Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists

Technical Design of a Low-Latitude Satellite Constellation for Ocean Observation with a Focus on Hainan Province, China

Acquiring high-quality images from space at low-latitude areas is challenging due to the orbital requirements of the satellites and the frequent cloud coverage. To address this issue, a low-latitude remote sensing satellite constellation-the Hainan Satellite Constellation (HSC)-was conceived with a spatial coverage-priority concept. This constellation integrates sensors with multispectral, hyperspectral, radar, and Automatic Identification System (AIS) capabilities for marine vessels with an onboard image processing technology. The design is tailored to the tropical/subtropical region. Once HSC becomes fully operational, it will provide high-frequency coverage in low-latitude regions, with a primary focus on ocean observations. The first four optical satellites (HN-1 01/02 and WC-1 01/02) were successfully launched in February 2022. They boast unique application characteristics, including satellite networking for ocean observations over large areas, onboard image processing and modeling for ship detection, as well as the synergy of onboard sensors with optical and ship AIS capabilities. This study focuses on the technical design and proposes implementation strategies for HSC, encompassing its technical characteristics, composition, and capacity. Additionally, it explores the construction of this satellite constellation and its uses while providing insights into potential follow-up satellites.

A Novel Approach to Pod Count Estimation Using a Depth Camera in Support of Soybean Breeding Applications

Improving soybean (Glycine max L. (Merr.)) yield is crucial for strengthening national food security. Predicting soybean yield is essential to maximize the potential of crop varieties. Non-destructive methods are needed to estimate yield before crop maturity. Various approaches, including the pod-count method, have been used to predict soybean yield, but they often face issues with the crop background color. To address this challenge, we explored the application of a depth camera to real-time filtering of RGB images, aiming to enhance the performance of the pod-counting classification model. Additionally, this study aimed to compare object detection models (YOLOV7 and YOLOv7-E6E) and select the most suitable deep learning (DL) model for counting soybean pods. After identifying the best architecture, we conducted a comparative analysis of the model's performance by training the DL model with and without background removal from images. Results demonstrated that removing the background using a depth camera improved YOLOv7's pod detection performance by 10.2% precision, 16.4% recall, 13.8% mAP@50, and 17.7% mAP@0.5:0.95 score compared to when the background was present. Using a depth camera and the YOLOv7 algorithm for pod detection and counting yielded a mAP@0.5 of 93.4% and mAP@0.5:0.95 of 83.9%. These results indicated a significant improvement in the DL model's performance when the background was segmented, and a reasonably larger dataset was used to train YOLOv7.

Multispectral versus texture features from ZiYuan-3 for recognizing on deciduous tree species with cloud and SVM models

Tree species recognition accuracy greatly affects forest remote sensing mapping and forestry resource monitoring. The multispectral and texture features of the remote sensing images from the ZiYuan-3 (ZY-3) satellite at two phenological phases of autumn and winter (September 29th and December 7th) were selected for constructing and optimizing sensitive spectral indices and texture indices. Multidimensional cloud model and support vector machine (SVM) model were constructed by the screened spectral and texture indices for remote sensing recognition of Quercus acutissima (Q. acutissima) and Robinia pseudoacacia (R. pseudoacacia) on Mount Tai. The results showed that, the correlation intensities of the constructed spectral indices with tree species were preferable in winter than in autumn. The spectral indices constructed by band 4 showed the superior correlation compared with other bands, both in the autumn and winter time phases. The optimal sensitive texture indices for both phases were mean, homogeneity and contrast for Q. acutissima, and contrast, dissimilarity and second moment for R. pseudoacacia. Spectral features were found to have a higher recognition accuracy than textural features for recognizing on both Q. acutissima and R. pseudoacacia, and winter showing superior recognition accuracy than autumn, especially for Q. acutissima. The recognition accuracy of the multidimensional cloud model (89.98%) does not show a superior advantage over the one-dimensional cloud model (90.57%). The highest recognition accuracy derived from a three-dimensional SVM was 84.86%, which was lower than the cloud model (89.98%) in the same dimension. This study is expected to provide technical support for the precise recognition and forestry management on Mount Tai.

Habitat configuration of the Yangtze finless porpoise in Poyang Lake under a shifting hydrological regime

Habitats of freshwater cetaceans are under increasing threats of deterioration globally. A complete understanding of long-term variations of habitat configurations is therefore critical. Poyang Lake in China contains a large and stable population of the Yangtze finless porpoise, a critically endangered freshwater cetacean species. However, constant water decline and intensified human activities in the lake since 2000 have led to uncertainty for porpoise conservation. We address this issue via remote sensing and hydrodynamic modeling of nine environmental variables during different seasons over the past two decades. The MaxEnt model was used to extrapolate changes in likely habitat configurations of the porpoise, and MARXAN algorithms delineated habitat protection priorities in different seasons. Results illustrate that flow velocity, water depth, Chl-a concentration, distance to grassland and boats greatly affect the porpoise distribution. Shifts in these environmental variables can lead to significant habitat decreases in all seasons. In particular, unstable hydrological regimes may force the porpoises to live in habitats with lower water depths for suitable flow velocity conditions in the dry season, and habitats are increasingly infringed by grassland and mudflats. High protection priority areas such as the northern channel and the estuaries of the tributaries urgently need long-term systematic and targeted surveys of ecosystem functionality and flexible management of anthropogenic activities. Combining remote sensing with hydrodynamic and species distribution models can also assist in understanding the situation of other aquatic species.

