Mapping Crop Cycles in China Using MODIS-EVI Time Series

As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA’s (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data

Mapping moth induced birch forest damage in northern Sweden, with MODIS satellite data

Large synchronous outbreaks of herbivory geometrids is regularly occurring at 9-10 years intervals when they reach peak densities in the Fennoscandian birch forest, in the northern part of Scandinavia (Tenow 1972, Bylund 1995). Climate change is likely to increase the frequency, intensity and extent of the outbreak due to increasing temperatures in the area (Callaghan 2010, Heliasz et al. 2011, Wolf et al. 2008). The consequence is a detrimental effect to the birch forest since the forest might not have enough time to recover between outbreaks, which will potentially decrease the proliferation and distribution of birch forest (Tenow et al. 2001, 2003, Karlsson et al 2004). This will have an ecological cost by making the forest inhabitable and non-resourceful for the animals and people that depend on it (Helle 2001). However, the effects on the Birch forest are not well known, therefore it is important to continue studying the distribution of forest damage, to gain a better understanding of its dynamics and the underlying spatio-temporal factors controlling the synchronous outbreaks. The most current year of infestation in the study area of the surroundings of lake Torneträsk in northern Sweden was 2012. To map the distribution and dynamics of the geometrids of the birch forest, time series data of MODIS 16-day NDVI composites were analyzed. To facilitate the analysis, a tree cover map with high resolution was created based on Lidar data. The Lidar based forest cover map was created to mask the forest. The topographical distribution of infested forest at four altitudinal intervals with 100 meter equidistance in between was also studied. A method was developed in this study to separate infested from non-infested forest with a threshold value based on z-score, which was successful at showing the distribution of the geometrid outbreak in 2012. The size of the infested area was 80km², equal to 54.3% of the forest in the area. If the forest classified as “likely infested” would have been included, the ratio of infested forest would increase to 64.4%. This is a significant proportion of the forest that will certainly affect the forest in future years. The topographical distribution of the infestation over the study area was relatively evenly distributed, without displaying any range of altitude that was more prone to infestation.Stora synkrona utbrott av björkmätare förekommer vart 9-10 år i de norra delarna av Skandinavien i den Fenoskandiska björkskogen. Björkmätaren konsumerar bladen och kan orsaka stora skador under ett toppår. Man misstänker att klimatförändringarna kommer att öka utbrottens omfattning och intensitet på grund av ökad temperatur i området. Konsekvenserna kan bli förödande för björkskogen, eftersom tiden mellan utbrotten riskerar att bli för korta för skogen att återhämta sig. Det kan resultera i att träden dör och skogen försvinner i de värst drabbade områdena. Om skogen minskar eller försvinner kan det få ekologiska konsekvenser för de djur som är beroende av den och människor som utnyttjar dess resurser. Hur skogen kommer att påverkas vet man inte säkert, därför är det viktigt att studera utbredningen av skogsskador och på så sätt få bättre kunskap om de underliggande faktorerna som kontrollerar utbrottens dynamik. Ett utbrott inträffade sommaren 2012 i studieområdet vilket var indelat i 2 olika stora överlappande område; Ett större som täcker området runt sjön Torneträsk i norra Sverige och ett mindre som täcker den västra delen av sjön runt Abisko. För att kartlägga omfattningen av björmatarutbrottet användes tidsserier av MODIS 16-dagars NDVI kompositer som analyserades men en utvecklad förändrings analys för projektet. Som en del i analysen skapades en högupplöst skogsutbredningskarta med Lidar data. Den topografiska utbredningen av utbrotten studerades också genom att dela in området i fyra höjdintervall med 100 meters ekvidistans mellan intervallen. För att identifiera skadade område utvecklades en metod baserat på standardiserade z-värde och definierade gränsvärde för klassificeringen av skogen. Det angripna området i Torneträsk var 251 km² stort, motsvarande 32% av skogen. I studieområdet i Abisko var utbrottet 80km2 stort, vilket motsvarar 54.3% av skogen i studieområdet. Om skogen klassad som ”troligen angripen” inkluderas i beräkningen, skulle den del av skogen som var angripen ökas ytterligare. Det är en signifikant andel skog som med stor sannolikhet kommer att påverka skogen i flera år. Den topografiska utbredningen av utbrottet i studieområdet var relativt jämnt fördelat, inget höjdintervall visade sig vara extra känsligt för att bli angripen

