Fine-scale mapping of vector habitats using very high resolution satellite imagery : a liver fluke case-study

The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of <5m resolution was acquired in the spring of 2013 for the area around Bruges, Belgium, a region where dairy farms suffer from liver fluke infections transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m(2) and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations

Recent global and regional trends in burned area and their compensating environmental controls

The apparent decline in the global incidence of fire between 1996 and 2015, as measured by satellite-observations of burned area, has been related to socioeconomic and land use changes. However, recent decades have also seen changes in climate and vegetation that influence fire and fire-enabled vegetation models do not reproduce the apparent decline. Given that the satellite-derived burned area datasets are still relatively short (<20 years), this raises questions both about the robustness of the apparent decline and what causes it. We use two global satellite-derived burned area datasets and a data-driven fire model to (1) assess the spatio-temporal robustness of the burned area trends and (2) to relate the trends to underlying changes in temperature, precipitation, human population density and vegetation conditions. Although the satellite datasets and simulation all show a decline in global burned area over ~20 years, the trend is not significant and is strongly affected by the start and end year chosen for trend analysis and the year-to-year variability in burned area. The global and regional trends shown by the two satellite datasets are poorly correlated for the common overlapping period (2001–2015) and the fire model simulates changes in global and regional burned area that lie within the uncertainties of the satellite datasets. The model simulations show that recent increases in temperature would lead to increased burned area but this effect is compensated by increasing wetness or increases in population, both of which lead to declining burned area. Increases in vegetation cover and density associated with recent greening trends lead to increased burned area in fuel-limited regions. Our analyses show that global and regional burned area trends result from the interaction of compensating trends in controls of wildfire at regional scales

What controls global fire? Evaluating emergent relationships in satellite observations and global vegetation models using machine learning

Fire is a major disturbance agent in terrestrial ecosystems. The occurrence and spread of wildfires is controlled by the interplay of human activities, weather conditions, and the conditions of vegetation and litter fuels. Most state-of-the-art global ecosystem models represent such controls to simulate fire effects on vegetation dynamics and global carbon cycling. However, global fire models poorly reproduce the observed dynamics and variability of fire burned area. Here we aim to identify and evaluate functional responses of global burned area to environmental and human controls. We use several global satellite, climate, and socioeconomic datasets, and simulations from the Fire Model Inter-comparison Project (FireMIP) [1] to predict the observed or modelled burned area with the random forest machine-learning algorithm. We then derive from the trained random forests individual conditional expectation curves [2], which represent emergent functional responses of burned area to controlling factors. These functional responses allow us to compare data- and model-derived sensitivities. FireMIP models mostly represent the emergent responses to climate variables but show diverse responses to human population, land cover, and vegetation. The models especially underestimate the emergent strong increase of burned area with increasing precedent plant productivity in many semi-arid ecosystems. The results suggest that FireMIP models misrepresent the links between plant productivity, biomass allocation, litter turnover, and fuel production. Additionally, the good performance of data-driven modelling approaches [3] suggests to develop hybrid global fire models to better represent and predict the role of fire dynamics for ecosystem functioning and vegetation-climate interactions. REFERENCES: 1. Rabin, S.S., Melton, J.R., Lasslop, G., Bachelet, D., Forrest, M., Hantson, S., Kaplan, J.O., Li, F., Mangeon, S., Ward, D.S., Yue, C., Arora, V.K., Hickler, T., Kloster, S., Knorr, W., Nieradzik, L., Spessa, A., Folberth, G.A., Sheehan, T., Voulgarakis, A., Kelley, D.I., Prentice, I.C., Sitch, S., Harrison, S., Arneth, A., 2017. The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions. Geosci Model Dev 10, 1175–1197. https://doi.org/10.5194/gmd-10-1175-2017 2. Goldstein, A., Kapelner, A., Bleich, J., Pitkin, E., 2013. Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation. ArXiv13096392 Stat. 3. Forkel, M., Dorigo, W., Lasslop, G., Teubner, I., Chuvieco, E., Thonicke, K., 2017. A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1). Geosci Model Dev 10, 4443–4476. https://doi.org/10.5194/gmd-10-4443-201

PiCAM: A Raspberry Pi‐based open‐source, low‐power camera system for monitoring plant phenology in Arctic environments

