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Tropical peatlands are important carbon stores that are vulnerable to drainage and conversion to agriculture. Protection and restoration of peatlands are increasingly recognised as key nature based solutions that can be implemented as part of climate change mitigation. Identification of peatland areas that are important for protection and restauration with regards to the state of their carbon stocks, are therefore vital for policy makers. In this paper we combined organic geochemical analysis by Rock-Eval (6) pyrolysis of peat collected from sites with different land management history and optical remote sensing products to assess if remotely sensed data could be used to predict peat conditions and carbon storage. The study used the North Selangor Peat Swamp forest, Malaysia, as the model system. Across the sampling sites the carbon stocks in the below ground peat was ca 12 times higher than the forest (median carbon stock held in ground vegetation 114.70 Mg ha-1 and peat soil 1401.51 Mg ha-1). Peat core sub-samples and litter collected from Fire Affected, Disturbed Forest, and Managed Recovery locations (i.e. disturbed sites) had different decomposition profiles than Central Forest sites. The Rock-Eval pyrolysis of the upper peat profiles showed that surface peat layers at Fire Affected, Disturbed Forest, and Managed Recovery locations had lower immature organic matter index (I-index) values (average I-index range in upper section 0.15 to -0.06) and higher refractory organic matter index (R -index) (average R-index range in upper section 0.51 to 0.65) compared to Central Forest sites indicating enhanced decomposition of the surface peat. In the top 50 cm section of the peat profile, carbon stocks were negatively related to the normalised burns ratio (NBR) (a satellite derived parameter) (Spearman’s rho = -0.664, S = 366, p-value =

The role of Earth observation (EO) data in addressing societal problems from environmental through to humanitarian should not be understated. Recent innovation in EO means provision of analysis ready data and data cubes, which allows for rapid use of EO data. This in combination with processing technologies, such as Google Earth Engine and open source algorithms/software for EO data integration and analyses, has afforded an explosion of information to answer research questions and/or inform policy making. However, there is still a need for both training and validation data within EO projects–often this can be challenging to obtain. It has been suggested that citizen science can help here to provide these data, yet there is some perceived hesitancy in using citizen science within EO projects. This paper reports on the Citizen Science 4 EO (Citizens4EO) project that aimed to obtain an in-depth understanding of researchers’ and practitioners’ experiences with citizen science data in EO within the UK. Through a mixed methods approach (online and in-depth surveys and a spotlight case study) it was found that although the benefits of using citizen science data in EO projects were many (and highlighted in the spotlighted “Slavery from Space” case study), there were a number of common concerns around using citizen science. These were around the mechanics of deploying citizen science and the unreliability of a potentially misinformed or undertrained citizen base. As such, comparing the results of this study with those of a similar survey undertaken in 2016, it is apparent that progress towards optimizing citizen science use in EO has been incremental but positive with evidence of the realization of the benefits of citizen science for EO (Citizens4EO). As such, we conclude by offering priority action areas to support further use of citizen science by the EO community within the UK, which ultimately should be adopted further afield.

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

Permafrost thaw resulting from climate warming is threatening to release carbon from high latitude peatlands. The aim of this research was to determine subsidence rates linked to permafrost thaw in sub-Arctic peatlands in Sweden using historical orthophotographic (orthophotos), Unoccupied Aerial Vehicle (UAV), and Interferometric Synthetic Aperture Radar (InSAR) data. The orthophotos showed that the permafrost palsa on the study sites have been contracting in their areal extent, with the greatest rates of loss between 2002 and 2008. The surface motion estimated from differential digital elevation models from the UAV data showed high levels of subsidence (maximum of ?25 cm between 2017 and 2020) around the edges of the raised palsa plateaus. The InSAR data analysis showed that raised palsa areas had the greatest subsidence rates, with maximum subsidence rates of 1.5 cm between 2017 and 2020; however, all wetland vegetation types showed subsidence. We suggest that the difference in spatial units associated with each sensor explains parts of the variation in the subsidence levels recorded. We conclude that InSAR was able to identify the areas most at risk of subsidence and that it can be used to investigate subsidence over large spatial extents, whereas UAV data can be used to better understand the dynamics of permafrost degradation at a local level. These findings underpin a monitoring approach for these peatlands.

