A Review of Vegetation Phenological Metrics Extraction Using Time-Series, Multispectral Satellite Data

Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes, and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting general vegetation phenology stages (e.g., green onset, time or peak greenness, and growing season length) often termed “land surface phenology,” as well as more advanced methods that estimate species-specific phenological stages (e.g., silking stage of maize). Common data-processing methods, such as data smoothing, applied to prepare the time-series remote sensing observations to be applied to phenological detection methods are presented. Specific land surface phenology detection methods as well as species-specific phenology detection methods based on multispectral satellite data are then discussed. The impact of different error sources in the data on remote-sensing based phenology detection are also discussed in detail, as well as ways to reduce these uncertainties and errors. Joint analysis of multiscale observations ranging from satellite to more recent ground-based sensors is helpful for us to understand satellite-based phenology detection mechanism and extent phenology detection to regional scale in the future. Finally, emerging opportunities to further advance remote sensing of phenology is presented that includes observations from Cubesats, near-surface observations such as PhenoCams, and image data fusion techniques to improve the spatial resolution of time-series image data sets needed for phenological characterization

Exploring short-term climate change effects on rangelands and broad-leaved forests by free satellite data in Aosta Valley (Northwest Italy)

Satellite remote sensing is a power tool for the long-term monitoring of vegetation. This work, with reference to a regional case study, investigates remote sensing potentialities for describing the annual phenology of rangelands and broad-leaved forests at the landscape level with the aim of detecting eventual effects of climate change in the Alpine region of the Aosta Valley (Northwest (NW) Italy). A first analysis was aimed at estimating phenological metrics (PMs) from satellite images time series and testing the presence of trends along time. A further investigation concerned evapotranspiration from vegetation (ET) and its variation along the years. Additionally, in both the cases the following meteorological patterns were considered: air temperature anomalies, precipitation trends and the timing of yearly seasonal snow melt. The analysis was based on the time series (TS) of different MODIS collections datasets together with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) collection obtained through Google Earth Engine. Ground weather stations data from the Centro Funzionale VdA ranging from 2000 to 2019 were used. In particular, the MOD13Q1 v.6, MOD16A2 and MOD10A1 v.6 collections were used to derive PMs, ET and snow cover maps. The SRTM (shuttle radar topography mission) DTM (digital terrain model) was also used to describe local topography while the Coordination of Information on the Environment (CORINE) land cover map was adopted to investigate land use classes. Averagely in the area, rangelands and broad-leaved forests showed that the length of season is getting longer, with a general advance of the SOS (start of the season) and a delay in the EOS (end of the season). With reference to ET, significant increasing trends were generally observed. The water requirement from vegetation appeared to have averagely risen about 0.05 Kg·m−2 (about 0.5%) per year in the period 2000–2019, for a total increase of about 1 Kg·m−2 in 20 years (corresponding to a percentage difference in water requirement from vegetation of about 8%). This aspect can be particularly relevant in the bottom of the central valley, where the precipitations have shown a statistically significant decreasing trend in the period 2000–2019 (conversely, no significant variation was found in the whole territory). Additionally, the snowpack timing persistence showed a general reduction trend. PMs and ET and air temperature anomalies, as well as snow cover melting, proved to have significantly changed their values in the last 20 years, with a continuous progressive trend. The results encourage the adoption of remote sensing to monitor climate change effects on alpine vegetation, with particular focus on the relationship between phenology and other abiotic factors permitting an effective technological transfer

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

Estimating and monitoring land surface phenology in rangelands: A review of progress and challenges

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment

Caracterización de la fenología de Fagus sylvatica L. en poblaciones mediterráneas del Sistema Central español mediante datos Landsat OLI/ETM+ y Sentinel-2A/B

