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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 66178

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Special Issue Editor

Prof. Dr. Hanna Meyer

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Guest Editor

Institute of Landscape Ecology, University of Münster, 48149 Münster, Germany
Interests: remote sensing; machine learning; environmental monitoring

Special Issue Information

Dear Colleagues,

Today, environmental monitoring is becoming an increasingly important issue when considering climate and land cover change and its consequences for the environment. Current earth observation satellites provide information with advanced spatial and temporal details that increases the potential of remote sensing to reveal spatial and temporal patterns and trends. In this context, machine learning algorithms have shown to be a powerful method to link remote sensing information to relevant environmental variables by accounting for the complexity and nonlinearity found in nature. The combination of remote sensing data and machine learning methods hence offers great but not yet fully exploited possibilities to monitor environmental variables across disciplines (e.g., biodiversity research, agriculture, forestry, and climatology) and on different temporal and spatial scales. However, recent studies also indicate ongoing challenges when machine learning methods are applied to remote sensing data. Spatial and temporal dependencies in the data, for example, challenge the application of machine learning algorithms and call for new modelling strategies that take the characteristics of remote sensing data into account.

This Special Issue aims to advance the application of machine learning algorithms for remote sensing-based environmental monitoring. We welcome methodological contributions in terms of novel machine learning strategies and innovative developments towards the reliability and robustness of the results. We further welcome applied contributions that demonstrate the potential and the challenges of machine learning applied to remote sensing in the context of environmental monitoring.

We are looking forward to an interesting collection of contributions!

Prof. Dr. Hanna Meyer
Guest Editor

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Keywords

Earth observation
Ecosystem dynamics
Environmental change
Machine learning strategies
Predictive modelling
Satellite imagery
Time series

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Published Papers (8 papers)

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24 pages, 3136 KiB

Open AccessArticle

Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro

by Alice Ziegler, Hanna Meyer, Insa Otte, Marcell K. Peters, Tim Appelhans, Christina Behler, Katrin Böhning-Gaese, Alice Classen, Florian Detsch, Jürgen Deckert, Connal D. Eardley, Stefan W. Ferger, Markus Fischer, Friederike Gebert, Michael Haas, Maria Helbig-Bonitz, Andreas Hemp, Claudia Hemp, Victor Kakengi, Antonia V. Mayr, Christine Ngereza, Christoph Reudenbach, Juliane Röder, Gemma Rutten, David Schellenberger Costa, Matthias Schleuning, Axel Ssymank, Ingolf Steffan-Dewenter, Joseph Tardanico, Marco Tschapka, Maximilian G. R. Vollstädt, Stephan Wöllauer, Jie Zhang, Roland Brandl and Thomas Nauss Show full author list Hide full author list

Remote Sens. 2022, 14(3), 786; https://doi.org/10.3390/rs14030786 - 8 Feb 2022

Cited by 2 | Viewed by 4302

Abstract

The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results. Full article

(This article belongs to the Special Issue Machine Learning Methods for Environmental Monitoring)

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30 pages, 2190 KiB

Open AccessArticle

Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques?

by Patrick Schratz, Jannes Muenchow, Eugenia Iturritxa, José Cortés, Bernd Bischl and Alexander Brenning

Remote Sens. 2021, 13(23), 4832; https://doi.org/10.3390/rs13234832 - 28 Nov 2021

Cited by 8 | Viewed by 4013

Abstract

This study analyzed highly correlated, feature-rich datasets from hyperspectral remote sensing data using multiple statistical and machine-learning methods. The effect of filter-based feature selection methods on predictive performance was compared. In addition, the effect of multiple expert-based and data-driven feature sets, derived from the reflectance data, was investigated. Defoliation of trees (%), derived from in situ measurements from fall 2016, was modeled as a function of reflectance. Variable importance was assessed using permutation-based feature importance. Overall, the support vector machine (SVM) outperformed other algorithms, such as random forest (RF), extreme gradient boosting (XGBoost), and lasso (L1) and ridge (L2) regressions by at least three percentage points. The combination of certain feature sets showed small increases in predictive performance, while no substantial differences between individual feature sets were observed. For some combinations of learners and feature sets, filter methods achieved better predictive performances than using no feature selection. Ensemble filters did not have a substantial impact on performance. The most important features were located around the red edge. Additional features in the near-infrared region (800–1000 nm) were also essential to achieve the overall best performances. Filter methods have the potential to be helpful in high-dimensional situations and are able to improve the interpretation of feature effects in fitted models, which is an essential constraint in environmental modeling studies. Nevertheless, more training data and replication in similar benchmarking studies are needed to be able to generalize the results. Full article

(This article belongs to the Special Issue Machine Learning Methods for Environmental Monitoring)

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21 pages, 14903 KiB

Open AccessArticle

Learning to Identify Illegal Landfills through Scene Classification in Aerial Images

by Rocio Nahime Torres and Piero Fraternali

Remote Sens. 2021, 13(22), 4520; https://doi.org/10.3390/rs13224520 - 10 Nov 2021

