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Apple scab is a disease caused by the fungus Venturia inaequalis (Cke.) Wint. which can spread rapidly throughout orchards diminishing tree productivity and causing huge losses in marketable fruit. Efficient orchard reconnaissance and early detection of infections can inform fungicide applications for effective disease control and a range of new low-cost sensors offer a means of imaging orchards as the basis of scab detection. This study evaluates the potential contribution of three imaging devices: a multispectral (VIS-NIR) camera, thermal camera and a 3D sensor, for the detection of scab on young apple plants. In a controlled experiment, apple seedings were infected with scab and disease progression was imaged daily under natural illumination conditions in a glasshouse with minimal image processing. Whilst the thermal and 3D sensors images were deemed unsuitable for scab detection, the high-resolution multispectral imagery was exceptionally effective, with the NIR band (800–1000 nm) permitting the earliest scab detection due to the substantially lower reflectance of the fungal structures of V. inaequalis relative to healthy leaf tissue. We offer a model of near-infrared radiation interactions with the fungus and leaf interactions to explain reflectance characteristics of scab infected leaves throughout the growth cycle of the pathogen. The simple, low-cost remote-sensing approach developed here holds considerable promise for providing timely information on tree infection to improve the efficiency of apple scab disease management routines.

Water harvesting has a long history, but still plays an important role today by increasing crop productivity, combatting erosion, and improving water supplies. Geographical Information Systems (GIS) are used extensively to assess the suitability of sites for water harvesting but available tools fail to consider the synoptic topography of sites. Here, we report the creation of a novel, automated tool – “SiteFinder” – that evaluates potential locations by automatically calculating site-specific information, including structure parameters (height, length, and volume) and descriptors of the zone affected by the structure (storage capacity and area of influence) and the catchment area. Innovatively, compared to existing tools of this kind, SiteFinder works within a GIS environment. Thus, it allows the possibility of combining its outputs with larger Multi-Criteria Decision-Making processes to consider other bio-physical, socio-economic, and environmental factors. It utilises a Digital Elevation Model (DEM) and automatically analyses thousands of potential sites, computing site characteristics for different barrier heights that are dependent on the surrounding topography. It outputs values of eight parameters to aid planners in assessing the characteristics of sites as to their suitability for water harvesting. We conducted case studies using 30 × 30 m gridded DEMs to automatically evaluate several thousand sites and, by filtering the tool outputs, successfully identified sites with characteristics appropriate for scenarios at three spatial scales: large dams for nationally significant water supply reservoirs (383 sites analysed; 5 filtered sites with barriers up to 30 m in height); large gully erosion control dams for regional-scale interventions (4,586 sites analysed; 6 filtered sites with barriers up to 3.6 m in height); and local, community-based earth embankment projects (801 sites analysed; 6 filtered sites with barriers up to 2 m in height). A higher resolution (1

Apple scab is a disease caused by the fungus Venturia inaequalis (Cke.) Wint. which can spread rapidly throughout orchards diminishing tree productivity and causing huge losses in marketable fruit. Efficient orchard reconnaissance and early detection of infections can inform fungicide applications for effective disease control and a range of new low-cost sensors offer a means of imaging orchards as the basis of scab detection. This study evaluates the potential contribution of three imaging devices: a multispectral (VIS-NIR) camera, thermal camera and a 3D sensor, for the detection of scab on young apple plants. In a controlled experiment, apple seedings were infected with scab and disease progression was imaged daily under natural illumination conditions in a glasshouse with minimal image processing. Whilst the thermal and 3D sensors images were deemed unsuitable for scab detection, the high-resolution multispectral imagery was exceptionally effective, with the NIR band (800–1000 nm) permitting the earliest scab detection due to the substantially lower reflectance of the fungal structures of V. inaequalis relative to healthy leaf tissue. We offer a model of near-infrared radiation interactions with the fungus and leaf interactions to explain reflectance characteristics of scab infected leaves throughout the growth cycle of the pathogen. The simple, low-cost remote-sensing approach developed here holds considerable promise for providing timely information on tree infection to improve the efficiency of apple scab disease management routines.

Water harvesting has a long history, but still plays an important role today by increasing crop productivity, combatting erosion, and improving water supplies. Geographical Information Systems (GIS) are used extensively to assess the suitability of sites for water harvesting but available tools fail to consider the synoptic topography of sites. Here, we report the creation of a novel, automated tool – “SiteFinder” – that evaluates potential locations by automatically calculating site-specific information, including structure parameters (height, length, and volume) and descriptors of the zone affected by the structure (storage capacity and area of influence) and the catchment area. Innovatively, compared to existing tools of this kind, SiteFinder works within a GIS environment. Thus, it allows the possibility of combining its outputs with larger Multi-Criteria Decision-Making processes to consider other bio-physical, socio-economic, and environmental factors. It utilises a Digital Elevation Model (DEM) and automatically analyses thousands of potential sites, computing site characteristics for different barrier heights that are dependent on the surrounding topography. It outputs values of eight parameters to aid planners in assessing the characteristics of sites as to their suitability for water harvesting. We conducted case studies using 30 × 30 m gridded DEMs to automatically evaluate several thousand sites and, by filtering the tool outputs, successfully identified sites with characteristics appropriate for scenarios at three spatial scales: large dams for nationally significant water supply reservoirs (383 sites analysed; 5 filtered sites with barriers up to 30 m in height); large gully erosion control dams for regional-scale interventions (4,586 sites analysed; 6 filtered sites with barriers up to 3.6 m in height); and local, community-based earth embankment projects (801 sites analysed; 6 filtered sites with barriers up to 2 m in height). A higher resolution (1

