Suchergebnisse

Olive tree growing is an important economic activity in many countries, mostly in the Mediterranean Basin, Argentina, Chile, Australia, and California. Although recent intensification techniques organize olive groves in hedgerows, most olive groves are rainfed and the trees are scattered (as in Spain and Italy, which account for 50% of the world's olive oil production). Accurate measurement of trees biovolume is a first step to monitor their performance in olive production and health. In this work, we use one of the most accurate deep learning instance segmentation methods (Mask R-CNN) and unmanned aerial vehicles (UAV) images for olive tree crown and shadow segmentation (OTCS) to further estimate the biovolume of individual trees. We evaluated our approach on images with different spectral bands (red, green, blue, and near infrared) and vegetation indices (normalized difference vegetation index—NDVI—and green normalized difference vegetation index—GNDVI). The performance of red-green-blue (RGB) images were assessed at two spatial resolutions 3 cm/pixel and 13 cm/pixel, while NDVI and GNDV images were only at 13 cm/pixel. All trained Mask R-CNN-based models showed high performance in the tree crown segmentation, particularly when using the fusion of all dataset in GNDVI and NDVI (F1-measure from 95% to 98%). The comparison in a subset of trees of our estimated biovolume with ground truth measurements showed an average accuracy of 82%. Our results support the use of NDVI and GNDVI spectral indices for the accurate estimation of the biovolume of scattered trees, such as olive trees, in UAV images. ; Russian Foundation for Basic Research (RFBR) 19-01-00215 20-07-00370 ; European Research Council (ERC) European Commission 647038 ; Spanish Government RYC-2015-18136 ; Consejeria de Economia, Conocimiento y Universidad de la Junta de Andalucia P18-RT-1927 ; DETECTOR A-RNM-256-UGR18 ; European Research and Development Funds (ERDF) program

Wildfire disturbances can cause modifications in different dimensions of ecosystem functioning, i.e., the flows of matter and energy. There is an increasing need for methods to assess such changes, as functional approaches offer advantages over those focused solely on structural or compositional attributes. In this regard, remote sensing can support indicators for estimating a wide variety of effects of fire on ecosystem functioning, beyond burn severity assessment. These indicators can be described using intra-annual metrics of quantity, seasonality, and timing, called Ecosystem Functioning Attributes (EFAs). Here, we propose a satellite-based framework to evaluate the impacts, at short to medium term (i.e., from the year of fire to the second year after), of wildfires on four dimensions of ecosystem functioning: (i) primary productivity, (ii) vegetation water content, (iii) albedo, and (iv) sensible heat. We illustrated our approach by comparing inter-annual anomalies in satellite-based EFAs in the northwest of the Iberian Peninsula, from 2000 to 2018. Random Forest models were used to assess the ability of EFAs to discriminate burned vs. unburned areas and to rank the predictive importance of EFAs. Together with effect sizes, this ranking was used to select a parsimonious set of indicators for analyzing the main effects of wildfire disturbances on ecosystem functioning, for both the whole study area (i.e., regional scale), as well as for four selected burned patches with different environmental conditions (i.e., local scale). With both high accuracies (area under the receiver operating characteristic curve (AUC) > 0.98) and effect sizes (Cohen's |d| > 0.8), we found important effects on all four dimensions, especially on primary productivity and sensible heat, with the best performance for quantity metrics. Different spatiotemporal patterns of wildfire severity across the selected burned patches for different dimensions further highlighted the importance of considering the multi-dimensional effects of wildfire disturbances on key aspects of ecosystem functioning at different timeframes, which allowed us to diagnose both abrupt and lagged effects. Finally, we discuss the applicability as well as the potential advantages of the proposed approach for more comprehensive assessments of fire severity. ; Portuguese national funds through FCT-Foundation for Science and Technology, I.P., under the GreenRehab project PCIF/RPG/0077/2017 ; Junta de Andalucia P18-RT-1927 ; Project DETECTOR A-RNM-256-UGR18 ; European Union Funds for Regional Development ; Portuguese Foundation for Science and Technology European Commission ; Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) European Commission ; European Social Fund, within the 2014-2020 EU Strategic Framework, through FCT SFRH/BD/99469/2014 ; Individual Scientific Employment Stimulus Program (2017), through FCT CEECIND/02331/2017