Polynomial trends of vegetation phenology in Sahelian to equatorial Africa using remotely sensed time series from 1983 to 2005

Popular science Our understanding of global warming can be achieved in different ways. One way is to study the phenological parameters of vegetation. Phenology or seasonality of vegetation can be identified from several parameters such as: the start of the growing season (SOS), end of the growing season (EOS), amplitude of the season (AMP), and length of the growing season (LOS). Changes of these parameters represent the cyclic changes of vegetation. Nowadays, imagery satellite data are reliable and widely-used sources to study the vegetation changes. Phenology parameters are derived from time series of vegetation indices (VI) that can be computed from satellite imagery. In this thesis, long-term dataset of GIMMS NDVI from 1983 to 2005 was used to extract and analyze vegetation phenology over Sahelian to equatorial areas. The TIMESAT software package was also used as an automated method to extract the parameters. Recent researches have shown the changes via analyzing the linear trends of the vegetation indices or lately through studying the linear trend of phenological parameters. Since changes of vegetation are not always simply linear, the overall aim of this thesis was to study vegetation changes through analysis of non-linear trends and more complex mathematical functions of phenology parameters, and via finding the relationship between the phenology parameters and soil moisture. Driving forces behind changes in phenology parameters including land cover, soil texture and rainfall were also taken in to consideration. The results illustrated that non-linear trends can detect notable proportions of vegetation changes in the study area. Not only significant portions of areas with linear trends could be represented using non-linear trends, but also these trends increased the precision of phenology change detection. Regarding the climate driver forces results showed that the vegetation phenology changes followed soil moisture variations. However the trends of vegetation changes has not especially followed land cover, soil texture and geographic characteristics although in some limited cases these driver forces are related to the changes.Global warming has both short and long term effects on seasonal phenological cycles of vegetation. Phenology parameters of vegetation such as start, end, length and amplitude of season can describe life cycle events of vegetation. In this thesis, long-term dataset of GIMMS NDVI time series from 1983 to 2005 was used to extract and analyze vegetation phenology over Sahelian to equatorial areas and TIMESAT software package was used as an automated method to extract the parameters. The overall aim of this thesis was to study vegetation changes through analysis of polynomial trends of phenology parameters. Phenology parameters were analyzed to detect hidden changes in vegetation dynamics. Through comparing polynomial trends of vegetation parameters and soil moisture, the relationship between the phenology parameters and soil moisture was detected and the role of climate driver forces (including land cover, soil texture and rainfall) behind the changes in phenology parameters were investigated. The results illustrated that polynomial trends can detect notable proportions of vegetation changes in the Sahel using remotely sensed data. Significant portions of areas with linear trends could be represented through quadratic and cubic trends, and these trends increased the precision of phenology change detection. Furthermore, in some areas vegetation changes were not detected neither through linear regressions nor polynomial trends. In such areas, polynomial hidden trends could be applied for detecting the fluctuations of vegetation parameters. In summation, applying polynomial trend analysis to time-series of satellite data is a powerful tool for investigating trends and variations in vegetation in semi-arid to sub-humid regions, like the Sahel. Regarding the climate driver forces, results showed that the vegetation phenology changes followed soil moisture variations, and in most occurrences, moderate correlations were found between SOS, EOS, and soil moisture. The trends of vegetation changes did not spatially follow land cover and soil types of the study area. However, in some limited cases, land cover, soil texture and geographic characteristics such as elevation were related to the changes

REMOTE SENSING DATA ANALYSIS FOR ENVIRONMENTAL AND HUMANITARIAN PURPOSES. The automation of information extraction from free satellite data.

