Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries

Assessing water availability in Mediterranean regions affected by water conflicts through MODIS data time series analysis

Water scarcity is a widespread problem in arid and semi-arid regions such as the western Mediterranean coastal areas. The irregularity of the precipitation generates frequent droughts that exacerbate the conflicts among agriculture, water supply and water demands for ecosystems maintenance. Besides, global climate models predict that climate change will cause Mediterranean arid and semi-arid regions to shift towards lower rainfall scenarios that may exacerbate water conflicts. The purpose of this study is to find a feasible methodology to assess current and monitor future water demands in order to better allocate limited water resources. The interdependency between a vegetation index (NDVI), land surface temperature (LST), precipitation (current and future), and surface water resources availability in two watersheds in southeastern Spain with serious difficulties in meeting water demands was investigated. MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI and LST products (as proxy of drought), precipitation maps (generated from climate station records) and reservoir storage gauging information were used to compute times series anomalies from 2001 to 2014 and generate regression images and spatial regression models. The temporal relationship between reservoir storage and time series of satellite images allowed the detection of different and contrasting water management practices in the two watersheds. In addition, a comparison of current precipitation rates and future precipitation conditions obtained from global climate models suggests high precipitation reductions, especially in areas that have the potential to contribute significantly to groundwater storage and surface runoff, and are thus critical to reservoir storage. Finally, spatial regression models minimized spatial autocorrelation effects, and their results suggested the great potential of our methodology combining NDVI and LST time series to predict future scenarios of water scarcity.Published versio

Near Real-Time Disturbance Detection in Terrestrial Ecosystems Using Satellite Image Time Series: Drought Detection in Somalia

Near real-time monitoring of ecosystem disturbances is critical for addressing impacts on carbon dynamics, biodiversity, and socio-ecological processes. Satellite remote sensing enables cost-effective and accurate monitoring at frequent time steps over large areas. Yet, generic methods to detect disturbances within newly captured satellite images are lacking. We propose a generic time series based disturbance detection approach by modelling stable historical behaviour to enable detection of abnormal changes within newly acquired data. Time series of vegetation greenness provide a measure for terrestrial vegetation productivity over the last decades covering the whole world and contain essential information related land cover dynamics and disturbances. Here, we assess and demonstrate the method by (1) simulating time series of vegetation greenness data from satellite data with different amount of noise, seasonality and disturbances representing a wide range of terrestrial ecosystems, (2) applying it to real satellite greenness image time series between February 2000 and July 2011 covering Somalia to detect drought related vegetation disturbances. First, simulation results illustrate that disturbances are successfully detected in near real-time while being robust for seasonality and noise. Second, major drought related disturbance corresponding with most drought stressed regions in Somalia are detected from mid 2010 onwards and confirm proof-of-concept of the method. The method can be integrated within current operational early warning systems and has the potential to detect a wide variety of disturbances (e.g. deforestation, flood damage, etc.). It can analyse in-situ or satellite data time series of biophysical indicators from local to global scale since it is fast, does not depend on thresholds or definitions and does not require time series gap filling.early warning, real-time monitoring, global change, disturbance, time series, remote sensing, vegetation and climate dynamics

THE BIG PICTURE - SATELLITE REMOTE SENSING APPLICATIONS IN RANGELAND ASSESSMENT AND CROP INSURANCE

Livestock Production/Industries, Risk and Uncertainty,

A multi-temporal phenology based classification approach for Crop Monitoring in Kenya

The SBAM (Satellite Based Agricultural Monitoring) project, funded by the Italian Space Agency aims at: developing a validated satellite imagery based method for estimating and updating the agricultural areas in the region of Central-Africa; implementing an automated process chain capable of providing periodical agricultural land cover maps of the area of interest and, possibly, an estimate of the crop yield. The project aims at filling the gap existing in the availability of high spatial resolution maps of the agricultural areas of Kenya. A high spatial resolution land cover map of Central-Eastern Africa including Kenya was compiled in the year 2000 in the framework of the Africover project using Landsat images acquired, mostly, in 1995. We investigated the use of phenological information in supporting the use of remotely sensed images for crop classification and monitoring based on Landsat 8 and, in the near future, Sentinel 2 imagery. Phenological information on crop condition was collected using time series of NDVI (Normalized Difference Vegetation Index) based on Landsat 8 images. Kenyan countryside is mainly characterized by a high number of fragmented small and medium size farmlands that dramatically increase the difficulty in classification; 30 m spatial resolution images are not enough for a proper classification of such areas. So, a pan-sharpening FIHS (Fast Intensity Hue Saturation) technique was implemented to increase image resolution from 30 m to 15 m. Ground test sites were selected, searching for agricultural vegetated areas from which phenological information was extracted. Therefore, the classification of agricultural areas is based on crop phenology, vegetation index behaviour retrieved from a time series of satellite images and on AEZ (Agro Ecological Zones) information made available by FAO (FAO, 1996) for the area of interest. This paper presents the results of the proposed classification procedure in comparison with land cover maps produced in the past years by other projects. The results refer to the Nakuru County and they were validated using field campaigns data. It showed a satisfactory overall accuracy of 92.66 % which is a significant improvement with respect to previous land cover maps