Cited by 41 | Viewed by 5290

Abstract

Illegal landfills are uncontrolled disposals of waste that cause severe environmental and health risk. Discovering them as early as possible is of prominent importance for preventing hazards, such as fire pollution and leakage. Before the digital era, the only means to detect illegal waste dumps was the on site inspection of potentially suspicious sites, a procedure extremely costly and impossible to scale to a vast territory. With the advent of Earth observation technology, scanning the territory via aerial images has become possible. However, manual image interpretation remains a complex and time-consuming task that requires expert skill. Photo interpretation can be partially automated by embedding the expert knowledge within a data driven classifier trained with samples provided by human annotators. In this paper, the detection of illegal landfills is formulated as a multi-scale scene classification problem. Scene elements positioning and spatial relations constitute hints of the presence of illegal waste dumps. A dataset of ≈3000 images (20 cm resolution per pixel) was created with the help of expert photo interpreters. A combination of ResNet50 and Feature Pyramid Network (FPN) elements accounting for different object scales achieves 88% precision with an 87% of recall in a test area. The results proved the feasibility of applying convolutional neural networks for scene classification in this scenario to optimize the process of waste dumps detection. Full article

(This article belongs to the Special Issue Machine Learning Methods for Environmental Monitoring)

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25 pages, 91711 KiB

Open AccessArticle

Coral Reef Mapping with Remote Sensing and Machine Learning: A Nurture and Nature Analysis in Marine Protected Areas

by Camila Brasil Louro da Silveira, Gil Marcelo Reuss Strenzel, Mauro Maida, Ana Lídia Bertoldi Gaspar and Beatrice Padovani Ferreira

Remote Sens. 2021, 13(15), 2907; https://doi.org/10.3390/rs13152907 - 24 Jul 2021

Cited by 27 | Viewed by 8634

Abstract

Mapping habitats is essential to assist strategic decisions regarding the use and protection of coral reefs. Coupled with machine learning (ML) algorithms, remote sensing has allowed detailed mapping of reefs at meaningful scales. Here we integrated WorldView-3 and Landsat-8 imagery and ML techniques to produce a map of suitable habitats for the occurrence of a model species, the hydrocoral Millepora alcicornis, in coral reefs located inside marine protected areas in Northeast Brazil. Conservation and management efforts in the region were also analyzed, integrating human use layers to the ecological seascape. Three ML techniques were applied: two to derive base layers, namely geographically weighted regressions for bathymetry and support vector machine classifier (SVM) for habitat mapping, and one to build the species distribution model (MaxEnt) for Millepora alcicornis, a conspicuous and important reef-building species in the area. Additionally, human use was mapped based on the presence of tourists and fishers. SVM yielded 15 benthic classes (e.g., seagrass, sand, coral), with an overall accuracy of 79%. Bathymetry and its derivative layers depicted the topographical complexity of the area. The Millepora alcicornis distribution model identified distance from the shore and depth as topographical factors limiting the settling and growth of coral colonies. The most important variables were ecological, showing the importance of maintaining high biodiversity in the ecosystem. The comparison of the habitat suitability model with species absence and human use maps indicated the impact of direct human activities as potential inhibitors of coral development. Results reinforce the importance of the establishment of no-take zones and other protective measures for maintaining local biodiversity. Full article

(This article belongs to the Special Issue Machine Learning Methods for Environmental Monitoring)

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18 pages, 7841 KiB

Open AccessArticle

Large-Scale River Mapping Using Contrastive Learning and Multi-Source Satellite Imagery

by Zhihao Wei, Kebin Jia, Pengyu Liu, Xiaowei Jia, Yiqun Xie and Zhe Jiang

Remote Sens. 2021, 13(15), 2893; https://doi.org/10.3390/rs13152893 - 23 Jul 2021

Cited by 14 | Viewed by 4158

Abstract

River system is critical for the future sustainability of our planet but is always under the pressure of food, water and energy demands. Recent advances in machine learning bring a great potential for automatic river mapping using satellite imagery. Surface river mapping can provide accurate and timely water extent information that is highly valuable for solid policy and management decisions. However, accurate large-scale river mapping remains challenging given limited labels, spatial heterogeneity and noise in satellite imagery (e.g., clouds and aerosols). In this paper, we propose a new multi-source data-driven method for large-scale river mapping by combining multi-spectral imagery and synthetic aperture radar data. In particular, we build a multi-source data segmentation model, which uses contrastive learning to extract the common information between multiple data sources while also preserving distinct knowledge from each data source. Moreover, we create the first large-scale multi-source river imagery dataset based on Sentinel-1 and Sentinel-2 satellite data, along with 1013 handmade accurate river segmentation mask (which will be released to the public). In this dataset, our method has been shown to produce superior performance (F1-score is 91.53%) over multiple state-of-the-art segmentation algorithms. We also demonstrate the effectiveness of the proposed contrastive learning model in mapping river extent when we have limited and noisy data. Full article

(This article belongs to the Special Issue Machine Learning Methods for Environmental Monitoring)

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29 pages, 8523 KiB

Open AccessArticle

Random Forest Spatial Interpolation

by Aleksandar Sekulić, Milan Kilibarda, Gerard B.M. Heuvelink, Mladen Nikolić and Branislav Bajat