We thank R Romero-Calcerrada and J M Requena-Mullor for helpful discussions, and three anonymous reviewers for their constructive suggestions to improve this paper. We also thank the Spanish Ministry of Economy and Business (Project CGL2014-61610-EXP) for the financial support, as well as the Spanish Ministry of Education for the FPU Predoctoral Fellowship of MPR (FPU14/06782) and MTTG (16/02214). MPR gratefully acknowledges funding from Universidad de Almeria for a research stay at the Laboratory of Regional Analysis and Remote Sensing (LART) of University of Buenos Aires to develop this study. This research was done within the LTSER Platforms of the Arid Iberian South East-Spain (LTER_EU_ES_027) and Sierra Nevada/Granada (ES- SNE)-Spain (LTER_EU_ES_010), and contributes to the Global Land Programme. ; The spatial mapping of social-ecological system (SES) archetypes constitutes a fundamental tool to operationalize the SES concept in empirical research. Approaches to detect, map, and characterize SES archetypes have evolved over the last decade towards more integrative and comparable perspectives guided by SES conceptual frameworks and reference lists of variables. However, hardly any studies have investigated how to empirically identify the most relevant set of indicators to map the diversity of SESs. In this study, we propose a data-driven methodological routine based on multivariate statistical analysis to identify the most relevant indicators for mapping and characterizing SES archetypes in a particular region. Taking Andalusia (Spain) as a case study, we applied this methodological routine to 86 indicators representing multiple variables and dimensions of the SES. Additionally, we assessed how the empirical relevance of these indicators contributes to previous expert and empirical knowledge on key variables for characterizing SESs. We identified 29 key indicators that allowed us to map 15 SES archetypes encompassing natural, mosaic, agricultural, and urban systems, which uncovered contrasting land sharing and land sparing patterns throughout the territory. We found synergies but also disagreements between empirical and expert knowledge on the relevance of variables: agreement on their widespread relevance (32.7% of the variables, e.g. crop and livestock production, net primary productivity, population density); relevance conditioned by the context or the scale (16.3%, e.g. land protection, educational level); lack of agreement (20.4%, e.g. economic level, land tenure); need of further assessments due to the lack of expert or empirical knowledge (30.6%). Overall, our data-driven approach can contribute to more objective selection of relevant indicators for SES mapping, which may help to produce comparable and generalizable empirical knowledge on key variables for characterizing SESs, as well as to derive more representative descriptions and causal factor configurations in SES archetype analysis. ; Spanish Government CGL2014-61610-EXP FPU14/06782 16/02214 ; Universidad de Almeria ; LTSER Platforms of the Arid Iberian South East-Spain LTER_EU_ES_027 ; Sierra Nevada/Granada (ES- SNE)-Spain LTER_EU_ES_010

"Offering a balanced review of differing approaches based on remote sensing methods to monitor ecosystem services related to biodiversity conservation, carbon and water cycles, and the energy balance of the terrestrial ecosystem, this book identifies the relevant issues and challenges of assessment, presents cutting-edge sensing techniques, uses globally implemented tools to quantify ecosystem functions, and provides numerous examples of successful monitoring programs. Covering recent developments undertaken on the global and national stage from earth observation satellite data, it includes valuable lessons and recommendations and novel ways to improve current global monitoring systems"--

In the "digital conservation" age, big data from Earth observations and from social media have been increasingly used to tackle conservation challenges. Here, we combined information from those two digital sources in a multimodel inference framework to identify, map, and predict the potential for nature's cultural contributions to people in two contrasting UNESCO biosphere reserves: Doñana and Sierra Nevada (Spain). The content analysis of Flickr pictures revealed different cultural contributions, according to the natural and cultural values of the two reserves. Those contributions relied upon landscape variables computed from Earth observation data: the variety of colors and vegetation functioning that characterize Doñana landscapes, and the leisure facilities, accessibility features, and heterogeneous landscapes that shape Sierra Nevada. Our findings suggest that social media and Earth observations can aid in the cost-efficient monitoring of nature's contributions to people, which underlie many Sustainable Development Goals and conservation targets in protected areas worldwide. ; European Union'sHorizon 2020 research and innovation programme,Grant/Award Number: 641762; Program for Excellent Units of the Plan Propio de Investigación of the University of Granada; European Union; University of Granada, Spain; Portuguese Science Foundation, Grant/Award Numbers: DL57/2016/ICETA/EEC2018/13, CEECIND/02331/2017

