large-scale flood hazard assessments continue to rely instead on freely-available global Digital Elevation Models, despite the significant vertical biases known to affect them. To predict (and thereby reduce) these biases, we apply a fully-convolutional neural network (FCN), a form of artificial neural network originally developed for image segmentation which is capable of learning from multi-variate spatial patterns at different scales. We assess its potential by training such a model on a wide variety of remote-sensed input data (primarily multi-spectral imagery), using high-resolution, LiDAR-derived Digital Terrain Models published by the New Zealand government as the reference topography data. In parallel, two more widely used machine learning models are also trained, in order to provide benchmarks against which the novel FCN may be assessed. We find that the FCN outperforms the other models (reducing root mean square error in the testing dataset by 71%), likely due to its ability to learn from spatial patterns at multiple scales, rather than only a pixel-by-pixel basis. Significantly for flood hazard modelling applications, corrections were found to be especially effective along rivers and their floodplains. However, our results also suggest that models are likely to be biased towards the land cover and relief conditions most prevalent in their training data, with further work required to assess the importance of limiting training data inputs to those most representative of the intended application area(s). Full article

(This article belongs to the Section AI Remote Sensing)

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17 pages, 14347 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Remote Sensing-Based Assessment of Water Resources in the Arabian Peninsula

by Youssef Wehbe and Marouane Temimi

Remote Sens. 2021, 13(2), 247; https://doi.org/10.3390/rs13020247 - 13 Jan 2021

Cited by 34 | Viewed by 5759

Abstract

A better understanding of the spatiotemporal distribution of water resources is crucial for the sustainable development of hyper-arid regions. Here, we focus on the Arabian Peninsula (AP) and use remotely sensed data to (i) analyze the local climatology of total water storage (TWS), [...] Read more.

A better understanding of the spatiotemporal distribution of water resources is crucial for the sustainable development of hyper-arid regions. Here, we focus on the Arabian Peninsula (AP) and use remotely sensed data to (i) analyze the local climatology of total water storage (TWS), precipitation, and soil moisture; (ii) characterize their temporal variability and spatial distribution; and (iii) infer recent trends and change points within their time series. Remote sensing data for TWS, precipitation, and soil moisture are obtained from the Gravity Recovery and Climate Experiment (GRACE), the Tropical Rainfall Measuring Mission (TRMM), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), respectively. The study relies on trend analysis, the modified Mann–Kendall test, and change point detection statistics. We first derive 10-year (2002–2011) seasonal averages from each of the datasets and intercompare their spatial organization. In the absence of large-scale in situ data, we then compare trends from GRACE TWS retrievals to in situ groundwater observations locally over the subdomain of the United Arab Emirates (UAE). TWS anomalies vary between −6.2 to 3.2 cm/month and −6.8 to −0.3 cm/month during the winter and summer periods, respectively. Trend analysis shows decreasing precipitation trends (−2.3 × 10⁻⁴ mm/day) spatially aligned with decreasing soil moisture trends (−1.5 × 10⁻⁴ g/cm³/month) over the southern part of the AP, whereas the highest decreasing TWS trends (−8.6 × 10⁻² cm/month) are recorded over areas of excessive groundwater extraction in the northern AP. Interestingly, change point detection reveals increasing precipitation trends pre- and post-change point breaks over the entire AP region. Significant spatial dependencies are observed between TRMM and GRACE change points, particularly over Yemen during 2010, revealing the dominant impact of climatic changes on TWS depletion. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Water Cycle Studies: Sciences and Societal Applications)

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18 pages, 8314 KiB

Open AccessEditor’s ChoiceArticle

Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet Remote-Sensing Satellite Images

by Yulu Chen, Rongjun Qin, Guixiang Zhang and Hessah Albanwan

Remote Sens. 2021, 13(2), 208; https://doi.org/10.3390/rs13020208 - 8 Jan 2021

Cited by 52 | Viewed by 7192

Abstract

The spread of the COVID-19 since the end of 2019 has reached an epidemic level and has quickly become a global public health crisis. During this period, the responses for COVID-19 were highly diverse and decentralized across countries and regions. Understanding the dynamics [...] Read more.

