Airborne LiDAR technology is widely used in archaeology and over the past decade has emerged as an accurate tool to describe anthropomorphic landforms. Archaeological features are traditionally ...emphasised on a LiDAR-derived Digital Terrain Model (DTM) using multiple Visualisation Techniques (VTs), and occasionally aided by automated feature detection or classification techniques. Such an approach offers limited results when applied to heterogeneous structures (different sizes, morphologies), which is often the case for archaeological remains that have been altered throughout the ages. This study proposes to overcome these limitations by developing a multi-scale analysis of topographic position combined with supervised machine learning algorithms (Random Forest). Rather than highlighting individual topographic anomalies, the multi-scalar approach allows archaeological features to be examined not only as individual objects, but within their broader spatial context. This innovative and straightforward method provides two levels of results: a composite image of topographic surface structure and a probability map of the presence of archaeological structures. The method was developed to detect and characterise megalithic funeral structures in the region of Carnac, the Bay of Quiberon, and the Gulf of Morbihan (France), which is currently considered for inclusion on the UNESCO World Heritage List. As a result, known archaeological sites have successfully been geo-referenced with a greater accuracy than before (even when located under dense vegetation) and a ground-check confirmed the identification of a previously unknown Neolithic burial mound in the commune of Carnac.
Aims
(a) Evaluate the potential of Unmanned Aerial Vehicle (UAV) technology for very high resolution monitoring vegetation dynamics. (b) Describe and explain the colonization pattern of dewatered ...alluvial deposits by vegetation during early successional stages at an intra‐annual scale.
Location
Sélune River, Normandy, France.
Methods
We assessed vegetation cover using models based on UAV imagery and field samples at very fine spatial (cm) and temporal (intra‐annual) scales. A UAV flight was conducted every two months from January to July 2015, and vegetation was measured during image acquisition phases. Vegetation cover was mapped for each image acquisition date (four UAV orthomosaics) using a nonlinear regression model (Support Vector Regression algorithm). Then, the maps of vegetation cover were compared to evaluate the colonization process.
Results
Vegetation cover was predicted from UAV with high accuracy (mean correlation coefficient: 0.90). Analysis of the maps revealed that colonization of the alluvial deposits by vegetation was rapid in spring.
Conclusions
This study shows that intra‐annual vegetation dynamics on alluvial deposits is rapid and that the colonization pattern can be observed in early successional stages. Very high spatial resolution images acquired by UAV can be used to create detailed maps to evaluate vegetation cover development.
In this study, we show that very high spatial resolution images acquired by UAV can be used to create detailed maps to evaluate vegetation cover development. Accurate vegetation cover maps allow the description and explanation of the very fast colonization pattern of dewatered alluvial deposits by vegetation during early successional stages at an intra‐annual scale.
The open access availability of satellite images from new sensors characterized by various spatial and temporal resolutions provides new challenges and possibilities for biodiversity conservation. ...Methodologies aiming at characterizing vegetation type, phenology, and function can now benefit from metric spatial resolution imagery combined with an improved revisit capability. Here, we test hybrid methods and data fusion, using very high spatial resolution (VHSR) sensors in different complex landscapes encompassing three French biogeographical regions.
The methodological approach presented herein has a generic value in response to national conservation targets based on the concept of essential biodiversity variables accessed by remote sensing (RS‐enabled EBVs). We focused on deriving five RS‐enabled EBVs from natural and seminatural open ecosystems: (1) ecosystem distribution, (2) land cover, (3) heterogeneity, (4) primary productivity and (5) vegetation phenology. The challenge was to develop a method that would be technically feasible, economically viable, and sustainable in time.
We demonstrated that it is possible to derive key parameters required to develop a set of EBVs from remote sensing (RS) data. The combined use of remote sensing data sources with various spatial, temporal, and spectral resolutions is essential to obtain different indicators of natural habitats.
One major current challenge for an improved contribution of RS to conservation is to strengthen multiple collaborative frameworks among remote sensing scientists, conservation biologists, and ecologists in order to increase the efficiency of methodological exchange and draw benefits for successful conservation planning strategies.