Despite the growth rates of photovoltaic solar parks, their potential to alter land surface temperature remains unclear. Yet, resolving temperature impacts is pivotal to understanding the implications for ecosystem function, and the consequences for society due to perturbations to ecosystem service supply and natural capital stores. Here, for the first time, we demonstrate the existence of a solar park land surface temperature cool island effect that extends beyond the solar park boundary, using Landsat satellite imagery. The cool island effect was quantified for two large ground-mounted solar parks, Longyangxia (850 megawatts) in China and Stateline (300 megawatts) in the United States of America, where the effect was confirmed using field-based measurements. At both sites, the cooling extended up to 730 m away from the solar park boundary with localized reductions in LST of up to 2.3 °C. These cool islands could affect large areas of the land surface as solar parks proliferate across the world, with notable positive or negative impacts on ecosystem function. Given the potential implications for ecosystem processes, including carbon feedbacks to climate change and the carbon intensity of the electricity produced, improved understanding of solar park LST impacts is required. Specifically, this knowledge is needed to inform the development of sustainable land use and energy policies considering the rapid growth of solar park developments.

To explore the feasibility of meeting recently proposed large-scale tree planting targets, a UK wide assessment of land available for afforestation was carried out, considering a range of physical, environmental and policy constraints in three hypothetical planting scenarios. Results show there is sufficient space to meet these targets in all three scenarios, even if planting is prevented on good to moderate quality agricultural land and within protected areas. However, this would require planting on a large proportion of unconstrained land, especially for the more ambitious targets, which is unevenly distributed across the UK. This would limit opportunities for spatially targeting woodland creation, which may restrict the provision of additional ecosystem services such as air pollution control and recreation, and induce widespread negative impacts on landscapes and communities. In order to overcome these limitations, relaxing constraints, such as permitting afforestation of higher quality agricultural land, will need to be considered. Meeting many of the proposed afforestation targets would result in a transformational change in British land cover, which could replace or significantly impact the business models of tens of thousands of farms, and see the replacement of hundreds of thousands to millions of hectares of grassland, arable and horticultural land and other land covers. This would require rates of planting that far exceed those seen historically. Policies and mechanisms that could be used to encourage this planting, both by the state and private sectors, are discussed.

Despite the growth rates of photovoltaic solar parks, their potential to alter land surface temperature remains unclear. Yet, resolving temperature impacts is pivotal to understanding the implications for ecosystem function, and the consequences for society due to perturbations to ecosystem service supply and natural capital stores. Here, for the first time, we demonstrate the existence of a solar park land surface temperature cool island effect that extends beyond the solar park boundary, using Landsat satellite imagery. The cool island effect was quantified for two large ground-mounted solar parks, Longyangxia (850 megawatts) in China and Stateline (300 megawatts) in the United States of America, where the effect was confirmed using field-based measurements. At both sites, the cooling extended up to 730 m away from the solar park boundary with localized reductions in LST of up to 2.3 °C. These cool islands could affect large areas of the land surface as solar parks proliferate across the world, with notable positive or negative impacts on ecosystem function. Given the potential implications for ecosystem processes, including carbon feedbacks to climate change and the carbon intensity of the electricity produced, improved understanding of solar park LST impacts is required. Specifically, this knowledge is needed to inform the development of sustainable land use and energy policies considering the rapid growth of solar park developments.

To explore the feasibility of meeting recently proposed large-scale tree planting targets, a UK wide assessment of land available for afforestation was carried out, considering a range of physical, environmental and policy constraints in three hypothetical planting scenarios. Results show there is sufficient space to meet these targets in all three scenarios, even if planting is prevented on good to moderate quality agricultural land and within protected areas. However, this would require planting on a large proportion of unconstrained land, especially for the more ambitious targets, which is unevenly distributed across the UK. This would limit opportunities for spatially targeting woodland creation, which may restrict the provision of additional ecosystem services such as air pollution control and recreation, and induce widespread negative impacts on landscapes and communities. In order to overcome these limitations, relaxing constraints, such as permitting afforestation of higher quality agricultural land, will need to be considered. Meeting many of the proposed afforestation targets would result in a transformational change in British land cover, which could replace or significantly impact the business models of tens of thousands of farms, and see the replacement of hundreds of thousands to millions of hectares of grassland, arable and horticultural land and other land covers. This would require rates of planting that far exceed those seen historically. Policies and mechanisms that could be used to encourage this planting, both by the state and private sectors, are discussed.

To prevent further loss of our vital ecosystem services we must understand the linkages to their supporting natural capital attributes. Systematic literature reviews synthesise evidence of natural capital attribute to ecosystem service (NC-ES) linkages. However, such reviews rarely account for the context dependency of evidence that is derived from individual studies undertaken for a particular purpose, at a specific spatial scale or geographic location. To address this deficiency, we developed the LiNCAGES (Linking Natural Capital Attribute Groups to Ecosystem Services) platform for investigating the context dependency of literature-based evidence for NC-ES linkages. We demonstrate the application of the LiNCAGES platform using the OpenNESS systematic literature review of NC-ES linkages. A hypothetical use case scenario of a small-scale European forest manager is described. We find evidence for many NC-ES linkages, and trade-offs and synergies between services, is severely diminished or non-existent under certain contexts, such as larger spatial scales and European study location. The LiNCAGES platform provides a flexible tool that researchers can use to support collation, exploration and synthesis of literature-based evidence on NC-ES linkages. This is vital for providing credible and salient evidence to stakeholders on important NC-ES linkages that occur under their context, to guide effective management strategies.