The spread of the COVID-19 since the end of 2019 has reached an epidemic level and has quickly become a global public health crisis. During this period, the responses for COVID-19 were highly diverse and decentralized across countries and regions. Understanding the dynamics of human mobility change at high spatial temporal resolution is critical for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders, regional lockdowns and travel restrictions) during the pandemic. However, this requires collecting traffic data at scale, which is time-consuming, cost-prohibitive and often not available (e.g., in underdeveloped countries). Therefore, spatiotemporal analysis through processing periodical remote-sensing images is very beneficial to enable efficient monitoring at the global scale. In this paper, we present a novel study that utilizes high temporal Planet multispectral images (from November 2019 to September 2020, on average 7.1 days of frequency) to detect traffic density in multiple cities through a proposed morphology-based vehicle detection method and evaluate how the traffic data collected in such a manner reflect mobility pattern changes in response to COVID-19. Our experiments at city-scale detection, demonstrate that our proposed vehicle detection method over this 3 m resolution data is able to achieve a detection level at an accuracy of 68.26% in most of the images, and the observations’ trends coincide with existing public data of where available (lockdown duration, traffic volume, etc.), further suggesting that such high temporal Planet data with global coverage (although not with the best resolution), with well-devised detection algorithms, can sufficiently provide traffic details for trend analysis to better facilitate informed decision making for extreme events at the global level. Full article

(This article belongs to the Special Issue Recent Advances in Land Cover Classification and Change Detection in 2D and 3D)

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8 pages, 3128 KiB

Open AccessEditor’s ChoiceTechnical Note

Assessing the Behavioural Responses of Small Cetaceans to Unmanned Aerial Vehicles

by Joana Castro, Francisco O. Borges, André Cid, Marina I. Laborde, Rui Rosa and Heidi C. Pearson

Remote Sens. 2021, 13(1), 156; https://doi.org/10.3390/rs13010156 - 5 Jan 2021

Cited by 25 | Viewed by 9347

Abstract

Unmanned Aerial Vehicles (UAVs), or drones, have recently emerged as a relatively affordable and accessible method for studying wildlife. Vertical Take-off and Landing (VTOL) UAVs are appropriate for morphometric, behavioural, abundance and demographic studies of marine mammals, providing a stable, nonintrusive and highly [...] Read more.

Unmanned Aerial Vehicles (UAVs), or drones, have recently emerged as a relatively affordable and accessible method for studying wildlife. Vertical Take-off and Landing (VTOL) UAVs are appropriate for morphometric, behavioural, abundance and demographic studies of marine mammals, providing a stable, nonintrusive and highly manoeuvrable platform. Previous studies using VTOL UAVs have been conducted on various marine mammal species, but specific studies regarding behavioural responses to these devices are limited and scarce. The aim of this study was to evaluate the immediate behavioural responses of common (Delphinus delphis) and bottlenose (Tursiops truncatus) dolphins to a VTOL UAV flown at different altitudes. A multirotor (quadcopter) UAV with an attached GoPro camera was used. Once a dolphin group was located, the UAV was flown at a starting height of 50 m directly above the group, subsequently descending 5 m every 30 s until reaching 5 m. We assessed three behavioural responses to a VTOL UAV at different heights: (i) direction changes, (ii) swimming speed and (iii) diving. Responses by D. delphis (n = 15) and T. truncatus (n = 10) groups were analysed separately. There were no significant responses of T. truncatus to any of the studied variables. For D. delphis, however, there were statistically significant changes in direction when the UAV was flown at a height of 5 m. Our results indicate that UAVs do not induce immediate behavioural responses in common or bottlenose dolphins when flown at heights > 5 m, demonstrating that the use of VTOL UAVs to study dolphins has minimal impact on the animals. However, we advise the use of the precautionary principle when interpreting these results as characteristics of this study site (e.g., high whale-watching activity) may have habituated dolphins to anthropogenic disturbance. Full article

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31 pages, 149067 KiB

Open AccessEditor’s ChoiceArticle

Deep Learning Based Thin Cloud Removal Fusing Vegetation Red Edge and Short Wave Infrared Spectral Information for Sentinel-2A Imagery

by Jun Li, Zhaocong Wu, Zhongwen Hu, Zilong Li, Yisong Wang and Matthieu Molinier

Remote Sens. 2021, 13(1), 157; https://doi.org/10.3390/rs13010157 - 5 Jan 2021

Cited by 35 | Viewed by 7530

Abstract

Thin clouds seriously affect the availability of optical remote sensing images, especially in visible bands. Short-wave infrared (SWIR) bands are less influenced by thin clouds, but usually have lower spatial resolution than visible (Vis) bands in high spatial resolution remote sensing images (e.g., [...] Read more.

