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Remote Sensing for Habitat Mapping

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 35089

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Cristina Tarantino

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Institute of Atmospheric Pollution Research (IIA), National Research Council of Italy (CNR), c/o Interateneo Physics Department, University of Bari, Via Amendola 173, 70126 Bari, Italy
Interests: remote sensing; classification; land cover/land use mapping; habitat mapping; change detection; invasive species monitoring; time series analysis; GIS environments
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Dr. Maria Adamo

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Institute of Atmospheric Pollution Research (IIA), National Research Council of Italy (CNR), c/o Interateneo Physics Department, University of Bari, Via Amendola 173, 70126 Bari, Italy
Interests: optical remote sensing; land cover/land use mapping; habitat mapping; time series analysis; oil spill monitoring; wind fields retrieval from SAR
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Dr. Valeria Tomaselli

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Institute of Biosciences and BioResources (IBBR), National Research Council of Italy (CNR), Via Amendola 165/A, 70126 Bari, Italy
Interests: plant community diversity; vegetation mapping; habitat mapping; habitat taxonomies; LCCS taxonomy; expert knowledge; plant phenology

Special Issue Information

Dear Colleagues,

The mapping of natural and semi-natural habitats is increasingly required in environmental policies, as well as in spatial planning, land management, and the designation of protected areas. Habitats are effective indicators of biodiversity and their periodic and consistent monitoring, in terms of extent, status, and changes can provide an effective tool for policy makers engaged in the conservation plans. This is in accordance with the GEO strategies planned for 2016–2025 period and the attainment of SDG 15 for preserving biodiversity and ecosystem sustainability.

Remote sensing data and techniques offer significant opportunities for long-term habitats monitoring because of the availability of a large amount of multi-temporal data from past and current spaceborne missions with continuity provided by planned future missions. Routinely, mapping can be generated and intra-annual and inter-annual changes quantified providing synoptic spatial views of expansive landscapes and regions from the integration of remote sensed (RS) data with in situ and ancillary data.

Due to the great relevance and interest in this theme, there are a great deal of questions to be answered concerning, for example, the best methods and standards to use in acquiring and processing data, habitat classification terms and systems, as well as the reliability of the maps produced depending on the scale adopted, this Special Issue is inviting manuscripts on the following topics:

RS data and techniques for identification, mapping, and assessment of different habitat types, their conditions and/or conservation, at different spatial and temporal scales;
Remote sensing and habitats characterization for different marine and terrestrial environments, from coastal areas to mountain regions, from large, homogenous, and spatially continuous units to highly fragmented, heterogeneous and spatially discontinuous landscapes (e.g., mosaics);
Satellite time series analysis for long-term habitat mapping;
Habitat change maps from RS data;
Integration of RS data with in situ data and expert knowledge;
Habitat taxonomies and semantics in a framework of integration of RS data and in situ data;
Indicators from RS data for the habitat modeling.

Dr. Cristina Tarantino
Dr. Maria Adamo
Dr. Valeria Tomaselli
Guest Editors

Manuscript Submission Information

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Keywords

Remote sensing
Time series analysis
Habitat mapping
Habitat modeling
In situ data
Land cover/Land Use (LC/LU)
LC/LU and habitat Taxonomies
Change detection
Open Access
Multiple scales

Published Papers (9 papers)

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Research

24 pages, 4701 KiB

Open AccessArticle

The Habitat Map of Switzerland: A Remote Sensing, Composite Approach for a High Spatial and Thematic Resolution Product

by Bronwyn Price, Nica Huber, Anita Nussbaumer and Christian Ginzler

Remote Sens. 2023, 15(3), 643; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030643 - 21 Jan 2023

Cited by 2 | Viewed by 2615

Abstract

Habitat maps at high thematic and spatial resolution and broad extents are fundamental tools for biodiversity conservation, the planning of ecological networks and the management of ecosystem services. To derive a habitat map for Switzerland, we used a composite methodology bringing together the best available spatial data and distribution models. The approach relies on the segmentation and classification of high spatial resolution (1 m) aerial imagery. Land cover data, as well as habitat and species distribution models built on Earth observation data from Sentinel 1 and 2, Landsat, Planetscope and LiDAR, inform the rule-based classification to habitats defined by the hierarchical Swiss Habitat Typology (TypoCH). A total of 84 habitats in 32 groups and 9 overarching classes are mapped in a spatially explicit manner across Switzerland. Validation and plausibility analysis with four independent datasets show that the mapping is broadly plausible, with good accuracy for most habitats, although with lower performance for fine-scale and linear habitats, habitats with restricted geographical distributions and those predominantly characterised by understorey species, especially forest habitats. The resulting map is a vector dataset available for interactive viewing and download from open EnviDat data sharing platform. The methodology is semi-automated to allow for updates over time. Full article