Consistent data are seldom available for whole-catchment flood modelling in many developing regions, hence this study aimed to explore an integrated approach for flood modelling and mapping by combining available segmented hydrographic, topographic, floodplain roughness, calibration, and validation datasets using a two-dimensional Caesar-Lisflood hydrodynamic model to quantify and recreate the extent and impact of the historic 2012 flood in Nigeria. Available segments of remotely-sensed and in situ datasets (including hydrological, altimetry, digital elevation model, bathymetry, aerial photo, optical imagery, and radar imagery data) available to different degrees in the Niger-South hydrological area were systematically integrated to draw maximum benefits from all available data. Retrospective modelling, calibration, and validation were undertaken for the whole Niger- South hydrological catchment area of Nigeria, and then these data were segmented into sub-domains for re-validation to understand how data variability and uncertainties impact the accuracy of model outcomes. Furthermore, aerial photos were applied for the first time in the study area for flood model validation and for understanding how different physio-environmental properties influenced the synthetic aperture radar flood delineation capacity in the Niger Delta region of Nigeria. This study demonstrates how the complementary strengths of open, readily available geospatial datasets and tools can be leveraged to model and map flooding within acceptable levels of uncertainty for flood risk management.

The valuation of natural capital within individual farms could inform environmentally beneficial land use change and form the basis of agricultural subsidy schemes based on the provision of ecosystem services. Land cover extents can be used in a benefit transfer approach to produce monetary valuations of natural capital rapidly and at low cost. However, the methodology has not before been used within individual farms, and the impact of land cover data characteristics on the accuracy of valuations is uncertain. Here, we apply the approach to five UK farms of contrasting size, configuration and farming style, using three widely available land cover products. Results show that the land cover product used has a substantial impact on valuations, with differences of up to 58%, and the magnitude of this effect varies considerably according to the landscape structure of the farm. At most sites, valuation differences are driven by the extent of woodland recorded in the landscape, with higher resolution land cover products incorporating larger amounts of woodland through inclusion of smaller patches, leading to higher overall valuations. Integrating more accurate land cover data and accounting for the condition, configuration and location of natural capital has potential to improve the accuracy of valuations.

Within the agrifood sector, the production of high yields is a driver for UK orchard husbandry. Currently, orchard tree management is typically a non-discriminatory method with all trees subjected to the same interventions. Previous studies indicate that structural complexity of individual orchard trees is an indicator for future yield, which can guide the management of individual trees. However, data on the structure of individual trees is often limited. This study investigated the suitability of using remote sensing methods to capture data that can be used to quantify tree structure. Descriptive metrics based on the mathematical assessment of self-affinity and dimensionality were applied to the remotely-sensed data to quantify tree structure, and were also analysed for suitability as a predictor of fruit yield. The findings suggest that while proximal photogrammetry is informative, terrestrial LiDAR data can be used to quantify structural complexity most effectively and this approach holds greater potential for informing orchard management.

The study involves an object-based segmentation method to extract feature changes in tropical rainforest cover using Landsat image and airborne LiDAR (ALS). Disturbance event that are represents the changes are examined by the classification of multisensor data; that is a highly accurate ALS with different resolutions of multispectral Landsat image. Disturbance Index (DI) derived from Tasseled Cap Transformation, Normalized Difference Vegetation Index (NDVI), and the ALS height are the variables for object-based segmentation process. The classification is categorized into two classes; disturbed and non-disturbed forest cover using Nearest Neighbor (NN), Random Forest (RF) and Support Vector Machine (SVM). The overall accuracy ranging from 88% to 96% and kappa ranging from 0.79 to 0.91. Mcnemar’s test p-value (<0.05) is applied to check the classification for each method used which is RF 0.03 and SVM 0.01. The accuracy increases when the integration of ALS in Landsat image (SpectralLandsat; and SpectralLandsat + HeightALS).

Extreme flood events are becoming more frequent and intense in recent times, owing to climate change and other anthropogenic factors. Nigeria, the case-study for this research experiences recurrent flooding, with the most disastrous being the 2012 flood event that resulted in unprecedented damage to infrastructure, displacement of people, socio-economic disruption, and loss of lives. To mitigate and minimize the impact of such floods now and in the future, effective planning is required, underpinned by analytics based on reliable data and information. Such data are seldom available in many developing regions, owing to financial, technical, and organizational drawbacks that result in short-length and inadequate historical data that are prone to uncertainties if directly applied for flood frequency estimation. This study applies regional Flood Frequency Analysis (FFA) to curtail deficiencies in historical data, by agglomerating data from various sites with similar hydro-geomorphological characteristics and is governed by a similar probability distribution, differing only by an “index-flood”; as well as accounting for climate variability effect. Data from 17 gauging stations within the Ogun-Osun River Basin in Western Nigeria were analysed, resulting in the delineation of 3 sub-regions, of which 2 were homogeneous and 1 heterogeneous. The Generalized Logistic distribution was fitted to the annual maximum flood series for the 2 homogeneous regions to estimate flood magnitudes and the probability of occurrence while accounting for climate variability. The influence of climate variability on flood estimates in the region was linked to the Madden-Julian Oscillation (MJO) climate indices and resulted in increased flood magnitude for regional and direct flood frequency estimates varying from 0% - 35% and demonstrate that multi-decadal changes in atmospheric conditions influence both small and large floods. The results reveal the value of considering climate variability for f

The study involves an object-based segmentation method to extract feature changes in tropical rainforest cover using Landsat image and airborne LiDAR (ALS). Disturbance event that are represents the changes are examined by the classification of multisensor data; that is a highly accurate ALS with different resolutions of multispectral Landsat image. Disturbance Index (DI) derived from Tasseled Cap Transformation, Normalized Difference Vegetation Index (NDVI), and the ALS height are the variables for object-based segmentation process. The classification is categorized into two classes; disturbed and non-disturbed forest cover using Nearest Neighbor (NN), Random Forest (RF) and Support Vector Machine (SVM). The overall accuracy ranging from 88% to 96% and kappa ranging from 0.79 to 0.91. Mcnemar’s test p-value (<0.05) is applied to check the classification for each method used which is RF 0.03 and SVM 0.01. The accuracy increases when the integration of ALS in Landsat image (SpectralLandsat; and SpectralLandsat + HeightALS).

