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Tropical forests provide essential ecosystem services related to human livelihoods. However, the distribution and condition of tropical forests are under significant pressure, causing shrinkage and risking biodiversity loss across the tropics. Tanzania is currently undergoing significant forest cover changes, but monitoring is limited, in part due to a lack of remote sensing knowledge, tools and methods. This study has demonstrated a comprehensive approach to creating a national-scale forest monitoring system using Earth Observation data to inform decision making, policy formulation, and combat biodiversity loss. A systematically wall-to-wall forest baseline was created for 2018 through the application of Landsat 8 imagery. The classification was developed using the extreme gradient boosting (XGBoost) machine-learning algorithm, and achieved an accuracy of 89% and identified 45.76% of the country’s area to be covered with forest. Of those forested areas, 45% was found within nationally protected areas. Utilising an innovative methodology based on a forest habitat suitability analysis, the forest baseline was classified into forest types, with an overall accuracy of 85%. Woodlands (open and closed) were found to make up 79% of Tanzania’s forests. To map changes in forest extent, an automated system for downloading and processing of the Landsat imagery was used along with the XGBoost classifiers trained to define the national forest extent, where Landsat 8 scenes were individually downloaded and processed and the identified changes summarised on an annual basis. Forest loss identified for 2019 was found to be 157,204 hectares, with an overall accuracy of 82%. These forest losses within Tanzania have already triggered ecological problems and alterations in ecosystem types and species loss. Therefore, a forest monitoring system, such as the one presented in this study, will enhance conservation programmes and support efforts to save the last remnants of Tanzania’s pristine forests.

Wetlands are among the most productive natural ecosystems in the world, generally being important biodiversity hotspots. However, the complex nature of these landscapes together with the fragile and dynamic relationships among the organisms inhabiting these regions, make wetland ecosystems especially vulnerable to environmental disturbance, such as climate change. Thus, developing new automated systems which allow continuous monitoring and mapping of wetland dynamics is crucial for preserving their natural health. Synthetic aperture radar (SAR) systems have proven useful in monitoring and mapping the hydrological processes of wetland ecosystems through the use of polarimetric change detection techniques. Nonetheless, most of these flood change detectors rely on static detection approaches, generally covering a limited period of time (e.g., pre and post flooding scenario comparison), thus failing in providing continuous information about the diverse hydrological mechanisms. In this context, this research presents a novel approach for monitoring the hydrological dynamics of wetlands in a continuous and near-real-time manner using dense Sentinel-1 image time-series. In this work, we have enhanced our recently developed algorithm based on cumulative sums (SAR-CUSUM), to include polarimetric information, which allows to classify the type of change due to the flood. The new processing stack exploits the polarimetric information of dual-pol Sentinel-1 dense time series for detecting floods and provide some separation between open water and flooded vegetation areas. The outcomes derived from this study emphasize the capabilities of dense SAR time-series for environmental monitoring while providing a useful tool which could be integrated into rapid response and wetland conservation management plans.

We present an image processing method for accurately segmenting crop plots from Unmanned Aerial System imagery (UAS). The use of UAS for agricultural monitoring has increased significantly, emerging as a potentially cost effective alternative to manned aerial surveys and field work for remotely assessing crop state. The accurate segmentation of small densely-packed crop plots from UAS imagery over extensive areas is an important component of this monitoring activity in order to assess the state of different varieties and treatment regimes in a timely and cost-effective manner. Despite its importance, a reliable crop plot segmentation approach eludes us, with best efforts being relying on significant manual parameterization. The segmentation method developed uses a combination of edge detection and Hough line detection to establish the boundaries of each plot with pixel/point based metrics calculated for each plot segment. We show that with limited parameterization, segmentation of crop plots consistently over 89% accuracy are possible on different crop types and conditions. This is comparable to results obtained from rice paddies where the plant material in plots is sharply contrasted with the water, and represents a considerable improvement over previous methods for typical dry land crops.