Multisensor UAS mapping of Plant Species and Plant Functional Types in Midwestern Grasslands

Uncrewed aerial systems (UASs) have emerged as powerful ecological observation platforms capable of filling critical spatial and spectral observation gaps in plant physiological and phenological traits that have been difficult to measure from space-borne sensors. Despite recent technological advances, the high cost of drone-borne sensors limits the widespread application of UAS technology across scientific disciplines. Here, we evaluate the tradeoffs between off-the-shelf and sophisticated drone-borne sensors for mapping plant species and plant functional types (PFTs) within a diverse grassland. Specifically, we compared species and PFT mapping accuracies derived from hyperspectral, multispectral, and RGB imagery fused with light detection and ranging (LiDAR) or structure-for-motion (SfM)-derived canopy height models (CHM). Sensor–data fusion were used to consider either a single observation period or near-monthly observation frequencies for integration of phenological information (i.e., phenometrics). Results indicate that overall classification accuracies for plant species and PFTs were highest in hyperspectral and LiDAR-CHM fusions (78 and 89%, respectively), followed by multispectral and phenometric–SfM–CHM fusions (52 and 60%, respectively) and RGB and SfM–CHM fusions (45 and 47%, respectively). Our findings demonstrate clear tradeoffs in mapping accuracies from economical versus exorbitant sensor networks but highlight that off-the-shelf multispectral sensors may achieve accuracies comparable to those of sophisticated UAS sensors by integrating phenometrics into machine learning image classifiers.

Leveraging Machine Learning and Geo-Tagged Citizen Science Data to Disentangle the Factors of Avian Mortality Events at the Species Level

Abrupt environmental changes can affect the population structures of living species and cause habitat loss and fragmentations in the ecosystem. During August–October 2020, remarkably high mortality events of avian species were reported across the western and central United States, likely resulting from winter storms and wildfires. However, the differences of mortality events among various species responding to the abrupt environmental changes remain poorly understood. In this study, we focused on three species, Wilson’s Warbler, Barn Owl, and Common Murre, with the highest mortality events that had been recorded by citizen scientists. We leveraged the citizen science data and multiple remotely sensed earth observations and employed the ensemble random forest models to disentangle the species responses to winter storm and wildfire. We found that the mortality events of Wilson’s Warbler were primarily impacted by early winter storms, with more deaths identified in areas with a higher average daily snow cover. The Barn Owl’s mortalities were more identified in places with severe wildfire-induced air pollution. Both winter storms and wildfire had relatively mild effects on the mortality of Common Murre, which might be more related to anomalously warm water. Our findings highlight the species-specific responses to environmental changes, which can provide significant insights into the resilience of ecosystems to environmental change and avian conservations. Additionally, the study emphasized the efficiency and effectiveness of monitoring large-scale abrupt environmental changes and conservation using remotely sensed and citizen science data.

OUP accepted manuscript

Extreme events have increased in frequency globally, with a simultaneous surge in scientific interest about their ecological responses, particularly in sensitive freshwater, coastal, and marine ecosystems. We synthesized observational studies of extreme events in these aquatic ecosystems, finding that many studies do not use consistent definitions of extreme events. Furthermore, many studies do not capture ecological responses across the full spatial scale of the events. In contrast, sampling often extends across longer temporal scales than the event itself, highlighting the usefulness of long-term monitoring. Many ecological studies of extreme events measure biological responses but exclude chemical and physical responses, underscoring the need for integrative and multidisciplinary approaches. To advance extreme event research, we suggest prioritizing pre- and postevent data collection, including leveraging long-term monitoring; making intersite and cross-scale comparisons; adopting novel empirical and statistical approaches; and developing funding streams to support flexible and responsive data collection.

A Comprehensive Overview of Technologies for Species and Habitat Monitoring and Conservation

The range of technologies currently used in biodiversity conservation is staggering, with innovative uses often adopted from other disciplines and being trialed in the field. We provide the first comprehensive overview of the current (2020) landscape of conservation technology, encompassing technologies for monitoring wildlife and habitats, as well as for on-the-ground conservation management (e.g., fighting illegal activities). We cover both established technologies (routinely deployed in conservation, backed by substantial field experience and scientific literature) and novel technologies or technology applications (typically at trial stage, only recently used in conservation), providing examples of conservation applications for both types. We describe technologies that deploy sensors that are fixed or portable, attached to vehicles (terrestrial, aquatic, or airborne) or to animals (biologging), complemented with a section on wildlife tracking. The last two sections cover actuators and computing (including web platforms, algorithms, and artificial intelligence).