This work is aimed at investigating technical possibilities to provide information on environmental parameters that can be used for risk management. The World food Program (WFP) is the United Nations Agency which is involved in risk management for fighting hunger in least-developed and low-income countries, where victims of natural and manmade disasters, refugees, displaced people and the hungry poor suffer from severe food shortages. Risk management includes three different phases (pre-disaster, response and post disaster) to be managed through different activities and actions. Pre disaster activities are meant to develop and deliver risk assessment, establish prevention actions and prepare the operative structures for managing an eventual emergency or disaster. In response and post disaster phase actions planned in the pre-disaster phase are executed focusing on saving lives and secondly, on social economic recovery. In order to optimally manage its operations in the response and post disaster phases, WFP needs to know, in order to estimate the impact an event will have on future food security as soon as possible, the areas affected by the natural disaster, the number of affected people, and the effects that the event can cause to vegetation. For this, providing easy-to-consult thematic maps about the affected areas and population, with adequate spatial resolution, time frequency and regular updating can result determining. Satellite remote sensed data have increasingly been used in the last decades in order to provide updated information about land surface with an acceptable time frequency. Furthermore, satellite images can be managed by automatic procedures in order to extract synthetic information about the ground condition in a very short time and can be easily shared in the web. The work of thesis, focused on the analysis and processing of satellite data, was carried out in cooperation with the association ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action), a center of research which works in cooperation with the WFP in order to provide IT products and tools for the management of food emergencies caused by natural disasters. These products should be able to facilitate the forecasting of the effects of catastrophic events, the estimation of the extension and location of the areas hit by the event, of the affected population and thereby the planning of interventions on the area that could be affected by food insecurity. The requested features of the instruments are: • Regular updating • Spatial resolution suitable for a synoptic analysis • Low cost • Easy consultation Ithaca is developing different activities to provide georeferenced thematic data to WFP users, such a spatial data infrastructure for storing, querying and manipulating large amounts of global geographic information, and for sharing it between a large and differentiated community; a system of early warning for floods, a drought monitoring tool, procedures for rapid mapping in the response phase in a case of natural disaster, web GIS tools to distribute and share georeferenced information, that can be consulted only by means of a web browser. The work of thesis is aimed at providing applications for the automatic production of base georeferenced thematic data, by using free global satellite data, which have characteristics suitable for analysis at a regional scale. In particular the main themes of the applications are water bodies and vegetation phenology. The first application aims at providing procedures for the automatic extraction of water bodies and will lead to the creation and update of an historical archive, which can be analyzed in order to catch the seasonality of water bodies and delineate scenarios of historical flooded areas. The automatic extraction of phenological parameters from satellite data will allow to integrate the existing drought monitoring system with information on vegetation seasonality and to provide further information for the evaluation of food insecurity in the post disaster phase. In the thesis are described the activities carried on for the development of procedures for the automatic processing of free satellite data in order to produce customized layers according to the exigencies in format and distribution of the final users. The main activities, which focused on the development of an automated procedure for the extraction of flooded areas, include the research of an algorithm for the classification of water bodies from satellite data, an important theme in the field of management of the emergencies due to flood events. Two main technologies are generally used: active sensors (radar) and passive sensors (optical data). Advantages for active sensors include the ability to obtain measurements anytime, regardless of the time of day or season, while passive sensors can only be used in the daytime cloud free conditions. Even if with radar technologies is possible to get information on the ground in all weather conditions, it is not possible to use radar data to obtain a continuous archive of flooded areas, because of the lack of a predetermined frequency in the acquisition of the images. For this reason the choice of the dataset went in favor of MODIS (Moderate Resolution Imaging Spectroradiometer), optical data with a daily frequency, a spatial resolution of 250 meters and an historical archive of 10 years. The presence of cloud coverage prevents from the acquisition of the earth surface, and the shadows due to clouds can be wrongly classified as water bodies because of the spectral response very similar to the one of water. After an analysis of the state of the art of the algorithms of automated classification of water bodies in images derived from optical sensors, the author developed an algorithm that allows to classify the data of reflectivity and to temporally composite them in order to obtain flooded areas scenarios for each event. This procedure was tested in the Bangladesh areas, providing encouraging classification accuracies. For the vegetation theme, the main activities performed, here described, include the review of the existing methodologies for phenological studies and the automation of the data flow between inputs and outputs with the use of different global free satellite datasets. In literature, many studies demonstrated the utility of the NDVI (Normalized Difference Vegetation Index) indices for the monitoring of vegetation dynamics, in the study of cultivations, and for the survey of the vegetation water stress. The author developed a procedure for creating layers of phenological parameters which integrates the TIMESAT software, produced by Lars Eklundh and Per Jönsson, for processing NDVI indices derived from different satellite sensors: MODIS (Moderate Resolution Imaging Spectroradiometer), AVHRR (Advanced Very High Resolution Radiometer) AND SPOT (Système Pour l'Observation de la Terre) VEGETATION. The automated procedure starts from data downloading, calls in a batch mode the software and provides customized layers of phenological parameters such as the starting of the season or length of the season and many others

Remote sensing phenology at European northern latitudes - From ground spectral towers to satellites

