Burned area detection and mapping using Sentinel-1 backscatter coefficient and thermal anomalies

This paper presents a burned area mapping algorithm based on change detection of Sentinel-1 backscatter data guided by thermal anomalies. The algorithm self-adapts to the local scattering conditions and it is robust to variations of input data availability. The algorithm applies the Reed-Xiaoli detector (RXD) to distinguish anomalous changes of the backscatter coefficient. Such changes are linked to fire events, which are derived from thermal anomalies (hotspots) acquired during the detection period by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Land cover maps were used to account for changing backscatter behaviour as the RXD is class dependent. A machine learning classifier (random forests) was used to detect burned areas where hotspots were not available. Burned area perimeters derived from optical images (Landsat-8 and Sentinel-2) were used to validate the algorithm results. The validation dataset covers 21 million hectares in 18 locations that represent the main biomes affected by fires, from boreal forests to tropical and sub-tropical forests and savannas. A mean Dice coefficient (DC) over all studied locations of 0.59±0.06 (±confidence interval, 95%) was obtained. Mean omission (OE) and commission errors (CE) were 0.43±0.08 and 0.37±0.06, respectively. Comparing results with the MODIS based MCD64A1 Version 6, our detections are quite promising, improving on average DC by 0.13 and reducing OE and CE by 0.12 and 0.06, respectively.European Space AgencyMinisterio de Educación, Cultura y Deport

Continuous Forest Monitoring Using Cumulative Sums of Sentinel-1 Timeseries

Forest degradation is recognized as a major environmental threat on a global scale. The recent rise in natural and anthropogenic destruction of forested ecosystems highlights the need for developing new, rapid, and accurate remote sensing monitoring systems, which capture forested land transformations. In spite of the great technological advances made in airborne and spaceborne sensors over the past decades, current Earth observation (EO) change detection methods still need to overcome numerous limitations. Optical sensors have been commonly used for detecting land use and land cover changes (LULCC), however, the requirement of certain technical and environmental conditions (e.g., sunlight, not cloud-coverage) restrict their use. More recently, synthetic aperture radar (SAR)-based change detection approaches have been used to overcome these technical limitations, but they commonly rely on static detection approaches (e.g., pre and post disturbance scenario comparison) that are slow to monitor change. In this context, this paper presents a novel approach for mapping forest structural changes in a continuous and near-real-time manner using dense Sentinel-1 image time-series. Our cumulative sum−spatial mean corrected (CUSU-SMC) algorithm approach is based on cumulative sum statistical analysis, which allows the continuous monitoring of radar signal variations, derived from forest structural change. Taking advantage of the high data availability offered by the Sentinel-1 (S-1) C-band constellation, we used an S-1 ground range detected (GRD) dual (VV, VH) polarization timeseries, formed by a total of 84 images, to monitor clear-cutting operations carried out in a Scottish forest during 2019. The analysis showed a user’s accuracy of 82% for the (conservative) detection approach. The use of a post-processing neighbor filter increased the detection performance to a user’s accuracy of 86% with an overall accuracy of 77% for areas of a minimum extent of 0.4ha. To further validate the detection performance of the method, the CUSU-SMC change detector was tested against commonly-used pairwise change detection approaches for the same period. These results emphasize the capabilities of dense SAR time-series for environmental monitoring and provide a useful tool for optimizing national forest inventories

3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function

Dear Colleagues, The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties, and can thus be crucially shaped and changed by various biotic and abiotic factors on multiple spatial scales. The magnitude and extent of these changes in recent decades calls for enhanced mitigation and adaption measures. Remote sensing data and methods are the main complementary sources of up-to-date synoptic and objective information of forest ecology. Due to the inherent 3D nature of forest ecosystems, the analysis of 3D sources of remote sensing data is considered to be most appropriate for recreating the forest’s compositional, structural and functional dynamics. In this Special Issue of Forests, we published a set of state-of-the-art scientific works including experimental studies, methodological developments and model validations, all dealing with the general topic of 3D remote sensing-assisted applications in forest ecology. We showed applications in forest ecology from a broad collection of method and sensor combinations, including fusion schemes. All in all, the studies and their focuses are as broad as a forest’s ecology or the field of remote sensing and, thus, reflect the very diverse usages and directions toward which future research and practice will be directed