Aim: Climate change is pressing extra strain on the already degraded forest ecosystem in Tanzania. However, it is mostly unknown how climate change will affect the distribution of forests in the future. We aimed to model the impacts of climate change on natural forests to help inform national-level conservation and mitigation strategies. Location: Tanzania. Methods: We conducted maximum entropy (MaxEnt) modelling to simulate forest habitat suitability using the Tanzanian national forest inventory survey (1,307 occurrences) and environmental data. Changes in forest habitats were simulated under two Representative Concentration Pathways (RCPs) emission scenarios RCP 4.5 and RCP 8.5 for 2055 and 2085. Results: The results indicate that climate change will threaten forest communities, especially fragmented strips of montane forests. Even under optimistic emission scenario, the extent of montane forest is projected to almost halve by 2085, intersecting many biodiversity hotspots across the Eastern Arc Mountains. Similarly, climate change is predicted to threaten microhabitat forests (i.e. thickets), with losses exceeding 70% by 2085 (RCP8.5). Other forest habitats are predicted to decrease (lowland forest and woodland) representing essential ecological networks, whereas suitable habitats for carbon-rich mangroves are predicted to expand by more than 40% at both scenarios. Conclusions: Climate change will impact forests by accelerating habitat loss, and fragmentation and the remaining land suitable for forests will also be subject to pressures associated with rising demand for food and biofuels. These changes are likely to increase the probability of adverse impacts to the country's indigenous flora and fauna. Our findings, therefore, call for a shift in conservation efforts, focusing on (i) the enhanced management of existing protected areas that can absorb the impacts of future climate change, and (ii) expanding conservation efforts into newly suitable regions through effective land use planning and land reclamation, helping to preserve and enhance forest connectivity between fragmented patches.

Continental-scale models of malaria climate suitability typically couple well-established temperature-response models with basic estimates of vector habitat availability using rainfall as a proxy. Here we show that across continental Africa, the estimated geographic range of climatic suitability for malaria transmission is more sensitive to the precipitation threshold than the thermal response curve applied. To address this problem we use downscaled daily climate predictions from seven GCMs to run a continental-scale hydrological model for a process-based representation of mosquito breeding habitat availability. A more complex pattern of malaria suitability emerges as water is routed through drainage networks and river corridors serve as year-round transmission foci. The estimated hydro-climatically suitable area for stable malaria transmission is smaller than previous models suggest and shows only a very small increase in state-of-the-art future climate scenarios. However, bigger geographical shifts are observed than with most rainfall threshold models and the pattern of that shift is very different when using a hydrological model to estimate surface water availability for vector breeding., Prior studies mapping climatologically suitable areas for malaria transmission have used relatively simple thresholds for precipitation. Here the authors show that when models incorporate hydrological processes a more complex pattern of malaria suitability emerges in Africa and future shifts in suitability are more pronounced.

Background The Barotse floodplains of the upper Zambezi River and its tributaries are a highly dynamic environment, with seasonal flooding and transhumance presenting a shifting mosaic of potential larval habitat and human and livestock blood meals for malaria vector mosquitoes. However, limited entomological surveillance has been undertaken to characterize the vector community in these floodplains and their environs. Such information is necessary as, despite substantial deployment of insecticide-treated nets (ITNs) and indoor residual spraying (IRS) against Anopheles vectors, malaria transmission persists across Barotseland in Zambia’s Western Province. Methods Geographically extensive larval surveys were undertaken in two health districts along 102 km of transects, at fine spatial resolution, during a dry season and following the peak of the successive wet season. Larvae were sampled within typical Anopheles flight range of human settlements and identified through genetic sequencing of cytochrome c oxidase I and internal transcribed spacer two regions of mitochondrial and nuclear DNA. This facilitated detailed comparison of taxon-specific abundance patterns between ecological zones differentiated by hydrological controls. Results An unexpected paucity of primary vectors was revealed, with An. gambiae s.l. and An. funestus representing < 2% of 995 sequenced anophelines. Potential secondary vectors predominated in the vector community, primarily An. coustani group species and An. squamosus. While the distribution of An. gambiae s.l. in the study area was highly clustered, secondary vector species were ubiquitous across the landscape in both dry and wet seasons, with some taxon-specific relationships between abundance and ecological zones by season. Conclusions The diversity of candidate vector species and their high relative abundance observed across diverse hydro-ecosystems indicate a highly adaptable transmission system, resilient to environmental variation and, potentially, interventions that target only part of the vector community. Larval survey results imply that residual transmission of malaria in Barotseland is being mediated predominantly by secondary vector species, whose known tendencies for crepuscular and outdoor biting renders them largely insensitive to prevalent vector control methods. Graphical Abstract