Extreme flood events are becoming more frequent and intense in recent times, owing to climate change and other anthropogenic factors. Nigeria, the case-study for this research experiences recurrent flooding, with the most disastrous being the 2012 flood event that resulted in unprecedented damage to infrastructure, displacement of people, socio-economic disruption, and loss of lives. To mitigate and minimize the impact of such floods now and in the future, effective planning is required, underpinned by analytics based on reliable data and information. Such data are seldom available in many developing regions, owing to financial, technical, and organizational drawbacks that result in short-length and inadequate historical data that are prone to uncertainties if directly applied for flood frequency estimation. This study applies regional Flood Frequency Analysis (FFA) to curtail deficiencies in historical data, by agglomerating data from various sites with similar hydro-geomorphological characteristics and is governed by a similar probability distribution, differing only by an “index-flood”; as well as accounting for climate variability effect. Data from 17 gauging stations within the Ogun-Osun River Basin in Western Nigeria were analysed, resulting in the delineation of 3 sub-regions, of which 2 were homogeneous and 1 heterogeneous. The Generalized Logistic distribution was fitted to the annual maximum flood series for the 2 homogeneous regions to estimate flood magnitudes and the probability of occurrence while accounting for climate variability. The influence of climate variability on flood estimates in the region was linked to the Madden-Julian Oscillation (MJO) climate indices and resulted in increased flood magnitude for regional and direct flood frequency estimates varying from 0% - 35% and demonstrate that multi-decadal changes in atmospheric conditions influence both small and large floods. The results reveal the value of considering climate variability for f

The valuation of natural capital within individual farms could inform environmentally beneficial land use change and form the basis of agricultural subsidy schemes based on the provision of ecosystem services. Land cover extents can be used in a benefit transfer approach to produce monetary valuations of natural capital rapidly and at low cost. However, the methodology has not before been used within individual farms, and the impact of land cover data characteristics on the accuracy of valuations is uncertain. Here, we apply the approach to five UK farms of contrasting size, configuration and farming style, using three widely available land cover products. Results show that the land cover product used has a substantial impact on valuations, with differences of up to 58%, and the magnitude of this effect varies considerably according to the landscape structure of the farm. At most sites, valuation differences are driven by the extent of woodland recorded in the landscape, with higher resolution land cover products incorporating larger amounts of woodland through inclusion of smaller patches, leading to higher overall valuations. Integrating more accurate land cover data and accounting for the condition, configuration and location of natural capital has potential to improve the accuracy of valuations.

To prevent further loss of our vital ecosystem services we must understand the linkages to their supporting natural capital attributes. Systematic literature reviews synthesise evidence of natural capital attribute to ecosystem service (NC-ES) linkages. However, such reviews rarely account for the context dependency of evidence that is derived from individual studies undertaken for a particular purpose, at a specific spatial scale or geographic location. To address this deficiency, we developed the LiNCAGES (Linking Natural Capital Attribute Groups to Ecosystem Services) platform for investigating the context dependency of literature-based evidence for NC-ES linkages. We demonstrate the application of the LiNCAGES platform using the OpenNESS systematic literature review of NC-ES linkages. A hypothetical use case scenario of a small-scale European forest manager is described. We find evidence for many NC-ES linkages, and trade-offs and synergies between services, is severely diminished or non-existent under certain contexts, such as larger spatial scales and European study location. The LiNCAGES platform provides a flexible tool that researchers can use to support collation, exploration and synthesis of literature-based evidence on NC-ES linkages. This is vital for providing credible and salient evidence to stakeholders on important NC-ES linkages that occur under their context, to guide effective management strategies.

This paper describes how wind engineering, geospatial science and crop agronomy disciplines can be combined to provide a decision support system for farmers to manage lodging risk, using the Crop Failure Assessment due to Lodging Losses (CROPFALL) framework. CROPFALL calculates lodging risk using information about topography, land cover, soil type and meteorological data, combined with a mechanistic model of lodging, and crop parameters. The risk of lodging is calculated at regional, farm, field and sub-field scales. The impact of crop management changes on lodging risk is simulated, demonstrating that modest crop management changes can achieve large reductions in lodging risk.

In many regions of the world, especially in developing countries, river network data are outdated or completely absent, yet such information is critical for supporting important functions such as flood mitigation efforts, land use and transportation planning, and the management of water resources. In this study a new method was developed for delineating river networks using Sentinel-1 imagery. Unsupervised classification was applied to multi-temporal Sentinel-1 data to discriminate water bodies from other land cover types then the outputs were combined to generate a single persistent water bodies product. A thinning algorithm was then used to delineate river centre lines which were converted into vector features and built into a topologically structured geometric network. The complex river system of the Niger Delta was used to compare the performance of the Sentinel-based method against alternative freely available waterbody products from USGS, ESA and OpenStreetMap and a river network derived from a SRTM DEM. From both raster-based and vector-based accuracy assessments it was found that the Sentinel-based river network products were superior to the comparator data sets by a substantial margin. The resulting geometric river network was used to perform flow routing analysis which is important for a variety of environmental management and planning applications. The approach developed in this study holds considerable potential for generating up to date, detailed river network data for the many countries globally where such data are deficient.