Comparing synthetic aperture radar and LiDAR for above-ground biomass estimation in Glen Affric, Scotland

Quantifying above-ground biomass (AGB) and carbon sequestration has been a significant focus of attention within the UNFCCC and Kyoto Protocol for improvement of national carbon accounting systems (IPCC, 2007; UNFCCC, 2011). A multitude of research has been carried out in relatively flat and homogeneous forests (Ranson & Sun, 1994; Beaudoin et al.,1994; Kurvonen et al., 1999; Austin et al., 2003; Dimitris et al., 2005), yet forests in the highlands, which generally form heterogeneous forest cover and sparse woodlands with mountainous terrain have been largely neglected in AGB studies (Cloude et al., 2001; 2008; Lumsdon et al., 2005; 2008; Erxue et al., 2009, Tan et al., 2010; 2011a; 2011b; 2011c; 2011d). Since mountain forests constitute approximately 28% of the total global forest area (Price and Butt, 2000), a better understanding of the slope effects is of primary importance in AGB estimation. The main objective of this research is to estimate AGB in the aforementioned forest in Glen Affric, Scotland using both SAR and LiDAR data. Two types of Synthetic Aperture Radar (SAR) data were used in this research: TerraSAR-X, operating at X-band and ALOS PALSAR, operating at L-band, both are fully polarimetric. The former data was acquired on 13 April 2010 and of the latter, two scenes were acquired on 17 April 2007 and 08 June 2009. Airborne LiDAR data were acquired on 09 June 2007. Two field measurement campaigns were carried out, one of which was done from winter 2006 to spring 2007 where physical parameters of trees in 170 circular plots were measured by the Forestry Commission team. Another intensive fieldwork was organised by myself with the help of my fellow colleagues and it comprised of tree measurement in two transects of 200m x 50m at a relatively flat and dense plantation forest and 400m x 50m at hilly and sparse semi-natural forest. AGB is estimated for both the transects to investigate the effectiveness of the proposed method at plot-level. This thesis evaluates the capability of polarimetric Synthetic Aperture Radar data for AGB estimation by investigating the relationship between the SAR backscattering coefficient and AGB and also the relationship between the decomposed scattering mechanisms and AGB. Due to the terrain and heterogeneous nature of the forests, the result from the backscatter-AGB analysis show that these forests present a challenge for simple AGB estimation. As an alternative, polarimetric techniques were applied to the problem by decomposing the backscattering information into scattering mechanisms based on the approach by Yamaguchi (2005; 2006), which are then regressed to the field measured AGB. Of the two data sets, ALOS PALSAR demonstrates a better estimation capacity for AGB estimation than TerraSAR-X. The AGB estimated results from SAR data are compared with AGB derived from LiDAR data. Since tree height is often correlated with AGB (Onge et al., 2008; Gang et al., 2010), the effectiveness of the tree height retrieval from LiDAR is evaluated as an indicator of AGB. Tree delineation was performed before AGB of individual trees were calculated allometrically. Results were validated by comparison to the fieldwork data. The amount of overestimation varies across the different canopy conditions. These results give some indication of when to use LiDAR or SAR to retrieve forest AGB. LiDAR is able to estimate AGB with good accuracy and the R2 value obtained is 0.97 with RMSE of 14.81 ton/ha. The R2 and RMSE obtained for TerraSAR-X are 0.41 and 28.5 ton/ha, respectively while for ALOS PALSAR data are 0.70 and 23.6 ton/ha, respectively. While airborne LiDAR data with very accurate height measurement and consequent three-dimensional (3D) stand profiles which allows investigation into the relationship between height, number density and AGB, it's limited to small coverage area, or large areas but at large cost. ALOS PALSAR, on the other hand, can cover big coverage area but it provide a lower resolution, hence, lower estimation accuracy

Caracterización de la respuesta SAR de banda X en áreas quemadas en dos incendios forestales de la zona central de Chile

