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Introducing WIW for Detecting the Presence of Water in Wetlands with Landsat and Sentinel Satellites
No full textMany wetlands are characterized by a vegetation cover of variable height and density over time. Tracking spatio-temporal changes in inundation patterns of these wetlands remains a challenge for remote sensing. Water In Wetlands (WIW) was predicted using a dichotomous partitioning of reflectance values encoded based on ground-truth (n = 4038) and optical-space derived (n = 7016) data covering all land cover types (n = 17) found in the Rh?ne delta, southern France. The models were developed with spectral data from Sentinel 2, Landsat 7, and Landsat 8 sensors, hence providing a monitoring tool that covers a 35-year period (same sensor for Landsat 5 and 7). A single model combining the near infrared (NIR 0.1558 to 0.1804, depending on sensors) and short-wave infrared (SWIR2 0.0871 to 0.1131) wavelengths was identified by three independent analyses, each one using a dierent satellite. Overall accuracy of water maps ranged from 89% to 94% for the training samples and from 90% to 94% for the validation samples, encompassing standard water indices that systematically underestimate flooding duration under vegetation cover. Sentinel 2 provided the highest performance with a kappa coecient of 0.82 for both samples. Such tool will be most useful for monitoring the water dynamics of seasonal wetlands, which are particularly sensitive to climate change while providing multiple services to humankind. Considering the high temporal resolution of Sentinel 2 (every 5 days), cumulative water maps built with the WIW logical rule could further be used for mapping a wide range of wetlands which are either periodically or permanently flooded
Multispectral approach for identifying invasive plant species based on flowering phenology characteristics
No full textInvasive plant species (IPS) are the second biggest threat to biodiversity after habitat loss. Since the spatial extent of IPS is essential for managing the invaded ecosystem, the current study aims at identifying and mapping the aggressive IPS of Acacia salicina and Acacia saligna, to understand better the key factors influencing their distribution in the coastal plain of Israel. This goal was achieved by integrating airborne-derived hyperspectral imaging and multispectral earth observation for creating species distribution maps. Hyperspectral data, in conjunction with high spatial resolution species distribution maps, were used to train the multispectral images at the species level. We incorporated a series of statistical models to classify the IPS location and to recognize their distribution and density. We took advantage of the phenological flowering stages of Acacia trees, as obtained by the multispectral images, for the support vector machine classification procedure. The classification yielded an overall Kappa coefficient accuracy of 0.89. We studied the e effect of various environmental and human factors on IPS density by using a random forest machine learning model, to understand the mechanisms underlying successful invasions, and to assess where IPS have a higher likelihood of occurring. This algorithm revealed that the high density of Acacia most closely related to elevation, temperature pattern, and distances from rivers, settlements, and roads. Our results demonstrate how the integration of remote-sensing data with different data sources can assist in determining IPS proliferation and provide detailed geographic information for conservation and management efforts to prevent their future spread
Optimal dates for assessing long-term changes in tree cover in the semi-arid biomes of South Africa using MODIS NDVI time series (2001-2018)
No full textThe varying proportions of tree and herbaceous cover in the grassland and savanna biomes of Southern Africa determine their capacity to provide ecosystem services. The asynchronous phenologies e.g. annual NDVI profiles of grasses and trees in these semi-arid landscapes provide an opportunity to estimate percentage tree-cover by determining the period of maximum contrast between grasses and trees. First, a 16-day NDVI time series was generated from MODIS NDVI data, i.e. MOD13A2 16-day NDVI composite data. Secondly, percentage tree-cover data for 100 sample polygons (4?4) pixels for areas that have not undergone change in tree cover between 2001 and 2018 were derived using high resolution Google Earth imagery. Next, a time series consisting of the coefficients of determination (R2) for the NDVI/tree-cover linear regression were computed for the 100 polygons. Lastly, a threshold R2>0.5 was used to determine the optimal period of the year for mapping tree-cover. It emerged that the narrow period from Julian day 161?177 (June 10?26) was the most consistent period with R2>0.5 in the region. 18 tree-cover maps (2001?2018) were generated using linear regression model coefficients derived from Julian day 161 for each year. Kendall correlation coefficient (tau) was used to determine areas of significant (p < 0.05 and p < 0.01) increasing or decreasing trend in tree-cover. Areas (polygons) that showed increasing tree-cover appeared to be more widespread in the trend map as compared to areas of decreasing tree-cover. An accuracy assessment of the map of increasing tree-cover was conducted using Google Earth high resolution images. Out of 330 and 200 mapped polygons verified using p < 0.05 and 0.01 thresholds, respectively, 180 (54% accuracy) and 132 (65% accuracy) showed evidence of tree recruitment. Farm abandonment appeared to have been the most important factor contributing to increasing tree-cover in the region
A comparison of remotely-sensed and inventory datasets for burned area in Mediterranean Europe
