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

Vegetation dynamics and their relationships with precipitation in Africa for drought monitoring purposes

Drought affects more people than any other natural disaster and results in serious economic, social and environmental costs. The development of effective drought monitoring and early warning has been a significant challenge because of the unique characteristics of drought. In fact, considering the multifaceted nature of drought phenomena (i.e. hydrological, meteorological, and agricultural), a comprehensive and integrated approach is required to define effective Early Warning Systems (EWS), which are thus based on the monitoring of different drought-related parameters and complex drought indicators. In such a context, several studies have shown how temporary changes of vegetation indices and their anomalies are strongly correlated with precipitations, especially in arid and semi-arid environments. Besides, satellite-derived vegetation indicators and climatic data have been widely used to study and monitoring droughts and included in the main existing EWS developed by the international community (e.g. global systems, such as US-AID FEWSNET, JRC MARS FOODSEC, FAO GIEWS, or designed for a specific area of interest, as in the case of MESA South Africa Drought Monitoring, the US Drought Monitor, and the JRC European Drought Observatory). In this work, a study aimed at investigating spatial and temporal vegetation dynamics in the whole Africa and their relationships with climate factors, considering as a base data long-term time-series of vegetation-related phenological parameters is proposed. The outcomes of this study have been used in order to define proper drought monitoring procedures to be used by ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action) for early warning purposes. In fact, in recent years, through its partnership with the World Food Programme (WFP), ITHACA has focused its efforts to develop an automated drought EWS, based on the monitoring of relevant environmental variables that allow the early detection of vegetation stress patterns and agricultural drought phenomena on a global scale, finally providing near real-time alerts about vegetation conditions and productivity. In particular, the fortnightly monitoring of satellite-derived vegetation indexes during growing seasons allows the early detection of water stress conditions of vegetation, and the assessment of derived phenological parameters. These parameters, coupled with the evaluation of precipitation conditions, allow the near real-time assessment of the vegetation productivity which can be expected at the end of the considered growing season. The timely detection of critical conditions in vegetation health and productivity, during a vegetation growing season, leads to the identification of the agricultural areas where crop failures are likely to occur. Finally, the proposed system incorporates a simplified drought vulnerability model, able to show food security conditions starting from the hazard situation evaluated in near real-time. The system outputs and information related to identified alerted areas are updated fortnightly and disseminated using a proper web display application. The described study has been conducted using time-series of vegetation phenological parameters extracted from satellite-derived NDVI datasets (global 15-day NDVI time-series, available from 2000 to present, at a 5.6 km spatial resolution, derived from the MODIS MOD13C1 Terra CMG dataset), and precipitation time-series obtained from the Tropical Rainfall Measuring Mission TRMM mission (0.25° x 0.25° spatial resolution) Multisatellite Precipitation Analysis estimation, computed at daily intervals (TRMM 3B-42 daily data), for period of 1998-present. For the purpose of the proposed statistical analysis, ten phenological metrics (the time for the start and the end of the season, the length of the season, the season base level, the time for the mid of the season, the largest NDVI data value during the season, the seasonal amplitude, the rate of increase at the beginning of the season and the rate of decrease at the end of the season, and, finally, the seasonal integral) have been extracted from the yearly NDVI function that best fits the original yearly NDVI time-series and considered for each vegetation growing season in the examined time interval (2000-2014). These metrics are able to describe synthetically the trend of the season in both the time and the integrated NDVI/time domains and are related to the seasonal vegetation productivity. Different precipitation fortnightly time-series have been used for the study, obtained taking into consideration different cumulating intervals (1-3-6-9-12 months values). Specific routines have been implemented in order to investigate, on a pixel basis, and to explain the statistical relationship between the considered time-series of phenological parameters and precipitation data. Obtained results have been spatially analyzed and aggregated taking into consideration different vegetation types, and maps showing the areas where the observed vegetation phenological parameters are largely dependent on rainfall patterns have been produced. Moreover, the precipitation cumulative interval and the period, in the year, when precipitation influence on vegetation productivity has proved to be significant, have been identified and discussed, also in relation to the rainfall seasonality and crop calendar in the examined area. The monitoring of vegetation conditions based on the analysis of phenological metrics, as originally provided in the ITHACA drought EWS, proved to effectively support WFP activities in several cases (i.e. Niger and Chad 2009, Sahel 2012, Horn of Africa crisis 2011). The final aim of conducted statistical study, object of this thesis work, was to correctly define the operational use of precipitation data for drought detection, in support to the vegetation monitoring procedures. The outcomes of the carried out work supported the planning and definition of effective procedures for the integration, where it is meaningful, in the ITHACA vegetation conditions monitoring activities , based on the analysis of phenological parameters, with the near real-time evaluation of precipitation deficits explained, for multiple time scales, using the Standard Precipitation Index (SPI). Indeed, the studied relationships between rainfall and vegetation dynamics allowed to determine the areas where the spatial and the temporal variability in vegetation conditions are closely related to the climate, and the best rainfall cumulating interval to be used for SPI monitoring purposes as well. In these areas, the fortnightly near real-time monitoring of the precipitation permits to earlier identify drought warnings, by considering also climate conditions before the start of the vegetation growing season. Moreover, in the same areas, the near real-time SPI analysis during the vegetation growing season supports the monitoring of phenological parameters in a way to identify very critical events characterized by both vegetation productivity and rainfall anomalies