Accurate crop yield estimates are important for governments, farmers, scientists and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate within-field wheat yield variability in a single year. The impact of data resolution and availability on yield estimation is explored using different combinations of input data. This was achieved by combining Sentinel-2 with environmental data (e.g. meteorological, topographical, soil moisture) for different periods throughout the growing season. Yield was estimated using Random Forest (RF) regression models. They were trained and validated using a dataset containing over 8000 points collected by combine harvester yield monitors from 39 wheat fields in the UK. The results demonstrate that it is possible to produce accurate maps of within-field yield variation at 10 m resolution using Sentinel-2 data (RMSE 0.66 t/ha). When combined with environmental data further improvements in accuracy can be obtained (RMSE 0.61 t/ha). We demonstrate that with knowledge of crop-type distribution it is possible to use these models, trained with data from a few fields, to estimate within-field yield variability on a landscape scale. Applying this method gives us a range of crop yield across the landscape of 4.09 to 12.22 t/ha, with a total crop production of approx. 289,000 t.

To assess the accuracy of individual tree crown (ITC) delineation techniques the same tree needs to be identified in two different datasets, for example, ground reference (GR) data and crowns delineated from LiDAR. Many studies use arbitrary metrics or simple linear-distance thresholds to match trees in different datasets without quantifying the level of agreement. For example, successful match-pairing is often claimed where two data points, representing the same tree in different datasets, are located within 5m of one another. Such simple measures are inadequate for representing the multi-variate nature of ITC delineations and generate misleading measures of delineation accuracy. In this study, we develop a new framework for objectively quantifying the agreement between GR and remotely-sensed tree datasets: the Accuracy of Remotely-sensed Biophysical Observation and Retrieval (ARBOR) framework. Using common biophysical properties of ITC delineated trees (location, height and crown area), trees represented in different data sets were modelled as overlapping Gaussian curves to facilitate a more comprehensive assessment of the level of agreement. Extensive testing quantified the limitations of some frequently used match-pairing methods, in particular, the Hausdorff distance algorithm. We demonstrate that within the ARBOR framework, the Hungarian combinatorial optimisation algorithm improves the match between datasets, while the Jaccard similarity coefficient is effective for measuring the correspondence between the matched data populations. The ARBOR framework was applied to GR and remotely-sensed tree data from a woodland study site to demonstrate how ARBOR can identify the optimum ITC delineation technique, out of four different methods tested, based on two measures of statistical accuracy. Using ARBOR will limit further reliance on arbitrary thresholds as it provides an objective approach for quantifying accuracy in the development and application of ITC delineatio

Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data.

Accurate crop yield estimates are important for governments, farmers, scientists and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate within-field wheat yield variability in a single year. The impact of data resolution and availability on yield estimation is explored using different combinations of input data. This was achieved by combining Sentinel-2 with environmental data (e.g. meteorological, topographical, soil moisture) for different periods throughout the growing season. Yield was estimated using Random Forest (RF) regression models. They were trained and validated using a dataset containing over 8000 points collected by combine harvester yield monitors from 39 wheat fields in the UK. The results demonstrate that it is possible to produce accurate maps of within-field yield variation at 10 m resolution using Sentinel-2 data (RMSE 0.66 t/ha). When combined with environmental data further improvements in accuracy can be obtained (RMSE 0.61 t/ha). We demonstrate that with knowledge of crop-type distribution it is possible to use these models, trained with data from a few fields, to estimate within-field yield variability on a landscape scale. Applying this method gives us a range of crop yield across the landscape of 4.09 to 12.22 t/ha, with a total crop production of approx. 289,000 t.

This paper describes how wind engineering, geospatial science and crop agronomy disciplines can be combined to provide a decision support system for farmers to manage lodging risk, using the Crop Failure Assessment due to Lodging Losses (CROPFALL) framework. CROPFALL calculates lodging risk using information about topography, land cover, soil type and meteorological data, combined with a mechanistic model of lodging, and crop parameters. The risk of lodging is calculated at regional, farm, field and sub-field scales. The impact of crop management changes on lodging risk is simulated, demonstrating that modest crop management changes can achieve large reductions in lodging risk.

In many regions of the world, especially in developing countries, river network data are outdated or completely absent, yet such information is critical for supporting important functions such as flood mitigation efforts, land use and transportation planning, and the management of water resources. In this study a new method was developed for delineating river networks using Sentinel-1 imagery. Unsupervised classification was applied to multi-temporal Sentinel-1 data to discriminate water bodies from other land cover types then the outputs were combined to generate a single persistent water bodies product. A thinning algorithm was then used to delineate river centre lines which were converted into vector features and built into a topologically structured geometric network. The complex river system of the Niger Delta was used to compare the performance of the Sentinel-based method against alternative freely available waterbody products from USGS, ESA and OpenStreetMap and a river network derived from a SRTM DEM. From both raster-based and vector-based accuracy assessments it was found that the Sentinel-based river network products were superior to the comparator data sets by a substantial margin. The resulting geometric river network was used to perform flow routing analysis which is important for a variety of environmental management and planning applications. The approach developed in this study holds considerable potential for generating up to date, detailed river network data for the many countries globally where such data are deficient.