Offering a balanced review of differing approaches based on remote sensing methods to monitor ecosystem services related to biodiversity conservation, carbon and water cycles, and the energy balance of the terrestrial ecosystem, this book identifies the relevant issues and challenges of assessment, presents cutting-edge sensing techniques, uses globally implemented tools to quantify ecosystem functions, and provides numerous examples of successful monitoring programs. Covering recent developments undertaken on the global and national stage from earth observation satellite data, it includes

Conservation biology must set geographic conservation priorities not only based on the compositional or structural but also on the functional dimensions of biodiversity. However, assessing functional diversity is challenging at the regional scale. We propose the use of satellite-derived Ecosystem Functional Types (EFTs), defined here as patches of land surface that share similar primary production dynamics, to incorporate such aspects of ecosystem functional diversity into the selection of protected areas. We applied the EFT approach to the Baja California Peninsula, Mexico, to characterize the regional heterogeneity of primary production dynamics in terms of EFTs; to set conservation priorities based on EFT richness and rarity; and to explore whether such EFT-based conservation priorities were consistent with and/or complementary to previous assessments focused on biodiversity composition and structure. EFTs were identified based on three ecosystem functional attributes derived from seasonal dynamics of the Enhanced Vegetation Index: the annual mean (proxy of primary production), the seasonal coefficient of variation (descriptor of seasonality), and the date of maximum (indicator of phenology). EFT-based priorities identified 26% of the peninsula as being of extreme or high priority and reinforced the value of the ecosystem functional diversity of areas already prioritized by traditional conservation assessments. In addition, our study revealed that biodiversity composition- and structure-based assessments had not identified the full range of important areas for EFT diversity and tended to better capture areas of high EFT rarity than those of high EFT richness. Our EFT-based assessment demonstrates how remotely sensed regional heterogeneity in ecosystem functions could reinforce and complement traditional conservation priority setting. ; European Union (EU) ; Spanish MINECO CGL2014-61610-EXP ; University of Almeria (PhD contract: research training program) ; European Union (EU) 641762 ; NASA 2016 GEOBON Work Programme Grant 80NSSC18K0446

We acknowledge the use of MODIS imagery obtained from NASA's Land Processes Distributed Active Archive Center (LP DAAC), available free of charge. The authors would like to thank the anonymous reviewers for providing comments and suggestions that helped to improve the quality of the original manuscript. ; Wildfire disturbances can cause modifications in different dimensions of ecosystem functioning, i.e., the flows of matter and energy. There is an increasing need for methods to assess such changes, as functional approaches offer advantages over those focused solely on structural or compositional attributes. In this regard, remote sensing can support indicators for estimating a wide variety of effects of fire on ecosystem functioning, beyond burn severity assessment. These indicators can be described using intra-annual metrics of quantity, seasonality, and timing, called Ecosystem Functioning Attributes (EFAs). Here, we propose a satellite-based framework to evaluate the impacts, at short to medium term (i.e., from the year of fire to the second year after), of wildfires on four dimensions of ecosystem functioning: (i) primary productivity, (ii) vegetation water content, (iii) albedo, and (iv) sensible heat. We illustrated our approach by comparing inter-annual anomalies in satellite-based EFAs in the northwest of the Iberian Peninsula, from 2000 to 2018. Random Forest models were used to assess the ability of EFAs to discriminate burned vs. unburned areas and to rank the predictive importance of EFAs. Together with effect sizes, this ranking was used to select a parsimonious set of indicators for analyzing the main effects of wildfire disturbances on ecosystem functioning, for both the whole study area (i.e., regional scale), as well as for four selected burned patches with different environmental conditions (i.e., local scale). With both high accuracies (area under the receiver operating characteristic curve (AUC) > 0.98) and effect sizes (Cohen's |d| > 0.8), we found important effects on all four dimensions, especially on primary productivity and sensible heat, with the best performance for quantity metrics. Different spatiotemporal patterns of wildfire severity across the selected burned patches for different dimensions further highlighted the importance of considering the multi-dimensional effects of wildfire disturbances on key aspects of ecosystem functioning at different timeframes, which allowed us to diagnose both abrupt and lagged effects. Finally, we discuss the applicability as well as the potential advantages of the proposed approach for more comprehensive assessments of fire severity. ; Portuguese national funds through FCT-Foundation for Science and Technology, I.P., under the GreenRehab project PCIF/RPG/0077/2017 ; Junta de Andalucia P18-RT-1927 ; European Union Funds for Regional Development ; Project DETECTOR A-RNM-256-UGR18 ; European Commission ; Portuguese Foundation for Science and Technology European Commission ; Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) European Commission ; European Social Fund, within the 2014-2020 EU Strategic Framework, through FCT SFRH/BD/99469/2014 ; Individual Scientific Employment Stimulus Program (2017), through FCT CEECIND/02331/2017

Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-019-40766-5. ; The ability of ecological niche models (ENMs) to produce robust predictions for different time frames (i.e. temporal transferability) may be hindered by a lack of ecologically relevant predictors. Model performance may also be affected by species traits, which may reflect different responses to processes controlling species distribution. In this study, we tested four primary hypotheses involving the role of species traits and environmental predictors in ENM performance and transferability. We compared the predictive accuracy of ENMs based upon (1) climate, (2) land-use/cover (LULC) and (3) ecosystem functional attributes (EFAs), and (4) the combination of these factors for 27 bird species within and beyond the time frame of model calibration. The combination of these factors significantly increased both model performance and transferability, highlighting the need to integrate climate, LULC and EFAs to improve biodiversity projections. However, the overall model transferability was low (being only acceptable for less than 25% of species), even under a hierarchical modelling approach, which calls for great caution in the use of ENMs to predict bird distributions under global change scenarios. Our findings also indicate that positive effects of species traits on predictive accuracy within model calibration are not necessarily translated into higher temporal transferability. ; This research was developed as part of the project ECOPOTENTIAL, which received funding from the European Union's Horizon 2020 Research and Innovation Programme under agreement No. 641762. We thank everyone who contributed to the fieldwork: Xosé Pardavila, Adrián Lamosa (Sorex, Ecoloxía e Medio Ambiente SL), Marta Arenas, Alberto Toupa and Fernando Martínez-Freiría. Field surveys were funded by the project INTERREG-POCTEC ('NATURA Xurés-Gerês'). A.R. was financially supported by the Xunta de Galicia (post-doctoral fellowship ED481B2016/084-0). Miquel Ninyerola and Meritxell Batalla (UAB) generated climate variables from data provided by the Spanish Meteorological Agency and the Spanish Ministry of Marine and Rural Environment within the MONTES-Consolider project (CSD2008-00040).

Despite their interest and threat status, the number of whales in world's oceans remains highly uncertain. Whales detection is normally carried out from costly sighting surveys, acoustic surveys or through high-resolution images. Since deep convolutional neural networks (CNNs) are achieving great performance in several computer vision tasks, here we propose a robust and generalizable CNN-based system for automatically detecting and counting whales in satellite and aerial images based on open data and tools. In particular, we designed a two-step whale counting approach, where the first CNN finds the input images with whale presence, and the second CNN locates and counts each whale in those images. A test of the system on Google Earth images in ten global whale-watching hotspots achieved a performance (F1-measure) of 81% in detecting and 94% in counting whales. Combining these two steps increased accuracy by 36% compared to a baseline detection model alone. Applying this cost-effective method worldwide could contribute to the assessment of whale populations to guide conservation actions. Free and global access to high-resolution imagery for conservation purposes would boost this process. ; S.T. was supported by the Ramón y Cajal Programme of the Spanish government (RYC-2015-18136). S.T., E.G., and F.H. were supported by the Spanish Ministry of Science under the project TIN2017-89517-P. D. A-S. received support from European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612, and from ERDF and Andalusian Government under the project GLOCHARID. D.A.-S. received support from NASA Work Programme on Group on Earth Observations - Biodiversity Observation Network (GEOBON) under grant 80NSSC18K0446, from project ECOPOTENTIAL, funded by European Union Horizon 2020 Research and Innovation Programme under grant agreement No. 641762, and from the Spanish Ministry of Science under project CGL2014-61610-EXP and grant JC2015-00316. M.R. received support from International mobility grant for prestigious researchers by (CEIMAR) International Campus of Excellence of the Sea.