(This article belongs to the Special Issue Remote Sensing for Habitat Mapping)

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20 pages, 4401 KiB

Open AccessArticle

A Machine Learning Framework for the Classification of Natura 2000 Habitat Types at Large Spatial Scales Using MODIS Surface Reflectance Data

by Fabian Sittaro, Christopher Hutengs, Sebastian Semella and Michael Vohland

Remote Sens. 2022, 14(4), 823; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040823 - 10 Feb 2022

Cited by 6 | Viewed by 2441

Abstract

Anthropogenic climate and land use change is causing rapid shifts in the distribution and composition of habitats with profound impacts on ecosystem biodiversity. The sustainable management of ecosystems requires monitoring programmes capable of detecting shifts in habitat distribution and composition at large spatial scales. Remote sensing observations facilitate such efforts as they enable cost-efficient modelling approaches that utilize publicly available datasets and can assess the status of habitats over extended periods of time. In this study, we introduce a modelling framework for habitat monitoring in Germany using readily available MODIS surface reflectance data. We developed supervised classification models that allocate (semi-)natural areas to one of 18 classes based on their similarity to Natura 2000 habitat types. Three machine learning classifiers, i.e., Support Vector Machines (SVM), Random Forests (RF), and C5.0, and an ensemble approach were employed to predict habitat type using spectral signatures from MODIS in the visible-to-near-infrared and short-wave infrared. The models were trained on homogenous Special Areas of Conservation that are predominantly covered by a single habitat type with reference data from 2013, 2014, and 2016 and tested against ground truth data from 2010 and 2019 for independent model validation. Individually, the SVM and RF methods achieved better overall classification accuracies (SVM: 0.72–0.93%, RF: 0.72–0.94%) than the C5.0 algorithm (0.66–0.93%), while the ensemble classifier developed from the individual models gave the best performance with overall accuracies of 94.23% for 2010 and 80.34% for 2019 and also allowed a robust detection of non-classifiable pixels. We detected strong variability in the cover of individual habitat types, which were reduced when aggregated based on their similarity. Our methodology is capable to provide quantitative information on the spatial distribution of habitats, differentiate between disturbance events and gradual shifts in ecosystem composition, and could successfully allocate natural areas to Natura 2000 habitat types. Full article

(This article belongs to the Special Issue Remote Sensing for Habitat Mapping)

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27 pages, 41642 KiB

Open AccessArticle

A Multi-Stage Approach Combining Very High-Resolution Satellite Image, GIS Database and Post-Classification Modification Rules for Habitat Mapping in Hong Kong

by Ivan H. Y. Kwong, Frankie K. K. Wong, Tung Fung, Eric K. Y. Liu, Roger H. Lee and Terence P. T. Ng

Remote Sens. 2022, 14(1), 67; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010067 - 24 Dec 2021

Cited by 7 | Viewed by 4693

Abstract

Identification and mapping of various habitats with sufficient spatial details are essential to support environmental planning and management. Considering the complexity of diverse habitat types in a heterogeneous landscape, a context-dependent mapping framework is expected to be superior to traditional classification techniques. With the aim to produce a territory-wide habitat map in Hong Kong, a three-stage mapping procedure was developed to identify 21 habitats by combining very-high-resolution satellite images, geographic information system (GIS) layers and knowledge-based modification rules. In stage 1, several classification methods were tested to produce initial results with 11 classes from a WorldView-2/3 image mosaic using a combination of spectral, textural, topographic and geometric variables. In stage 2, modification rules were applied to refine the classification results based on contextual properties and ancillary data layers. Evaluation of the classified maps showed that the highest overall accuracy was obtained from pixel-based random forest classification (84.0%) and the implementation of modification rules led to an average 8.8% increase in the accuracy. In stage 3, the classification scheme was expanded to all 21 habitats through the adoption of additional rules. The resulting habitat map achieved >80% accuracy for most of the evaluated classes and >70% accuracy for the mixed habitats when validated using field-collected points. The proposed mapping framework was able to utilize different information sources in a systematic and controllable workflow. While transitional mixed habitats were mapped using class membership probabilities and a soft classification method, the identification of other habitats benefited from the hybrid use of remote-sensing classification and ancillary data. Adaptive implementation of classification procedures, development of appropriate rules and combination with spatial data are recommended when producing an integrated and accurate map. Full article