Fusion neural networks for plant classification: learning to combine RGB, hyperspectral, and lidar data

Airborne remote sensing offers unprecedented opportunities to efficiently monitor vegetation, but methods to delineate and classify individual plant species using the collected data are still actively being developed and improved. The Integrating Data science with Trees and Remote Sensing (IDTReeS) plant identification competition openly invited scientists to create and compare individual tree mapping methods. Participants were tasked with training taxon identification algorithms based on two sites, to then transfer their methods to a third unseen site, using field-based plant observations in combination with airborne remote sensing image data products from the National Ecological Observatory Network (NEON). These data were captured by a high resolution digital camera sensitive to red, green, blue (RGB) light, hyperspectral imaging spectrometer spanning the visible to shortwave infrared wavelengths, and lidar systems to capture the spectral and structural properties of vegetation. As participants in the IDTReeS competition, we developed a two-stage deep learning approach to integrate NEON remote sensing data from all three sensors and classify individual plant species and genera. The first stage was a convolutional neural network that generates taxon probabilities from RGB images, and the second stage was a fusion neural network that “learns” how to combine these probabilities with hyperspectral and lidar data. Our two-stage approach leverages the ability of neural networks to flexibly and automatically extract descriptive features from complex image data with high dimensionality. Our method achieved an overall classification accuracy of 0.51 based on the training set, and 0.32 based on the test set which contained data from an unseen site with unknown taxa classes. Although transferability of classification algorithms to unseen sites with unknown species and genus classes proved to be a challenging task, developing methods with openly available NEON data that will be collected in a standardized format for 30 years allows for continual improvements and major gains for members of the computational ecology community. We outline promising directions related to data preparation and processing techniques for further investigation, and provide our code to contribute to open reproducible science efforts.

LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives

Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data.

The Road to Operationalization of Effective Tropical Forest Monitoring Systems

The urgency to preserve tropical forest remnants has encouraged the development of remote sensing tools and techniques to monitor diverse forest attributes for management and conservation. State-of-the-art methodologies for mapping and tracking these attributes usually achieve accuracies greater than 0.8 for forest cover monitoring; r-square values of ~0.5–0.7 for plant diversity, vegetation structure, and plant functional trait mapping, and overall accuracies of ~0.8 for categorical maps of forest attributes. Nonetheless, existing operational tropical forest monitoring systems only track single attributes at national to global scales. For the design and implementation of effective and integrated tropical forest monitoring systems, we recommend the integration of multiple data sources and techniques for monitoring structural, functional, and compositional attributes. We also recommend its decentralized implementation for adjusting methods to local climatic and ecological characteristics and for proper end-user engagement. The operationalization of the system should be based on all open-source computing platforms, leveraging international support in research and development and ensuring direct and constant user engagement. We recommend continuing the efforts to address these multiple challenges for effective monitoring.

Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield

In this study, we develop a method to estimate corn yield based on remote sensing data and ground monitoring data under different water treatments. Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. We found that leaf area index (LAI) inversion based on unmanned aerial vehicle (UAV) vegetation index has a high accuracy, with R2 and root mean square error (RMSE) values of 0.877 and 0.609, respectively. Maize yield estimation based on UAV remote sensing data and simple algorithm for yield (SAFY) crop model data assimilation has different yield estimation accuracy under different water treatments. This method can be used to estimate corn yield, where R2 is 0.855 and RMSE is 692.8kg/ha. Generally, the higher the water stress, the lower the estimation accuracy. Furthermore, we perform the yield estimate mapping at 2 m spatial resolution, which has a higher spatial resolution and accuracy than satellite remote sensing. The great potential of incorporating UAV observations with crop data to monitor crop yield, and improve agricultural management is therefore indicated.

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

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.

A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI

Satellite remote sensing has now become a unique tool for continuous and predictable monitoring of geosystems at various scales, observing the dynamics of different geophysical parameters of the environment. One of the essential problems with most satellite environmental monitoring methods is their sensitivity to atmospheric conditions, in particular cloud cover, which leads to the loss of a significant part of data, especially at high latitudes, potentially reducing the quality of observation time series until it is useless. In this paper, we present a toolbox for filling gaps in remote sensing time-series data based on machine learning algorithms and spatio-temporal statistics. The first implemented procedure allows us to fill gaps based on spatial relationships between pixels, obtained from historical time-series. Then, the second procedure is dedicated to filling the remaining gaps based on the temporal dynamics of each pixel value. The algorithm was tested and verified on Sentinel-3 SLSTR and Terra MODIS land surface temperature data and under different geographical and seasonal conditions. As a result of validation, it was found that in most cases the error did not exceed 1 °C. The algorithm was also verified for gaps restoration in Terra MODIS derived normalized difference vegetation index and land surface broadband albedo datasets. The software implementation is Python-based and distributed under conditions of GNU GPL 3 license via public repository.