Thin clouds seriously affect the availability of optical remote sensing images, especially in visible bands. Short-wave infrared (SWIR) bands are less influenced by thin clouds, but usually have lower spatial resolution than visible (Vis) bands in high spatial resolution remote sensing images (e.g., in Sentinel-2A/B, CBERS04, ZY-1 02D and HJ-1B satellites). Most cloud removal methods do not take advantage of the spectral information available in SWIR bands, which are less affected by clouds, to restore the background information tainted by thin clouds in Vis bands. In this paper, we propose CR-MSS, a novel deep learning-based thin cloud removal method that takes the SWIR and vegetation red edge (VRE) bands as inputs in addition to visible/near infrared (Vis/NIR) bands, in order to improve cloud removal in Sentinel-2 visible bands. Contrary to some traditional and deep learning-based cloud removal methods, which use manually designed rescaling algorithm to handle bands at different resolutions, CR-MSS uses convolutional layers to automatically process bands at different resolution. CR-MSS has two input/output branches that are designed to process Vis/NIR and VRE/SWIR, respectively. Firstly, Vis/NIR cloudy bands are down-sampled by a convolutional layer to low spatial resolution features, which are then concatenated with the corresponding features extracted from VRE/SWIR bands. Secondly, the concatenated features are put into a fusion tunnel to down-sample and fuse the spectral information from Vis/NIR and VRE/SWIR bands. Third, a decomposition tunnel is designed to up-sample and decompose the fused features. Finally, a transpose convolutional layer is used to up-sample the feature maps to the resolution of input Vis/NIR bands. CR-MSS was trained on 28 real Sentinel-2A image pairs over the globe, and tested separately on eight real cloud image pairs and eight simulated cloud image pairs. The average SSIM values (Structural Similarity Index Measurement) for CR-MSS results on Vis/NIR bands over all testing images were 0.69, 0.71, 0.77, and 0.81, respectively, which was on average 1.74% higher than the best baseline method. The visual results on real Sentinel-2 images demonstrate that CR-MSS can produce more realistic cloud and cloud shadow removal results than baseline methods. Full article

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20 pages, 22381 KiB

Open AccessEditor’s ChoiceArticle

InSAR Multitemporal Data over Persistent Scatterers to Detect Floodwater in Urban Areas: A Case Study in Beletweyne, Somalia

by Luca Pulvirenti, Marco Chini and Nazzareno Pierdicca

Remote Sens. 2021, 13(1), 37; https://doi.org/10.3390/rs13010037 - 24 Dec 2020

Cited by 16 | Viewed by 3517

Abstract

A stack of Sentinel-1 InSAR data in an urban area where flood events recurrently occur, namely Beletweyne town in Somalia, has been analyzed. From this analysis, a novel method to deal with the problem of flood mapping in urban areas has been derived. [...] Read more.

A stack of Sentinel-1 InSAR data in an urban area where flood events recurrently occur, namely Beletweyne town in Somalia, has been analyzed. From this analysis, a novel method to deal with the problem of flood mapping in urban areas has been derived. The approach assumes the availability of a map of persistent scatterers (PSs) inside the urban settlement and is based on the analysis of the temporal trend of the InSAR coherence and the spatial average of the exponential of the InSAR phase in each PS. Both interferometric products are expected to have high and stable values in the PSs; therefore, anomalous decreases may indicate that floodwater is present in an urban area. The stack of Sentinel-1 data has been divided into two subsets. The first one has been used as a calibration set to identify the PSs and determine, for each PS, reference values of the coherence and the spatial average of the exponential of the interferometric phase under standard non-flooded conditions. The other subset has been used for validation purposes. Flood maps produced by UNOSAT, analyzing very-high-resolution optical images of the floods that occurred in Beletweyne in April–May 2018, October–November 2019, and April–May 2020, have been used as reference data. In particular, the map of the April–May 2018 flood has been used for training purposes together with the subset of Sentinel-1 calibration data, whilst the other two maps have been used to validate the products generated by applying the proposed method. The main product is a binary map of flooded PSs that complements the floodwater map of rural/suburban areas produced by applying a well-consolidated algorithm based on intensity data. In addition, a flood severity map that labels the different districts of Beletweyne, as not, partially, or totally flooded has been generated to consolidate the validation. The results have confirmed the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Flood Mapping in Urban and Vegetated Areas)