No full textQuantitative estimate of observational uncertainty is an essential ingredient to correctly interpret changes in climatic and environmental variables such as wildfires. In this work we compare four state-of-the-art satellite fire products with the gridded, ground-based EFFIS dataset for Mediterranean Europe and analyse their statistical differences. The data are compared for spatial and temporal similarities at different aggregations to identify a spatial scale at which most of the observations provide equivalent results. The results of the analysis indicate that the datasets show high temporal correlation with each other (0.5/0.6) when aggregating the data at resolution of at least 1.0? or at NUTS3 level. However, burned area estimates vary widely between datasets. Filtering out satellite fires located on urban and crop land cover classes greatly improves the agreement with EFFIS data. Finally, in spite of the differences found in the area estimates, the spatial pattern is similar for all the datasets, with spatial correlation increasing as the resolution decreases. Also, the general reasonable agreement between satellite products builds confidence in using these datasets and in particular the most-recent developed dataset, FireCCI51, shows the best agreement with EFFIS overall. As a result, the main conclusion of the study is that users should carefully consider the limitations of the satellite fire estimates currently available, as their uncertainties cannot be neglected in the overall uncertainty estimate/cascade that should accompany global or regional change studies and that removing fires on human-dominated land areas is key to analyze forest fires estimation from satellite products
Data on alpine grassland diversity in Gran Paradiso National Park, Italy
No full textThe diversity of alpine grassland species and their functional traits constitute alpine ecosystem functioning and services that support human-wellbeing. However, alpine grassland diversity is threatened by land use and climate change. Field surveys and monitoring are necessary to understand and preserve such endangered ecosystems. Here we describe data on abundances (percentage cover) of 247 alpine plant species (including mosses and lichens) inside nine 20 m by 20 m plots that were subdivided into 2 m by 2 m subplots. The nine plots are located in Gran Paradiso National Park, Italy. They cover three distinct alpine vegetation subtypes (\u27pure\u27 natural grassland, sparsely vegetated \u27rocky\u27 grassland, and wetland) in each of three valleys (Bardoney, Colle de Nivolet and Levionaz) between 2200 and 2700 m a.s.l., i.e. above the treeline. The vegetation survey was conducted in 2015 at the peak of vegetation development during August. The dataset is provided as supplementary material and associated with the research article "Optimizing sampling effort and information content of biodiversity surveys: a case study of alpine grassland" [1]. See [1] for data interpretation
Protected Area management: Fusion and confusion with the ecosystem services approach
No full textFor many years, Protected Areas (PA) have been an important tool for conserving nature. Recently, also societal aspects have been introduced into PAmanagement via the introduction of the EcosystemServices (ES) approach. This review discusses the historical background of PAs, PA management, and the ES approach.We then discuss the relevance and applicability of the ES approach for PA management, including the different definitions of ES, different classification methods, and the ways in which ES are measured. We conclude that there are still major challenges ahead in using the ES approach in PA management and so recommendations are given on the way in which the ES approach should be integrated into PA management
DEIMS-SDR - A web portal to document research sites and their associated data
No full textClimate change and other drivers are affecting ecosystems around the globe. In order to enable a better understanding of ecosystem functioning and to develop mitigation and adaptation strategies in response to environmental change, a broad range of information, including in-situ observations of both biotic and abiotic parameters, needs to be considered. Access to sufficient and well documented in-situ data from long term observations is therefore one of the key requirements for modelling and assessing the effects of global change on ecosystems. Usually, such data is generated by multiple providers; often not openly available and with improper documentation. In this regard, metadata plays an important role in aiding the findability, accessibility and reusability of data as well as enabling reproducibility of the results leading to management decisions. This metadata needs to include information on the observation location and the research context. For this purpose we developed the Dynamic Ecological Information Management System - Site and Dataset Registry (DEIMS-SDR), a research and monitoring site registry (https://www.deims.org/) that not only makes it possible to describe insitu observation or experimentation sites, generating persistent, unique and resolvable identifiers for each site, but also to document associated data linked to each site. This article describes the system architecture and illustrates the linkage of contextual information to observational data. The aim of DEIMS-SDR is to be a globally comprehensive site catalogue describing a wide range of sites, providing a wealth of information, including each site\u27s location, ecosystems, facilities, measured parameters and research themes and enabling that standardised information to be openly available
Predicted climate shifts within terrestrial protected areas Worldwide.