A WEBGIS FRAMEWORK FOR DISSEMINATING PROCESSED REMOTELY SENSED ON LAND COVER TRANSFORMATIONS

Mediterranean regions have experienced significant soil degradation over the past decades. In this context, careful land observation using satellite data is crucial for understanding the long-term usage patterns of natural resources and facilitating their sustainable management to monitor and evaluate the potential degradation. Given the environmental and political interest on this problem, there is urgent need for a centralized repository and mechanism to share geospatial data, information and maps of land change. Geospatial data collecting is one of the most important task for many users because there are significant barriers in accessing and using data. This limit could be overcome by implementing a WebGIS through a combination of existing free and open source software for geographic information systems (FOSS4G). In this paper we preliminary discuss methods for collecting raster data in a geodatabase by processing open multi-temporal and multi-scale satellite data aimed at retrieving indicators for land degradation phenomenon (i.e. land cover/land use analysis, vegetation indices, trend analysis, etc.). Then we describe a methodology for designing a WebGIS framework in order to disseminate information through maps for territory monitoring. Basic WebGIS functions were extended with the help of POSTGIS database and OpenLayers libraries. Geoserver was customized to set up and enhance the website functions developing various advanced queries using PostgreSQL and innovative tools to carry out efficiently multi-layer overlay analysis. The end-product is a simple system that provides the opportunity not only to consult interactively but also download processed remote sensing data

A new station-enabled multi-sensor integrated index for drought monitoring

Remote sensing data are frequently incorporated into drought indices used widely by research and management communities to assess and diagnose current and historic drought events. The integrated drought indices combine multiple indicators and reflect drought conditions from a range of perspectives (i.e., hydrological, agricultural, meteorological). However, the success of most remote sensing based drought indices is constrained by geographic regions since their performance strongly depends on environmental factors such as land cover type, temperature, and soil moisture. To address this limitation, we propose a framework for a new integrated drought index that performs well across diverse climate regions. Our framework uses a geographically weighted regression model and principal component analysis to composite a range of vegetation and meteorological indices derived from multiple remote sensing platforms and in-situ drought indices developed from meteorological station data. Our new index, which we call the station-enabled Geographically Independent Integrated Drought Index (GIIDI_station), compared favorably with other common drought indices such as Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Vegetation Condition Index (VCI). Using Pearson correlation analyses between remote sensing and in-situ drought indices during the growing season (April to October) from 2002 to 2011, we show that GIIDI_station had the best correlations with in-situ drought indices. Across the entire study region of the continental United States, the performance of GIIDI_station was not affected by common environmental factors such as precipitation, temperature, land cover and soil conditions. Taken together, our results suggest that GIIDI_station has considerable potential to improve our ability of monitoring drought at regional scales, provided local meteorological station data are available

Canopy reflectance modeling in a tropical wooded grassland

Geometric/optical canopy reflectance modeling and spatial/spectral pattern recognition are used to study the form and structure of savanna in West Africa. An invertible plant canopy reflectance model is tested for its ability to estimate the amount of woody vegetation cover in areas of sparsely wooded grassland from remotely sensed data. Dry woodlands and wooded grasslands, commonly referred to as savannas, are important ecologically and economically in Africa, and cover approximately forty percent of the continent by some estimates. The Sahelian and Sudanian savanna make up the important and sensitive transition zone between the tropical forests and the arid Saharan region. The depletion of woody cover, used for fodder and fuel in these regions, has become a very severe problem for the people living there. LANDSAT Thematic Mapper (TM) data is used to stratify woodland and wooded grassland into areas of relatively homogeneous canopy cover, and then by applying an invertible forest canopy reflectance model to estimate directly the height and spacing of the trees in the stands. Since height and spacing are proportional to biomass in some cases, a successful application of the segmentation/modeling techniques will allow direct estimation of woody biomass, as well as cover density, over significant areas of these valuable and sensitive ecosystems. Sahelian savanna sites in the Gourma area of Mali being used by the NASA/GIMMS project (Global Inventory Modeling and Monitoring System, at Goddard Space Flight Center), in conjunction with CIPEA/Mali (Centre International pour l'Elevage en Afrique) will be used for testing the canopy model. The model will also be tested in a Sudanian zone crop/woodland area in the Region of Segou, Mali

Integration of remote sensing and GIS in studying vegetation trends and conditions in the gum arabic belt in North Kordofan, Sudan