Assessing Global Surface Water Inundation Dynamics Using Combined Satellite Information from SMAP, AMSR2 and Landsat

A method to assess global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fw(sub LBand)) retrievals were derived using SMAP H-polarization brightness temperature (Tb) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency (Tb) observations from AMSR2. The resulting (fw(sub LBand)) global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The (fw(sub LBand)) annual averages corresponded favourably (R=0.84, p<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly (fw(sub LBand)) averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable (fw(sub LBand)) performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) (fw(sub LBand)) results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m (fw(sub LBand)) retrievals showed favourable spatial accuracy for water (70.71%) and land (98.99%) classifications and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new (fw(sub LBand)) algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics and potential flood risk

Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing

Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both “greenness rising” and “greenness falling” transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground

The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan

We investigated and developed a prototype crop information system integrating 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data with other available remotely sensed imagery, field data, and knowledge as part of a wider project monitoring opium and cereal crops. NDVI profiles exhibited large geographical variations in timing, height, shape, and number of peaks, with characteristics determined by underlying crop mixes, growth cycles, and agricultural practices. MODIS pixels were typically bigger than the field sizes, but profiles were indicators of crop phenology as the growth stages of the main first-cycle crops (opium poppy and cereals) were in phase. Profiles were used to investigate crop rotations, areas of newly exploited agriculture, localized variation in land management, and environmental factors such as water availability and disease. Near-real-time tracking of the current years’ profile provided forecasts of crop growth stages, early warning of drought, and mapping of affected areas. Derived data products and bulletins provided timely crop information to the UK Government and other international stakeholders to assist the development of counter-narcotic policy, plan activity, and measure progress. Results show the potential for transferring these techniques to other agricultural systems

Potential of the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor for the monitoring of terrestrial chlorophyll fluorescence