Résumé
La disponibilité en libre accès des images satellitaires issues de nouveaux capteurs caractérisés par diverses résolutions spatiales et temporelles offre de nouveaux défis et de nouvelles possibilités pour la conservation de la biodiversité. Les méthodologies visant à caractériser, selon le type de végétation, la phénologie et le fonctionnement peuvent maintenant bénéficier de l'imagerie à résolution métrique combinée à une capacité de revisite améliorée. Nous testons ici des méthodes hybrides et la fusion de données mobilisant des images de capteurs à très haute résolution spatiale (THRS) dans différents paysages complexes englobant trois régions biogéographiques françaises.
L'approche méthodologique présentée ici a une valeur générique en réponse aux objectifs nationaux de conservation basés sur le concept de variables essentielles de biodiversité accessibles par télédétection (RS‐enabled EBVs). Nous nous sommes concentrés sur l'extraction de cinq EBVs accessibles par télédétection sur des écosystèmes ouverts naturels et semi naturels: (1) distribution de l’écosystème, (2) occupation du sol, (3) hétérogénéité, (4) productivité primaire et (5) phénologie de la végétation. Le défi était de développer une méthode qui serait techniquement faisable, économiquement viable et durable dans le temps.
Nous avons montré qu'il est possible de dériver les paramètres clés requis pour développer un ensemble de variables essentielles de biodiversité à partir des données de télédétection. L'utilisation combinée de sources de données de télédétection caractérisées par diverses résolutions spatiales, temporelles et spectrales est essentielle pour obtenir différents indicateurs d'habitats naturels.
L'un des principaux défis actuels pour une meilleure contribution de la télédétection à la conservation de la biodiversité est de renforcer les cadres de collaboration multiples entre les scientifiques de la télédétection, les biologistes de la conservation et les écologues afin d'améliorer l'efficacité des méthodologies et d'en tirer avantages.
Aims
The mapping and monitoring of natural vegetation is a challenging but important objective for environmental management. Although remote sensing has been used to map plant communities for several ...years, the maps produced are not sufficiently accurate to meet management requirements. This can be explained by the cumulative effects of floristic and spectral uncertainty. The objective of this study was to accurately map grassland plant communities using a comprehensive fuzzy approach in order to address floristic and spectral uncertainty.
Location
Sub‐brackish wet grasslands, Marais Poitevin, France.
Methods
We first created a compromise typology—floristically and spectrally consistent—to perform fuzzy noise clustering on a joint PCA matrix derived from vegetation relevés and remote sensing data. This typology had two levels, which corresponded to spectral signatures and plant communities, respectively. Second, we mapped grassland plant communities to predict the fuzzy model from the remote sensing data. We applied this approach using (1) a very high spatial resolution multispectral satellite image and a LiDAR‐derived Digital Terrain Model acquired on a 73 km2 wet grassland site and (2) more than 200 relevés collected in the field.
Results
The results show that (1) the compromise typology yields significantly higher mapping accuracy than classic phytosociological typology (62% and 26%, respectively); (2) compared to a crisp approach, the fuzzy approach improves mapping accuracy by 17 percentage points and (3) a single plant community can be defined by several (1–4) distinct spectral signatures.
Conclusions
The comprehensive fuzzy procedure successfully mapped herbaceous plant communities at the ecosystem scale using inexpensive remote sensing data. Floristic and spectral uncertainty was considered in a fuzzy approach, resulting in the mapping of nine herbaceous plant communities with acceptable accuracy. As the natural habitats were characterized at the plant community level, correspondence with functional properties of the species or with ecosystem services can be easily inferred. These encouraging results open up new ways to meet the requirements for monitoring the conservation status of natural habitats in the EU Habitats Directive.