The gum arabic belt in Sudan plays a significant role in environmental, social and economical aspects. The belt has suffered from deforestation and degradation due to natural hazards and human activities. This research was conducted in North Kordofan State, which is affected by modifications in conditions and composition of vegetation cover trends in the gum arabic belt as in the rest of the Sahelian Sudan zone. The application of remote sensing, geographical information system and satellites imageries with multi-temporal and spatial analysis of land use land cover provides the land managers with current and improved data for the purposes of effective management of natural resources in the gum arabic belt. This research investigated the possibility of identification, monitoring and mapping of the land use land cover changes and dynamics in the gum arabic belt during the last 35 years. Also a newly approach of object-based classification was applied for image classification. Additionally, the study elaborated the integration of conventional forest inventory with satellite imagery for Acacia senegal stands. The study used imageries from different satellites (Landsat and ASTER) and multi-temporal dates (MSS 1972, TM 1985, ETM+ 1999 and ASTER 2007) acquired in dry season (November). The imageries were geo-referenced and radiometrically corrected by using ENVI-FLAASH software. Image classification (pixel-based and object-based), post-classification change detection, 2x2 and 3x3 pixel windows and accuracy assessment were applied. A total of 47 field samples were inventoried for Acacia senegal tree’s variables in Elhemmaria forest. Three areas were selected and distributed along the gum arabic belt. Regression method analysis was applied to study the relationship between forest attributes and the ASTER imagery. Application of multi-temporal remote sensing data in gum arabic belt demonstrated successfully the identification and mapping of land use land cover into five main classes. Also NDVI categorisation provided a consistent method for land use land cover stratification and mapping. Forest dominated by Acacia senegal class was separated covering an area of 21% and 24% in the year 2007 for areas A and B, respectively. The land use land cover structure in the gum arabic belt has obvious changes and reciprocal conversions between the classes indicating the trends and conditions caused by the human interventions as well as ecological impacts on Acacia senegal trees. The study revealed a drastic loss of Acacia senegal cover by 25% during the period of 1972 to 2007.The results of the study revealed to a significant correlation (p ≤ 0.05) between the ASTER bands (VNIR) and vegetation indices (NDVI, SAVI, RVI) with stand density, volume, crown area and basal area of Acacia senegal trees. The derived 2x2 and 3x3 pixel windows methods successfully extracted the spectral reflectance of Acacia senegal trees from ASTER imagery. Four equations were developed and could be widely used and applied for monitoring the stand density, volume, basal area and crown area of Acacia senegal trees in the gum arabic belt considering the similarity between the selected areas. The pixel-based approach performed slightly better than the object-based approach in land use land cover classification in the gum arabic belt. The study come out with some valuable recommendations and comments which could contribute positively in using remotely sensed imagery and GIS techniques to explore management tools of Acacia senegal stands in order to maintain the tree component in the farming and the land use systems in the gum arabic belt

Performance of the Two-Source Energy Balance (TSEB) Model as a Tool for Monitoring the Response of Durum Wheat to Drought by High-Throughput Field Phenotyping

The current lack of efficient methods for high throughput field phenotyping is a constraint on the goal of increasing durum wheat yields. This study illustrates a comprehensive methodology for phenotyping this crop's water use through the use of the two-source energy balance (TSEB) model employing very high resolution imagery. An unmanned aerial vehicle (UAV) equipped with multispectral and thermal cameras was used to phenotype 19 durum wheat cultivars grown under three contrasting irrigation treatments matching crop evapotranspiration levels (ETc): 100%ETc treatment meeting all crop water requirements (450 mm), 50%ETc treatment meeting half of them (285 mm), and a rainfed treatment (122 mm). Yield reductions of 18.3 and 48.0% were recorded in the 50%ETc and rainfed treatments, respectively, in comparison with the 100%ETc treatment. UAV flights were carried out during jointing (April 4th), anthesis (April 30th), and grain-filling (May 22nd). Remotely-sensed data were used to estimate: (1) plant height from a digital surface model (H, R2 = 0.95, RMSE = 0.18m), (2) leaf area index from multispectral vegetation indices (LAI, R2 = 0.78, RMSE = 0.63), and (3) actual evapotranspiration (ETa) and transpiration (T) through the TSEB model (R2 = 0.50, RMSE = 0.24 mm/h). Compared with ground measurements, the four traits estimated at grain-filling provided a good prediction of days from sowing to heading (DH, r = 0.58–0.86), to anthesis (DA, r = 0.59–0.85) and to maturity (r = 0.67–0.95), grain-filling duration (GFD, r = 0.54–0.74), plant height (r = 0.62–0.69), number of grains per spike (NGS, r = 0.41–0.64), and thousand kernel weight (TKW, r = 0.37–0.42). The best trait to estimate yield, DH, DA, and GFD was ETa at anthesis or during grain filling. Better forecasts for yield-related traits were recorded in the irrigated treatments than in the rainfed one. These results show a promising perspective in the use of energy balance models for the phenotyping of large numbers of durum wheat genotypes under Mediterranean conditions.info:eu-repo/semantics/publishedVersio