Application of remote sensing technology to estimate productivity and assess phylogenetic heritability

PREMISE

Measuring plant productivity is critical to understanding complex community interactions. Many traditional methods for estimating productivity, such as direct measurements of biomass and cover, are resource intensive, and remote sensing techniques are emerging as viable alternatives.

METHODS

We explore drone-based remote sensing tools to estimate productivity in a tallgrass prairie restoration experiment and evaluate their ability to predict direct measures of productivity. We apply these various productivity measures to trace the evolution of plant productivity and the traits underlying it.

RESULTS

The correlation between remote sensing data and direct measurements of productivity varies depending on vegetation diversity, but the volume of vegetation estimated from drone-based photogrammetry is among the best predictors of biomass and cover regardless of community composition. The commonly used normalized difference vegetation index (NDVI) is a less accurate predictor of biomass and cover than other equally accessible vegetation indices. We found that the traits most strongly correlated with productivity have lower phylogenetic signal, reflecting the fact that high productivity is convergent across the phylogeny of prairie species. This history of trait convergence connects phylogenetic diversity to plant community assembly and succession.

DISCUSSION

Our study demonstrates (1) the importance of considering phylogenetic diversity when setting management goals in a threatened North American grassland ecosystem and (2) the utility of remote sensing as a complement to ground measurements of grassland productivity for both applied and fundamental questions.

A Review of the Sustainability Concept and the State of SDG Monitoring Using Remote Sensing

The formulation of the 17 sustainable development goals (SDGs) was a major leap forward in humankind’s quest for a sustainable future, which likely began in the 17th century, when declining forest resources in Europe led to proposals for the re-establishment and conservation of forests, a strategy that embodies the great idea that the current generation bears responsibility for future generations. Global progress toward SDG fulfillment is monitored by 231 unique social-ecological indicators spread across 169 targets, and remote sensing (RS) provides Earth observation data, directly or indirectly, for 30 (18%) of these indicators. Unfortunately, the UN Global Sustainable Development Report 2019—The Future is Now: Science for Achieving Sustainable Development concluded that, despite initial efforts, the world is not yet on track for achieving most of the SDG targets. Meanwhile, through the EO4SDG initiative by the Group on Earth Observations, the full potential of RS for SDG monitoring is now being explored at a global scale. As of April 2020, preliminary statistical data were available for 21 (70%) of the 30 RS-based SDG indicators, according to the Global SDG Indicators Database. Ten (33%) of the RS-based SDG indicators have also been included in the SDG Index and Dashboards found in the Sustainable Development Report 2019—Transformations to Achieve the Sustainable Development Goals. These statistics, however, do not necessarily reflect the actual status and availability of raw and processed geospatial data for the RS-based indicators, which remains an important issue. Nevertheless, various initiatives have been started to address the need for open access data. RS data can also help in the development of other potentially relevant complementary indicators or sub-indicators. By doing so, they can help meet one of the current challenges of SDG monitoring, which is how best to operationalize the SDG indicators.

Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements

The retention of structural elements such as habitat trees in forests managed for timber production is essential for fulfilling the objectives of biodiversity conservation. This paper seeks to predict tree-related microhabitats (TreMs) by close-range remote sensing parameters. TreMs, such as cavities or crown deadwood, are an established tool to quantify the suitability of habitat trees for biodiversity conservation. The aim to predict TreMs based on remote sensing (RS) parameters is supposed to assist a more objective and efficient selection of retention elements. The RS parameters were collected by the use of terrestrial laser scanning as well as unmanned aerial vehicles structure from motion point cloud generation to provide a 3D distribution of plant tissue. Data was recorded on 135 1-ha plots in Germany. Statistical models were used to test the influence of 28 RS predictors, which described TreM richness (R2: 0.31) and abundance (R2: 0.31) in moderate precision and described a deviance of 44% for the abundance and 38% for richness of TreMs. Our results indicate that multiple RS techniques can achieve moderate predictions of TreM occurrence. This method allows a more efficient and objective selection of retention elements such as habitat trees that are keystone features for biodiversity conservation, even if it cannot be considered a full replacement of TreM inventories due to the moderate statistical relationship at this stage.

A Trillion Coral Reef Colors: Deeply Annotated Underwater Hyperspectral Images for Automated Classification and Habitat Mapping

This paper describes a large dataset of underwater hyperspectral imagery that can be used by researchers in the domains of computer vision, machine learning, remote sensing, and coral reef ecology. We present the details of underwater data acquisition, processing and curation to create this large dataset of coral reef imagery annotated for habitat mapping. A diver-operated hyperspectral imaging system (HyperDiver) was used to survey 147 transects at 8 coral reef sites around the Caribbean island of Curaçao. The underwater proximal sensing approach produced fine-scale images of the seafloor, with more than 2.2 billion points of detailed optical spectra. Of these, more than 10 million data points have been annotated for habitat descriptors or taxonomic identity with a total of 47 class labels up to genus- and species-levels. In addition to HyperDiver survey data, we also include images and annotations from traditional (color photo) quadrat surveys conducted along 23 of the 147 transects, which enables comparative reef description between two types of reef survey methods. This dataset promises benefits for efforts in classification algorithms, hyperspectral image segmentation and automated habitat mapping.