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16 pages, 5089 KiB

Open AccessEditor’s ChoiceArticle

A First Assessment of the 2018 European Drought Impact on Ecosystem Evapotranspiration

by Kazi Rifat Ahmed, Eugénie Paul-Limoges, Uwe Rascher and Alexander Damm

Remote Sens. 2021, 13(1), 16; https://doi.org/10.3390/rs13010016 - 22 Dec 2020

Cited by 17 | Viewed by 4417

Abstract

The combined heatwave and drought in 2018 notably affected the state and functioning of European ecosystems. The severity and distribution of this extreme event across ecosystem types and its possible implication on ecosystem water fluxes are still poorly understood. This study estimates spatio-temporal [...] Read more.

The combined heatwave and drought in 2018 notably affected the state and functioning of European ecosystems. The severity and distribution of this extreme event across ecosystem types and its possible implication on ecosystem water fluxes are still poorly understood. This study estimates spatio-temporal changes in evapotranspiration (ET) during the 2018 drought and heatwave and assesses how these changes are distributed in European ecosystems along climatic gradients. We used the ET eight-day composite product from the MODerate Resolution Imaging Spectroradiometer (MODIS) together with meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF ERA5). Our results indicate that ecosystem ET was strongly reduced (up to −50% compared to a 10-year reference period) in areas with extreme anomalies in surface air temperature (T_sa) and precipitation (P) in central, northern, eastern, and western Europe. Northern and Eastern Europe had prolonged anomalies of up to seven months with extreme intensities (relative and absolute) of T_sa, P, and ET. Particularly, agricultural areas, mixed natural vegetation, and non-irrigated agricultural areas were the most affected by the increased temperatures in northern Europe. Our results show contrasting drought impacts on ecosystem ET between the North and South of Europe as well as on ecosystem types. Full article

(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET) II)

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18 pages, 9298 KiB

Open AccessEditor’s ChoiceArticle

H-YOLO: A Single-Shot Ship Detection Approach Based on Region of Interest Preselected Network

by Gang Tang, Shibo Liu, Iwao Fujino, Christophe Claramunt, Yide Wang and Shaoyang Men

Remote Sens. 2020, 12(24), 4192; https://doi.org/10.3390/rs12244192 - 21 Dec 2020

Cited by 31 | Viewed by 6854

Abstract

Ship detection from high-resolution optical satellite images is still an important task that deserves optimal solutions. This paper introduces a novel high-resolution image network-based approach based on the preselection of a region of interest (RoI). This pre-selected network first identifies and extracts a [...] Read more.

Ship detection from high-resolution optical satellite images is still an important task that deserves optimal solutions. This paper introduces a novel high-resolution image network-based approach based on the preselection of a region of interest (RoI). This pre-selected network first identifies and extracts a region of interest from input images. In order to efficiently match ship candidates, the principle of our approach is to distinguish suspected areas from the images based on hue, saturation, value (HSV) differences between ships and the background. The whole approach is the basis of an experiment with a large ship dataset, consisting of Google Earth images and HRSC2016 datasets. The experiment shows that the H-YOLO network, which uses the same weight training from a set of remote sensing images, has a 19.01% higher recognition rate and a 16.19% higher accuracy than applying the you only look once (YOLO) network alone. After image preprocessing, the value of the intersection over union (IoU) is also greatly improved. Full article

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17 pages, 4009 KiB

Open AccessEditor’s ChoiceArticle

Design and Development of a Smart Variable Rate Sprayer Using Deep Learning

by Nazar Hussain, Aitazaz A. Farooque, Arnold W. Schumann, Andrew McKenzie-Gopsill, Travis Esau, Farhat Abbas, Bishnu Acharya and Qamar Zaman

Remote Sens. 2020, 12(24), 4091; https://doi.org/10.3390/rs12244091 - 15 Dec 2020

Cited by 57 | Viewed by 7941

Abstract

The uniform application (UA) of agrochemicals results in the over-application of harmful chemicals, increases crop input costs, and deteriorates the environment when compared with variable rate application (VA). A smart variable rate sprayer (SVRS) was designed, developed, and tested using deep learning (DL) [...] Read more.

