This PDF file contains the front matter associated with SPIE Proceedings Volume 11527, including the Title Page, Copyright information, and Table of Contents.

Organised by the University of Edinburgh and SPIE, this interdisciplinary meeting will highlight newly operational satellite systems providing new sensors supporting sustainability. Advances in the processing of big satellite data will be presented alongside novel analytics, including those producing actionable sustainability intelligence.

Organised by the University of Edinburgh and SPIE, this interdisciplinary meeting will highlight newly operational satellite systems providing new sensors supporting sustainability. Advances in the processing of big satellite data will be presented alongside novel analytics, including those producing actionable sustainability intelligence.

Organised by the University of Edinburgh and SPIE, this interdisciplinary meeting will highlight newly operational satellite systems providing new sensors supporting sustainability. Advances in the processing of big satellite data will be presented alongside novel analytics, including those producing actionable sustainability intelligence.

There are substantial challenges in providing safe, affordable, and nutritious food for a healthy and active human population globally. At present, instances of undernourishment and micronutrient deficiencies are widespread and occur in the same communities as obesity – forming what has been termed as the ‘triple burden of malnourishment’. This triple burden of malnourishment affects the health of individuals, increases demand for medical services, and can reduce the productive capacity of a society. Concurrently, ecological and atmospheric conditions are approaching critical tipping points that will have a bearing on the ecosystems services that are required to produce our food. By 2064, the human population is predicted to reach 9.7 billion people – 2 billion more than 20201 . With this growth in population it will be increasingly challenging to address the triple burden of malnourishment and maintain or improve ecosystem services. This introduction serves as a brief overview of how remote sensing has been employed to inform decisions that will address these challenges.

The operational integration of Earth observation (EO) into the analysis of rural poverty and broader dynamics of human wellbeing is in its early stages. There is considerable scope for novel applications given the current proliferation of technological and computational capabilities. To develop this research agenda, it is necessary to synthesise scholarly contributions to the field in order to disseminate findings and stimulate debate, while catalysing uptake and development of methodologies. We conducted a systematic review of the scientific literature that investigates the novel applications of satellite EO for monitoring socioeconomic conditions and poverty in rural spaces of the Global South. We consider the challenges and opportunities for achieving evidence-based policymaking at finer temporal and spatial scales than is currently practised when measuring socioeconomic conditions. We investigate these challenges and the opportunities for integrating EO into monitoring poverty and human wellbeing in the context of sustainable rural development. Overall evidence suggests that the extensive spatial coverage and accessibility of data at different resolutions, paired with near real-time observations and a five-decade temporal legacy of satellite EO primes these data products for monitoring rural wellbeing. Our findings indicate a requirement to develop EO approaches for monitoring poverty dimensions across multiple spatial and temporal scales. Further requirements include testing the performance of methodologies in different social-ecological systems, to interrogate the performance of EO metrics when predicting different measures of rural poverty and wellbeing, and to operationalise the integration of disparate datasets.

Sars-CoV-2 is spread through contact between people and an understanding of where people are in contact with each other is necessary to prevent its spread. In this paper, the residential building density of Bulawayo was considered a proxy for high density of people. OpenStreetMap (OSM) building data was downloaded and converted from polygon to point data for use in the analysis. World View 2 data was used to visually map those areas where data was missing in OSM. More automated methods were attempted using eCognition however the short turnaround time of the project limited the success of this approach and work in this regard in ongoing. Land use attribute data was joined to the building shape file in order to select only those building which were designated as residential in nature. The residential building density was calculated per hectare and a hot spot analysis of the residential building density determined statistically significant clusters of high density residential buildings. The high density areas are mostly located in the west of the City, where new settlements are being created to accommodate new arrivals to the city. The East is typified by low density housing, largely a legacy of the City’s colonial past. A series of maps which could be printed on A3 paper were produced for the City. The maps displayed both the results of the hot spot analysis and land use and these have been made available to City officials to help in planning their response to the COVID-19 pandemic.

