Innovative technologies for terrestrial remote sensing

[In lieu of abstract, extract from first page] Characterizing and monitoring terrestrial, or land, surface features, such as forests, deserts, and cities, are fundamental and continuing goals of Earth Observation (EO). EO imagery and related technologies are essential for increasing our scientific understanding of environmental processes, such as carbon capture and albedo change, and to manage and safeguard environmental resources, such as tropical forests, particularly over large areas or the entire globe. This measurement or observation of some property of the land surface is central to a wide range of scientific investigations and industrial operations, involving individuals and organizations from many different backgrounds and disciplines. However, the process of observing the land provides a unifying theme for these investigations, and in practice there is much consistency in the instruments used for observation and the techniques used to map and model the environmental phenomena of interest. There is therefore great potential benefit in exchanging technological knowledge and experience among the many and diverse members of the terrestrial EO community

Analysing Slavery through Satellite Technology: How Remote Sensing Could Revolutionise Data Collection to Help End Modern Slavery

An estimated 40.3 million people are enslaved globally across a range of industries. Whilst these industries are known, their scale can hinder the fight against slavery. Some industries using slave labour are visible in satellite imagery, including mining, brick kilns, fishing and shrimp farming. Satellite data can provide supplementary details for large scales which cannot be easily gathered on the ground. This paper reviews previous uses of remote sensing in the humanitarian and human rights sectors and demonstrates how Earth Observation as a methodology can be applied to help achieve the United Nations Sustainable Development Goal target 8.7

Understanding the co‐occurrence of tree loss and modern slavery to improve efficacy of conservation actions and policies

Locations where populations are most reliant on forests and their ecosystem services for subsistence and development are also areas where modern slavery persists. These issues are noted within the Sustainable Development Goals (SDGs), both target 15.2 and 8.7 respectively. Often activities using slavery perpetuate deforestation, bolstering a slavery‐environment nexus; which has been examined by comparing modern slavery estimates against environmental protection levels. This study assesses the relationship between tree loss and modern slavery focusing on four countries: Brazil, Ghana, Indonesia, and Mozambique. Previously mapped levels of tree loss and predicted future levels of loss have been compared against modern slavery estimates from the Global Slavery Index 2016 and illegal logging analyses to determine an estimate of the risk for slavery related tree loss. These results provide an insight in to the co‐occurrence between modern slavery and tree loss due to a number of activities that are highlighted, including mining, illegal logging, and agricultural practices. The co‐occurrence is both complex, and yet, beyond coincidental. Implications for both national and global policy are noted assessing the benefits that could be achieved by limiting tree loss and ending modern slavery; of benefit to both the conservation and antislavery communities

Satellite remote sensing to monitor species diversity: potential and pitfalls

Assessing the level of diversity in plant communities from field‐based data is difficult for a number of practical reasons: (1) establishing the number of sampling units to be investigated can be difficult; (2) the choice of sample design can impact on results; and (3) defining the population of concern can be challenging. Satellite remote sensing (SRS) is one of the most cost‐effective approaches to identify biodiversity hotspots and predict changes in species composition. This is because, in contrast to field‐based methods, it allows for complete spatial coverages of the Earth's surface under study over a short period of time. Furthermore, SRS provides repeated measures, thus making it possible to study temporal changes in biodiversity. Here, we provide a concise review of the potential of satellites to help track changes in plant species diversity, and provide, for the first time, an overview of the potential pitfalls associated with the misuse of satellite imagery to predict species diversity. Our work shows that, while the assessment of alpha‐diversity is relatively straightforward, calculation of beta‐diversity (variation in species composition between adjacent locations) is challenging, making it difficult to reliably estimate gamma‐diversity (total diversity at the landscape or regional level). We conclude that an increased collaboration between the remote sensing and biodiversity communities is needed in order to properly address future challenges and developments

Models of upland species’ distributions are improved by accounting for geodiversity