Global environmental changes are rapidly affecting species' distributions and habitat suitability worldwide, requiring a continuous update of biodiversity status to support effective decisions on conservation policy and management. In this regard, satellite-derived Ecosystem Functional Attributes (EFAs) offer a more integrative and quicker evaluation of ecosystem responses to environmental drivers and changes than climate and structural or compositional landscape attributes. Thus, EFAs may hold advantages as predictors in Species Distribution Models (SDMs) and for implementing multi-scale species monitoring programs. Here we describe a modelling framework to assess the predictive ability of EFAs as Essential Biodiversity Variables (EBVs) against traditional datasets (climate, land-cover) at several scales. We test the framework with a multi-scale assessment of habitat suitability for two plant species of conservation concern, both protected under the EU Habitats Directive, differing in terms of life history, range and distribution pattern (Iris boissieri and Taxus baccata). We fitted four sets of SDMs for the two test species, calibrated with: interpolated climate variables; landscape variables; EFAs; and a combination of climate and landscape variables. EFAbased models performed very well at the several scales (AUCmedian from 0.881±0.072 to 0.983±0.125), and similarly to traditional climate-based models, individually or in combination with land-cover predictors (AUCmedian from 0.882±0.059 to 0.995±0.083). Moreover, EFAbased models identified additional suitable areas and provided valuable information on functional features of habitat suitability for both test species (narrowly vs. widely distributed), for both coarse and fine scales. Our results suggest a relatively small scale-dependence of the predictive ability of satellite-derived EFAs, supporting their use as meaningful EBVs in SDMs from regional and broader scales to more local and finer scales. Since the evaluation of species' conservation status and habitat quality should as far as possible be performed based on scalable indicators linking to meaningful processes, our framework may guide conservation managers in decision-making related to biodiversity monitoring and reporting schemes. ; This research was developed as part of the ECOPOTENTIAL project financed by European Union's Horizon 2020 research and innovation program under grant agreement No. 641762. SAC, DAS and JPH received funding from the ECOPOTENTIAL project. JG was supported by FCT (Portuguese Science Foundation) through PhD grant SFRH/BD/90112/2012. DAS received funding from Ministerio de Educación, Cultura y Deporte, JC2015-00316 grant, and Ministerio de Ciencia e Innovación, CGL2014-61610-EXP project.

There 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 Earth TM 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). ; Siham Tabik was supported by the Ramón y Cajal Programme (RYC-2015-18136). ; The work was partially supported by the Spanish Ministry of Science and Technology under the projects: TIN2014-57251-P, CGL2014-61610-EXP, CGL2010-22314 and grant JC2015-00316, and ERDF and Andalusian Government under the projects: GLOCHARID, RNM-7033, P09-RNM-5048 and P11-TIC-7765. ; This research was also developed as part of project ECOPOTENTIAL, which received funding from the European Union Horizon 2020 Research and Innovation Programme under grant agreement No. 641762, and by the European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612.

Global environmental changes are rapidly affecting species' distributions and habitat suitability worldwide, requiring a continuous update of biodiversity status to support effective decisions on conservation policy and management. In this regard, satellite-derived Ecosystem Functional Attributes (EFAs) offer a more integrative and quicker evaluation of ecosystem responses to environmental drivers and changes than climate and structural or compositional landscape attributes. Thus, EFAs may hold advantages as predictors in Species Distribution Models (SDMs) and for implementing multi-scale species monitoring programs. Here we describe a modelling framework to assess the predictive ability of EFAs as Essential Biodiversity Variables (EBVs) against traditional datasets (climate, land-cover) at several scales. We test the framework with a multi-scale assessment of habitat suitability for two plant species of conservation concern, both protected under the EU Habitats Directive, differing in terms of life history, range and distribution pattern (Iris boissieri and Taxus baccata). We fitted four sets of SDMs for the two test species, calibrated with: interpolated climate variables; landscape variables; EFAs; and a combination of climate and landscape variables. EFA-based models performed very well at the several scales (AUCmedian from 0.881±0.072 to 0.983±0.125), and similarly to traditional climate-based models, individually or in combination with land-cover predictors (AUCmedian from 0.882±0.059 to 0.995±0.083). Moreover, EFA-based models identified additional suitable areas and provided valuable information on functional features of habitat suitability for both test species (narrowly vs. widely distributed), for both coarse and fine scales. Our results suggest a relatively small scale-dependence of the predictive ability of satellite-derived EFAs, supporting their use as meaningful EBVs in SDMs from regional and broader scales to more local and finer scales. Since the evaluation of species' conservation status and ...