Tesis (Magister en Aplicaciones Espaciales de Alerta y Respuesta Temprana a Emergencias)--Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, 2017.El presente estudio tiene por objetivo caracterizar la respuesta radar de banda X en áreas quemadas en dos incendios forestales de la zona central de Chile. Uno caracterizado por vegetación nativa del tipo esclerófilo y el otro por plantaciones forestales de Pinus radiata D.Don. Para el análisis de la situación previa, la estimación de la superficie quemada y el análisis de cambio entre la situación previa y posterior a los incendios forestales, se utilizaron imágenes Landsat 8, mientras que para el análisis de la respuesta radar en banda X, se usaron imágenes COSMO SkyMed modo PingPong , con diferentes configuraciones de adquisición. En el incendio de vegetación nativa esclerófila las bandas cruzadas VH y HV mostraron mayor utilidad en el monitoreo de áreas quemadas, mientras que en el sector con plantaciones forestales de Pinus radiata, fueron las bandas co-polarizadas HH, VV.The aim of this study is characterize the X-band radar response in burned areas of two wildfires in Central Zone of Chile. One characterized by native sclerophyllous vegetation and the other by forestry plantations of Pinus radiata D.Don. For the analysis of the previous situation, burned area estimation and change analysis between previous and after wildfire situation, optical images from Landsat 8 satellite were used, while for the analysis of the X-band radar response, COSMO SkyMed images PingPong mode, whit different configurations acquisition were used. The results indicate opposite behaviors in the two wildfires analized. In the fire of native sclerophyll vegetation, cross polarizations (VH and HV) showed most useful in burned areas monitoring, while in forest plantations of Pinus radiata sector, were co-polarized bands HH, VV which presented increased sensitivity to the effect of fire

Metsissä tapahtuvien häiriöiden ja niistä palautumisen seurannan mahdollisuuksien parantaminen laajoilla alueilla

Information needs associated with forest monitoring have become increasingly complex. Data to support these information needs are required to be systematically generated, spatially exhaustive, spatially explicit, and to capture changes at a spatial and temporal resolution that is commensurate with both natural and anthropogenic impacts. Moreover, reporting obligations impose additional expectations of transparency, repeatability, and data provenance. The overall objective of this dissertation was to address these needs and improve capacity for large-area monitoring of forest disturbance and subsequent recovery. Landsat time series (LTS) enhance opportunities for forest monitoring, particularly for post-disturbance recovery assessments, while best-available pixel (BAP) compositing approaches allow LTS approaches to be applied over large forest extents. In substudies I and IV, forest monitoring information needs were identified and linked to image compositing criteria and data availability in Canada and Finland. In substudy II, methods were developed and demonstrated for generating large-area, gap-filled Landsat BAP image composites that preserve detected changes, generate continuous change metrics, and provide foundational, annual data to support forest monitoring. In substudy III a national monitoring framework was prototyped at scale over the 650 Mha of Canada’s forest ecosystems, providing a detailed analysis of areas disturbed by wildfire and harvest for a 25-year period (1985–2010), as well as characterizing short- and long-term recovery. New insights on spectral recovery metrics