Organised by the University of Edinburgh and SPIE, this interdisciplinary meeting will highlight newly operational satellite systems providing new sensors supporting sustainability. Advances in the processing of big satellite data will be presented alongside novel analytics, including those producing actionable sustainability intelligence.

The Copernicus Climate Change Service (C3S), operated by ECMWF on behalf of the European Commission, provides climate services built around a comprehensive suite of data products. These products include multidecadal estimates of the atmospheric state, based on atmospheric reanalysis, and a range of observational datasets on Essential Climate Variables (ECVs). Atmospheric reanalyses are now regarded as valuable sources of information for monitoring trends in the global atmospheric state and employ highly optimised methods for combining observations of meteorological variables, both in-situ and satellite. The most recent C3S global atmospheric reanalysis, ERA5, covering the period 1979-2019 (to be extended to 1950) is now available and since its release in early 2019 has a rapidly growing user base, currently numbering more than 30,000. It uses a recent version of the ECMWF Numerical Weather Prediction (NWP) system to assimilate observations (87 billion for the period 1979 - 2018) in order to analyse the atmospheric state. Satellite observations are a key input to reanalyses and the range of observations assimilated are reviewed. ECVs derived from satellite and in-situ observations, spanning land, atmosphere, ocean and biosphere domains, produced as part of international collaborations, are available via the C3S Climate Data Store (CDS). The aspiration of C3S is to further develop the CDS to include a wider range of (∼ 35) ECVs in the next phase of the Copernicus programme (2021-2027).

The opportunity to perform an update to the existing habitat map across the whole of Abu Dhabi Emirate provides a unique assessment of the benefits and challenges associated with using satellite imagery for the purposes of establishing a higher accuracy habitat map. The first habitat study of Abu Dhabi using Very-High-Resolution (VHR) satellite imagery, undertaken in 2014, provided a baseline for assessing changes in habitats. The approach for the current update combines the relative computational ease and consistency of processing Landsat-8 data to extract broad habitats with the ability to identify and extract high levels of detail in WorldView imagery accurately, over a compressed project timescale. Variations in habitats over time can be attributed to environmental change and/or human development and provide a basis for investigation of biodiversity stressors and an understanding of the environmental and economic implications of activities within Abu Dhabi and the wider Gulf region. Understanding the direction of change is a valuable precursor to informed decision making and the formation of appropriately targeted mitigation strategies. The new map uses reduced minimum mapping units (MMU), meaning a greater level of detail is present, and also identifies habitats where none were previously mapped. The balance between detail and consistency between map products should be considered carefully for each new map iteration so that map products display pertinent information but also remain comparable.

In the present document, methodologies for non-destructive techniques are developed to estimate the index of vegetable biomass of a study area that will undergo an intervention and whose location is near the city of Medell´ın, Colombia. The techniques proposed and compared are: multispectral photogrammetric images, photogrammetric images in the visible spectrum and analysis of SAR products. These techniques were chosen with the possibility of estimating the biomass index of a forest to perform an intervention of the study area, given that an urban construction will be carried out in this space.

Synthetic Aperture Radar data has a unique potential for continuous forest mapping as it is not affected by cloud cover. While longer wavelengths such as L-band are commonly used for forest applications, in this paper we assess the aptitude of C-band Sentinel-1 data for this purpose, for which there is much interest due to its high temporal resolution (5 days) and `free, full and open' data policy. We tested its ability to distinguish forest from non-forest in six study sites, located in Alaska, Colombia, Finland, Florida, Indonesia, and the UK. Using the time series for a full year significantly increases the classification accuracy compared to a single scene (a mean of 84.6% compared to 76.8% across the study sites). Our results show that we can further improve the mean accuracy to 86.4% when only considering the annual mean and standard deviation of VV and VH backscatter. In this case, separation accuracies of up to 93% (in Finland) are possible, though in the worst case (Indonesia) the highest possible accuracy using these variables was 82%. The best overall performance was observed when using a Support Vector Machine classifier, outperforming random forest, k-Nearest-Neighbors and Quadratic Discriminant Analysis. We further show that the small information content we found in the phase data is an artifact of terrain slope orientation and has a negligible impact on classifier performance. We thus conclude that for the purposes of forest mapping the smaller file size and easier to process GRD data is sufficient, with little benefit to downloading the SLC data. Possible uses of the phase data in this context relate to its temporal coherence, which was not tested in this study.

