A high spatial resolution woody cover map for Great Britain: preliminary results

Small-scale woody features such as hedgerows and small patches of trees provide valuable ecosystem services and are important for biodiversity conservation. However, there is currently no dataset available for mapping these features at a national scale. A product has been developed for mapping these features which combines airborne radar data (NEXTMap®) and optical imagery from satellites. The NEXTMap® DIFF product provides canopy height information at 5 x 5 m spatial resolution and this dataset was used to identify ‘tall’ features in the landscape. NDVI imagery was then used to separate tall vegetation from other tall features such as buildings and rocky outcrops. This method was successful in identifying small-scale woody features but worked less well for large areas of woodland. Therefore, these larger areas were filled in using the Land Cover Map 2007 dataset to produce the final woody features product with a binary (woody/non-woody) classification at a 5 x 5 m spatial resolution. The product was verified against aerial photography and initial results are promising. Work is ongoing to refine the classification and to produce a woody features map for the whole of Great Britain. This product has numerous potential applications, including investigations of habitat connectivity, catchment run-off processes and quantification of carbon stocks

Regional-scale high spatial resolution mapping of aboveground net primary productivity (ANPP) from field survey and Landsat data: a case study for the country of Wales

This paper presents an alternative approach for high spatial resolution vegetation productivity mapping at a regional scale, using a combination of Normalised Difference Vegetation Index (NDVI) imagery and widely distributed ground-based Above-ground Net Primary Production (ANPP) estimates. Our method searches through all available single-date NDVI imagery to identify the images which give the best NDVI–ANPP relationship. The derived relationships are then used to predict ANPP values outside of field survey plots. This approach enables the use of the high spatial resolution (30 m) Landsat 8 sensor, despite its low revisit frequency that is further reduced by cloud cover. This is one of few studies to investigate the NDVI–ANPP relationship across a wide range of temperate habitats and strong relationships were observed (R2 = 0.706), which increased when only grasslands were considered (R2 = 0.833). The strongest NDVI–ANPP relationships occurred during the spring “green-up” period. A reserved subset of 20% of ground-based ANPP estimates was used for validation and results showed that our method was able to estimate ANPP with a RMSE of 15–21%. This work is important because we demonstrate a general methodological framework for mapping of ANPP from local to regional scales, with the potential to be applied to any temperate ecosystems with a pronounced green up period. Our approach allows spatial extrapolation outside of field survey plots to produce a continuous surface product, useful for capturing spatial patterns and representing small-scale heterogeneity, and well-suited for modelling applications. The data requirements for implementing this approach are also discussed

Low-cost electronic sensors for environmental research: pitfalls and opportunities

Repeat observations underpin our understanding of environmental processes, but financial constraints often limit scientists’ ability to deploy dense networks of conventional commercial instrumentation. Rapid growth in the Internet-Of-Things (IoT) and the maker movement is paving the way for low-cost electronic sensors to transform global environmental monitoring. Accessible and inexpensive sensor construction is also fostering exciting opportunities for citizen science and participatory research. Drawing on 6 years of developmental work with Arduino-based open-source hardware and software, extensive laboratory and field testing, and incor- poration of such technology into active research programmes, we outline a series of successes, failures and lessons learned in designing and deploying environmental sensors. Six case studies are presented: a water table depth probe, air and water quality sensors, multi-parameter weather stations, a time-sequencing lake sediment trap, and a sonic anemometer for monitoring sand transport. Schematics, code and purchasing guidance to reproduce our sensors are described in the paper, with detailed build instructions hosted on our King’s College London Geography Environmental Sensors Github repository and the FreeStation project website. We show in each case study that manual design and construction can produce research-grade scientific instrumentation (mean bias error for calibrated sensors –0.04 to 23%) for a fraction of the conventional cost, provided rigorous, sensor-specific calibration and field testing is conducted. In sharing our collective experiences with build-it- yourself environmental monitoring, we intend for this paper to act as a catalyst for physical geographers and the wider environmental science community to begin incorporating low-cost sensor development into their research activities. The capacity to deploy denser sensor networks should ultimately lead to superior envi- ronmental monitoring at the local to global scales

Optical types of inland and coastal waters

Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in-water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions

Optical types of inland and coastal waters

Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in‐water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions

Remote sensing for the study of ecohydrology in East African Rift lakes

This thesis investigates remote sensing as a tool for monitoring the ecohydological sustainability of alkaline-saline lakes in the East African Rift Valley, of critical importance to Lesser Flamingos (Phoenicopterus minor). In Lake Bogoria, an algorithm was developed for retrieving chlorophyll-a as an indicator of cyanobacterial biomass - the Lesser Flamingos' primary food source. Results showed a strong linear relationship between Chl-a and the top-of-atmosphere Landsat ETM+ band ratio R[subscript 835]=R[subscript 660] (R[superscript 2] = 0.801; SE = 70 µg l[superscript -1]); valid for Chl-a up to 800 µg l[superscript -1]. At Lake Natron, the sole breeding site for Lesser Flamingos in East Africa, breeding is hydrologically dependent. Landsat-derived lake surface area estimates and ground-based observations of flamingo breeding showed that breeding takes place on a receding lake level. Upper and lower limits for which breeding is feasible were de fined (700-750 km[superscript 2] and 150-180 km[superscript 2] respectively) based on the presence of islands in Landsat imagery. Extending to a regional scale, a Landsat-based optical classi fication scheme was developed for alkaline-saline lakes; the scheme was able to distinguish six classes with a classifi cation accuracy of 73% when verifi ed against in situ measurements. Classifi ed imagery showed the potential importance to flamingos of the food resources off ered by Lake Logipi. Long-term timeseries of Chl-a and other environment variables for Lake Bogoria, from satellite datasets, showed that direct rainfall and lake levels were both weakly related to Chl-a and between them accounted for 20% of the variance in Chl-a. Examination of Landsat imagery showed common features associated with cyanobacterial bloom collapse in Lake Bogoria, which suggested three plausible explanations for these events. Hence, the results of this thesis have improved understanding of the connections between ecological and hydrological processes in alkaline-saline lakes and the role these lakes play in supporting the Lesser Flamingo species