Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities

Rapinel, Sébastien; Mony, Cendrine; Lecoq, Lucie; Clément, Bernard; Thomas, Alban; Hubert‐Moy, Laurence

doi:10.1016/j.rse.2019.01.018

Cited by 139 publications

(124 citation statements)

References 60 publications

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“…The use of fine-scale remote-sensing variables may thus provide a cost-effective tool to better support conservation planning with reduced survey costs [36], which may be crucial for rare and vulnerable species [97,98]. Higher mapping accuracy, especially when identifying grassland and linear land cover features, could be increased with images possessing very-high spectral and spatial resolutions, namely from data having a resolution spanning around 5m of detail, as suggested by Thornton et al [99] and Rapinel et al [100], possibly fulfilled through fusion of Sentinel 2 data [101]. Nevertheless, the use of very-high resolution data may be prohibitive for SDMs applications over larger areas due to its acquisitions costs.…”

Section: Discussionmentioning

confidence: 99%

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

et al. 2020

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Accurate mapping is a main challenge for endangered small-sized terrestrial species. Freely available spatio-temporal data at high resolution from multispectral satellite offer excellent opportunities for improving predictive distribution models of such species based on fine-scale habitat features, thus making it easier to achieve comprehensive biodiversity conservation goals. However, there are still few examples showing the utility of remote-sensing-based products in mapping microhabitat suitability for small species of conservation concern. Here, we address this issue using Sentinel-2 sensor-derived habitat variables, used in combination with more commonly used explanatory variables (e.g., topography), to predict the distribution of the endangered Cabrera vole (Microtus cabrerae) in agrosilvopastorial systems. Based on vole surveys conducted in two different seasons over a~176,000 ha landscape in Southern Portugal, we assessed the significance of each predictor in explaining Cabrera vole occurrence using the Boruta algorithm, a novel Random forest variant for dealing with high dimensionality of explanatory variables. Overall, results showed a strong contribution of Sentinel-2-derived variables for predicting microhabitat suitability of Cabrera voles. In particular, we found that photosynthetic activity (NDI45), specific spectral signal (SWIR1), and landscape heterogeneity (Rao's Q) were good proxies of Cabrera voles' microhabitat, mostly during temporally greener and wetter conditions. In addition to remote-sensing-based variables, the presence of road verges was also an important driver of voles' distribution, highlighting their potential role as refuges and/or corridors. Overall, our study supports the use of remote-sensing data to predict microhabitat suitability for endangered small-sized species in marginal areas that potentially hold most of the biodiversity found in human-dominated landscapes. We believe our approach can be widely applied to other species, for which detailed habitat mapping over large spatial extents is difficult to obtain using traditional descriptors. This would certainly contribute to improving conservation planning, thereby contributing to global conservation efforts in landscapes that are managed for multiple purposes.

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Section: Discussionmentioning

confidence: 99%

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

et al. 2020

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“…The results suggest that the Mediterranean forest plant association has distinct remotely sensed phenological behaviors and that the main seasonal phenological variations, useful to discriminate the contiguous Mediterranean habitats [17,19,20], extracted from the NDVI Landsat 8 time series using FPCA, are efficacy predictors for mapping the forest plant associations. Thus, the supervised random forest classification, similarly to Zhu and Liu [55], revealed that the main remotely sensed phenological seasonal variations (expressed by the first four pixel-based FPCA scores) contributed to the high OA (87.5%) of the map, much more than the lithological and topographic features.…”

Section: Mapping Performance and Methodological Considerationsmentioning

confidence: 97%

“…The increasing availability of remote sensing data free of charge of high spatial and temporal resolution (e.g., Sentinel-2 and Landsat-8) offers dense multi-temporal measures of greenness, such as the Normalized Difference Vegetation Index (NDVI) [14] times series, that are a useful proxy for the seasonal and inter-annual vegetation phenological changes [15]. These changes have proved useful in the discrimination of contiguous Mediterranean habitats [16][17][18] and to produce phenology-based mapping of the vegetation [19,20].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…Learning curves were used to optimize model parameters. Cross validation was used to calculate the evaluation metrics of model performance [45]. For each cross validation, the whole data set was randomly split into 70% training set and 30% test set.…”

Section: Building Of Machine Learning Modelsmentioning

confidence: 99%

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

Huang¹,

Guo²,

Chen³

et al. 2020

Preprint

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Water quality retrieval for small urban waterbodies by remote sensing get used to be difficult due to coarse spatial resolution of the remote sensing imagery. The recently launched Sentinel-2 produces imagery with a spatial resolution of 10 m. It provides an opportunity to solve the problem of retrieving water quality for small waterbodies. Additionally, many water management issues also require fine resolution of imagery, e.g. illegal discharge to an urban waterbody. Since illegal discharges are an important issue for urban water management, chemical oxygen demand (COD), total phosphorous (TP), and total nitrogen (TN) were chosen as the target parameters for water quality retrieval in this study. COD, TP and TN, however, are non-optically active parameters. There were limited studies in the past to retrieve these parameters in comparison with optically active parameters, e.g. Chlorophyll-A etc. This study compared three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Neural Networks (NN), to investigate the opportunity to retrieve the above non-optically active parameters. Results showed that R 2 of TP, TN, and COD by NN, RF and SVR were 0.94, 0.88, and 0.86, respectively. The performances of water quality retrieval for these non-optically active parameters were significantly improved by the optimized machine learning models. These models hence solved the problem to use remote sensing data to retrieve these non-optically active water quality parameters and provided a new monitoring strategy for small waterbodies. Water quality mapping obtained by Sentinel-2 imagery provided a full spatial coverage of the water quality characterization for the entire water surface. Compared with water samples collecting and testing, it greatly reduced labor cost, reagents cost, and waste treatment cost. It also may help identify illegal discharges to urban waterbodies. The method developed in this research provides a new practical and efficient water quality monitoring strategy in managing water with consideration of environmental sustainability.

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Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities

Cited by 139 publications

References 60 publications

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

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

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

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