Plant phenology exerts major influences on carbon, water, and energy exchanges between atmosphere and ecosystems, provides feedbacks to climate, and affects ecosystem functioning and services. Great efforts have been spent in studying plant phenology over the past decades, but there are still large uncertainties and disputations in phenology estimation, trends, and its climate sensitivities. This thesis aims to reduce these uncertainties through analyzing ground spectral sampling, developing methods for in situ light sensor calibration, and exploring a new spectral index for reliable retrieval of remote sensing phenology and climate sensitivity estimation at European northern latitudes. The ground spectral towers use light sensors of either nadir or off-nadir viewing to measure reflected radiation, yet how plants in the sensor view contribute differently to the measured signals, and necessary in situ calibrations are often overlooked, leading to great uncertainties in ground spectral sampling of vegetation. It was found that the ground sampling points in the sensor view follow a Cauchy distribution, which is further modulated by the sensor directional response function. We proposed in situ light sensor calibration methods and showed that the user in situ calibration is more reliable than manufacturer’s lab calibration when our proposed calibration procedures are followed. By taking the full advantages of more reliable and standardized reflectance, we proposed a plant phenology vegetation index (PPI), which is derived from a radiative transfer equation and uses red and near infrared reflectance. PPI shows good linearity with canopy green leaf area index, and is correlated with gross primary productivity, better than other vegetation indices in our test. With suppressed snow influences, PPI shows great potentials for retrieving phenology over coniferous-dominated boreal forests. PPI was used to retrieve plant phenology from MODIS nadir BRDF-adjusted reflectance at European northern latitudes for the period 2000-2014. We estimated the trend of start of growing season (SOS), end of growing season (EOS), length of growing season (LOS), and the PPI integral for the time span, and found significant changes in most part of the region, with an average rate of -0.39 days·year-1 in SOS, 0.48 days·year-1 in EOS, 0.87 days·year-1 in LOS, and 0.79%·year-1 in the PPI integral over the past 15 years. We found that the plant phenology was significantly affected by climate in most part of the region, with an average sensitivity to temperature: SOS at -3.43 days·°C-1, EOS at 1.27 days·°C-1, LOS at 3.16 days·°C-1, and PPI integral at 2.29 %·°C-1, and to precipitation: SOS at 0.28 days∙cm-1, EOS at 0.05 days∙cm-1, LOS at 0.04 days∙cm-1, and PPI integral at -0.07%∙cm-1. These phenology variations were significantly related to decadal variations of atmospheric circulations, including the North Atlantic Oscillation and the Arctic Oscillation. The methods developed in this thesis can help to improve the reliability of long-term field spectral measurements and to reduce uncertainties in remote sensing phenology retrieval and climate sensitivity estimation

Métricas fenológicas da vegetação de pastagens do Rio Grande do Sul, Brasil

Considering that plant phenology studies allow establishing relationships between phenological patterns of vegetation and changes caused by climate variability, the aim of this study was to obtain phenological metrics for the predominant grassland typologies in Rio Grande do Sul State, Brazil (latitudes 27º 05’ and 33º 45’ S and longitudes 49° 43’ and 57º 39’ W) and to evaluate the spatial-temporal distribution pattern of these metrics under the influence of the subtropical climate variability. The phenological metrics were obtained based on the time series of the EVI (Enhanced Vegetation Index), of the sensor MODIS (Moderate Resolution Imaging Spectroradiometer), for the period from 2001 to 2014, through the Timesat program. Eleven phenological metrics were extracted, identifying the presence of two spatial distribution patterns of grass-dominated typologies in the Rio Grande do Sul state, Brazil, one located in the south-central region and the other located in the northeast, along the coast and in the western portion of the state. In addition, it was also observed that the phenological pattern of the grassland vegetation of the state of Rio Grande do Sul, Brazil, is controlled by the seasonality of vegetation, mainly associated with the variations in air temperature.Considerando que o estudo da fenologia vegetal permite estabelecer relações entre o padrão fenológico da vegetação e as alterações causadas pela variabilidade climática, o objetivo deste trabalho foi obter métricas fenológicas para as tipologias de vegetação de pastagens predominantes no Rio Grande do Sul (latitudes 27º 05’ e 33º 45’ S e longitudes 49° 43’ e 57º 39’ W) e avaliar o padrão de distribuição espaçotemporal destas métricas sob influência da variabilidade climática subtropical. A obtenção de 11 métricas fenológicas foi realizada com base na série temporal do EVI (Enhanced Vegetation Index) do sensor MODIS (Moderate Resolution Imaging Spectroradiometer), para o período de 2001 a 2014, por meio do programa Timesat. Identificou-se a presença de dois grupos espaciais de tipologias campestres no Estado do Rio Grande do Sul, um na região centro-sul e outro na região nordeste, ao longo do litoral e no oeste do Estado. Observou-se ainda que o padrão fenológico da vegetação de pastagens do Rio Grande do Sul é controlado pela sazonalidade da vegetação associada, principalmente, à variação da temperatura do ar