Recent estimates suggest that more than 40 million people worldwide are in situations of modern slavery and other forms of labor exploitation. UN Sustainable Development Goal 8.7 addresses this problem and urges stakeholders to take effective measures to end all forms of labor exploitation by 2030. Labor exploitation is often a direct consequence of forced migration, and humanitarian operations have a key role to play in tackling this issue worldwide. Academic research can facilitate this by providing the necessary decision-making tools to support antislavery practitioners in humanitarian organizations and governments. For effective resource allocation, these practitioners need tools to help them systematically identify and assess the risks of labor exploitation in an area. In this paper, we develop a multi-method approach that combines various data sources to capture the issue’s complex and multidimensional nature. Through satellite remote sensing, we first identify 50 informal settlements hosting migrant workers in the strawberry production area of Southern Greece. We then apply a multi-criteria decision analysis (MCDA) method to a subset of six informal settlements in order to evaluate their labor exploitation risks based on eight criteria. In addition to being practically implemented by a humanitarian organization and a government agency in Greece, our paper advances research on humanitarian operations and labor exploitation by elucidating how a multi-method approach can be used for data-driven prioritization of interventions against labor exploitation. Our approach offers opportunities for other applications in the field of humanitarian operations.

Information on forest disturbance is crucial for tropical forest management and global carbon cycle analysis. The long-term collection of data from the Landsat missions provides some of the most valuable information for understanding the processes of global tropical forest disturbance. However, there are substantial uncertainties in the estimation of non-mechanized, small-scale (i.e., small area) clearings in tropical forests with Landsat series images. Because the appearance of small-scale openings in a tropical tree canopy are often ephemeral due to fast-growing vegetation, and because clouds are frequent in tropical regions, it is challenging for Landsat images to capture the logging signal. Moreover, the spatial resolution of Landsat images is typically too coarse to represent spatial details about small-scale clearings. In this paper, by fusing all available Landsat and Sentinel-2 images, we proposed a method to improve the tracking of small-scale tropical forest disturbance history with both fine spatial and temporal resolutions. First, yearly composited Landsat and Sentinel-2 self-referenced normalized burn ratio (rNBR) vegetation index images were calculated from all available Landsat-7/8 and Sentinel-2 scenes during 2016–2019. Second, a deep-learning based downscaling method was used to predict fine resolution (10 m) rNBR images from the annual coarse resolution (30 m) Landsat rNBR images. Third, given the baseline Landsat forest map in 2015, the generated fine-resolution Landsat rNBR images and original Sentinel-2 rNBR images were fused to produce the 10 m forest disturbance map for the period 2016–2019. From data comparison and evaluation, it was demonstrated that the deep-learning based downscaling method can produce fine-resolution Landsat rNBR images and forest disturbance maps that contain substantial spatial detail. In addition, by fusing downscaled fine-resolution Landsat rNBR images and original Sentinel-2 rNBR images, it was possible to produce state-of-the-art forest disturbance maps with OA values more than 87% and 96% for the small and large study areas, and detected 11% to 21% more disturbed areas than either the Sentinel-2 or Landsat-7/8 time-series alone. We found that 1.42% of the disturbed areas indentified during 2016–2019 experienced multiple forest disturbances. The method has great potential to enhance work undertaken in relation to major policies such as the reducing emissions from deforestation and forest degradation (REDD+) programmes.