No full textProtected areas (PA) are refugia of biodiversity. However, anthropogenic climate change induces a redistribution of life on Earth that affects the effectiveness of PAs. When species are forced to migrate from protected to unprotected areas to track suitable climate, they often face degraded habitats in human-dominated landscapes and a higher extinction threat. Here, we assess how climate conditions are expected to shift within the world\u27s terrestrial PAs (n = 137,432). PAs in the temperate and northern high-latitude biomes are predicted to obtain especially high area proportions of climate conditions that are novel within the PA network at the local, regional and global scale by the end of this century. These PAs are predominantly small, at low elevation, with low environmental heterogeneity, high human pressure, and low biotic uniqueness. Our results guide adaptation measures towards PAs that are strongly affected by climate change, and of low adaption capacity and high conservation value
Deep learning versus OBIA for scattered shrub detection with Google Earth imagery: ziziphus lotus as case study
No full textThere is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been traditionally performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image often cannot be extrapolated to a different image. Recently, deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in computer vision and are offering promising results in land cover mapping. This paper analyzes the potential of CNN-based methods for detection of plant species of conservation concern using free high-resolution Google EarthTM images and provides an objective comparison with the state-of-the-art OBIA-methods. We consider as case study the detection of Ziziphus lotus shrubs, which are protected as a priority habitat under the European Union Habitats Directive. Compared to the best performing OBIA-method, the best CNN-detector achieved up to 12% better precision, up to 30% better recall and up to 20% better balance between precision and recall. Besides, the knowledge that CNNs acquired in the first image can be re-utilized in other regions, which makes the detection process very fast. A natural conclusion of this work is that including CNN-models as classifiers, e.g., ResNet-classifier, could further improve OBIA methods. The provided methodology can be systematically reproduced for other species detection using our codes available through (https://github.com/EGuirado/CNN-remotesensing)
Spatiotemporal dynamics of plant diversity and endemism during primary succession on an oceanic-volcanic island
No full textQuestions: How does the diversity of native, endemic and alien plant species, as well as the diversity of plant life forms, change during primary succession on lava flows of an oceanic?volcanic island? How do environmental factors such as moisture and soil properties alter diversity during primary succession? Location: La Palma, Canary Islands. Methods: We recorded vascular plants and bryophytes in 210 plots on a chronosequence of nine lava flows spanning approx. 6,000 years and covering an elevational range of 1,100 m. In a subset (n = 78 plots) we collected and analyzed soil samples for soil nitrogen and plant?available phosphorus. We used generalized linear models, variance partitioning and structural equation models (SEMs) to analyze the data. Results: Species richness, endemic richness and alien richness increased with time. Natives dominated during early successional stages, whereas endemics and aliens increased with time. At early successional stages, vascular plants and bryophytes had an equal contribution to the species pool, while vascular plants increased up to an 80% contribution at later stages. In the variance partitioning and SEMs, time was the only consistent factor influencing different aspects of diversity during succession (species richness, endemic richness and percent endemism). Only for percent endemism did soil attributes have a substantial impact. Conclusion: Primary succession on lava flows on La Palma shows a pattern of increasing overall diversity, endemism and alien richness with time. Time is the only factor consistently explaining diversity and endemism, indicating that environmental influences such as climate and soil properties do not substantially alter them during primary succession. Our study contributes to understanding how different facets of diversity assemble through time by using an understudied, yet important island system, and, for the first time, specifically addresses how endemics contribute to the process of primary succession