Abstract Time‐lapse cameras have been widely used as a tool to monitor the timing of seasonal vegetation growth. These simple, relatively inexpensive systems can provide high‐frequency observations of leaf development and demography which are critical data sets needed to characterize plant phenology from species to landscapes. This is important for understanding how plants are responding to global changes, as well as for validating satellite‐derived phenology products. However, in remote regions including the high‐latitude Arctic, deploying time‐lapse cameras could be challenging. The remoteness and lack of widespread power and telecommunications infrastructure limit options for the installation, maintenance and retrieval of data and equipment, and make it difficult for cameras to survive in extreme weather (e.g. long cold winters). To improve our understanding of Arctic phenology, new technologies are required to address these challenges. Here, we present a novel, low‐power, compact, lightweight time‐lapse camera system, called power‐interval camera automation module (PiCAM). The PiCAM was designed with explicit consideration to simplify deployment (i.e. without a need for external power supplies) of camera systems and to address the challenges of camera survival in harsh Arctic environments. In this paper, we describe the design, setup and technical details of the PiCAM and provide a roadmap for how to build and operate these systems. As proof of concept, we deployed 26 PiCAMs at three low‐Arctic tundra sites on the Seward Peninsula, Alaska in early August 2021 for characterizing Arctic plant phenology. Of the 26 PiCAMs, 70% remained active at the point of our revisit in late July 2022 despite the extreme winter temperatures they experienced (< −30°C, heavy snow cover). We extracted key plant phenology metrics from the PiCAMs and captured strong differences across key Arctic plant species. We showed that the PiCAM has the potential to be widely used for monitoring plant phenology across the broader Arctic region, addressing the need for ground‐based understanding of Arctic phenological diversity to develop knowledge of plant response to climate change and to validate remote sensing products

Marking behavior of Andean bears in an Ecuadorian cloud forest : A pilot study

Very little is known about marking behavior of the endangered Andean bear (Tremarctos ornatus). Here, we present a first detailed description of Andean bear marking behavior obtained using camera traps. From November 2012 to April 2013, we inspected 16 bear trails in the Napo province of eastern Ecuador, and installed camera traps (n = 3) at marking sites to document their marking behavior. We obtained 22 video recordings of Andean bears, all of which were captured during daytime. Almost all recordings (n = 18) contained behavior associated with marking. Tree-rubbing was the main behavioral display at marking sites, and consisted of 4 common activities: (1) tree-sniffing, (2) rubbing the neck and/or shoulders, (3) rubbing the flanks, and (4) rubbing the back. Bears also claw-marked and urinated while rubbing trees. We only observed scent-marking from males. Consistent with other bear species, we suggest that Andean bears communicate intra-specifically through their marking behavior

TESTgroup-BNL/PiCam: MEE Article Release

Release version used in Yang et al. 2023 Methods in Ecology and Evolution PiCAM is a custom-grade phenocam system designed by the Terrestrial Ecosystem Science & Technology group at Brookhaven National Laboratory. The PiCAM has the advantage of being compact, low power cost, and lightweight, particularly suitable for Arctic environments. It was desinged to operate for at least a fiscal year (6 images per day) with three AA lithium batteries. Given its compact and light-weight feature, PiCAM can be deployed on small hosts (e.g., stakes), addressing the challenges of deploying heavy infrastructure commonly needed for commercial phenocams

The Arctic-Boreal vulnerability experiment model benchmarking system

NASA’s Arctic-Boreal Vulnerability Experiment (ABoVE) integrates field and airborne data into modeling and synthesis activities for understanding Arctic and Boreal ecosystem dynamics. The ABoVE Benchmarking System (ABS) is an operational software package to evaluate terrestrial biosphere models against key indicators of Arctic and Boreal ecosystem dynamics, i.e.: carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance. The ABS utilizes satellite remote sensing data, airborne data, and field data from ABoVE as well as collaborating research networks in the region, e.g.: the Permafrost Carbon Network, the International Soil Carbon Network, the Northern Circumpolar Soil Carbon Database, AmeriFlux sites, the Moderate Resolution Imaging Spectroradiometer, the Orbiting Carbon Observatory 2, and the Soil Moisture Active Passive mission. The ABS is designed to be interactive for researchers interested in having their models accurately represent observations of key Arctic indicators: a user submits model results to the system, the system evaluates the model results against a set of Arctic-Boreal benchmarks outlined in the ABoVE Concise Experiment Plan, and the user then receives a quantitative scoring of model strengths and deficiencies through a web interface. This interactivity allows model developers to iteratively improve their model for the Arctic-Boreal Region by evaluating results from successive model versions. We show here, for illustration, the improvement of the Lund–Potsdam–Jena-Wald Schnee und Landschaft (LPJwsl) version model through the ABoVE ABS as a new permafrost module is coupled to the existing model framework. The ABS will continue to incorporate new benchmarks that address indicators of Arctic-Boreal ecosystem dynamics as they become available