Chandler, C. J., van der Heijden, G. M. F., Boyd, D. S., Cutler, M. E. J., Costa, H., Nilus, R., & Foody, G. M. (2021). Remote sensing liana infestation in an aseasonal tropical forest: addressing mismatch in spatial units of analyses. Remote Sensing in Ecology and Conservation, 7(3), 397-410. https://doi.org/10.1002/rse2.197 ---------------------------------------------------------------- The authors also thank the Natural Environment Research Council [NE/P004806/1 to MEJC, DSB, GMF, GMFvdH; NE/I528477/1 (ARSF MA14/11) to MEJC, DSB, GMF and NE/L002604/1 to CJC, GMF, GMFvdH]. The FCT (Fundação para a Ciência e a Tecnologia) under the project UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC) to HC. The authors also thank the University of Nottingham for an Anne McLaren Research Fellowship to GMFvdH which funded the collection of the ground data. The ability to accurately assess liana (woody vine) infestation at the landscape level is essential to quantify their impact on carbon dynamics and help inform targeted forest management and conservation action. Remote sensing techniques provide potential solutions for assessing liana infestation at broader spatial scales. However, their use so far has been limited to seasonal forests, where there is a high spectral contrast between lianas and trees. Additionally, the ability to align the spatial units of remotely sensed data with canopy observations of liana infestation requires further attention. We combined airborne hyperspectral and LiDAR data with a neural network machine learning classification to assess the distribution of liana infestation at the landscape-level across an aseasonal primary forest in Sabah, Malaysia. We tested whether an object-based classification was more effective at predicting liana infestation when compared to a pixel-based classification. We found a stronger relationship between predicted and observed liana infestation when using a pixel-based approach (RMSD = 27.0% ± 0.80) in comparison to an object-based approach (RMSD = 32.6% ± 4.84). However, there was no significant difference in accuracy for object- versus pixel-based classifications when liana infestation was grouped into three classes; Low [0–30%], Medium [31–69%] and High [70–100%] (McNemar’s χ2 = 0.211, P = 0.65). We demonstrate, for the first time, that remote sensing approaches are effective in accurately assessing liana infestation at a landscape scale in an aseasonal tropical forest. Our results indicate potential limitations in object-based approaches which require refinement in order to accurately segment imagery across contiguous closed-canopy forests. We conclude that the decision on whether to use a pixel- or object-based approach may depend on the structure of the forest and the ultimate application of the resulting output. Both approaches will provide a valuable tool to inform effective conservation and forest management. publishersversion published

© 2020 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. Climate change projections have identified the Yucatan Peninsula as being vulnerable to increasing drought. Understanding spatial and temporal precipitation variability and drought occurrence are therefore important. Drought monitoring in Mexico has been carried out only relatively recently and often without considering the long-term variability in both droughts and precipitation. This research explores the spatio-temporal variability of precipitation and occurrence of droughts at a much finer spatial resolution and over a longer temporal period than previous studies. Using statistical (cluster analysis and standardized precipitation index) and geostatistical (kriging) techniques, maps of precipitation and droughts are generated for the period 1980–2011. These show that whilst many previous studies have regarded the Yucatan Peninsula as a homogenous region with respect to precipitation, there are actually four distinctive clusters of precipitation amount, showing climatic variability across the Peninsula. The analyses also show that droughts in the Peninsula are regionalised. Twelve-month Standardized Precipitation Indices (SPI), calculated for individual stations and for precipitation surfaces, reveal distinct patterns of spatial and temporal variability. SPI surfaces indicate the occurrence of major droughts in 1981, 1986–1987, 1994, 1996, 2003, 2004 and 2009, but these rarely affect the whole Yucatan Peninsula uniformly. Wetter years, such as 1983, 1984, 1988, 1992, 1995, 2002 and 2005 sometimes reflect the impact of individual extreme events, such as hurricane Isidore in 2002. Our results show that drought can be regionalised, thus enhancing the quality of information about droughts in the area and providing evidence and support for future drought mitigation and environmental protection. These methods could usefully be applied elsewhere.