Transformation to Continuous Cover Forestry (CCF) is a long and difficult process in which frequent management interventions rapidly alter forest structure and dynamics with long lasting impacts. Therefore, a critical component of transformation is the acquisition of up-to-date forest inventory data to direct future management decisions. Recently, the use of single tree detection methods derived from unmanned aerial vehicle (UAV) has been identified as being a cost effective method for inventorying forests. However, the rapidly changing structure of forest stands in transformation amplifies the difficultly in transferability of current individual tree detection (ITD) methods. This study presents a novel ITD Bayesian parameter optimisation approach that uses quantile regression and external biophysical tree data sets to provide a transferable and low cost ITD approach to monitoring stands in transformation. We applied this novel method to 5 stands in a variety of transformation stages in the UK and to a independent test study site in California, USA, to assess the accuracy and transferability of this method. Requiring small amounts of training data (15 reference trees) this approach had a mean test accuracy (F-score = 0.88) and provided mean tree diameter estimates (RMSE = 5.6 cm) with differences that were not significance to the ground data (p < 0.05). We conclude that this method can be used to monitor forests stands in transformation and thus can also be applied to a wide range of forest structures with limited manual parameterisation between sites.

Knowledge of the location and extent of surface water and inundated vegetation is vital for a range of applications including flood risk management, biodiversity monitoring, quantifying greenhouse gas emissions, and mapping water-borne disease risk. Here, we present a new tool, TropWet, which enables users of all abilities to map wetlands in herbaceous dominated regions based on simple unmixing of optical Landsat satellite imagery in the Google Earth Engine. The results demonstrate transferability throughout the African continent with a high degree of accuracy (mean 91% accuracy, st. dev 2.6%, n = 10,800). TropWet demonstrated considerable improvements over existing globally available surface water datasets for mapping the extent of important wetlands like the Okavango, Botswana. TropWet was able to provide frequency inundation maps as an indicator of malarial mosquito aquatic habitat extent and persistence in Barotseland, Zambia. TropWet was able to map flood extent comparable to operational flood risk mapping products in the Zambezi Region, Namibia. Finally, TropWet was able to quantify the effects of the El Niño/Southern Oscillation (ENSO) events on the extent of photosynthetic vegetation and wetland extent across Southern Africa. These examples demonstrate the potential for TropWet to provide policy makers with crucial information to help make national, regional, or continental scale decisions regarding wetland conservation, flood/disease hazard mapping, or mitigation against the impacts of ENSO.

The availability of water at the ground surface for vector mosquito larval habitats is a critical environmental control of malaria transmission. While ambient air temperature controls the rate of several components of the malaria transmission cycle, extensive laboratory and field studies mean that suitable temperature ranges are well established. In contrast, estimation of surface water availability from current global datasets remains challenging. Instead, monthly rainfall is typically used as a proxy for habitat availability; a threshold of 80 mm per month has gained traction as a proxy for breeding habitat in continental-scale models of malaria climate suitability in Africa and has been applied across the globe. However, since complex and spatially variable hydrological processes (e.g. infiltration, evaporation, soil moisture storage, transfer through and storage in river networks) are omitted, a wide variety of rainfall thresholds are found in the literature that leads to large differences in environmental suitability estimates. Moreover, irrigated areas have been observed to provide suitable year-round habitat for Anopheles mosquitos but are not included in such models. Here we show that across continental Africa, the estimated geographic range of climatic suitability for malaria transmission is more sensitive to precipitation thresholds than the thermal response curves applied. To address this problem and provide a more physical-basis for larval habitat estimation, we use daily climate predictions from seven downscaled general circulation models to run a continental-scale hydrological model (Lisflood) for a process-based representation of mosquito breeding habitat availability. A more complex pattern of malaria climatic suitability emerges as water is routed through drainage networks and river corridors serve as year-round transmission foci. The area estimated to be hydro-climatically suitable for stable malaria transmission is smaller than previous models suggest; however, more people are found in longer-transmission season areas due to higher-density populations along rivers and in irrigated areas. Hydro-climatic predictions of malaria suitable areas show only a very small increase in state-of-the-art future climate scenarios; however, bigger geographical shifts are observed than with most rainfall threshold models and the pattern of that shift is very different when a hydrological model is used to estimate surface water availability for vector breeding.