The uniform application (UA) of agrochemicals results in the over-application of harmful chemicals, increases crop input costs, and deteriorates the environment when compared with variable rate application (VA). A smart variable rate sprayer (SVRS) was designed, developed, and tested using deep learning (DL) for VA application of agrochemicals. Real-time testing of the SVRS took place for detecting and spraying and/or skipping lambsquarters weed and early blight infected and healthy potato plants. About 24,000 images were collected from potato fields in Prince Edward Island and New Brunswick under varying sunny, cloudy, and partly cloudy conditions and processed/trained using YOLOv3 and tiny-YOLOv3 models. Due to faster performance, the tiny-YOLOv3 was chosen to deploy in SVRS. A laboratory experiment was designed under factorial arrangements, where the two spraying techniques (UA and VA) and the three weather conditions (cloudy, partly cloudy, and sunny) were the two independent variables with spray volume consumption as a response variable. The experimental treatments had six repetitions in a 2 × 3 factorial design. Results of the two-way ANOVA showed a significant effect of spraying application techniques on volume consumption of spraying liquid (p-value < 0.05). There was no significant effect of weather conditions and interactions between the two independent variables on volume consumption during weeds and simulated diseased plant detection experiments (p-value > 0.05). The SVRS was able to save 42 and 43% spraying liquid during weeds and simulated diseased plant detection experiments, respectively. Water sensitive papers’ analysis showed the applicability of SVRS for VA with >40% savings of spraying liquid by SVRS when compared with UA. Field applications of this technique would reduce the crop input costs and the environmental risks in conditions (weed and disease) like experimental testing. Full article

(This article belongs to the Special Issue Proximal/Remote Sensing Coupled with Chemometrics in Vegetation and Soil Sciences)

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16 pages, 11851 KiB

Open AccessEditor’s ChoiceArticle

Derivation of Shortwave Radiometric Adjustments for SNPP and NOAA-20 VIIRS for the NASA MODIS-VIIRS Continuity Cloud Products

by Kerry Meyer, Steven Platnick, Robert Holz, Steve Dutcher, Greg Quinn and Fred Nagle

Remote Sens. 2020, 12(24), 4096; https://doi.org/10.3390/rs12244096 - 15 Dec 2020

Cited by 19 | Viewed by 3316

Abstract

Climate studies, including trend detection and other time series analyses, necessarily require stable, well-characterized and long-term data records. For satellite-based geophysical retrieval datasets, such data records often involve merging the observational records of multiple similar, though not identical, instruments. The National Aeronautics and [...] Read more.

Climate studies, including trend detection and other time series analyses, necessarily require stable, well-characterized and long-term data records. For satellite-based geophysical retrieval datasets, such data records often involve merging the observational records of multiple similar, though not identical, instruments. The National Aeronautics and Space Administration (NASA) cloud mask (CLDMSK) and cloud-top and optical properties (CLDPROP) products are designed to bridge the observational records of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard NASA’s Aqua satellite and the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the joint NASA/National Oceanic and Atmospheric Administration (NOAA) Suomi National Polar-orbiting Partnership (SNPP) satellite and NOAA’s new generation of operational polar-orbiting weather platforms (NOAA-20+). Early implementations of the CLDPROP algorithms on Aqua MODIS and SNPP VIIRS suffered from large intersensor biases in cloud optical properties that were traced back to relative radiometric inconsistency in analogous shortwave channels on both imagers, with VIIRS generally observing brighter top-of-atmosphere spectral reflectance than MODIS (e.g., up to 5% brighter in the 0.67 µm channel). Radiometric adjustment factors for the SNPP and NOAA-20 VIIRS shortwave channels used in the cloud optical property retrievals are derived from an extensive analysis of the overlapping observational records with Aqua MODIS, specifically for homogenous maritime liquid water cloud scenes for which the viewing/solar geometry of MODIS and VIIRS match. Application of these adjustment factors to the VIIRS L1B prior to ingestion into the CLDMSK and CLDPROP algorithms yields improved intersensor agreement, particularly for cloud optical properties. Full article