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In a context of both long-term climatic changes and short-term climatic shocks, temporal dynamics profoundly influence ecosystems and societies. In low income contexts in the Tropics, where both exposure and vulnerability to climatic fluctuations is high, the frequency, duration, and trends in these fluctuations are important determinants of socio-ecological resilience. In this paper, the dynamics of six diverse socio-ecological systems (SES) across the Tropics – ranging from agricultural and horticultural systems in Africa and Oceania to managed forests in South East Asia and coastal systems in South America – are examined in relation to the 2015–16 El Niño, and the longer context of climatic variability in which this short-term ‘event’ occurred. In each case, details of the socio-ecological characteristics of the systems and the climate phenomena experienced during the El Niño event are described and reflections on the observed impacts of, and responses to it are presented. Drawing on these cases, we argue that SES resilience (or lack of) is, in part, a product of both long-term historical trends, as well as short-term shocks within this history. Political and economic lock-ins and dependencies, and the memory and social learning that originates from past experience, all contribute to contemporary system resilience. We propose that the experiences of climate shocks can provide a window of insight into future ecosystem responses and, when combined with historical perspectives and learning from multiple contexts and cases, can be an important foundation for efforts to build appropriate long-term resilience strategies to mediate impacts of changing and uncertain climates.

© 2018 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology. The modifiable areal unit problem is prevalent across many aspects of spatial analysis within ecology and conservation. The problem is particularly manifested when calculating metrics for extinction risk estimation, for example, area of occupancy (AOO). Although embedded in the International Union for the Conservation of Nature (IUCN) Red List criteria, AOO is often not used or is poorly applied. We evaluated new and existing methods for calculating AOO from occurrence records and devised a method for determining the minimum AOO with a uniform grid. We evaluated the grid cell shape, origin, and rotation with real-world and simulated data and reviewed the effects on AOO values and possible impacts for species already assessed on the IUCN Red List. The AOO varied by up to 80%, and a ratio of cells to points of 1:1.21 yielded the maximum variation in the number of occupied cells. These findings potentially impact 3% of existing species on the IUCN Red List and species not yet assessed. Our new method combined grid rotation and moving grid origin and gave fast, robust, and reproducible results and, in the majority of cases, achieved the minimum AOO. As well as determining minimum AOO, our method yielded a confidence interval that should be incorporated into existing tools that support species risk assessment. We recommend when recording AOO and other areal measurements that the methods; summary statistics across multiple iterations; angle and origin of the minimum grid; map projection; and datum be recorded, this will lead to more robust species risk assessments.

AIM: Rising global temperatures are predicted to increase ectotherms’ body temperatures, benefitting some species but threatening others. Biophysical models predict a key role for shade in buffering these effects, but the difficulty of measuring shade across broad spatial extents limits predictions of ectotherms’ thermal futures at the global scale. Here, we extend biophysical models of ectotherm body temperature to include effects of forest canopy shade, via leaf area index, and test whether considering remotely-sensed canopy density improves predictions of body temperature variation in heavily shaded habitats.LOCATION: Worldwide.TIME PERIOD: 1990–2010.MAJOR TAXA STUDIED: Lizards.METHODS: We test predictions from biophysical ecological theory for how body temperature should vary with microclimate for 269 lizard populations across open, semi-open, and closed habitats worldwide. We extend existing biophysical models to incorporate canopy shade effects via leaf area index, test whether body temperature varies with canopy density as predicted by theory, and evaluate the extent to which incorporating canopy density improves model performance in heavily-shaded areas.RESULTS: We find that body temperatures in open habitats, like deserts, vary with air temperature and incident solar radiation as predicted by biophysical equations, but these relationships break down in forests, where body temperatures become unpredictable. Incorporating leaf area index into our models revealed lower body temperatures in more heavily shaded environments, restoring the predictability of body temperature in forests.CONCLUSIONS: Although biophysical ecological theory can predict ectotherm body temperature in open habitats, like deserts, these relationships decay in closed forests. Models incorporating remotely sensed data on canopy density improved predictability of body temperatures in these habitats, providing an avenue to incorporate canopy shade effects into predictions of animals’ vulnerability to climate change. These results highlight the thermal threat of changes in canopy structure and loss of forest cover for the world’s ectotherms.