Global monitoring of sun-induced chlorophyll fluorescence (SIF) is improving our knowledge about the photosynthetic functioning of terrestrial ecosystems. The feasibility of SIF retrievals from spaceborne atmospheric spectrometers has been demonstrated by a number of studies in the last years. In this work, we investigate the potential of the upcoming TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite mission for SIF retrieval. TROPOMI will sample the 675–775 nm spectral window with a spectral resolution of 0.5 nm and a pixel size of 7 km × 7 km. We use an extensive set of simulated TROPOMI data in order to assess the uncertainty of single SIF retrievals and subsequent spatio-temporal composites. Our results illustrate the enormous improvement in SIF monitoring achievable with TROPOMI with respect to comparable spectrometers currently in-flight, such as the Global Ozone Monitoring Experiment-2 (GOME-2) instrument. We find that TROPOMI can reduce global uncertainties in SIF mapping by more than a factor of 2 with respect to GOME-2, which comes together with an approximately 5-fold improvement in spatial sampling. Finally, we discuss the potential of TROPOMI to map other important vegetation parameters at a global scale with moderate spatial resolution and short revisit time. Those include leaf photosynthetic pigments and proxies for canopy structure, which will complement SIF retrievals for a self-contained description of vegetation condition and functioning

The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems

Evaluation of soil and vegetation response to drought using SMOS soil moisture satellite observations

European Geosciences Union General Assembly 2014 (EGU2014), 27 april - 2 may 2014, Vienna, Austria.-- 1 pageSoil moisture plays an important role in determining the likelihood of droughts and floods that may affect an area. Knowledge of soil moisture distribution as a function of time and space is highly relevant for hydrological, ecological and agricultural applications, especially in water-limited or drought-prone regions. However, measuring soil moisture is challenging because of its high variability; point-scale in-situ measurements are scarce being remote sensing the only practical means to obtain regional- and global-scale soil moisture estimates. The ESA’s Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission ever designed to measuring the Earth’s surface soil moisture at near daily time scales with levels of accuracy previously not attained. Since its launch in November 2009, significant efforts have been dedicated to validate and fine-tune the retrieval algorithms so that SMOS-derived soil moisture estimates meet the standards required for a wide variety of applications. In this line, the SMOS Barcelona Expert Center (BEC) is distributing daily, monthly, and annual temporal averages of 0.25-deg global soil moisture maps, which have proved useful for assessing drought and water-stress conditions. In addition, a downscaling algorithm has been developed to combine SMOS and NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) data into fine-scale (< 1km) soil moisture estimates, which permits extending the applicability of the data to regional and local studies. Fine-scale soil moisture maps are currently limited to the Iberian Peninsula but the algorithm is dynamic and can be transported to any region. Soil moisture maps are generated in a near real-time fashion at BEC facilities and are used by Barcelona’s fire prevention services to detect extremely dry soil and vegetation conditions posing a risk of fire. Recently, they have been used to explain drought-induced tree mortality episodes and forest decline in the Catalonia region. These soil moisture products can also be a useful tool to monitor the effectiveness of land restoration management practices. The aim of this work is to demonstrate the feasibility of using SMOS soil moisture maps for monitoring drought and water-stress conditions. In previous research, SMOS-derived Soil Moisture Anomalies (SSMA), calculated in a ten-day basis, were shown to be in close relationship with well-known drought indices (the Standardized Precipitation Index and the Standardized Precipitation Evapotranspiration Index). In this work, SSMA have been calculated for the period 2010-2013 in representative arid, semi-arid, sub-humid and humid areas across global land biomes. The SSMA reflect the cumulative precipitation anomalies and is known to provide ’memory’ in the climate and hydrological system; the water retained in the soil after a rainfall event is temporally more persistent than the rainfall event itself, and has a greater persistence during periods of low precipitation. Besides, the Normalized Difference Vegetation Index (NDVI) from MODIS is used as an indicator of vegetation activity and growth. The NDVI time series are expected to reflect the changes in surface vegetation density and status induced by water-deficit conditions. Understanding the relationships between SSMA and NDVI concurrent time series should provide new insight about the sensitivity of land biomes to droughtPeer Reviewe

Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping

Acknowledgments We thank Johan Havelaar, Aeryon Labs Inc., AeronVironment Inc. and Aeronautics Inc. for kindly permitting the use of materials in Fig. 1.Peer reviewedPublisher PD

The use of remote sensing data for drought assessment and monitoring in southwest Asia

Drought / Monitoring / Indicators / Assessment / Remote sensing / Asia