(This article belongs to the Special Issue Analysis of Decadal-Scale Continuous Data Products from Weather Satellite Platforms)

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17 pages, 2993 KiB

Open AccessEditor’s ChoiceArticle

Analysis of Drought Impact on Croplands from Global to Regional Scale: A Remote Sensing Approach

by Gohar Ghazaryan, Simon König, Ehsan Eyshi Rezaei, Stefan Siebert and Olena Dubovyk

Remote Sens. 2020, 12(24), 4030; https://doi.org/10.3390/rs12244030 - 9 Dec 2020

Cited by 18 | Viewed by 5726

Abstract

Drought is one of the extreme climatic events that has a severe impact on crop production and food supply. Our main goal is to test the suitability of remote sensing-based indices to detect drought impacts on crop production from a global to regional [...] Read more.

Drought is one of the extreme climatic events that has a severe impact on crop production and food supply. Our main goal is to test the suitability of remote sensing-based indices to detect drought impacts on crop production from a global to regional scale. Moderate resolution imaging spectroradiometer (MODIS) based imagery, spanning from 2001 to 2017 was used for this task. This includes the normalized difference vegetation index (NDVI), land surface temperature (LST), and the evaporative stress index (ESI), which is based on the ratio of actual to potential evapotranspiration. These indices were used as indicators of drought-induced vegetation conditions for three main crops: maize, wheat, and soybean. The start and end of the growing season, as observed at 500 m resolution, were used to exclude the time steps that are outside of the growing season. Based on the three indicators, monthly standardized anomalies were estimated, which were used for both analyses of spatiotemporal patterns of drought and the relationship with yield anomalies. Anomalies in the ESI had higher correlations with maize and wheat yield anomalies than other indices, indicating that prolonged periods of low ESI during the growing season are highly correlated with reduced crop yields. All indices could identify past drought events, such as the drought in the USA in 2012, Eastern Africa in 2016–2017, and South Africa in 2015–2016. The results of this study highlight the potential of the use of moderate resolution remote sensing-based indicators combined with phenometrics for drought-induced crop impact monitoring. For several regions, droughts identified using the ESI and LST were more intense than the NDVI-based results. We showed that these indices are relevant for agricultural drought monitoring at both global and regional scales. They can be integrated into drought early warning systems, process-based crop models, as well as can be used for risk assessment and included in advanced decision-support frameworks. Full article

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

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28 pages, 16858 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Potential Replacement of Laurel Forest by a Novel Ecosystem in the Steep Terrain of an Oceanic Island

by Ram Sharan Devkota, Richard Field, Samuel Hoffmann, Anna Walentowitz, Félix Manuel Medina, Ole Reidar Vetaas, Alessandro Chiarucci, Frank Weiser, Anke Jentsch and Carl Beierkuhnlein

Remote Sens. 2020, 12(24), 4013; https://doi.org/10.3390/rs12244013 - 8 Dec 2020

Cited by 6 | Viewed by 5657

Abstract

Biological invasions are a major global threat to biodiversity and often affect ecosystem services negatively. They are particularly problematic on oceanic islands where there are many narrow-ranged endemic species, and the biota may be very susceptible to invasion. Quantifying and mapping invasion processes [...] Read more.

Biological invasions are a major global threat to biodiversity and often affect ecosystem services negatively. They are particularly problematic on oceanic islands where there are many narrow-ranged endemic species, and the biota may be very susceptible to invasion. Quantifying and mapping invasion processes are important steps for management and control but are challenging with the limited resources typically available and particularly difficult to implement on oceanic islands with very steep terrain. Remote sensing may provide an excellent solution in circumstances where the invading species can be reliably detected from imagery. We here develop a method to map the distribution of the alien chestnut (Castanea sativa Mill.) on the island of La Palma (Canary Islands, Spain), using freely available satellite images. On La Palma, the chestnut invasion threatens the iconic laurel forest, which has survived since the Tertiary period in the favourable climatic conditions of mountainous islands in the trade wind zone. We detect chestnut presence by taking advantage of the distinctive phenology of this alien tree, which retains its deciduousness while the native vegetation is evergreen. Using both Landsat 8 and Sentinel-2 (parallel analyses), we obtained images in two seasons (chestnuts leafless and in-leaf, respectively) and performed image regression to detect pixels changing from leafless to in-leaf chestnuts. We then applied supervised classification using Random Forest to map the present-day occurrence of the chestnut. Finally, we performed species distribution modelling to map the habitat suitability for chestnut on La Palma, to estimate which areas are prone to further invasion. Our results indicate that chestnuts occupy 1.2% of the total area of natural ecosystems on La Palma, with a further 12–17% representing suitable habitat that is not yet occupied. This enables targeted control measures with potential to successfully manage the invasion, given the relatively long generation time of the chestnut. Our method also enables research on the spread of the species since the earliest Landsat images. Full article