(This article belongs to the Special Issue Remote Sensing for Habitat Mapping)

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32 pages, 5896 KiB

Open AccessArticle

The Methodology for Identifying Secondary Succession in Non-Forest Natura 2000 Habitats Using Multi-Source Airborne Remote Sensing Data

by Katarzyna Osińska-Skotak, Aleksandra Radecka, Wojciech Ostrowski, Dorota Michalska-Hejduk, Jakub Charyton, Krzysztof Bakuła and Hubert Piórkowski

Remote Sens. 2021, 13(14), 2803; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142803 - 16 Jul 2021

Cited by 3 | Viewed by 2399

Abstract

The succession process of trees and shrubs is considered as one of the threats to non-forest Natura 2000 habitats. Poland, as a member of the European Union, is obliged to monitor these habitats and preserve them in the best possible condition. If threats are identified, it is necessary to take action—as part of the so-called active protection—that will ensure the preservation of habitats in a non-deteriorated condition. At present, monitoring of Natura 2000 habitats is carried out in expert terms, i.e., the habitat conservation status is determined during field visits. This process is time- and cost-intensive, and it is subject to the subjectivism of the person performing the assessment. As a result of the research, a methodology for the identification and monitoring of the succession process in non-forest Natura 2000 habitats was developed, in which multi-sensor remote sensing data are used—airborne laser scanner (ALS) and hyperspectral (HS) data. The methodology also includes steps required to analyse the dynamics of the succession process in the past, which is done using archival photogrammetric data (aerial photographs and ALS data). The algorithms implemented within the methodology include structure from motion and dense image matching for processing the archival images, segmentation and Voronoi tessellation for delineating the spatial extent of succession, machine learning random forest classifier, recursive feature elimination and t-distributed stochastic neighbour embedding algorithms for succession species differentiation, as well as landscape metrics used for threat level analysis. The proposed methodology has been automated and enables a rapid assessment of the level of threat for a whole given area, as well as in relation to individual Natura 2000 habitats. The prepared methodology was successfully tested on seven research areas located in Poland. Full article

(This article belongs to the Special Issue Remote Sensing for Habitat Mapping)

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34 pages, 15785 KiB

Open AccessArticle

Mapping Alkaline Fens, Transition Mires and Quaking Bogs Using Airborne Hyperspectral and Laser Scanning Data

by Sylwia Szporak-Wasilewska, Hubert Piórkowski, Wojciech Ciężkowski, Filip Jarzombkowski, Łukasz Sławik and Dominik Kopeć

Remote Sens. 2021, 13(8), 1504; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081504 - 14 Apr 2021

Cited by 8 | Viewed by 2524

Abstract

The aim of this study is to evaluate the effectiveness of the identification of Natura 2000 wetland habitats (Alkaline fens—code 7230, and Transition mires and quaking bogs—code 7140) depending on various remotely sensed (RS) data acquired from an airborne platform. Both remote sensing data and botanical reference data were gathered for mentioned habitats in the Lower (LB) and Upper Biebrza (UB) River Valley and the Janowskie Forest (JF) in different seasonal stages. Several different classification scenarios were tested, and the ones that gave the best results for analyzed habitats were indicated in each campaign. In the final stage, a recommended term of data acquisition, as well as a list of remote sensing products, which allowed us to achieve the highest accuracy mapping for these two types of wetland habitats, were presented. Designed classification scenarios integrated different hyperspectral products such as Minimum Noise Fraction (MNF) bands, spectral indices and products derived from Airborne Laser Scanning (ALS) data representing topography (developed in SAGA), or statistical products (developed in OPALS—Orientation and Processing of Airborne Laser Scanning). The image classifications were performed using a Random Forest (RF) algorithm and a multi-classification approach. As part of the research, the correlation analysis of the developed remote sensing products was carried out, and the Recursive Feature Elimination with Cross-Validation (RFE-CV) analysis was performed to select the most important RS sub-products and thus increase the efficiency and accuracy of developing the final habitat distribution maps. The classification results showed that alkaline fens are better identified in summer (mean F_1-SCORE equals 0.950 in the UB area, and 0.935 in the LB area), transition mires and quaking bogs that evolved on/or in the vicinity of alkaline fens in summer and autumn (mean F_1-SCORE equals 0.931 in summer, and 0.923 in autumn in the UB area), and transition mires and quaking bogs that evolved on dystrophic lakes in spring and summer (mean F_1-SCORE equals 0.953 in spring, and 0.948 in summer in the JF area). The study also points out that the classification accuracy of both wetland habitats is highly improved when combining selected hyperspectral products (MNF bands, spectral indices) with ALS topographical and statistical products. This article demonstrates that information provided by the synergetic use of data from different sensors can be used in mapping and monitoring both Natura 2000 wetland habitats for its future functional assessment and/or protection activities planning with high accuracy. Full article