To assess the accuracy of individual tree crown (ITC) delineation techniques the same tree needs to be identified in two different datasets, for example, ground reference (GR) data and crowns delineated from LiDAR. Many studies use arbitrary metrics or simple linear-distance thresholds to match trees in different datasets without quantifying the level of agreement. For example, successful match-pairing is often claimed where two data points, representing the same tree in different datasets, are located within 5m of one another. Such simple measures are inadequate for representing the multi-variate nature of ITC delineations and generate misleading measures of delineation accuracy. In this study, we develop a new framework for objectively quantifying the agreement between GR and remotely-sensed tree datasets: the Accuracy of Remotely-sensed Biophysical Observation and Retrieval (ARBOR) framework. Using common biophysical properties of ITC delineated trees (location, height and crown area), trees represented in different data sets were modelled as overlapping Gaussian curves to facilitate a more comprehensive assessment of the level of agreement. Extensive testing quantified the limitations of some frequently used match-pairing methods, in particular, the Hausdorff distance algorithm. We demonstrate that within the ARBOR framework, the Hungarian combinatorial optimisation algorithm improves the match between datasets, while the Jaccard similarity coefficient is effective for measuring the correspondence between the matched data populations. The ARBOR framework was applied to GR and remotely-sensed tree data from a woodland study site to demonstrate how ARBOR can identify the optimum ITC delineation technique, out of four different methods tested, based on two measures of statistical accuracy. Using ARBOR will limit further reliance on arbitrary thresholds as it provides an objective approach for quantifying accuracy in the development and application of ITC delineatio

This paper describes the preliminary work on a project to develop a model of the lodging of cereal and oil seed rape crops based on a generalised theoretical model embedded within an earth observation framework. The project will involve intensive field tests to observe the behaviour of isolated and interlocked crops in conditions of high wind and heavy rainfall, and to measure a range of crop and soil parameters throughout the growing season to allow the generalised lodging model to be calibrated. In its final form, the modelling framework will allow the risk of lodging for a particular field or region to be assessed both before and during the growing season so that strategic and tactical measures can be taken to reduce this risk.

This paper describes the preliminary work on a project to develop a model of the lodging of cereal and oil seed rape crops based on a generalised theoretical model embedded within an earth observation framework. The project will involve intensive field tests to observe the behaviour of isolated and interlocked crops in conditions of high wind and heavy rainfall, and to measure a range of crop and soil parameters throughout the growing season to allow the generalised lodging model to be calibrated. In its final form, the modelling framework will allow the risk of lodging for a particular field or region to be assessed both before and during the growing season so that strategic and tactical measures can be taken to reduce this risk.

Tropical forests are experiencing a growing fire problem driven by climatic change, agricultural expansion and forest degradation. Protected areas are an important feature of forest protection strategies, and sustainable use reserves (SURs) may be reducing fire prevalence since they promote sustainable livelihoods and resource management. However, the use of fire in swidden agriculture, and other forms of land management, may be undermining the effectiveness of SURs in meeting their conservation and sustainable development goals. We analyse MODIS derived hot pixels, TRMM rainfall data, Terra-Class land cover data, socio-ecological data from the Brazilian agro-census and the spatial extent of rivers and roads to evaluate whether the designation of SURs reduces fire occurrence in the Brazilian Amazon. Specifically, we ask (1) a. Is SUR location (i.e., de facto) or (1) b. designation (i.e. de jure) the driving factor affecting performance in terms of the spatial density of fires?, and (2), Does SUR creation affect fire management (i.e., the timing of fires in relation to previous rainfall)? We demonstrate that pre-protection baselines are crucial for understanding reserve performance. We show that reserve creation had no discernible impact on fire density, and that fires were less prevalent in SURs due to their characteristics of sparser human settlement and remoteness, rather than their status de jure. In addition, the timing of fires in relation to rainfall, indicative of local fire management and adherence to environmental law, did not improve following SUR creation. These results challenge the notion that SURs promote environmentally sensitive fire-management, and suggest that SURs in Amazonia will require special attention if they are to curtail future accidental wildfires, particularly as plans to expand the road infrastructure throughout the region are realised. Greater investment to support improved fire management by farmers living in reserves, in addition to

Unlike other investigations that use discrete return (DR) light detection and ranging (LiDAR) data for the visualisation and investigation of physical structure, this research attempts to investigate data relationships found within the LiDAR point cloud in order to infer the condition of the subject of interest, which for the purposes of this investigation are tree canopies. During DR LiDAR data capture, a laser pulse is emitted from the scanner and information about the subject is captured at only at specific points known as laser returns (r). Subsequently, not all of the laser waveform is recorded, meaning that although a general impression of the subject is captured in a point cloud, there will be areas of the subject between each data point that remain unrepresented in the dataset. This paper outlines preliminary research into attempting to discover what range of LiDAR metrics, and resulting data relationships, are the most suitable to identify the significance of the structural condition of tree canopies from a tree health perspective. This research contributes to a wider investigation of automated tree health assessment and the early identification of structural failure in trees using remote sensing techniques

Unlike other investigations that use discrete return (DR) light detection and ranging (LiDAR) data for the visualisation and investigation of physical structure, this research attempts to investigate data relationships found within the LiDAR point cloud in order to infer the condition of the subject of interest, which for the purposes of this investigation are tree canopies. During DR LiDAR data capture, a laser pulse is emitted from the scanner and information about the subject is captured at only at specific points known as laser returns (r). Subsequently, not all of the laser waveform is recorded, meaning that although a general impression of the subject is captured in a point cloud, there will be areas of the subject between each data point that remain unrepresented in the dataset. This paper outlines preliminary research into attempting to discover what range of LiDAR metrics, and resulting data relationships, are the most suitable to identify the significance of the structural condition of tree canopies from a tree health perspective. This research contributes to a wider investigation of automated tree health assessment and the early identification of structural failure in trees using remote sensing techniques