The Barotse floodplain in the Western Province of Zambia, is a major feature of the Upper Zambezi River, covering an area of 11,000km2, and is inundated annually by a flood cycle that ranges from minimum values in September, to peak levels in April. The annual flooding of the area provides a number of challenges, and critically is a significant component of the life cycle of mosquitos, the principle vector for the transmission of malaria. A research project, FLOODMAL, has been developed to apply process based modelling approaches to the life cycle of the mosquito in the floodplain. A significant component of this approach is the development of a 1D-2D model which can be used to predict the formation of water bodies that are essential to the mosquito breeding cycle. This research presents the uncertainties associated with developing the flood model, with an emphasis on model performance through simulation time. In a typical model exercise, the calibration of input parameters are associated with ensuring that model performance is optimised for representing the peak of a flood event. This can be at the cost of providing a consistent level of model performance throughout a simulation, which is essential in this research.Using the LISFLOOD-FP computer code, and TanDEM-X1 terrain data, a baseline model of the Barotse floodplain was developed for the 2009 and 2018 events. A set of initial model runs identified key processes to be represented in the model, including evaporation and infiltration. The calibration of the model was focused on defining parameters for surface roughness, channel roughness, evaporation, infiltration, and defining channel topography. A number of datasets were available for model calibration, such as LandSAT imagery to compare observed and modelled extent at various points throughout the year, and downstream river gauge data. To further understand the uncertainties associated with the modelling, sensitivity analysis was undertaken using an emulator- based approach to define the contribution of the input parameters to overall model variance. The results indicate that parameters that control the movement of water across the floodplain (surface roughness) are generally the most significant of the inputs at all points in the year, although the level of this significance changes at different phases.

Airborne Laser Scanning (ALS) measurements are increasingly vital in forest management and national forest inventories. Despite the growing reliance on ALS data, comparatively little research has examined the sensitivity of ALS measurements to varying survey conditions over commercially important forests. This study investigated: (i) how accurately the Discrete Anisotropic Radiative Transfer (DART) model was able to replicate small-footprint ALS measurements collected over Irish conifer plantations, and (ii) how survey characteristics influenced the precision of discrete-return metrics. A variance-based global sensitivity analysis demonstrated that discrete-return height distributions were accurately and consistently simulated across 100 forest inventory plots with few perturbations induced by varying acquisition parameters or ground topography. In contrast, discrete return density, canopy cover and the proportion of multiple returns were sensitive to fluctuations in sensor altitude, scanning angle, pulse repetition frequency and pulse duration. Our findings corroborate previous studies indicating that discrete-return heights are robust to varying acquisition parameters and may be reliable predictors for the indirect retrieval of forest inventory measurements. However, canopy cover and density metrics are only comparable for ALS data collected under similar acquisition conditions, precluding their universal use across different ALS surveys. Our study demonstrates that DART is a robust model for simulating discrete-return measurements over structurally complex forests; however, the replication of foliage morphology, density and orientation are important considerations for radiative transfer simulations using synthetic trees with explicitly defined crown architectures.

Background\ud The potential of drones to support public health interventions, such as malaria vector control, is beginning to be realised. Although permissions from civil aviation authorities are often needed for drone operations, the communities over which they fly tend to be ignored: How do affected communities perceive drones? Is drone deployment accepted by communities? How should communities be engaged?\ud \ud Methods\ud An initiative in Zanzibar, United Republic of Tanzania is using drones to map malarial mosqutio breeding sites for targeting larval source management interventions. A community engagement framework was developed, based on participatory research, across three communities where drones will be deployed, to map local perceptions of drone use. Costs associated with this exercise were collated.\ud \ud Results\ud A total of 778 participants took part in the study spanning a range of community and stakeholder groups. Overall there was a high level of acceptance and trust in drone use for public health research purposes. Despite this level of trust for drone operations this support was conditional: There was a strong desire for pre-deployment information across all stakeholder groups and regular updates of this information to be given about drone activities, as well as consent from community level governance. The cost of the perception study and resulting engagement strategy was US$24,411.\ud \ud Conclusions\ud Mapping and responding to community perceptions should be a pre-requisite for drone activity in all public health applications and requires funding. The findings made in this study were used to design a community engagement plan providing a simple but effective means of building and maintaining trust and acceptability. We recommend this an essential investment.

To feed the world increasing population, expansion in the area under arable cultivation is expected, with the majority projected to occur in Sub-Sahara Africa and Latin American countries. However, many existing Precision Agriculture (PA) techniques are difficult to transfer to agricultural systems in these regions as they rely on prohibitively expensive crop monitoring systems. Satellite Earth Observation (EO) has the ability to provide affordable solutions, particularly to identify yield-limiting conditions within site-specific management zones (MZs). This paper presents the Earth Observation-based Anomaly Detection (EOAD) approach, a novel system for the detection of in-field anomalies through automatic thresholding of optical Vegetation Index data, based on their deviation from a normal distribution. The EOAD sets dynamic thresholds for the pixel values within a parcel by removing the atypical values in increments from the tails towards the median until the distribution is normal. The distribution normality is assessed based upon measures of skewness and kurtosis for each iteration. The anomaly detection approach demonstrated a strong agreement, 80% overall accuracy, with identified in-field anomalies when applied to rice plots in the Ibague Plateau, Colombia, using both Sentinel-2 and PlanetScope imagery. Areas identified as anomalous during the booting stage were shown to be significantly (p ⩽ 0.005) associated with a decrease in final yield. Additionally, the percentage of anomalies detected with the EOAD improved the detection of underperforming plots in early growth stages. Using freely available data and software, this automated approach demonstrates an exciting potential for use in improving agricultural practices in low-resource regions.