were provided by substudies V and VI. In substudies V, the utility of spectral measures of recovery were evaluated and confirmed against benchmarks of forest cover and height derived from airborne laser scanning data. In substudy VI the influence of field-measured structure and composition on spectral recovery were examined and quantified. By focusing on four key aspects of forest monitoring systems: information needs, data availability, methods development, and information outcomes, the component studies demonstrated that combining BAP compositing and LTS analysis approaches provides data with the requisite characteristics to support large-area forest monitoring, while also enabling a more comprehensive assessment of forest disturbance and recovery.Tietotarpeista metsien seurantaan liittyen on tullut entistä monitahoisempia. Jotta näihin tietotarpeisiin voidaan vastata, aineistojen tulee olla systemaattisesti tuotettuja, spatiaalisesti kattavia ja yksiselitteisiä, sekä niiden avulla tulee olla mahdollista havaita muutokset spatiaalisella ja temporaalisella resoluutiolla, jotka ovat yhteismitallisia sekä luonnon että ihmisen vaikutusten kanssa. Lisäksi raportointivelvollisuudet asettavat edelleen vaatimuksia läpinäkyvyyden, toistettavuuden ja aineistojen alkuperän suhteen. Väitöskirjan tavoitteena oli keskittyä näihin tarpeisiin sekä parantaa metsissä tapahtuvien häiriöiden ja niistä palautumisen seurannan mahdollisuuksia laajoilla alueilla. Landsat-aikasarja tehostaa metsien seurannan mahdollisuuksia, erityisesti metsissä tapahtuvien häiriöiden jälkeisen palautumisen arviointia, kun taas komposiittikuvien tuottamisen lähestymistapa, jossa hyödynnetään parhaita saatavilla olevia pikseleitä, mahdollistaa Landsat-aikasarjan hyödyntämisen laajoilla metsäalueilla. Osajulkaisuissa I ja IV tunnistettiin metsien seurannan tietotarpeita ja liitettiin niitä komposiittikuvan tuottamisen kriteereihin sekä aineistojen saatavuuteen Kanadassa ja Suomessa. Osajulkaisussa II kehitettiin menetelmiä ja havainnollistettiin niitä tuottamalla laajan alueen yhtenäiset Landsat-komposiittikuvat, joista tunnistettiin muutokset, laskettiin jatkuvat muutospiirteet sekä tuotettiin vuosittaiset tiedot, jotka ovat olennaisen tärkeitä metsien seurannan kannalta. Osajulkaisussa III kansallista seurantamenetelmää testattiin Kanadan yli 650 Mha metsäekosysteemien alueella ja se mahdollisti yksityiskohtaiset analyysit kohteissa, joissa oli tapahtunut metsäpalo tai päätehakkuu edellisen 25 vuoden aikana (1985-2010). Lisäksi metsien lyhyen ja pitkän ajan palautumista pystytiin arvioimaan. Palautumista kuvaavista sävyarvopiirteistä tuotettiin lisätietoa osajulkaisuissa V ja VI. Osajulkaisussa V metsien palautumista kuvaavien sävyarvojen hyödyllisyyttä arvioitiin ja vahvistettiin vertaamalla niitä metsien peitteisyyden ja pituuden kriteereihin, jotka saatiin lentolaserkeilausaineistosta. Osajulkaisussa VI tutkittiin ja määrällistettiin maastossa mitattujen metsän rakenteen ja puulajisuhteiden vaikutusta palautumista kuvaaviin sävyarvoihin. Keskittymällä metsien seurantajärjestelmien neljään tärkeimpään näkökulmaan, tietotarpeeseen, aineistojen saatavuuteen, menetelmäkehitykseen, ja tuotettuun tietoon, väitöskirjatutkimukset osoittivat, että yhdistämällä lähestymistapa, jossa komposiittikuvat tuotetiin hyödyntämällä parhaita saatavilla olevia pikseleitä, ja Landsat-aikasarja on mahdollista tuottaa sellaista tietoa ja aineistoja, joissa on tarvittavat ominaisuudet laajojen alueiden metsien seurantaa varten, samalla kun mahdollistetaan myös kokonaisvaltaisempi arviointi metsissä tapahtuvista häiriöistä ja metsien palautumisesta niiden jälkeen