Land Cover Classification of Complex Agroecosystems in the Non-Protected Highlands of the Galapagos Islands

The humid highlands of the Galapagos are the islands’ most biologically productive regions and a key habitat for endemic animal and plant species. These areas are crucial for the region’s food security and for the control of invasive plants, but little is known about the spatial distribution of its land cover. We generated a baseline high-resolution land cover map of the agricultural zones and their surrounding protected areas. We combined the high spatial resolution of PlanetScope images with the high spectral resolution of Sentinel-2 images in an object-based classification using a RandomForest algorithm. We used images collected with an unmanned aerial vehicle (UAV) to verify and validate our classified map. Despite the astounding diversity and heterogeneity of the highland landscape, our classification yielded useful results (overall Kappa: 0.7, R2: 0.69) and revealed that across all four inhabited islands, invasive plants cover the largest fraction (28.5%) of the agricultural area, followed by pastures (22.3%), native vegetation (18.6%), food crops (18.3%), and mixed forest and pioneer plants (11.6%). Our results are consistent with historical trajectories of colonization and abandonment of the highlands. The produced dataset is designed to suit the needs of practitioners of both conservation and agriculture and aims to foster collaboration between the two areas.

Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semi-Empirical Models and SNAP Biophysical Processor Approach

Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for crop evapotranspiration monitoring. Using Sentinel-2 (S2) spectral information, this study performed a comparative analysis of empirical (vegetation indices), semi-empirical (CLAIR model with fixed and calibrated extinction coefficient) and artificial neural network S2 products derived from the Sentinel Application Platform Software (SNAP) biophysical processor (ANN S2 products) approaches for the estimation of LAI and CCC. Four independent in situ collected datasets of LAI and CCC, obtained with standard instruments (LAI-2000, SPAD) and a smartphone application (PocketLAI), were used. The ANN S2 products present good statistics for LAI (R2 > 0.70, root mean square error (RMSE) < 0.86) and CCC (R2 > 0.75, RMSE < 0.68 g/m2) retrievals. The normalized Sentinel-2 LAI index (SeLI) is the index that presents good statistics in each dataset (R2 > 0.71, RMSE < 0.78) and for the CCC, the ratio red-edge chlorophyll index (CIred-edge) (R2 > 0.67, RMSE < 0.62 g/m2). Both indices use bands located in the red-edge zone, highlighting the importance of this region. The LAI CLAIR model with a fixed extinction coefficient value produces a R2 > 0.63 and a RMSE < 1.47 and calibrating this coefficient for each study area only improves the statistics in two areas (RMSE ≈ 0.70). Finally, this study analyzed the influence of the LAI parameter estimated with the different methodologies in the calculation of crop potential evapotranspiration (ETc) with the adapted Penman–Monteith (FAO-56 PM), using a multi-temporal dataset. The results were compared with ETc estimated as the product of the reference evapotranspiration (ETo) and on the crop coefficient (Kc) derived from FAO table values. In the absence of independent reference ET data, the estimated ETc with the LAI in situ values were considered as the proxy of the ground-truth. ETc estimated with the ANN S2 LAI product is the closest to the ETc values calculated with the LAI in situ (R2 > 0.90, RMSE < 0.41 mm/d). Our findings indicate the good validation of ANN S2 LAI and CCC products and their further suitability for the implementation in evapotranspiration retrieval of agricultural areas.

UAV-Based High Resolution Thermal Imaging for Vegetation Monitoring, and Plant Phenotyping Using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap Cameras

The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with decreased cost and greater accessibility of both UAVs and thermal imaging sensors, has led to the widespread use of this technology, especially for precision agriculture and plant phenotyping. There are several thermal camera systems in the market that are available at a low cost. However, their efficacy and accuracy in various applications has not been tested. In this study, three commercially available UAV thermal cameras, including ICI 8640 P-series (Infrared Cameras Inc., USA), FLIR Vue Pro R 640 (FLIR Systems, USA), and thermoMap (senseFly, Switzerland) have been tested and evaluated for their potential for forest monitoring, vegetation stress detection, and plant phenotyping. Mounted on multi-rotor or fixed wing systems, these cameras were simultaneously flown over different experimental sites located in St. Louis, Missouri (forest environment), Columbia, Missouri (plant stress detection and phenotyping), and Maricopa, Arizona (high throughput phenotyping). Thermal imagery was calibrated using procedures that utilize a blackbody, handheld thermal spot imager, ground thermal targets, emissivityand atmospheric correction. A suite of statistical analyses, including analysis of variance (ANOVA), correlation analysis between camera temperature and plant biophysical and biochemical traits, and heritability were utilized in order to examine the sensitivity and utility of the cameras against selected plant phenotypic traits and in the detection of plant water stress. In addition, in reference to quantitative assessment of image quality from different thermal cameras, a non-reference image quality evaluator, which primarily measures image focus that is based on the spatial relationship of pixels in different scales, was developed. Our results show that (1) UAV-based thermal imaging is a viable tool in precision agriculture and (2) the three examined cameras are comparable in terms of their efficacy for plant phenotyping. Overall, accuracy, when compared against field measured ground temperature and estimating power of plant biophysical and biochemical traits, the ICI 8640 P-series performed better than the other two cameras, followed by FLIR Vue Pro R 640 and thermoMap cameras. Our results demonstrated that all three UAV thermal cameras provide useful temperature data for precision agriculture and plant phenotying, with ICI 8640 P-series presenting the best results among the three systems. Cost wise, FLIR Vue Pro R 640 is more affordable than the other two cameras, providing a less expensive option for a wide range of applications.