Fire in the forested tropics has profound environmental, economic, and social impacts at multiple geographical scales. Causes of tropical fires are widely documented, although research contributions are from many disciplines, and each tends to focus on specific facets of a research problem, which might limit understanding of fire as a complex social-ecological system. We conducted a systematic review to (1) examine geographic and methodological focus in tropical fire research; (2) identify which types of landholders are the focus of the research effort; (3) test for a research method effect on the variables, e. g., socio-political, economic, and climatic, identified as causes of and proposed management solutions to tropical fire; and (4) examine relationships between causal factors and proposed solutions. Results from 51 studies show distinct geographic and methodological tendencies in the literature. Few studies explicitly identify landholder types, and no social studies focused on large-landholders. Multiple drivers and potential solutions to preventing fire are identified and the research approach adopted had the strongest influence on the socioeconomic, direct fire management and landscape characteristics variables. There was an overall mismatch between identified cause and proposed management solution. These findings indicate that mixed method approaches are imperative to understanding the coupled human-nature system of fire and to improve rural development and management strategies to curtail tropical fire spread.

Fire in the forested tropics has profound environmental, economic, and social impacts at multiple geographical scales. Causes of tropical fires are widely documented, although research contributions are from many disciplines, and each tends to focus on specific facets of a research problem, which might limit understanding of fire as a complex social-ecological system. We conducted a systematic review to (1) examine geographic and methodological focus in tropical fire research; (2) identify which types of landholders are the focus of the research effort; (3) test for a research method effect on the variables, e. g., socio-political, economic, and climatic, identified as causes of and proposed management solutions to tropical fire; and (4) examine relationships between causal factors and proposed solutions. Results from 51 studies show distinct geographic and methodological tendencies in the literature. Few studies explicitly identify landholder types, and no social studies focused on large-landholders. Multiple drivers and potential solutions to preventing fire are identified and the research approach adopted had the strongest influence on the socioeconomic, direct fire management and landscape characteristics variables. There was an overall mismatch between identified cause and proposed management solution. These findings indicate that mixed method approaches are imperative to understanding the coupled human-nature system of fire and to improve rural development and management strategies to curtail tropical fire spread.

Grimsvotn is located beneath Vatnajokull, Europe's largest temperate ice cap. As a part of ongoing research on heat flux, morphological changes and volcanic processes at Grimsvotn, Airborne Thematic Mapper (ATM) imagery and aerial photographs were acquired in 2001. The thermal images illuminated distinct areas of geothermal activity along the southern caldera wall. In combination with meteorological data the images were used to estimate surface temperatures and heat flux from patches of open water along the margin of the ice shelf covering the Grimsvotn subglacial lake. It was found that water temperatures varied from 0°C to ∼45°C and that the heat flux to the atmosphere from open water varied from slightly negative values (net energy gain) up to 1000 W m-2. The total heat output from the ∼0.1 km2 of open water was estimated as ∼18 MW, about 1% of the base heat output of Grimsvotn. The aerial photographs were used to produce geomorphological maps of the caldera wall, including areas that cannot be safely mapped from the ground. This work indicates that thermal imagery can be an important supplement to ground-based measurements, and that combined optical and thermal remote sensing is a useful tool for spatially detailed monitoring of inaccessible and partly ice-covered volcanoes.

Grimsvotn is located beneath Vatnajokull, Europe's largest temperate ice cap. As a part of ongoing research on heat flux, morphological changes and volcanic processes at Grimsvotn, Airborne Thematic Mapper (ATM) imagery and aerial photographs were acquired in 2001. The thermal images illuminated distinct areas of geothermal activity along the southern caldera wall. In combination with meteorological data the images were used to estimate surface temperatures and heat flux from patches of open water along the margin of the ice shelf covering the Grimsvotn subglacial lake. It was found that water temperatures varied from 0°C to ∼45°C and that the heat flux to the atmosphere from open water varied from slightly negative values (net energy gain) up to 1000 W m-2. The total heat output from the ∼0.1 km2 of open water was estimated as ∼18 MW, about 1% of the base heat output of Grimsvotn. The aerial photographs were used to produce geomorphological maps of the caldera wall, including areas that cannot be safely mapped from the ground. This work indicates that thermal imagery can be an important supplement to ground-based measurements, and that combined optical and thermal remote sensing is a useful tool for spatially detailed monitoring of inaccessible and partly ice-covered volcanoes.

This paper examines why the growing gap-year phenomenon is important for university geography departments in the context of education and employment. The research examines the scale and types of gap years, and their effects on students. The study uses a multi-actor approach comprising information from national statistical sources, university departments, students who have taken gap years and commercial gap-year providers. The paper draws some lessons for geography departments such as the need to systematically record the effects of a gap year. It highlights some paradoxes of any expansion of the gap-year market; for example, that expansion may reduce the benefits of the gap-year experience and may narrow the types of gap year taken. Issues of social exclusion also arise.