Mangrove forests play a global role in providing ecosystem goods and services in addition to acting as carbon sinks, and are particularly vulnerable to climate change effects such as rising sea levels and increased salinity. For this reason, accurate long-term monitoring of mangrove ecosystems is vital. However, these ecosystems are extremely dynamic and data frequency is often reduced by cloud cover. The Continuous Change Detection and Classification (CCDC) method has the potential to overcome this by utilising every available observation on a per-pixel basis to build stable season-trend models of the underlying phenology. These models can then be used for land cover classification and to determine greening and browning trends. To demonstrate the utility of this approach, CCDC was applied to a 30-year time series of Landsat data covering an area of mangrove forest known as the Sundarbans. Spanning the delta formed by the confluence of the Ganges, Brahmaputra and Meghna river systems, the Sundarbans is the largest contiguous mangrove forest in the world. CCDC achieved an overall classification accuracy of 94.5% with a 99% confidence of being between 94.2% and 94.8%. Results showed that while mangrove extent in the Sundarbans has remained stable, around 25% of the area experienced an overall negative trend, probably due to the effect of die-back on Heritiera fomes. In addition, dates and magnitudes of change derived from CCDC were used to investigate damage and recovery from a major cyclone; 11% of the Sundarbans was found to have been affected by Cyclone Sidr in 2007, 47.6% of which had not recovered by mid-2018. The results indicate that while the Sundarbans forest is resilient to cyclone events, the long-term degrading effects of climate change could reduce this resilience to critical levels. The proposed methodology, while computationally expensive, also offers means by which the full Landsat archive can be analyzed and interpreted and should be considered for global application to mangrove monitoring.

Access to temporally dense time series such as data from the Landsat and Sentinel-2 missions has lead to an increase in methods which aim to monitor land cover change on a per-acquisition rather than a yearly basis. Evaluating the accuracy and limitations of these methods can be difficult because validation data are limited and often rely on human interpretation. Simulated time series offer an objective method for evaluating and comparing between change detection algorithms. A set of simulated time series was used to evaluate four change detection methods: (1) Breaks for Additive and Seasonal Trend (BFAST); (2) BFAST Monitor; (3) Continuous Change Detection and Classification (CCDC); and (4) Exponentially Weighted Moving Average Change Detection (EWMACD). In total, 151,200 simulations were generated to represent a range of abrupt, gradual, and seasonal changes. EWMACD was found to give the best performance overall, correctly identifying the true date of change in 76.6% of cases. CCDC performed worst (51.8%). BFAST performed well overall but correctly identified less than 10% of seasonal changes (changes in amplitude, length of season, or number of seasons). All methods showed some decrease in performance with increased noise and missing data, apart from BFAST Monitor which improved when data were removed. The following recommendations are made as a starting point for future studies: EWMACD should be used for detection of lower magnitude changes and changes in seasonality; CCDC should be used for robust detection of complete land cover class changes; EWMACD and BFAST are suitable for noisy datasets, depending on the application; and CCDC should be used where there are high quantities of missing data. The simulated datasets have been made freely available online as a foundation for future work.

Providing timely and accurate maps of surface water is valuable for mapping malaria risk and targeting disease control interventions. Radar satellite remote sensing has the potential to provide this information but current approaches are not suitable for mapping African malarial mosquito aquatic habitats that tend to be highly dynamic, often with emergent vegetation. We present a novel approach for mapping both open and vegetated water bodies using serial Sentinel-1 imagery for Western Zambia. This region is dominated by the seasonally inundated Upper Zambezi floodplain that suffers from a number of public health challenges. The approach uses open source segmentation and machine learning (extra trees classifier), applied to training data that are automatically derived using freely available ancillary data. Refinement is implemented through a consensus approach and Otsu thresholding to eliminate false positives due to dry flat sandy areas. The results indicate a high degree of accuracy (mean overall accuracy 92% st dev 3.6) providing a tractable solution for operationally mapping water bodies in similar large river floodplain unforested environments. For the period studied, 70% of the total water extent mapped was attributed to vegetated water, highlighting the importance of mapping both open and vegetated water bodies for surface water mapping.