On the Effects of InSAR Temporal Decorrelation and Its Implications for Land Cover Classification: The Case of the Ocean-Reclaimed Lands of the Shanghai Megacity

In this work, we focused on the ocean-reclaimed lands of the Shanghai coastal region and we evidenced how, over these areas, the interferometric synthetic aperture radar (InSAR) coherence maps exhibit peculiar behavior. In particular, by analyzing a sequence of Sentinel-1 SAR InSAR coherence maps, we found a significant coherence loss over time in correspondence to the ocean-reclaimed platforms that are substantially different from the coherence loss experienced in naturally-formed regions with the same type of land cover. We have verified whether this is due to the engineering geological conditions or the soil consolidation subsidence in ocean-reclaimed region. In this work, we combine the information coming from InSAR coherence maps and the retrieved temporal decorrelation model with that obtained by using optical Sentinel-2 data, and we performed land cover classification analyses in the zone of the Pudong International Airport. To estimate the accuracy of utilizing InSAR coherence information for land cover classification, in particular, we have analyzed what causes the difference of the InSAR coherence loss with the same type of land cover. The presented results show that the coherence models can be useful to distinguish roads and buildings, thus enhancing the accuracy of land cover classification compared with that allowable by using only Sentinel-2 data. In particular, the accuracy of classification increases from 75% to 86%.

Biodiversity monitoring, earth observations and the ecology of scale

Human activity and land-use change are dramatically altering the sizes, geographical distributions and functioning of biological populations worldwide, with tremendous consequences for human well-being. Yet our ability to measure, monitor and forecast biodiversity change - crucial to addressing it - remains limited. Biodiversity monitoring systems are being developed to improve this capacity by deriving metrics of change from an array of in situ data (e.g. field plots or species occurrence records) and Earth observations (EO; e.g. satellite or airborne imagery). However, there are few ecologically based frameworks for integrating these data into meaningful metrics of biodiversity change. Here, I describe how concepts of pattern and scale in ecology could be used to design such a framework. I review three core topics: the role of scale in measuring and modelling biodiversity patterns with EO, scale-dependent challenges linking in situ and EO data and opportunities to apply concepts of pattern and scale to EO to improve biodiversity mapping. From this analysis emerges an actionable approach for measuring, monitoring and forecasting biodiversity change, highlighting key opportunities to establish EO as the backbone of global-scale, science-driven conservation.

Monitoring protected areas from space: A multi-temporal assessment using raptors as biodiversity surrogates

Monitoring protected areas (PAs) is essential for systematic evaluation of their effectiveness in terms of habitat protection, preservation and representativeness. This study illustrates how the use of species distribution models that combine remote sensing data and information about biodiversity surrogates can contribute to develop a systematic protocol for monitoring PAs. In particular, we assessed the effectiveness of the Natura 2000 (N2000) network, for conserving and preserving the representativeness of seven raptor species in a highly-dynamic landscape in northwest Spain between 2001 and 2014. We also evaluated the cost-effectiveness of the N2000 network by using the total area under protection as a proxy for conservation costs. Overall, the N2000 network was found to poorly represent the habitats of the raptor species. Despite the low representativeness, this network showed a high degree of effectiveness due to increased overall habitat availability for generalist and forest specialist species between 2001 and 2014. Nevertheless, additional protected areas should be established in the near future to increase their representativeness, and thus ensure the protection of open-habitat specialist species and their priority habitats. In addition, proactive conservation measures in natural and semi-natural ecosystems (in particular, montane heathlands) will be essential for long-term protection of Montagu's harrier (species listed in the Annex I of the Bird Directive), and thus complying with the current European Environmental Legislation. This study sheds light on how the development and application of new protected area indices based on the combined use of freely-available satellite data and species distribution models may contribute substantially to the cost-efficiency of the PA monitoring systems, and to the 'Fitness Check' process of EU Nature Directives.