(This article belongs to the Special Issue Remote Sensing for Habitat Mapping)

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29 pages, 6457 KiB

Open AccessArticle

Intra-Annual Sentinel-2 Time-Series Supporting Grassland Habitat Discrimination

by Cristina Tarantino, Luigi Forte, Palma Blonda, Saverio Vicario, Valeria Tomaselli, Carl Beierkuhnlein and Maria Adamo

Remote Sens. 2021, 13(2), 277; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020277 - 14 Jan 2021

Cited by 23 | Viewed by 3320

Abstract

The present study aims to discriminate four semi-arid grassland habitats in a Mediterranean Natura 2000 site, Southern Italy, involving 6210/E1.263, 62A0/E1.55, 6220/E1.434 and X/E1.61-E1.C2-E1.C4 (according to Annex I of the European Habitat Directive/EUropean Nature Information System (EUNIS) taxonomies). For this purpose, an intra-annual time-series of 30 Sentinel-2 images, embedding phenology information, were investigated for 2018. The methodology adopted was based on a two-stage workflow employing a Support Vector Machine classifier. In the first stage only four Sentinel-2 multi-season images were analyzed, to provide an updated land cover map from where the grassland layer was extracted. The layer obtained was then used for masking the input features to the second stage. The latter stage discriminated the four grassland habitats by analyzing several input features configurations. These included multiple spectral indices selected from the time-series and the Digital Terrain Model. The results obtained from the different input configurations selected were compared to evaluate if the phenology information from time-series could improve grassland habitats discrimination. The highest F1 values (95.25% and 80.27%) were achieved for 6210/E1.263 and 6220/E1.434, respectively, whereas the results remained stable (97,33%) for 62A0/E1.55 and quite low (75,97%) for X/E1.61-E1.C2-E1.C4. However, since for all the four habitats analyzed no single configuration resulted effective, a Majority Vote algorithm was applied to achieve a reduction in classification uncertainty. Full article

(This article belongs to the Special Issue Remote Sensing for Habitat Mapping)

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31 pages, 7408 KiB

Open AccessArticle

Knowledge-Based Classification of Grassland Ecosystem Based on Multi-Temporal WorldView-2 Data and FAO-LCCS Taxonomy

by Maria Adamo, Valeria Tomaselli, Cristina Tarantino, Saverio Vicario, Giuseppe Veronico, Richard Lucas and Palma Blonda

Remote Sens. 2020, 12(9), 1447; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091447 - 03 May 2020

Cited by 22 | Viewed by 3897

Abstract

Grassland ecosystems can provide a variety of services for humans, such as carbon storage, food production, crop pollination and pest regulation. However, grasslands are today one of the most endangered ecosystems due to land use change, agricultural intensification, land abandonment as well as climate change. The present study explores the performance of a knowledge-driven GEOgraphic-Object—based Image Analysis (GEOBIA) learning scheme to classify Very High Resolution (VHR) images for natural grassland ecosystem mapping. The classification was applied to a Natura 2000 protected area in Southern Italy. The Food and Agricultural Organization Land Cover Classification System (FAO-LCCS) hierarchical scheme was instantiated in the learning phase of the algorithm. Four multi-temporal WorldView-2 (WV-2) images were classified by combining plant phenology and agricultural practices rules with prior-image spectral knowledge. Drawing on this knowledge, spectral bands and entropy features from one single date (Post Peak of Biomass) were firstly used for multiple-scale image segmentation into Small Objects (SO) and Large Objects (LO). Thereafter, SO were labelled by considering spectral and context-sensitive features from the whole multi-seasonal data set available together with ancillary data. Lastly, the labelled SO were overlaid to LO segments and, in turn, the latter were labelled by adopting FAO-LCCS criteria about the SOs presence dominance in each LO. Ground reference samples were used only for validating the SO and LO output maps. The knowledge driven GEOBIA classifier for SO classification obtained an OA value of 97.35% with an error of 0.04. For LO classification the value was 75.09% with an error of 0.70. At SO scale, grasslands ecosystem was classified with 92.6%, 99.9% and 96.1% of User’s, Producer’s Accuracy and F1-score, respectively. The findings reported indicate that the knowledge-driven approach not only can be applied for (semi)natural grasslands ecosystem mapping in vast and not accessible areas but can also reduce the costs of ground truth data acquisition. The approach used may provide different level of details (small and large objects in the scene) but also indicates how to design and validate local conservation policies. Full article