[EN] The Spanish Central Range hosts some of the southernmost populations of Fagus sylvatica L. (European beech). Recent cartography indicates that these populations are expanding, going up-streams and gaining ground to oak forests of Quercus pyrenaica Willd., heather-lands, and pine plantations. Understanding the spectral phenology of European beech populations—which leaf flush occurs earlier than other vegetation formations—in this Mediterranean mountain range will provide insights of the species recent dynamics, and will enable modelling its performance under future climate oscillations. Intra-annual series of 211 Landsat OLI/ETM+ images, acquired between April 2013-December 2019, and 217 Sentinel-2A/B images, acquired between April 2017-December 2019, were employed to characterize the spectral phenology of European beech populations and five other vegetation types for comparison in an area of 108000 ha. Vegetation indices (VI) including the Normalized Difference Vegetation Index (NDVI) and Tasseled Cap Angle (TCA) from Landsat, and the NDVI and Enhanced Vegetation Index (EVI) from Sentinel-2 were retrieved from sample pixels. The temporal series of these VI were modelled with Savitzky-Golay and double logistic functions, and assessed with TIMESAT software, enabling the parametric characterization of European beech spectral phenology in the area with the start, length, and end of season, as well as peak time and value. The length of beech phenological season was similar when portrayed by Landsat and Sentinel-2 NDVI time series (214 and 211 days on average for the common period 2017-2019) although start and end differed. Compared with NDVI counterparts the TCA season started and peaked later, and the EVI season was shorter. Sentinel-2 NDVI peaked higher than Landsat NDVI. The European beech had an earlier (21 days on average) start of season than competing oak forests. Joint analysis of data from the virtual constellation Landsat/ Sentinel-2 and calibration with field observations may enable more detailed knowledge of phenological traits at the landscape scale.[ES] Algunas de las poblaciones más meridionales de Fagus sylvatica L. (haya) se encuentran en el Sistema Central español. La cartografía reciente de estas poblaciones indica que están expandiéndose a lo largo de arroyos y ganando terreno a robledales de Quercus pyrenaica Willd., brezales, y pinares. Conocer la fenología espectral de estos hayedos mediterráneos de montaña, cuya apertura de hojas se adelanta a la de otras formaciones vegetales permitiría inferir su dinámica reciente y modelizar su comportamiento frente a futuras oscilaciones climáticas. Se utilizaron 211 imágenes Landsat OLI/ETM+ adquiridas entre abril 2013-diciembre 2019 y 217 imágenes Sentinel-2A/B adquiridas entre abril 2017-diciembre 2019 para caracterizar la fenología espectral de hayedos y otras cinco formaciones vegetales en 108000 ha. Se calcularon y analizaron índices de vegetación: Normalized Difference Vegetation Index (NDVI) y Tasseled Cap Angle (TCA) con datos Landsat, NDVI y Enhanced Vegetation Index (EVI) con Sentinel-2. Se extrajeron las series temporales de estos índices en píxeles muestra para analizar mediante software TIMESAT, ajustando modelos Savitzky-Golay y función logística, y describiendo paramétricamente la fenología espectral: inicio, fin, y duración de temporada, así como momento y valor máximo del índice. Las series NDVI de Landsat y Sentinel-2 representaron una duración similar de la temporada fenológica (214 y 211 días para el periodo común de análisis, 2017-2019), aunque inicio y fin no coincidieron. Comparando con las curvas NDVI homólogas, la temporada TCA comenzó y alcanzó el pico máximo antes, y la temporada EVI fue más corta. Los valores máximos de NDVI en las series Sentinel-2 fueron más altos que los de Landsat. Los hayedos comenzaron la temporada fenológica de media 21 días antes que los robledales. El análisis conjunto de datos de la constelación virtual Landsat/Sentinel-2 y la calibración con observaciones de campo permitirá conocer mejor la fenología a escala de paisaje.This work was funded by the Spanish Ministry of Science, Innovation and University through projects: AGL2013-46028-R “Forest manage-ment facing the change in forest ecosystems dynamics: a multiscale approach (SCALyFOR)” and AGL201676769-C2-1-R “Influence of nat-ural disturbance regimes and management on forests dynamics, structure and carbon balance (FORESTCHANGE)”. Field work assistance by Diego Galán, Belén Oñate, and Gregorio Cerezo, and the support of José Juárez Benítez, director of the Sierra Norte de Guadalajara Natural Park are much appreciated.Gómez, C.; Alejandro, P.; Montes, F. (2020). Phenological characterization of Fagus sylvatica L. in Mediterranean populations of the Spanish Central Range with Landsat OLI/ETM+ and Sentinel-2A/B. Revista de Teledetección. 0(55):71-80. https://doi.org/10.4995/raet.2020.13561OJS7180055Augspurger, C.K. 2013. Reconstructing patterns of temperature, phenology, and frost damage over 124 years: Spring damage risk is increasing. Ecology 94, 41-50. https://doi.org/10.1890/12-0200.1Bolton, D.K., Gray, J.M, Melaas, E.K., Moon, M., Eklundh, L., Friedl, M.A. 2020. Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery, Remote Sensing of Environment, 240, 111685. https://doi.org/10.1016/j.rse.2020.111685Bucha T, Koren, M. 2017. Phenology of the beech forests in the Western Carpathians from MODIS for 2000-2015. iForest (Biosciences and Forestry), 10, 537-546. https://doi.org/10.3832/ifor2062-010Crist, E.P. 1985. A TM Tasseled Cap equivalent transformation for reflectance factor data. Remote Sensing of Environment, 17, 301-306. https://doi.org/10.1016/0034-4257(85)90102-6Delhon, C., Thiébault, S. 2005. The migration of beech (Fagus sylvatica L.) up the Rhone: the Mediterranean history of a "mountain" species. Veget. Hist. Archaeobot., 14, 119-132. https://doi.org/10.1007/s00334-005-0068-9Dittmar, C., Elling, W. 2006. Phenological phases of common beech (Fagus sylvatica L.) and their dependence on region and altitude in Southern Germany. European Journal of Forest Research, 125, 181-188. https://doi.org/10.1007/s10342-005-0099-xDrusch, M., Del Bello, U., Carlier, S., Colin, O., Fernández, V., Gascon, F., Hoesrch, B., Isola, C., Labertini, P., Marimort, P., Meygret, A., Spoto, F., Sya, O., Marchese, F., Bargellini, P. 2012. Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120, 25-36. https://doi.org/10.1016/j.rse.2011.11.026Eklundh, L., Jönsson, P. 2017. Timesat 3.3 Software Manual, Lund and Malmö University, Sweden.Fang, J., Lechovicz, M.J. 2006. Climatic limits for the present distribution of beech (Fagus L.) species in the world. Journal of Biogeography, 33, 1804-1819. https://doi.org/10.1111/j.1365-2699.2006.01533.xFu, Y.H., Piao S., Op de Beeck, M.O., Cong, N., Zhao, H., Zhang, Y., Menzel, A., Janssens, I.A., 2014. Recent spring phenology shifts in western Central Europe based on multiscale observations. Global Ecology and Biogeography, 23(11), 1255-1263. https://doi.org/10.1111/geb.12210Gerard F.F., George, C.T., Hayman, G., Chavana- Bryant, C., Weedon, G.P. 2020. Leaf phenology amplitude derived from MODIS NDVI and EVI: maps of leaf phenology synchrony for Meso- and South America. Geosciences Data Journal, 00, 1-14. https://doi.org/10.1002/gdj3.87Gao, B.C. 1996. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257-266. https://doi.org/10.1016/S0034-4257(96)00067-3Gil, L., Náger, J.A., Aranda-García, I., González- Doncel, I., Gonzalo-Jiménez, J., López de Heredia, U., Millerón, M., Nanos, N., Perea García-Calvo, R., Rodríguez-Calcerrada, J., Valbuena-Carabaña, M. 2010. El Hayedo de Montejo: una gestión sostenible. Dirección General del Medio Ambiente, Spain, 151 pp.Gómez, C., Alejandro, P., Aulló-Maestro, I., Hernández, L., Sánchez de Dios, R., Sainz-Ollero, H., Velázquez, J.C., Montes, F. 2019. Presence of European beech in its Spanish southernmost limit characterized with Landsat intra-annual time series. Proceedings of the AIT 2018, IX Conference of the Italian Society of Remote Sensing.Gonzalo, J. 2010. Diagnosis fitoclimática de la España peninsular. Hacia un modelo de clasificación funcional de la vegetación y de los ecosistemas peninsulares españoles. Serie Técnica: Naturaleza y Parques Nacionales. Ministerio de Medio Ambiente y Medio Rural y Marino. Organismo Autónomo Parques Nacionales.Herrera, S., Gutiérrez, J.M., Ancell, R., Pons, M.R., Frías, M.D., Fernández, J. 2012. Development and Analysis of a 50 year high-resolution daily gridded precipitation dataset over Spain (Spain02). International Journal of Climatology, 32, 74-85. https://doi.org/10.1002/joc.2256Houston Durrant, T., de Rigo, D., Candullo, G. 2016. Fagus sylvatica and other beeches in Europe: distribution, habitat, usage and threats in San Miguel Ayanz, J., de Rigo, D., Candullo, G., Houston Durrant, T., Mauri, A. (eds.) European Atlas of Forest Tree Species. Publication Office of the European Union, Luxembourg, pp.e012b90Jönsson, P., Cai., Z., Melaas, E., Friedl, M., Eklundh, L. 2018. A method for robust estimation of vegetation seasonality from Landsat and Sentinel-2 time series data. Remote Sensing, 10, 365. https://doi.org/10.3390/rs10040635Li, J., Roy, D.P. 2017. A global analysis of Sentinel- 2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing, 9, 902. https://doi.org/10.3390/rs9090902Liu, H.Q., Huete, A.R. 1995. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33, 457-465. https://doi.org/10.1109/TGRS.1995.8746027Melaas, E.K., Friedl, M.A., Zhu, Z. 2013. Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data. Remote Sensing of Environment, 132, 176-185. https://doi.org/10.1016/j.rse.2013.01.011Melaas, E.K., Sulla-Menashe, D., Gray, J.M., Black, T.A., Morin, T.H., Andrew, D.R., Friedl, M.A. 2016. Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat. Remote Sensing of Environment, 186, 452-464. https://doi.org/10.1016/j.rse.2016.09.014Powell, S.L., Cohen, W.B., Healey, S.P., Kennedy, R.E., Moisen, G.G., Pierce, K.B., Ohmann, J.L. 2010. Quantification of live aboveground biomass dynamics with Landsat time-series and field inventory data: a comparison of empirical modeling approaches. Remote Sensing of Environment, 114, 1053-1068. https://doi.org/10.1016/j.rse.2009.12.018Rubio-Cuadrado, A., Camarero, J.J., Del Río, M., Sánchez-González, M., Ruiz-Peinado, R., Bravo- Oviedo, A., Gil, L., Montes, F. 2018. Long-term impacts of drought on growth and forest dynamics in a temperate beech-oak-birch forest. Agricultural and Forest Meteorology, 259, 48-59. https://doi.org/10.1016/j.agrformet.2018.04.015Ruiz-Labourdette, D., Nogués-Bravo, D., Sainz- Ollero, H., Schmitz, M.F., Pineda, F.D. 2012. Forest composition in Mediterranean mountains is projected to shift along the entire elevational gradient under climate change. Journal of Biogeography, 39, 162-176. https://doi.org/10.1111/j.1365-2699.2011.02592.xSánchez de Dios, R., Hernández, L., Montes, F., Sainz- Ollero, H., Cañellas, I. 2016. Tracking the leading edge of Fagus sylvatica in North-Western Iberia: Holocene migration inertia, forest succession and recent global change. Perspectives in Plant Ecology, Evolution and Systematics, 20, 11-21. https://doi.org/10.1016/j.ppees.2016.03.001Sánchez de Dios, R., Gómez, .C, Aulló, I., Cañellas, I., Gea-Izquierdo, G., Montes, F., Sain-Ollero, H., Velázquez, J.C., Hernández, L. 2020. Fagus sylvatica L. peripheral populations in the Mediterranean Iberian Peninsula: climatic or anthropic relicts? Ecosystems. https://doi.org/10.1007/s10021-020-00513-8Stanimirova, R., Cai, Z., Melaas, E.K., Gray, J.M., Eklundh, L., Jönsson, P., Friedl, M.A. 2019. An empirical assessment of the MODIS land cover dynamics and TIMESAT land surface phenology algorithms. Remote Sensing, 11, 2201. https://doi.org/10.3390/rs11192201Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150. https://doi.org/10.1016/0034-4257(79)90013-0Van Rossum, G., Drake, F.L., 2009. Python 3 reference manual. Soho Books. Scotts Valley, CA, USA. 244 pp.Wulder, M.A., et al. 2019. Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225, 127-147. https://doi.org/10.1016/j.rse.2019.02.015Zeng, L., Wardlow B.D., Xiang, D., Hu, S., Li, D. 2020. A review of phenological metrics extraction using time-series, multispectral data. Remote Sensing of Environment, 237, 111511. https://doi.org/10.1016/j.rse.2019.11151