Mapping plant communities from satellite imagery remains challenging because of floristic and spectral uncertainties. Here, we address this issue using a fuzzy approach considering a 73 km2 grassland area. We reveal that: (a) nine plant communities can be mapped from multispectral image with 62% accuracy, and (b) compared to a crisp approach, the fuzzy method improves mapping accuracy by ~20%.
This paper is concerned with the estimation of the dominant orientation of textured patches that appear in a number of images (remote sensing, biology or natural sciences for instance). It is based ...on the maximization of a criterion that deals with the coefficients enclosed in the different bands of a wavelet decomposition of the original image. More precisely, we search for the orientation that best concentrates the energy of the coefficients in a single direction. To compare the wavelet coefficients between the different bands, we use the Kullback–Leibler divergence on their distribution, this latter being assumed to behave like a Generalized Gaussian Density. The space–time localization of the wavelet transform allows to deal with any polygon that may be contained in a single image. This is of key importance when one works with (non-rectangular) segmented objects. We have applied the same methodology but using other criteria to compare the distributions, in order to highlight the benefit of the Kullback–Leibler divergence. In addition, the methodology is validated on synthetic and real situations and compared with a state-of-the-art approach devoted to orientation estimation.
Monitoring vegetation cover during winter is a major environmental and scientific issue in agricultural areas. From an environmental viewpoint, the presence and type of vegetation cover in winter ...influences the transport of pollutants to water resources. From a methodological viewpoint, characterizing spatio-temporal dynamics of land cover and land use at the field scale is challenging due to the diversity of farming strategies and practices in winter. The objective of this study was to evaluate the respective advantages of Sentinel optical and SAR time-series to identify land use in winter. To this end, Sentinel-1 and -2 time-series were classified using Support Vector Machine and Random Forest algorithms in a 130 km² agricultural area. From the classification, the Sentinel-2 time-series identified winter land use more accurately (overall accuracy (OA) = 75%, Kappa index = 0.70) than that of Sentinel-1 (OA = 70%, Kappa = 0.66) but a combination of the Sentinel-1 and -2 time-series was the most accurate (OA = 81%, Kappa = 0.77). Our study outlines the effectiveness of Sentinel-1 and -2 for identify land use in winter, which can help to change agricultural practices.
Wetlands, which provide multiple functions and ecosystem services, have decreased and been degraded worldwide for several decades due to human activities and climate change. Managers and scientists ...need tools to characterize and monitor wetland areas, structure, and functions in the long term and at regional and global scales and assess the effects of planning policies on their conservation status. The Landsat earth observation program has collected satellite images since 1972, which makes it the longest global earth observation record with respect to remote sensing. In this review, we describe how Landsat data have been used for long-term (≥20 years) wetland monitoring. A total of 351 articles were analyzed based on 5 topics and 22 attributes that address long-term wetland monitoring and Landsat data analysis issues. Results showed that (1) the open access Landsat archive successfully highlights changes in wetland areas, structure, and functions worldwide; (2) recent progress in artificial intelligence (AI) and machine learning opens new prospects for analyzing the Landsat archive; (3) most unexplored wetlands can be investigated using the Landsat archive; (4) new cloud-computing tools enable dense Landsat times-series to be processed over large areas. We recommend that future studies focus on changes in wetland functions using AI methods along with cloud computing. This review did not include reports and articles that do not mention the use of Landsat imagery.
Advances in remote sensing (RS) technology in recent years have increased the interest in including RS data into one-class classifiers (OCCs). However, this integration is complex given the ...interdisciplinary issues involved. In this context, this review highlights the advances and current challenges in integrating RS data into OCCs to map vegetation classes. A systematic review was performed for the period 2013–2020. A total of 136 articles were analyzed based on 11 topics and 30 attributes that address the ecological issues, properties of RS data, and the tools and parameters used to classify natural vegetation. The results highlight several advances in the use of RS data in OCCs: (i) mapping of potential and actual vegetation areas, (ii) long-term monitoring of vegetation classes, (iii) generation of multiple ecological variables, (iv) availability of open-source data, (v) reduction in plotting effort, and (vi) quantification of over-detection. Recommendations related to interdisciplinary issues were also suggested: (i) increasing the visibility and use of available RS variables, (ii) following good classification practices, (iii) bridging the gap between spatial resolution and site extent, and (iv) classifying plant communities.