(This article belongs to the Special Issue Remote Sensing for Habitat Mapping)

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22 pages, 3449 KiB

Open AccessArticle

Mapping Mediterranean Forest Plant Associations and Habitats with Functional Principal Component Analysis Using Landsat 8 NDVI Time Series

by Simone Pesaresi, Adriano Mancini, Giacomo Quattrini and Simona Casavecchia

Remote Sens. 2020, 12(7), 1132; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071132 - 02 Apr 2020

Cited by 27 | Viewed by 5424

Abstract

The classification of plant associations and their mapping play a key role in defining habitat biodiversity management, monitoring, and conservation strategies. In this work we present a methodological framework to map Mediterranean forest plant associations and habitats that relies on the application of the Functional Principal Component Analysis (FPCA) to the remotely sensed Normalized Difference Vegetation Index (NDVI) time series. FPCA, considering the chronological order of the data, reduced the NDVI time series data complexity and provided (as FPCA scores) the main seasonal NDVI phenological variations of the forests. We performed a supervised classification of the FPCA scores combined with topographic and lithological features of the study area to map the forest plant associations. The supervised mapping achieved an overall accuracy of 87.5%. The FPCA scores contributed to the global accuracy of the map much more than the topographic and lithological features. The results showed that (i) the main seasonal phenological variations (FPCA scores) are effective spatial predictors to obtain accurate plant associations and habitat maps; (ii) the FPCA is a suitable solution to simultaneously express the relationships between remotely sensed and ecological field data, since it allows us to integrate these two different perspectives about plant associations in a single graph. The proposed approach based on the FPCA is useful for forest habitat monitoring, as it can contribute to produce periodically detailed vegetation-based habitat maps that reflect the “current” status of vegetation and habitats, also supporting the study of plant associations. Full article

(This article belongs to the Special Issue Remote Sensing for Habitat Mapping)

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28 pages, 7368 KiB

Open AccessArticle

Analysis of Using Dense Image Matching Techniques to Study the Process of Secondary Succession in Non-Forest Natura 2000 Habitats

by Katarzyna Osińska-Skotak, Łukasz Jełowicki, Krzysztof Bakuła, Dorota Michalska-Hejduk, Justyna Wylazłowska and Dominik Kopeć

Remote Sens. 2019, 11(8), 893; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11080893 - 12 Apr 2019

Cited by 64 | Viewed by 3368

Abstract

Secondary succession is considered a threat to non-forest Natura 2000 habitats. Currently available data and techniques such as airborne laser scanning (ALS) data processing can be used to study this process. Thanks to these techniques, information about the spatial extent and the height of research objects—trees and shrubs—can be obtained. However, only archival aerial photographs can be used to conduct analyses of the stage of succession process that took place in the 1960s or 1970s. On their basis, the extent of trees and shrubs can be determined using photointerpretation, but height information requires stereoscopic measurements. State-of-the-art dense image matching (DIM) algorithms provide the ability to automate this process and create digital surface models (DSMs) that are much more detailed than ones obtained using image matching techniques developed a dozen years ago. This research was part of the HabitARS project on the Ostoja Olsztyńsko-Mirowska Natura 2000 protected site (PLH240015). The source data included archival aerial photographs (analogue and digital) acquired from various phenological periods from 1971–2015, ALS data from 2016, and data from botanical campaigns. First, using the DIM algorithms, point clouds were generated and converted to DSMs. Heights interpolated from the DSMs were compared with stereoscopic measurements (1971–2012) and ALS data (2016). Then, the effectiveness of tree and shrub detection was analysed, considering the relationship between the date and the parameters of aerial images acquisition and DIM effects. The results showed that DIM can be used successfully in tree and shrub detection and monitoring, but the source images must meet certain conditions related to their quality. Based on the extensive material analysed, the detection of small trees and shrubs in aerial photographs must have a scale greater than 1:13,000 or a 25 cm GSD (Ground Sample Distance) at most, an image acquisition date from June–September (the period of full foliage in Poland), and good radiometric quality. Full article

(This article belongs to the Special Issue Remote Sensing for Habitat Mapping)

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