(This article belongs to the Special Issue Earth Observations for Biodiversity and Ecosystems of Mediterranean-Type Climate Regions)

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28 pages, 6397 KiB

Open AccessEditor’s ChoiceArticle

Accuracy Assessment of GEDI Terrain Elevation and Canopy Height Estimates in European Temperate Forests: Influence of Environmental and Acquisition Parameters

by Markus Adam, Mikhail Urbazaev, Clémence Dubois and Christiane Schmullius

Remote Sens. 2020, 12(23), 3948; https://doi.org/10.3390/rs12233948 - 2 Dec 2020

Cited by 117 | Viewed by 9249

Abstract

Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver [...] Read more.

Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver these height estimates on a near global scale. This paper analyzes the accuracy of the first version of GEDI ground elevation and canopy height estimates in two study areas with temperate forests in the Free State of Thuringia, central Germany. Digital terrain and canopy height models derived from airborne laser scanning data are used as reference heights. The influence of various environmental and acquisition parameters (e.g., canopy cover, terrain slope, beam type) on GEDI height metrics is assessed. The results show a consistently high accuracy of GEDI ground elevation estimates under most conditions, except for areas with steep slopes. GEDI canopy height estimates are less accurate and show a bigger influence of some of the included parameters, specifically slope, vegetation height, and beam sensitivity. A number of relatively high outliers (around 9–13% of the measurements) is present in both ground elevation and canopy height estimates, reducing the estimation precision. Still, it can be concluded that GEDI height metrics show promising results and have potential to be used as a basis for further investigations. Full article

(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)

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31 pages, 12046 KiB

Open AccessEditor’s ChoiceArticle

Optimizing Near Real-Time Detection of Deforestation on Tropical Rainforests Using Sentinel-1 Data

by Juan Doblas, Yosio Shimabukuro, Sidnei Sant’Anna, Arian Carneiro, Luiz Aragão and Claudio Almeida

Remote Sens. 2020, 12(23), 3922; https://doi.org/10.3390/rs12233922 - 30 Nov 2020

Cited by 46 | Viewed by 10893

Abstract

Early Warning Systems (EWS) for near real-time detection of deforestation are a fundamental component of public policies focusing on the reduction in forest biomass loss and associated CO₂ emissions. Most of the operational EWS are based on optical data, which are severely [...] Read more.

Early Warning Systems (EWS) for near real-time detection of deforestation are a fundamental component of public policies focusing on the reduction in forest biomass loss and associated CO₂ emissions. Most of the operational EWS are based on optical data, which are severely limited by the cloud cover in tropical environments. Synthetic Aperture Radar (SAR) data can help to overcome this observational gap. SAR measurements, however, can be altered by atmospheric effects on and variations in surface moisture. Different techniques of time series (TS) stabilization have been used to mitigate the instability of C-band SAR measurements. Here, we evaluate the performance of two different approaches to SAR TS stabilization, harmonic deseasonalization and spatial stabilization, as well as two deforestation detection techniques, Adaptive Linear Thresholding (ALT) and maximum likelihood classification (MLC). We set up a rigorous, Amazon-wide validation experiment using the Google Earth Engine platform to sample and process Sentinel-1A data of nearly 6000 locations in the whole Brazilian Amazonian basin, generating more than 8M processed samples. Half of those locations correspond to non-degraded forest areas, while the other half pertained to 2019 deforested areas. The detection results showed that the spatial stabilization algorithm improved the results of the MLC approach, reaching 94.36% global accuracy. The ALT detection algorithm performed better, reaching 95.91% global accuracy, regardless of the use of any stabilization method. The results of this experiment are being used to develop an operational EWS in the Brazilian Amazon. Full article