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

Evaluation of the impact of climate and human induced changes on the Nigerian forest using remote sensing

The majority of the impact of climate and human induced changes on forest are related to climate variability and deforestation. Similarly, changes in forest phenology due to climate variability and deforestation has been recognized as being among the most important early indicators of the impact of environmental change on forest ecosystem functioning. Comprehensive data on baseline forest cover changes including deforestation is required to provide background information needed for governments to make decision on Reducing Emissions from Deforestation and Forest Degradation (REED). Despite the fact that Nigeria ranks among the countries with highest deforestation rates based on Food and Agricultural Organization estimates, only a few studies have aimed at mapping forest cover changes at country scales. However, recent attempts to map baseline forest cover and deforestation in Nigeria has been based on global scale remote sensing techniques which do not confirm with ground based observations at country level. The aim of this study is two-fold: firstly, baseline forest cover was estimated using an ‘adaptive’ remote sensing model that classified forest cover with high accuracies at country level for the savanna and rainforest zones. The first part of this study also compared the potentials of different MODIS data in detecting forest cover changes at regional (cluster level) scale. The second part of this study explores the trends and response of forest phenology to rainfall across four forest clusters from 2002 to 2012 using vegetation index data from the MODIS and rainfall data obtained from the TRMM.Tertiary Education Trust Fund, Nigeri

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