Remote Sens. 2020, 12(10), 1687; https://doi.org/10.3390/rs12101687 - 25 May 2020

Cited by 173 | Viewed by 22686

Abstract

For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques. Kriging with external drift and regression kriging have become basic techniques that benefit both from spatial autocorrelation and covariate information. More recently, machine learning techniques, such as random forest and gradient boosting, have become increasingly popular and are now often used for spatial interpolation. Some attempts have been made to explicitly take the spatial component into account in machine learning, but so far, none of these approaches have taken the natural route of incorporating the nearest observations and their distances to the prediction location as covariates. In this research, we explored the value of including observations at the nearest locations and their distances from the prediction location by introducing Random Forest Spatial Interpolation (RFSI). We compared RFSI with deterministic interpolation methods, ordinary kriging, regression kriging, Random Forest and Random Forest for spatial prediction (RFsp) in three case studies. The first case study made use of synthetic data, i.e., simulations from normally distributed stationary random fields with a known semivariogram, for which ordinary kriging is known to be optimal. The second and third case studies evaluated the performance of the various interpolation methods using daily precipitation data for the 2016–2018 period in Catalonia, Spain, and mean daily temperature for the year 2008 in Croatia. Results of the synthetic case study showed that RFSI outperformed most simple deterministic interpolation techniques and had similar performance as inverse distance weighting and RFsp. As expected, kriging was the most accurate technique in the synthetic case study. In the precipitation and temperature case studies, RFSI mostly outperformed regression kriging, inverse distance weighting, random forest, and RFsp. Moreover, RFSI was substantially faster than RFsp, particularly when the training dataset was large and high-resolution prediction maps were made. Full article

(This article belongs to the Special Issue Machine Learning Methods for Environmental Monitoring)

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27 pages, 22737 KiB

Open AccessArticle

Tree Crown Delineation Algorithm Based on a Convolutional Neural Network

by José R. G. Braga, Vinícius Peripato, Ricardo Dalagnol, Matheus P. Ferreira, Yuliya Tarabalka, Luiz E. O. C. Aragão, Haroldo F. de Campos Velho, Elcio H. Shiguemori and Fabien H. Wagner

Remote Sens. 2020, 12(8), 1288; https://doi.org/10.3390/rs12081288 - 18 Apr 2020

Cited by 89 | Viewed by 11452

Abstract

Tropical forests concentrate the largest diversity of species on the planet and play a key role in maintaining environmental processes. Due to the importance of those forests, there is growing interest in mapping their components and getting information at an individual tree level to conduct reliable satellite-based forest inventory for biomass and species distribution qualification. Individual tree crown information could be manually gathered from high resolution satellite images; however, to achieve this task at large-scale, an algorithm to identify and delineate each tree crown individually, with high accuracy, is a prerequisite. In this study, we propose the application of a convolutional neural network—Mask R-CNN algorithm—to perform the tree crown detection and delineation. The algorithm uses very high-resolution satellite images from tropical forests. The results obtained are promising—the

R e c a l l

P r e c i s i o n

, and

F_{1}

score values obtained were were

0.81

0.91

, and

0.86

, respectively. In the study site, the total of tree crowns delineated was

59,062

. These results suggest that this algorithm can be used to assist the planning and conduction of forest inventories. As the algorithm is based on a Deep Learning approach, it can be systematically trained and used for other regions. Full article

(This article belongs to the Special Issue Machine Learning Methods for Environmental Monitoring)

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11 pages, 3370 KiB

Open AccessTechnical Note

Semi-Automatic Generation of Training Samples for Detecting Renewable Energy Plants in High-Resolution Aerial Images

by Maximilian Kleebauer, Daniel Horst and Christoph Reudenbach

Remote Sens. 2021, 13(23), 4793; https://doi.org/10.3390/rs13234793 - 26 Nov 2021

Cited by 6 | Viewed by 1992

Abstract

Deep learning (DL)—in particular convolutional neural networks (CNN)—methods are widely spread in object detection and recognition of remote sensing images. In the domain of DL, there is a need for large numbers of training samples. These samples are mostly generated based on manual identification. Identifying and labelling these objects is very time-consuming. The developed approach proposes a partially automated procedure for the sample creation and avoids manual labelling of rooftop photovoltaic (PV) systems. By combining address data of existing rooftop PV systems from the German Plant Register, the Georeferenced Address Data and the Official House Surroundings Germany, a partially automated generation of training samples is achieved. Using a selection of 100,000 automatically generated samples, a network using a RetinaNet-based architecture combining ResNet101, a feature pyramid network, a classification and a regression network is trained, applied on a large area and post-filtered by intersection with additional automatically identified locations of existing rooftop PV systems. Based on a proof-of-concept application, a second network is trained with the filtered selection of approximately 51,000 training samples. In two independent test applications using high-resolution aerial images of Saarland in Germany, buildings with PV systems are detected with a precision of at least 92.77 and a recall of 84.47. Full article

(This article belongs to the Special Issue Machine Learning Methods for Environmental Monitoring)

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