Forest attributes mapping with SAR data in the romanian South-Eastern Carpathians requirements and outcomes

Esta tesis doctoral se centra en la estimación de variables forestales en la zona Sureste de los Cárpatos Rumanos a partir de imágenes de radar de apertura sintética. La investigación abarca parte del preprocesado de las imágenes, métodos de generación de mosaicos y la extracción de la cobertura de bosque, sus subtipos o su biomasa. La tesis se desarrolló en el Instituto Nacional de Investigación y Desarrollo Forestal Marín Dracea (INCDS) y la Universidad de Alcalá (UAH) gracias a varios proyectos: el proyecto EO-ROFORMON del INCDS (Prototyping an Earth-Observation based monitoring and forecasting system for the Romanian forests), y el proyecto EMAFOR de la UAH (Synthetic Aperture Radar (SAR) enabled Analysis Ready Data (ARD) cubes for efficient monitoring of agricultural and forested landscapes). El proyecto EO-ROFORMON fue financiado por la Autoridad Nacional para la Investigación Científica de Rumania y el Fondo Europeo de Desarrollo Regional. El proyecto EMAFOR fue financiado por la Comunidad Autónoma de Madrid (España). El objetivo de esta tesis es el desarrollo de algoritmos para la extracción de variables forestales de uso general como la cobertura, el tipo o la biomasa del bosque a partir de imagen de radar de apertura sintética. Para alcanzar dicho propósito se analizaron posibles fuentes de sesgo sistemático que podrían aparecer en zonas de montaña (ej., normalización topográfica, generación de mosaicos), y se aplicaron técnicas de aprendizaje de máquina para tareas de clasificación y regresión. La tesis contiene ocho secciones: una introducción, cinco publicaciones en revistas o actas de congresos indexados, una pendiente de publicación (quinto capítulo) y las conclusiones. La introducción contextualiza la importancia del bosque, cómo se recoge la información sobre su estado (ej., inventario forestal) y las iniciativas o marcos legislativos que requieren dicha información. A continuación, se describe cómo la teledetección puede complementar la información de inventario forestal, detallando el contexto histórico de las distintas tecnologías, su funcionamiento, y cómo pueden ser aplicadas para la extracción de información forestal. Por último, se describe la problemática y el monitoreo del bosque en Rumanía, detallando el objetivo de la tesis y su estructura. El primer capítulo analiza la influencia del modelo digital de elevaciones (MDE) en la calidad de la normalización topográfica, analizando tres MDE globales (SRTM, AW3D y TanDEM-X DEM) y uno nacional (PNOA-LiDAR). Los experimentos se basan en la comparación entre órbitas, con un MDE de referencia, y la variación del acierto en la clasificación dependiendo del MDE empleado para la normalización. Los resultados muestran una menor diferencia ente órbitas al utilizar un MDE con una mejor resolución (ej. TanDEM-X, PNOA-LIDAR), especialmente en el caso de zonas con fuertes pendientes o formas del terreno complejas, como pueden ser los valles. En zonas de alta montaña las imágenes de radar de apertura sintética (SAR) sufren frecuentes distorsiones. Estas distorsiones dependen de la geometría de adquisición, por lo que es posible combinar imágenes adquiridas desde varias órbitas para que la cobertura sea lo más completa posible. El segundo capítulo evalúa dos metodologías para la clasificación de usos del suelo utilizando datos de Sentinel-1 adquiridos desde varias órbitas. El primer método crea clasificaciones por órbita y las combina, mientras que el segundo genera un mosaico con datos de múltiples órbitas y lo clasifica. El acierto obtenido mediante combinación de clasificaciones es ligeramente mayor, mientras que la clasificación de mosaicos tiene importantes omisiones de las zonas boscosas debido a problemas en la normalización topográfica y a los efectos direccionales. El tercer capítulo se enfoca en separar la cobertura forestal de otras coberturas del suelo (urbano, vegetación baja, agua) analizando la utilidad de las variables basadas en la coherencia interferométrica. En él se realizan tres clasificaciones de máquina vector-soporte basadas en un conjunto concreto de variables. El primer conjunto contiene las estadísticas anuales de la retrodispersión (media y desviación típica anual), el segundo añade la coherencia a largo plazo (separación temporal mayor a un año), el tercero incluye las estadísticas de la coherencia a corto plazo (mínima separación temporal). Utilizar variables basadas en la coherencia aumenta el acierto de la clasificación hasta un 5% y reduce los errores de omisión de la cobertura forestal. El cuarto capítulo evalúa la posibilidad de detectar talas selectivas utilizando datos de Sentinel-1 y Sentinel-2. Sus resultados muestran que la detección resulta muy difícil debido a la saturación de los sensores y la confusión introducida por el efecto de la fenología. El quinto capítulo se centra en la clasificación de tipos de bosque basado en una serie temporal de datos Sentinel-1. Se basa en la creación de un conjunto de modelos que describen la relación entre la retrodispersión y el ángulo local de incidencia para un determinado tipo de bosque y fecha concreta. Para cada píxel se calcula el residuo respecto al modelo de cada uno de los tipos de bosque, acumulando dichos residuos a lo largo de la serie temporal. Hecho esto, cada píxel es asignado al tipo de bosque que acumula un menor residuo. Los resultados son prometedores, mostrando que frondosas y coníferas tienen un comportamiento distintivo, y que es posible separar ambos tipos de bosque con un alto grado de acierto. El sexto capítulo está dedicado a la estimación de biomasa utilizando datos Sentinel-1, ALOS PALSAR y regresión Random Forest. Se obtiene un error similar para ambos sensores a pesar de utilizar una banda diferente (band-C vs. -L), con poca reducción en el error cuando ambas bandas se utilizan conjuntamente. Sin embargo, el ajuste de un estimador adaptado a las condiciones locales de Rumanía sí ofreció una reducción de del error al ser comparado con las estimaciones globales de biomasa