Mapping natural habitats remains challenging, especially at a national scale. Although new open‐access variables for vegetation and its environment and increased spatial resolution derived from ...satellite remote sensing data are available at the global scale, the relevance of these new variables for fine‐grained mapping of natural habitats at a national scale remains underexplored. This study aimed to map the fine‐grained pattern of four heathland habitats throughout France (550 000 km2). Environmental (bioclimatic, soil and topographic) and spectral (vegetation) variables derived from MODerate resolution Imaging Spectroradiometer, Advanced Spaceborne Thermal Emission and Reflection Radiometer, and Sentinel‐2 satellite data were analyzed using the MaxEnt classifier. Open‐access field databases were used to calibrate and validate the classification, based on the threshold‐independent area under the curve (AUC) index and the conventional F1‐score. For each heathland habitat, potential and actual areas were mapped using environmental and spectral variables, respectively. The results showed high classification accuracy for potential (AUC 0.92–0.99) and actual (AUC 0.88–0.99) suitability maps of the four heathland habitats. Visual interpretation of maps of the probability of occurrence indicated that the fine‐grained distribution of heathland habitat was detected satisfactorily. However, although the accuracy of the crisp map of combined classifications of actual heathland habitats was high (overall accuracy 0.72), estimated producer's accuracies in terms of proportion of area were low (<0.25). This study provides the first fine‐grained pattern maps of heathland habitats at a national scale, thus highlighting the value of combining environmental and spectral variables derived from open‐remote sensing data and open‐source field databases. These suitability maps could support the identification of heathland habitats in the framework of national conservation policies.
Mapping natural habitats remains challenging, especially at the national scale. Here, we mapped the fine‐grained pattern of four heathland habitats throughout France (550 000 km2) using open‐source remote sensing data, including Sentinel‐2 time series. This study provides the first fine‐grained map of heathland habitats at a national scale, highlighting the value of combining environmental and vegetation variables. This map will support the monitoring and evaluation of the conservation status of natural habitats.
Monitoring forest–agriculture mosaics is crucial for understanding landscape heterogeneity and managing biodiversity. Mapping these mosaics from remotely sensed imagery remains challenging, since ...ecological gradients from forested to agricultural areas make characterizing vegetation more difficult. The recent synthetic aperture radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) time series provide a great opportunity to monitor forest–agriculture mosaics due to their high spatial and temporal resolutions. However, while a few studies have used the temporal resolution of S-2 time series alone to map land cover and land use in cropland and/or forested areas, S-1 time series have not yet been investigated alone for this purpose. The combined use of S-1 & S-2 time series has been assessed for only one or a few land cover classes. In this study, we assessed the potential of S-1 data alone, S-2 data alone, and their combined use for mapping forest–agriculture mosaics over two study areas: a temperate mountainous landscape in the Cantabrian Range (Spain) and a tropical forested landscape in Paragominas (Brazil). Satellite images were classified using an incremental procedure based on an importance rank of the input features. The classifications obtained with S-2 data alone (mean kappa index = 0.59–0.83) were more accurate than those obtained with S-1 data alone (mean kappa index = 0.28–0.72). Accuracy increased when combining S-1 and 2 data (mean kappa index = 0.55–0.85). The method enables defining the number and type of features that discriminate land cover classes in an optimal manner according to the type of landscape considered. The best configuration for the Spanish and Brazilian study areas included 5 and 10 features, respectively, for S-2 data alone and 10 and 20 features, respectively, for S-1 data alone. Short-wave infrared and VV and VH polarizations were key features of S-2 and S-1 data, respectively. In addition, the method enables defining key periods that discriminate land cover classes according to the type of images used. For example, in the Cantabrian Range, winter and summer were key for S-2 time series, while spring and winter were key for S-1 time series.
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