Satellite Earth Observation (EO) is often used as a cost-effective method to report on the condition of remote and inaccessible peatland areas. Current EO techniques are primarily limited to reporting on the vegetation classes and properties of the immediate peat surface using optical data, which can be used to infer peatland condition. Another useful indicator of peatland condition is that of surface motion, which has the potential to report on mass accumulation and loss of peat. Interferometic SAR (InSAR) techniques can provide this using data from space. However, the most common InSAR techniques for information extraction, such as Persistent Scatterers’ Interferometry (PSI), have seen limited application over peat as they are primarily tuned to work in areas of high coherence (i.e., on hard, non-vegetated surfaces only). A new InSAR technique, called the Intermittent Small BAseline Subset (ISBAS) method, has been recently developed to provide measurements over vegetated areas from SAR data acquired by satellite sensors. This paper examines the feasibility of the ISBAS technique for monitoring long-term surface motion over peatland areas of the Flow Country, in the northeast of Scotland. In particular, the surface motions estimated are compared with ground data over a small forested area (namely the Bad a Cheo forest Reserve). Two sets of satellite SAR data are used: ERS C-band images, covering the period 1992–2000, and Sentinel-1 C-band images, covering the period 2015–2016. We show that the ISBAS measurements are able to identify surface motion over peatland areas, where subsidence is a consequence of known land cover/land use. In particular, the ISBAS products agree with the trend of surface motion, but there are uncertainties with their magnitude and direction (vertical). It is concluded that there is a potential for the ISBAS method to be able to report on trends in subsidence and uplift over peatland areas, and this paper suggests avenues for further investigation, but this requires a well-resourced validation campaign.

Aim: To explore the scale dependence of relationships between novel measures of geodiversity and species richness of both native and alien vascular plants. \ud \ud Location: Great Britain.\ud \ud Time period: Data collected 1995–2015. Major taxa: Vascular plants.\ud \ud Methods: We calculated the species richness of terrestrial native and alien vascular plants (6,932 species in total) across the island of Great Britain at grain sizes of 1 km2 (n=219,964) and 100 km2 (n=2,121) and regional extents of 25–250 km diameter, centred around each 100-km2 cell. We compiled geodiversity data on landforms, soils, hydrological and geological features using existing national datasets, and used a newly developed geomorphometric method to extract land- form coverage data (e.g., hollows, ridges, valleys, peaks). We used these as predictors of species richness alongside climate, commonly used topographic metrics, land-cover variety and human population.We analysed species richness across scales using boosted regression tree (BRT) modelling and compared models with and without geodiversity data.\ud \ud Results: Geodiversity significantly improved models over and above the widely used topographic metrics, particularly at smaller extents and the finer grain size, and slightly more so for native species richness. For each increase in extent, the contribution of climatic variables increased and that of geodiversity decreased. Of the geodiversity variables, automatically extracted landform data added the most explanatory power, but hydrology (rivers, lakes) and materials (soil, superficial deposits, geology) were also important. \ud \ud Main conclusions: Geodiversity improves our understanding of, and our ability to model, the relationship between species richness and abiotic heterogeneity at multiple spatial scales by allowing us to get closer to the real-world physical processes that affect patterns of life. The greatest benefit comes from measuring the constituent parts of geodiversity separately rather than one combined variable (as in most of the few studies to date). Automatically extracted landform data, the use of which is novel in ecology and biogeography, proved particularly valuable in our study.

Forest inventory studies in the Amazon indicate a large terrestrial carbon sink. However, field plots may fail to represent forest mortality processes at landscape-scales of tropical forests. Here we characterize the frequency distribution of disturbance events in natural forests from 0.01 ha to 2,651 ha size throughout Amazonia using a novel combination of forest inventory, airborne lidar and satellite remote sensing data. We find that small-scale mortality events are responsible for aboveground biomass losses of ~1.28 Pg Cy-1 over the entire Amazon region. We also find that intermediate-scale disturbances account for losses of ~0.01 Pg Cy-1, and that the largest-scale disturbances as a result of blow-downs only account for losses of ~0.003 Pg Cy-1. Simulation of growth and mortality indicates that even when all carbon losses from intermediate and large-scale disturbances are considered, these are outweighed by the net biomass accumulation by tree growth, supporting the inference of an Amazon carbon sink.