Growing stock volume estimation in temperate forsted areas using a fusion approach with SAR Satellites Imagery

Forest monitoring plays a central role in the context of global warming mitigation and in the assessment of forest resources. To meet these challenges, significant efforts have been made by scientists to develop new feasible remote sensing techniques for the retrieval of forest parameters. However, much work remains to be done in this area, in particular in establishing global assessments of forest biomass. In this context, this Ph.D. Thesis presents a complete methodology for estimating Growing Stock Volume (GSV) in temperate forested areas using a fusion approach based on Synthetic-Aperture Radar (SAR) satellite imagery. The investigations which were performed focused on the Thuringian Forest, which is located in Central Germany. The satellite data used are composed of an extensive set of L-band (ALOS PALSAR) and X-band (TerraSAR-X, TanDEM-X, Cosmo-SkyMed) images, which were acquired in various sensor configurations (acquisition modes, polarisations, incidence angles). The available ground data consists of a forest inventory delivered by the local forest offices. Weather measurements and a LiDAR DEM complete the datasets. The research showed that together with the topography, the forest structure and weather conditions generally limited the sensitivity of the SAR signal to GSV. The best correlations were obtained with ALOS PALSAR (R2 = 0.61) and TanDEM-X (R2 = 0.72) interferometric coherences. These datasets were chosen for the retrieval of GSV in the Thuringian Forest and led with regressions to an root-mean-square error (RMSE) in the range of 100─200 m3ha-1. As a final achievement of this thesis, a methodology for combining the SAR information was developed. Assuming that there are sufficient and adequate remote sensing data, the proposed fusion approach may increase the biomass maps accuracy, their spatial extension and their updated frequency. These characteristics are essential for the future derivation of accurate, global and robust forest biomass maps

Remote Sensing and Geosciences for Archaeology

This book collects more than 20 papers, written by renowned experts and scientists from across the globe, that showcase the state-of-the-art and forefront research in archaeological remote sensing and the use of geoscientific techniques to investigate archaeological records and cultural heritage. Very high resolution satellite images from optical and radar space-borne sensors, airborne multi-spectral images, ground penetrating radar, terrestrial laser scanning, 3D modelling, Geographyc Information Systems (GIS) are among the techniques used in the archaeological studies published in this book. The reader can learn how to use these instruments and sensors, also in combination, to investigate cultural landscapes, discover new sites, reconstruct paleo-landscapes, augment the knowledge of monuments, and assess the condition of heritage at risk. Case studies scattered across Europe, Asia and America are presented: from the World UNESCO World Heritage Site of Lines and Geoglyphs of Nasca and Palpa to heritage under threat in the Middle East and North Africa, from coastal heritage in the intertidal flats of the German North Sea to Early and Neolithic settlements in Thessaly. Beginners will learn robust research methodologies and take inspiration; mature scholars will for sure derive inputs for new research and applications

Continuous Monitoring of Environmental Disturbances by Cumulative Sums of Dense SAR Satellite Timeseries

Climate change together with growing socio-economic pressures are leading to a significant increase in alterations to natural ecosystems. The alteration of natural cycles and dynamics through the direct destruction or continuous degradation, are threatening the conservation of these natural spaces on a global scale. Satellite remote sensing is a suitable solution for large-scale monitoring and evaluation of natural landscapes under threat, as it provides a consistent source of information for both historical and updated environmental studies. However, most current remote sensing-based environmental monitoring tools still present certain limitations which hinder access to continuous and real-time information. The design and development of new methods and approaches to environmental remote sensing is required to mitigate the current environmental degradation trends. This thesis analyses the current challenges associated with environmental monitoring to focus on the development of new change detection methods applied to the study of environmental disturbances in highly dynamic natural ecosystems. By exploiting the frequent monitoring capabilities of Synthetic Aperture Radar (SAR) dense timeseries, this research introduces new approaches based on Cumulative Sum (CuSum) strategies for continuous and near-real-time investigation. These approaches have been applied to monitor permanent and cyclical disturbances in highly threatened forest and wetland ecosystems. The main scientific contribution of this thesis is the introduction of three novel SAR-based change detection approaches capable of exploiting dense satellite imagery time series for continuous and near-real-time monitoring. The outcome of this research provides environmental managers with a fully operational alternative tool capable of rapid and continuous monitoring of environmental dynamics