Lake Poopó is a large endorheic lake located in the Andean Mountain Range Plateau or Altiplano, Bolivia. A general decline in the lake water level has been observed in the last two decades, with devastating impacts for the fishing and cattle breeding livelihoods of local communities. Several factors have been associated to the lake decline: climate warming, intensification of quinoa farming, mining activities and population growth. Being an endorheic catchment, evapotranspiration (ET) losses are expected to be the main water output mechanism. This study analysed ET, vegetation and precipitation spatio-temporal trends in the Lake Poopo catchment using MODIS ET and normalized difference vegetation index (NDVI) products, and CHIRPS-v2 precipitation products validated to in-situ meteorological data for the period 2002 to 2015. ET, NDVI and precipitation trends were investigated for individual sub-catchments, for the wet and rainy seasons, and for land use type. Our analysis revealed that ET losses and NDVI in the wet season have increased throughout the study period, while they remained approximately stationary in the dry season. An analysis across land cover types showed that only croplands experienced an increase in NDVI and ET losses, while natural covers showed either constant or decreasing NDVI trends, together with increases in ET. The comparatively larger increase in vegetation and ET over agricultural regions, strongly suggests that cropping practices exacerbated water losses in these areas. Additionally, an intensity-duration analysis of the wet season in the Altiplano has shown a sustained shortening of the season, exacerbating the uneven distribution of water inputs throughout the year and highlighting the need for adequate water resources planning. Our study results provide essential quantitative information for the sustainable and socially-fair joint planning of water resources and land use in the Lake Poopó catchment.

Filed spectroscopy is a modern tool for studying crop characteristics. The main objective of the current study is to use field spectroradiometer ASD-4 to generate remotely sensed empirical chlorophyll content models to estimate Chl. (a,b and total chlorophyll) on Valencia orange under arid region and determine which spectral band on the spectrum recognize to chlorophyll content . The main input parameters of these models are spectral data either in the form of spectral reflectance data released from Field spectroradiometer ASD-4 in the form of spectral vegetation indices. The spectral bands ranged from 350 nm to 2500 nm to select the best band to recognize chlorophyll content. The four vegetation indices that are forms of ratios between red and near-infrared bands are (Chlorophyll Index Red Edge 710, Simple Ratio 695/760, Red-Edge Position Linear Interpolation and Normalized Difference NIR/Red). This aim was achieved by select field in wadi elmolak, Ismaeilia rejoin, Egypt, 42 samples were collected and produce Partial least Square model (PLS) between laboratory (Chl. a,b and carotein) contents and field spectroradiometer as well as PLS between field measurements of chl. using (CM-1000) and laboratory measurements in the study area. Many statistical procedures, including a principal component analysis (PCA), multiple linear regression model (MLR), PLS were used. Correlation co-efficient between actual and predicted between physiochemical chlorophyll a,b and total properties (Chlorophyll Index Red Edge 710, Simple Ratio 695/760, Red-Edge Position Linear Interpolation and Normalized Difference NIR/Red), CM-1000 and spectroscopy data of coefficient of determination (R2) were R2 = 0.912,0.727, 0.864 and 0.918 and 0.817 respectively. On thr other hand the selection band to determine the best wavelength to determine chlorophyll content was W- 504 with high accuracy R2 0.734. It is complemented that the VIS-NIR-SWIR spectroscopy study can be effectively tool for studied chlorophyll properties. Also, PLS is a good tool for detecting the relationships between spectral properties and Lab. Measurements