Context: Recent research suggests that novel geodiversity data on landforms, hydrology and surface materials can improve biodiversity models at the landscape scale by quantifying abiotic variability more effectively than commonly used measures of spatial heterogeneity. However, few studies consider whether these variables can account for, and improve our understanding of, species’ distributions.Objectives: Assess the role of geodiversity components as macro-scale controls of plant species’ distributions in a montane landscape.Methods: We used an innovative approach to quantifying a landscape, creating an ecologically meaningful geodiversity dataset that accounted for hydrology, morphometry (landforms derived from geomorphometric techniques), and soil parent material (data from expert sources). We compared models with geodiversity to those just using topographic metrics (e.g. slope and elevation) and climate data. Species distribution models (SDMs) were produced for ‘rare’ (N=76) and ‘common’ (N=505) plant species at 1 km2 resolution for the Cairngorms National Park, Scotland.Results: The addition of automatically produced landform geodiversity data and hydrological features to a basic SDM (climate, elevation, and slope) resulted in a significant improvement in model fit across all common species’ distribution models. Adding further geodiversity data on surface materials resulted in a less consistent statistical improvement, but added considerable conceptual value to many individual rare and common SDMs.Conclusions: The geodiversity data used here helped us capture the abiotic environment’s heterogeneity and allowed for explicit links between the geophysical landscape and species’ ecology. It is encouraging that relatively simple and easily produced geodiversity data have the potential to improve SDMs. Our findings have important implications for applied conservation and support the need to consider geodiversity in management

Predicting residential building age from map data

The age of a building influences its form and fabric composition and this in turn is critical to inferring its energy performance. However, often this data is unknown. In this paper, we present a methodology to automatically identify the construction period of houses, for the purpose of urban energy modelling and simulation. We describe two major stages to achieving this – a per-building classification model and post-classification analysis to improve the accuracy of the class inferences. In the first stage, we extract measures of the morphology and neighbourhood characteristics from readily available topographic mapping, a high-resolution Digital Surface Model and statistical boundary data. These measures are then used as features within a random forest classifier to infer an age category for each building. We evaluate various predictive model combinations based on scenarios of available data, evaluating these using 5-fold cross-validation to train and tune the classifier hyper-parameters based on a sample of city properties. A separate sample estimated the best performing cross-validated model as achieving 77% accuracy. In the second stage, we improve the inferred per-building age classification (for a spatially contiguous neighbourhood test sample) through aggregating prediction probabilities using different methods of spatial reasoning. We report on three methods for achieving this based on adjacency relations, near neighbour graph analysis and graph-cuts label optimisation. We show that post-processing can improve the accuracy by up to 8 percentage points

Long-term peatland condition assessment via surface motion monitoring using the ISBAS DInSAR technique over the Flow Country, Scotland

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

Remote sensing restores predictability of ectotherm body temperature in the world’s forests

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

Automated classification metrics for energy modelling of residential buildings in the UK with open algorithms

Estimating residential building energy use across large spatial extents is vital for identifying and testing effective strategies to reduce carbon emissions and improve urban sustainability. This task is underpinned by the availability of accurate models of building stock from which appropriate parameters may be extracted. For example, the form of a building, such as whether it is detached, semi-detached, terraced etc and its shape may be used as part of a typology for defining its likely energy use. When these details are combined with information on building construction materials or glazing ratio, it can be used to infer the heat transfer characteristics of different properties. However, these data are not readily available for energy modelling or urban simulation. Although this is not a problem when the geographic scope corresponds to a small area and can be hand-collected, such manual approaches cannot be easily applied at the city or national scale. In this paper, we demonstrate an approach that can automatically extract this information at the city scale using off-the-shelf products supplied by a National Mapping Agency. We present two novel techniques to create this knowledge directly from input geometry. The first technique is used to identify built form based upon the physical relationships between buildings. The second technique is used to determine a more refined internal/external wall measurement and ratio. The second technique has greater metric accuracy and can also be used to address problems identified in extracting the built form. A case study is presented for the City of Nottingham in the United Kingdom using two data products provided by the Ordnance Survey of Great Britain (OSGB): MasterMap and AddressBase. This is followed by a discussion of a new categorisation approach for housing form for urban energy assessment