This study presents a new global baseline of mangrove extent for 2010 and has been released as the first output of the Global Mangrove Watch (GMW) initiative. This is the first study to apply a globally consistent and automated method for mapping mangroves, identifying a global extent of 137,600 km 2 . The overall accuracy for mangrove extent was 94.0% with a 99% likelihood that the true value is between 93.6–94.5%, using 53,878 accuracy points across 20 sites distributed globally. Using the geographic regions of the Ramsar Convention on Wetlands, Asia has the highest proportion of mangroves with 38.7% of the global total, while Latin America and the Caribbean have 20.3%, Africa has 20.0%, Oceania has 11.9%, North America has 8.4% and the European Overseas Territories have 0.7%. The methodology developed is primarily based on the classification of ALOS PALSAR and Landsat sensor data, where a habitat mask was first generated, within which the classification of mangrove was undertaken using the Extremely Randomized Trees classifier. This new globally consistent baseline will also form the basis of a mangrove monitoring system using JAXA JERS-1 SAR, ALOS PALSAR and ALOS-2 PALSAR-2 radar data to assess mangrove change from 1996 to the present. However, when using the product, users should note that a minimum mapping unit of 1 ha is recommended and that the error increases in regions of disturbance and where narrow strips or smaller fragmented areas of mangroves are present. Artefacts due to cloud cover and the Landsat-7 SLC-off error are also present in some areas, particularly regions of West Africa due to the lack of Landsat-5 data and persistence cloud cover. In the future, consideration will be given to the production of a new global baseline based on 10 m Sentinel-2 composites.

Clouds play a key role in hydroclimatological variability by modulating the surface energy balance and air temperature. This study utilizes MODIS cloud cover data, with corroboration from global meteorological reanalysis (ERA-Interim) cloud estimates, to describe a cloud climatology for the upper Indus River basin. It has specific focus on tributary catchments in the northwest of the region, which contribute a large fraction of basin annual runoff, including 65% of flow originating above Besham, Pakistan or 50 km3 yr−1 in absolute terms. In this region there is substantial cloud cover throughout the year, with spatial means of 50%–80% depending on the season. The annual cycles of catchment spatial mean daytime and nighttime cloud cover fraction are very similar. This regional diurnal homogeneity belies substantial spatial variability, particularly along seasonally varying vertical profiles (based on surface elevation). Correlations between local near-surface air temperature observations and MODIS cloud cover fraction confirm the strong linkages between local atmospheric conditions and near-surface climate variability. These correlations are interpreted in terms of seasonal and diurnal variations in apparent cloud radiative effect and its influence on near-surface air temperature in the region. The potential role of cloud radiative effect in recognized seasonally and diurnally asymmetrical temperature trends over recent decades is also assessed by relating these locally observed trends to ERA-Interim-derived trends in cloud cover fraction. Specifically, reduction in nighttime cloud cover fraction relative to daytime conditions over recent decades appears to provide a plausible physical mechanism for the observed nighttime cooling of surface air temperature in summer months.

A methodology is developed to estimate daily river discharge at an ungauged site using remote sensing data. Use is made of ERS-2 and ENVISAT satellite altimetry to provide a time series of river channel stage levels and longitudinal channel slope and Landsat satellite imagery to provide a range of channel widths over a 50 km reach of river. The data are substituted into the Bjerklie et al. (2003) equation, which is based on the Manning's resistance equation and has been developed using a global database of channel hydraulic information and discharge measurements. Our methodology has been applied at three locations on the Mekong and Ob Rivers and validated against daily in situ discharge measurements. The results show Nash–Sutcliffe efficiency values of 0.90 at Nakhon Phanom and 0.86 at Vientiane on the Mekong, and 0.86 at Kalpashevo on the Ob. Copyright © 2012 John Wiley & Sons, Ltd.