This paper examines why the growing gap-year phenomenon is important for university geography departments in the context of education and employment. The research examines the scale and types of gap years, and their effects on students. The study uses a multi-actor approach comprising information from national statistical sources, university departments, students who have taken gap years and commercial gap-year providers. The paper draws some lessons for geography departments such as the need to systematically record the effects of a gap year. It highlights some paradoxes of any expansion of the gap-year market; for example, that expansion may reduce the benefits of the gap-year experience and may narrow the types of gap year taken. Issues of social exclusion also arise.

This study created and tested predictive models developed using airborne imaging spectrometer and light detection and ranging (LIDAR)instruments for estimating the concentrations of photosynthetic pigments in broad-leaved and coniferous forest plantations. Data were acquired using a Compact Airborne Spectrographic Imager (CASI) and an Airborne Laser Terrain Mapping (ALTM) 1020 instrument in midsummer for study sites in the New Forest, England, along with concomitant in situ measurements of canopy properties. The stands used displayed a wide variation in the biophysical and biochemical properties of interest. When employing the imaging spectrometer data alone, there were no relationships between any spectral variables (band reflectance, band ratios, or first derivatives of reflectance) and canopy biophysical and biochemical properties when both broad-leaved and coniferous stands were analysed as a combined data set. However, for the broad-leaved stands alone, curvilinear relationships were found between the wavelength position of the red edge (kRE) and pigment concentrations per unit ground area (e.g., R2=0.88** for chlorophyll a [Chl a]) and per unit leaf mass (e.g., R2=0.76** for Chl a). The predictive value of these models was somewhat limited; for example, the root mean squared error (RMSE) was 300 mg m-2 (27% of the mean) for Chl a concentration per unit ground area and 1.17 mg g-1 (24% of the mean) for Chl a concentration per unit leaf mass. A ratio of a near-infrared and a green band (865 nm/553 nm) was linearly related to leaf area index (LAI) of the broad-leaved stands (R2=0.71**) and the regression model was a reasonable predictor of the LAI for the independent test sites (RMSE=0.88; 18.6% of the mean). Canopy height information derived from the ALTM data was used to mask out canopy gap areas from the CASI imagery of each stand. This process had limited impact on the relationships between spectral and canopy variables for the broad-leaved stands, and kRE re

This study created and tested predictive models developed using airborne imaging spectrometer and light detection and ranging (LIDAR)instruments for estimating the concentrations of photosynthetic pigments in broad-leaved and coniferous forest plantations. Data were acquired using a Compact Airborne Spectrographic Imager (CASI) and an Airborne Laser Terrain Mapping (ALTM) 1020 instrument in midsummer for study sites in the New Forest, England, along with concomitant in situ measurements of canopy properties. The stands used displayed a wide variation in the biophysical and biochemical properties of interest. When employing the imaging spectrometer data alone, there were no relationships between any spectral variables (band reflectance, band ratios, or first derivatives of reflectance) and canopy biophysical and biochemical properties when both broad-leaved and coniferous stands were analysed as a combined data set. However, for the broad-leaved stands alone, curvilinear relationships were found between the wavelength position of the red edge (kRE) and pigment concentrations per unit ground area (e.g., R2=0.88** for chlorophyll a [Chl a]) and per unit leaf mass (e.g., R2=0.76** for Chl a). The predictive value of these models was somewhat limited; for example, the root mean squared error (RMSE) was 300 mg m-2 (27% of the mean) for Chl a concentration per unit ground area and 1.17 mg g-1 (24% of the mean) for Chl a concentration per unit leaf mass. A ratio of a near-infrared and a green band (865 nm/553 nm) was linearly related to leaf area index (LAI) of the broad-leaved stands (R2=0.71**) and the regression model was a reasonable predictor of the LAI for the independent test sites (RMSE=0.88; 18.6% of the mean). Canopy height information derived from the ALTM data was used to mask out canopy gap areas from the CASI imagery of each stand. This process had limited impact on the relationships between spectral and canopy variables for the broad-leaved stands, and kRE re

Gaps within woodland canopies have important ecological roles and their spatial characteristics influence regeneration processes and the diversity and distribution species. This paper investigates the potential of airborne imaging spectrometer data for characterizing the spatial properties of gaps within deciduous woodlands. Imagery obtained in summer with an Compact Airborne Spectrographic Imager (CASI) was classified to produce an accurate map of canopy gaps within several deciduous woodlands. Imagery obtained in winter proved much less informative. Principal components analysis (PCA) of separate and combined summer and winter images revealed PCs that provided information on woodland canopy structure and could be classified to give an improved delineation of canopy gaps. The spatial characteristics within the woodlands were quantified using a raster-based GIS and pattern-process relationships were used to infer the relative ecological status of the different woodlands. The ability of the aircraft to fly at short notice and the high sensitivity and high spatial and spectral resolution of the airborne spectrometer offered many advantages over broad-band satellite sensors for this application.

The directional spectral reflectance of deciduous forests changes markedly throughout the year as a consequence of the phenology of the canopy and understorey and changes in the spatial arrangement and density of scene components. This paper presents the results of a 1-year experiment using a tower-mounted spectroradiometer to measure the seasonal changes in the nadir reflectance properties of ash and beech canopies. Seasonal variations in reflectance in red wavelengths were found to be inversely related to percentage cover, whilst variations in near-infrared reflectance were directly related to percentage cover although for both wavelength regions changes in reflectance were not always exactly synchronous with changes in percentage cover. A strong rectilinear relation was observed between NDVI and percentage cover when data from both species were combined. The shape of the seasonal NDVI profile revealed differences between the two species that was ascribed to phenological differences between ash and beech canopies. The position of the wavelength of maximum slope in the region 680-750 nm, was found to shift between two domains depending on the particular association of scene elements presented to the sensor as canopy cover changed.