Responses of seasonal indicators to extreme droughts in southwest China

Significant impact of extreme droughts on human society and ecosystem has occurred in many places of the world, for example, Southwest China (SWC). Considerable research concentrated on analyzing causes and effects of droughts in SWC, but few studies have examined seasonal indicators, such as variations of surface water and vegetation phenology. With the ongoing satellite missions, more and more earth observation data become available to environmental studies. Exploring the responses of seasonal indicators from satellite data to drought is helpful for the future drought forecast and management. This study analyzed the seasonal responses of surface water and vegetation phenology to drought in SWC using the multi-source data including Seasonal Water Area (SWA), Permanent Water Area (PWA), Start of Season (SOS), End of Season (EOS), Length of Season (LOS), precipitation, temperature, solar radiation, evapotranspiration, the Palmer Drought Severity Index (PDSI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and data from water conservancy construction. The results showed that SWA and LOS effectively revealed the development and recovery of droughts. There were two obvious drought periods from 2000 to 2017. In the first period (from August 2003 to June 2007), SWA decreased by 11.81% and LOS shortened by 5 days. They reduced by 21.04% and 9 days respectively in the second period (from September 2009 to June 2014), which indicated that there are more severe droughts in the second period. The SOS during two drought periods delayed by 3~6 days in spring, while the EOS advanced 1~3 days in autumn. All of PDSI, SWA and LOS could reflect the period of droughts in SWC, but the LOS and PDSI were very sensitive to the meteorological events, such as precipitation and temperature, while the SWA performed a more stable reaction to drought and could be a good indicator for the drought periodicity. This made it possible for using SWA in drought forecast because of the strong correlation between SWA and drought. Our results improved the understanding of seasonal responses to extreme droughts in SWC, which will be helpful to the drought monitoring and mitigation for different seasons in this ecologically fragile region

The CACAO Method for Smoothing, Gap Filling, and Characterizing Seasonal Anomalies in Satellite Time Series

Consistent, continuous, and long time series of global biophysical variables derived from satellite data are required for global change research. A novel climatology fitting approach called CACAO (Consistent Adjustment of the Climatology to Actual Observations) is proposed to reduce noise and fill gaps in time series by scaling and shifting the seasonal climatological patterns to the actual observations. The shift and scale CACAO parameters adjusted for each season allow quantifying shifts in the timing of seasonal phenology and inter-annual variations in magnitude as compared to the average climatology. CACAO was assessed first over simulated daily Leaf Area Index (LAI) time series with varying fractions of missing data and noise. Then, performances were analyzed over actual satellite LAI products derived from AVHRR Long-Term Data Record for the 1981-2000 period over the BELMANIP2 globally representative sample of sites. Comparison with two widely used temporal filtering methods-the asymmetric Gaussian (AG) model and the Savitzky-Golay (SG) filter as implemented in TIMESAT-revealed that CACAO achieved better performances for smoothing AVHRR time series characterized by high level of noise and frequent missing observations. The resulting smoothed time series captures well the vegetation dynamics and shows no gaps as compared to the 50-60% of still missing data after AG or SG reconstructions. Results of simulation experiments as well as confrontation with actual AVHRR time series indicate that the proposed CACAO method is more robust to noise and missing data than AG and SG methods for phenology extraction

From global observations to local information: The Earth Observation Monitor

Earth Observation (EO) data are available around the globe and can be used for a range of applications. To support scientists and local stakeholders in the usage of information from space, barriers, especially in data processing, need to be reduced. To meet this need, the software framework Earth Observation Monitor provides access and analysis tools for global EO vegetation time-series data based on standard-compliant geo-processing services. Data are automatically downloaded from several data providers, processed, and time-series analysis tools for vegetation analyses extract further information. A web portal and a mobile application have been developed to show the usage of interoperable geospatial web services and to simplify the access and analysis of global EO time-series data. All steps from data download to analysis are automated and provided as operational geo-processing services. Open-source software has been used to develop the services and client applications