Earthworks such as embankments and cuttings are integral to road and rail networks but can be prone to instability, necessitating rigorous and continual monitoring. To date, the potential of remote sensing for earthwork hazard assessment has been largely overlooked. However, techniques such as airborne laser scanning (ALS) are now ripe for addressing these challenges. This research presents the development of a novel hazard assessment strategy, combining high-resolution remote sensing with a numerical modeling approach. The research was implemented at a railway test site located in northern England, U.K.; ALS data and multispectral aerial imagery facilitated the determination of key slope stability variables, which were then used to parameterize a coupled hydrological-geotechnical model, in order to simulate slope behavior under current and future climates. A software toolset was developed to integrate the core elements of the methodology and determine resultant slope failure hazard which could then be mapped and queried within a geographical information system environment. Results indicate that the earthworks are largely stable, which is in broad agreement with the management company's slope hazard grading data, and in terms of morphological analysis, the remote methodology was able to correctly identify 99% of earthworks classed as embankments and 100% of cuttings. The developed approach provides an effective and practicable method for remotely quantifying slope failure hazard at fine spatial scales (0.5 m) and for prioritizing and reducing on-site inspection.

Hardy, A. J., Barr, S., Mills, J., Miller, P. (2012). Characterising soil moisture in transport corridor environments using airborne LIDAR and CASI data. Hydrological Processes, 26 (13), 1925-1936

This study demonstrates a globally applicable method for monitoring mangrove forest extent at high spatial resolution. A 2010 mangrove baseline was classified for 16 study areas using a combination of ALOS PALSAR and Landsat composite imagery within a random forests classifier. A novel map-to-image change method was used to detect annual and decadal changes in extent using ALOS PALSAR/JERS-1 imagery. The map-to-image method presented makes fewer assumptions of the data than existing methods, is less sensitive to variation between scenes due to environmental factors (e.g., tide or soil moisture) and is able to automatically identify a change threshold. Change maps were derived from the 2010 baseline to 1996 using JERS-1 SAR and to 2007, 2008 and 2009 using ALOS PALSAR. This study demonstrated results for 16 known hotspots of mangrove change distributed globally, with a total mangrove area of 2,529,760 ha. The method was demonstrated to have accuracies consistently in excess of 90% (overall accuracy: 92.2–93.3%, kappa: 0.86) for mapping baseline extent. The accuracies of the change maps were more variable and were dependent upon the time period between images and number of change features. Total change from 1996 to 2010 was 204,850 ha (127,990 ha gain, 76,860 ha loss), with the highest gains observed in French Guiana (15,570 ha) and the highest losses observed in East Kalimantan, Indonesia (23,003 ha). Changes in mangrove extent were the consequence of both natural and anthropogenic drivers, yielding net increases or decreases in extent dependent upon the study site. These updated maps are of importance to the mangrove research community, particularly as the continual updating of the baseline with currently available and anticipated spaceborne sensors. It is recommended that mangrove baselines are updated on at least a 5-year interval to suit the requirements of policy makers.

Much of the world’s transport networks are located on cuttings and embankments. In the UK many of these earthwork structures were constructed in the mid-19th Century and are therefore susceptible to slope instability. Instability in transport corridors tends to manifest itself through shallow slope failures. The trigger for such failures is an increase in pore water pressure which is directly affected by an increase in soil moisture. Transport network operators carry out appraisals of their earthworks by making crude observations of soil moisture conditions in the absence of an efficient technique for making in situ measurements. Remote sensing offers the potential to monitor long stretches of transport corridor but currently there is no method for characterising soil moisture that can be considered operational or cost effective. This study explores the use of airborne Light Detection and Ranging (LiDAR) to map soil moisture distribution for a transport corridor near Haltwhistle, Northumberland. Terrain analysis calculations, namely potential solar radiation and a topographic wetness index, are applied to a fine scale DEM interpolated from the LiDAR point data using the AnuDEM routine which ensures consideration of hydrological flow.

Background\ud \ud Larval source management strategies can play an important role in malaria elimination programmes, especially for tackling outdoor biting species and for eliminating parasite and vector populations when they are most vulnerable during the dry season. Effective larval source management requires tools for identifying geographic foci of vector proliferation and malaria transmission where these efforts may be concentrated. Previous studies have relied on surface topographic wetness to indicate hydrological potential for vector breeding sites, but this is unsuitable for karst (limestone) landscapes such as Zanzibar where water flow, especially in the dry season, is subterranean and not controlled by surface topography.\ud \ud Methods\ud \ud We examine the relationship between dry and wet season spatial patterns of diagnostic positivity rates of malaria infection amongst patients reporting to health facilities on Unguja, Zanzibar, with the physical geography of the island, including land cover, elevation, slope angle, hydrology, geology and geomorphology in order to identify transmission hot spots using Boosted Regression Trees (BRT) analysis.\ud \ud Results\ud \ud The distribution of both wet and dry season malaria infection rates can be predicted using freely available static data, such as elevation and geology. Specifically, high infection rates in the central and southeast regions of the island coincide with outcrops of hard dense limestone which cause locally elevated water tables and the location of dolines (shallow depressions plugged with fine-grained material promoting the persistence of shallow water bodies).\ud \ud Conclusions\ud \ud This analysis provides a tractable tool for the identification of malaria hotspots which incorporates subterranean hydrology, which can be used to target larval source management strategies.