(This article belongs to the Section Environmental Remote Sensing)

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23 pages, 7459 KiB

Open AccessEditor’s ChoiceArticle

Novel Techniques for Void Filling in Glacier Elevation Change Data Sets

by Thorsten Seehaus, Veniamin I. Morgenshtern, Fabian Hübner, Eberhard Bänsch and Matthias H. Braun

Remote Sens. 2020, 12(23), 3917; https://doi.org/10.3390/rs12233917 - 29 Nov 2020

Cited by 8 | Viewed by 4557

Abstract

The increasing availability of digital elevation models (DEMs) facilitates the monitoring of glacier mass balances on local and regional scales. Geodetic glacier mass balances are obtained by differentiating DEMs. However, these computations are usually affected by voids in the derived elevation change data [...] Read more.

The increasing availability of digital elevation models (DEMs) facilitates the monitoring of glacier mass balances on local and regional scales. Geodetic glacier mass balances are obtained by differentiating DEMs. However, these computations are usually affected by voids in the derived elevation change data sets. Different approaches, using spatial statistics or interpolation techniques, were developed to account for these voids in glacier mass balance estimations. In this study, we apply novel void filling techniques, which are typically used for the reconstruction and retouche of images and photos, for the first time on elevation change maps. We selected 6210 km² of glacier area in southeast Alaska, USA, covered by two void-free DEMs as the study site to test different inpainting methods. Different artificially voided setups were generated using manually defined voids and a correlation mask based on stereoscopic processing of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) acquisition. Three “novel” (Telea, Navier–Stokes and shearlet) as well as three “classical” (bilinear interpolation, local and global hypsometric methods) void filling approaches for glacier elevation data sets were implemented and evaluated. The hypsometric approaches showed, in general, the worst performance, leading to high average and local offsets. Telea and Navier–Stokes void filling showed an overall stable and reasonable quality. The best results are obtained for shearlet and bilinear void filling, if certain criteria are met. Considering also computational costs and feasibility, we recommend using the bilinear void filling method in glacier volume change analyses. Moreover, we propose and validate a formula to estimate the uncertainties caused by void filling in glacier volume change computations. The formula is transferable to other study sites, where no ground truth data on the void areas exist, and leads to higher accuracy of the error estimates on void-filled areas. In the spirit of reproducible research, we publish a software repository with the implementation of the novel void filling algorithms and the code reproducing the statistical analysis of the data, along with the data sets themselves. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Glaciology)

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26 pages, 7267 KiB

Open AccessEditor’s ChoiceArticle

Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments

by Wei Chen, Yunzhi Chen, Paraskevas Tsangaratos, Ioanna Ilia and Xiaojing Wang

Remote Sens. 2020, 12(23), 3854; https://doi.org/10.3390/rs12233854 - 25 Nov 2020

Cited by 71 | Viewed by 5904

Abstract

The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algorithms (GA) are used as a feature selection method, whereas the [...] Read more.

The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algorithms (GA) are used as a feature selection method, whereas the particle swarm optimization (PSO) method is used to optimize the structural parameters of two ML models, support vector machines (SVM) and artificial neural network (ANN). A well-defined spatial database, which included 335 landslides and twelve landslide-related variables (elevation, slope angle, slope aspect, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, distance to faults, distance to river, lithology, and hydrological cover) are considered for the analysis, in the Achaia Regional Unit located in Northern Peloponnese, Greece. The outcome of the study illustrates that both ML models have an excellent performance, with the SVM model achieving the highest learning accuracy (0.977 area under the receiver operating characteristic curve value (AUC)), followed by the ANN model (0.969). However, the ANN model shows the highest prediction accuracy (0.800 AUC), followed by the SVM (0.750 AUC) model. Overall, the proposed ML models highlights the necessity of feature selection and tuning procedures via evolutionary optimization algorithms and that such approaches could be successfully used for landslide susceptibility mapping as an alternative investigation tool. Full article

(This article belongs to the Special Issue Spatial Modelling of Natural Hazards and Water Resources through Remote Sensing, GIS and Machine Learning Methods)

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