A new method for ecoacoustics? Toward the extraction and evaluation of ecologically-meaningful soundscape components using sparse coding methods

Passive acoustic monitoring is emerging as a promising non-invasive proxy for ecological complexity with potential as a tool for remote assessment and monitoring (Sueur & Farina, 2015). Rather than attempting to recognise species-specific calls, either manually or automatically, there is a growing interest in evaluating the global acoustic environment. Positioned within the conceptual framework of ecoacoustics, a growing number of indices have been proposed which aim to capture community-level dynamics by (e.g., Pieretti, Farina & Morri, 2011; Farina, 2014; Sueur et al., 2008b) by providing statistical summaries of the frequency or time domain signal. Although promising, the ecological relevance and efficacy as a monitoring tool of these indices is still unclear. In this paper we suggest that by virtue of operating in the time or frequency domain, existing indices are limited in their ability to access key structural information in the spectro-temporal domain. Alternative methods in which time-frequency dynamics are preserved are considered. Sparse-coding and source separation algorithms (specifically, shift-invariant probabilistic latent component analysis in 2D) are proposed as a means to access and summarise time-frequency dynamics which may be more ecologically-meaningful.

Opportunities for the application of advanced remotely-sensed data in ecological studies of terrestrial animal movement

Animal movement patterns in space and time are a central aspect of animal ecology. Remotely-sensed environmental indices can play a key role in understanding movement patterns by providing contiguous, relatively fine-scale data that link animal movements to their environment. Still, implementation of newly available remotely-sensed data is often delayed in studies of animal movement, calling for a better flow of information to researchers less familiar with remotely-sensed data applications. Here, we reviewed the application of remotely-sensed environmental indices to infer movement patterns of animals in terrestrial systems in studies published between 2002 and 2013. Next, we introduced newly available remotely-sensed products, and discussed their opportunities for animal movement studies. Studies of coarse-scale movement mostly relied on satellite data representing plant phenology or climate and weather. Studies of small-scale movement frequently used land cover data based on Landsat imagery or aerial photographs. Greater documentation of the type and resolution of remotely-sensed products in ecological movement studies would enhance their usefulness. Recent advancements in remote sensing technology improve assessments of temporal dynamics of landscapes and the three-dimensional structures of habitats, enabling near real-time environmental assessment. Online movement databases that now integrate remotely-sensed data facilitate access to remotely-sensed products for movement ecologists. We recommend that animal movement studies incorporate remotely-sensed products that provide time series of environmental response variables. This would facilitate wildlife management and conservation efforts, as well as the predictive ability of movement analyses. Closer collaboration between ecologists and remote sensing experts could considerably alleviate the implementation gap. Ecologists should not expect that indices derived from remotely-sensed data will be directly analogous to field-collected data and need to critically consider which remotely-sensed product is best suited for a given analysis.

A compact 3D omnidirectional range sensor of high resolution for robust reconstruction of environments

In this paper, an accurate range sensor for the three-dimensional reconstruction of environments is designed and developed. Following the principles of laser profilometry, the device exploits a set of optical transmitters able to project a laser line on the environment. A high-resolution and high-frame-rate camera assisted by a telecentric lens collects the laser light reflected by a parabolic mirror, whose shape is designed ad hoc to achieve a maximum measurement error of 10 mm when the target is placed 3 m away from the laser source. Measurements are derived by means of an analytical model, whose parameters are estimated during a preliminary calibration phase. Geometrical parameters, analytical modeling and image processing steps are validated through several experiments, which indicate the capability of the proposed device to recover the shape of a target with high accuracy. Experimental measurements show Gaussian statistics, having standard deviation of 1.74 mm within the measurable range. Results prove that the presented range sensor is a good candidate for environmental inspections and measurements.

Deforestation and fragmentation of natural forests in the upper Changhua watershed, Hainan, China: implications for biodiversity conservation

Hainan, the largest tropical island in China, belongs to the Indo-Burma biodiversity hotspot. The Changhua watershed is a center of endemism for plants and birds and the cradle of Hainan's main rivers. However, this area has experienced recent and ongoing deforestation and habitat fragmentation. To quantify habitat loss and fragmentation of natural forests, as well as the land-cover changes in the Changhua watershed, we analyzed Landsat images obtained in 1988, 1995, and 2005. Land-cover dynamics analysis showed that natural forests increased in area (97,909 to 104,023 ha) from 1988 to 1995 but decreased rapidly to 76,306 ha over the next decade. Rubber plantations increased steadily throughout the study period while pulp plantations rapidly expanded after 1995. Similar patterns of land cover change were observed in protected areas, indicating a lack of enforcement. Natural forests conversion to rubber and pulp plantations has a general negative effect on biodiversity, primarily through habitat fragmentation. The fragmentation analysis showed that natural forests area was reduced and patch number increased, while patch size and connectivity decreased. These land-cover changes threatened local biodiversity, especially island endemic species. Both natural forests losses and fragmentation should be stopped by strict enforcement to prevent further damage. Preserving the remaining natural forests and enforcing the status of protected areas should be a management priority to maximize the watershed's biodiversity conservation value.