This paper summarizes a number of instrumental designs and configurations that have been developed by the Middle Atlantic Mass Spectrometry Laboratory for the miniaturization of time-of-flight mass spectrometers that will be used in environmental, bioagent detection, and diagnostics applications. All are “linear” designs, that is: ions are focused to a detector located within a line-of-sight of the ion source; no reflectrons are used. All are also “dynamic” instruments, using pulsed ion extraction and/or other time-dependent electric fields. These mass analyzers have flight paths of approximately 3 inches, with mass resolutions of up to one part in 1200 and mass ranges over 66 kdaltons. The use of non-homogeneous electric fields is also proposed to further improve that performance. Some examples are given for the analysis of peptides and bacterial spores.

For the period 1996-2010, we provide the first indication of the drivers behind mangrove land cover and land use change across the (pan-)tropics using time-series Japanese Earth Resources Satellite (JERS-1) Synthetic Aperture Radar (SAR) and Advanced Land Observing Satellite (ALOS) Phased Array-type L-band SAR (PALSAR) data. Multi-temporal radar mosaics were manually interpreted for evidence of loss and gain in forest extent and its associated driver. Mangrove loss as a consequence of human activities was observed across their entire range. Between 1996-2010 12% of the 1168 1°x1° radar mosaic tiles examined contained evidence of mangrove loss, as a consequence of anthropogenic degradation, with this increasing to 38% when combined with evidence of anthropogenic activity prior to 1996. The greatest proportion of loss was observed in Southeast Asia, whereby approximately 50% of the tiles in the region contained evidence of mangrove loss, corresponding to 18.4% of the global mangrove forest tiles. Southeast Asia contained the greatest proportion (33.8%) of global mangrove forest. The primary driver of anthropogenic mangrove loss was found to be the conversion of mangrove to aquaculture/agriculture, although substantial advance of mangroves was also evident in many regions.

Background\ud \ud Larval source management is a promising component of integrated malaria control and elimination. This requires development of a framework to target productive locations through process-based understanding of habitat hydrology and geomorphology.\ud \ud Methods\ud \ud We conducted the first catchment scale study of fine resolution spatial and temporal variation in Anopheles habitat and productivity in relation to rainfall, hydrology and geomorphology for a high malaria transmission area of Tanzania.\ud \ud Results\ud \ud Monthly aggregates of rainfall, river stage and water table were not significantly related to the abundance of vector larvae. However, these metrics showed strong explanatory power to predict mosquito larval abundances after stratification by water body type, with a clear seasonal trend for each, defined on the basis of its geomorphological setting and origin.\ud \ud Conclusion\ud \ud Hydrological and geomorphological processes governing the availability and productivity of Anopheles breeding habitat need to be understood at the local scale for which larval source management is implemented in order to effectively target larval source interventions. Mapping and monitoring these processes is a well-established practice providing a tractable way forward for developing important malaria management tools.

Background There is a growing awareness that if we are to achieve the ambitious goal of malaria elimination, we must compliment indoor-based vector control interventions (such as bednets and indoor spraying) with outdoor-based interventions such as larval source management (LSM). The effectiveness of LSM is limited by our capacity to identify and map mosquito aquatic habitats. This study provides a proof of concept for the use of a low-cost (< $1000) drone (DJI Phantom) for mapping water bodies in seven sites across Zanzibar including natural water bodies, irrigated and non-irrigated rice paddies, peri-urban and urban locations. Results With flying times of less than 30 min for each site, high-resolution (7 cm) georeferenced images were successfully generated for each of the seven sites, covering areas up to 30 ha. Water bodies were readily identifiable in the imagery, as well as ancillary information for planning LSM activities (access routes to water bodies by road and foot) and public health management (e.g. identification of drinking water sources, mapping individual households and the nature of their construction). Conclusion The drone-based surveys carried out in this study provide a low-cost and flexible solution to mapping water bodies for operational dissemination of LSM initiatives in mosquito vector-borne disease elimination campaigns. Generated orthomosaics can also be used to provide vital information for other public health planning activities. Electronic supplementary material The online version of this article (doi:10.1186/s13071-017-1973-3) contains supplementary material, which is available to authorized users.