Foraging ecology of three sympatric ungulate species - Behavioural and resource maps indicate differences between chamois, ibex and red deer

BACKGROUND

The spatial distribution of forage resources is a major driver of animal movement patterns. Understanding where animals forage is important for the conservation of multi-species communities, since interspecific competition can emerge if different species use the same depletable resources. However, determining forage resources in a spatially continuous fashion in alpine grasslands at high spatial resolution was challenging up to now, because terrain heterogeneity causes vegetation characteristics to vary at small spatial scales, and methods for detection of behavioural phases in animal movement patterns were not widely available. We delineated areas coupled to the foraging behaviour of three sympatric ungulate species (chamois, ibex, red deer) using Time Local Convex Hull (T-LoCoH), a non-parametric utilisation distribution method incorporating spatial and temporal autocorrelation structure of GPS data. We used resource maps of plant biomass and plant nitrogen content derived from high-resolution airborne imaging spectroscopy data, and multinomial logistic regression to compare the foraging areas of the three ungulate species.

RESULTS

We found significant differences in plant biomass and plant nitrogen content between the core foraging areas of chamois, ibex and red deer. Core foraging areas of chamois were characterised by low plant biomass and low to medium plant nitrogen content. Core foraging areas of ibex were, in contrast, characterised by high plant nitrogen content, but varied in plant biomass, and core foraging areas of red deer had high plant biomass, but varied in plant nitrogen content.

CONCLUSIONS

Previous studies carried out in the same study area found no difference in forage consumed by chamois, ibex and red deer. Methodologically, those studies were based on micro-histological analysis of plant fragments identifying them to plant family or functional type level. However, vegetation properties such as productivity (biomass) or plant nutrient content can vary within vegetation communities, especially in highly heterogeneous landscapes. Thus, the combination of high spatial resolution resource maps with a utilisation distribution method allowing to generate behavioural maps (T-LoCoH) provides new insights into the foraging ecology of the three sympatric species, important for their conservation and to monitor expected future changes.

Multi-sensor fusion of Landsat 8 thermal infrared (TIR) and panchromatic (PAN) images

Data fusion is defined as the combination of data from multiple sensors such that the resulting information is better than would be possible when the sensors are used individually. The multi-sensor fusion of panchromatic (PAN) and thermal infrared (TIR) images is a good example of this data fusion. While a PAN image has higher spatial resolution, a TIR one has lower spatial resolution. In this study, we have proposed an efficient method to fuse Landsat 8 PAN and TIR images using an optimal scaling factor in order to control the trade-off between the spatial details and the thermal information. We have compared the fused images created from different scaling factors and then tested the performance of the proposed method at urban and rural test areas. The test results show that the proposed method merges the spatial resolution of PAN image and the temperature information of TIR image efficiently. The proposed method may be applied to detect lava flows of volcanic activity, radioactive exposure of nuclear power plants, and surface temperature change with respect to land-use change.

Movement-based estimation and visualization of space use in 3D for wildlife ecology and conservation

Advances in digital biotelemetry technologies are enabling the collection of bigger and more accurate data on the movements of free-ranging wildlife in space and time. Although many biotelemetry devices record 3D location data with x, y, and z coordinates from tracked animals, the third z coordinate is typically not integrated into studies of animal spatial use. Disregarding the vertical component may seriously limit understanding of animal habitat use and niche separation. We present novel movement-based kernel density estimators and computer visualization tools for generating and exploring 3D home ranges based on location data. We use case studies of three wildlife species – giant panda, dugong, and California condor – to demonstrate the ecological insights and conservation management benefits provided by 3D home range estimation and visualization for terrestrial, aquatic, and avian wildlife research.

High-resolution satellite imagery is an important yet underutilized resource in conservation biology

Technological advances and increasing availability of high-resolution satellite imagery offer the potential for more accurate land cover classifications and pattern analyses, which could greatly improve the detection and quantification of land cover change for conservation. Such remotely-sensed products, however, are often expensive and difficult to acquire, which prohibits or reduces their use. We tested whether imagery of high spatial resolution (≤5 m) differs from lower-resolution imagery (≥30 m) in performance and extent of use for conservation applications. To assess performance, we classified land cover in a heterogeneous region of Interior Atlantic Forest in Paraguay, which has undergone recent and dramatic human-induced habitat loss and fragmentation. We used 4 m multispectral IKONOS and 30 m multispectral Landsat imagery and determined the extent to which resolution influenced the delineation of land cover classes and patch-level metrics. Higher-resolution imagery more accurately delineated cover classes, identified smaller patches, retained patch shape, and detected narrower, linear patches. To assess extent of use, we surveyed three conservation journals (Biological Conservation, Biotropica, Conservation Biology) and found limited application of high-resolution imagery in research, with only 26.8% of land cover studies analyzing satellite imagery, and of these studies only 10.4% used imagery ≤5 m resolution. Our results suggest that high-resolution imagery is warranted yet under-utilized in conservation research, but is needed to adequately monitor and evaluate forest loss and conversion, and to delineate potentially important stepping-stone fragments that may serve as corridors in a human-modified landscape. Greater access to low-cost, multiband, high-resolution satellite imagery would therefore greatly facilitate conservation management and decision-making.