Comparison of Machine Learning Methods Applied to SAR Images for Forest Classification in Mediterranean Areas

In this paper, multifrequency synthetic aperture radar (SAR) images from ALOS/PALSAR, ENVISAT/ASAR and Cosmo‐SkyMed sensors were studied for forest classification in a test area in Central Italy (San Rossore), where detailed in‐situ measurements were available. A preliminary discrimination of the main land cover classes and forest types was carried out by exploiting the synergy among L‐, C‐ and X‐bands and different polarizations. SAR data were preliminarily inspected to assess the capabilities of discriminating forest from non‐forest and separating broadleaf from coniferous forests. The temporal average backscattering coefficient (°) was computed for each sensor‐polarization pair and labeled on a pixel basis according to the reference map. Several classification methods based on the machine learning framework were applied and validated considering different features, in order to highlight the contribution of bands and polarizations, as well as to assess the classifiers’ performance. The experimental results indicate that the different surface types are best identified by using all bands, followed by joint L‐ and X‐ bands. In the former case, the best overall average accuracy (83.1%) is achieved by random forest classification. Finally, the classification maps on class edges are discussed to highlight the misclassification errors

A novel application of satellite radar data: measuring carbon sequestration and detecting degradation in a community forestry project in Mozambique

Background: It is essential that systems for measuring changes in carbon stocks for Reducing Emissions from Deforestation and Forest Degradation (REDD) projects are accurate, reliable and low cost. Widely used systems involving classifying optical satell

A Bayesian approach to combine Landsat and ALOS PALSAR time series for near real-time deforestation detection

To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat- and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series

Mapping Spatial Variations of Structure and Function Parameters for Forest Condition Assessment of the Changbai Mountain National Nature Reserve

Forest condition is the baseline information for ecological evaluation and management. The National Forest Inventory of China contains structural parameters, such as canopy closure, stand density and forest age, and functional parameters, such as stand volume and soil fertility. Conventionally forest conditions are assessed through parameters collected from field observations, which could be costly and spatially limited. It is crucial to develop modeling approaches in mapping forest assessment parameters from satellite remote sensing. This study mapped structure and function parameters for forest condition assessment in the Changbai Mountain National Nature Reserve (CMNNR). The mapping algorithms, including statistical regression, random forests, and random forest kriging, were employed with predictors from Advanced Land Observing Satellite (ALOS)-2, Sentinel-1, Sentinel-2 satellite sensors, digital surface model of ALOS, and 1803 field sampled forest plots. Combined predicted parameters and weights from principal component analysis, forest conditions were assessed. The models explained spatial dynamics and characteristics of forest parameters based on an independent validation with all r values above 0.75. The root mean square error (RMSE) values of canopy closure, stand density, stand volume, forest age and soil fertility were 4.6%, 33.8%, 29.4%, 20.5%, and 14.3%, respectively. The mean assessment score suggested that forest conditions in the CMNNR are mainly resulted from spatial variations of function parameters such as stand volume and soil fertility. This study provides a methodology on forest condition assessment at regional scales, as well as the up-to-date information for the forest ecosystem in the CMNNR

Remote Sensing of Savannas and Woodlands

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome

Satellite monitoring of harmful algal blooms (HABs) to protect the aquaculture industry

Harmful algal blooms (HABs) can cause sudden and considerable losses to fish farms, for example 500,000 salmon during one bloom in Shetland, and also present a threat to human health. Early warning allows the industry to take protective measures. PML's satellite monitoring of HABs is now funded by the Scottish aquaculture industry. The service involves processing EO ocean colour data from NASA and ESA in near-real time, and applying novel techniques for discriminating certain harmful blooms from harmless algae. Within the AQUA-USERS project we are extending this capability to further HAB species within several European countries

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

Applications of Satellite Earth Observations section - NEODAAS: Providing satellite data for efficient research

The NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) provides a central point of Earth Observation (EO) satellite data access and expertise for UK researchers. The service is tailored to individual users’ requirements to ensure that researchers can focus effort on their science, rather than struggling with correct use of unfamiliar satellite data

Métodos de classificação de imagens de satélite para delineamento de banhados

As Áreas Úmidas (AUs) são ecossistemas de importância global, que apresentam altos níveis de diversidade ecológica e produtividade primária e secundária. Os Banhados são um tipo de AU, característicos nos estados do Sul do Brasil, no Uruguai e na Argentina. O delineamento e classificação desses ecossistemas é uma tarefa árdua, dada as características estruturais hidrológicas, de solos, de cobertura vegetal e espectrais. No estado Rio Grande do Sul os Banhados são considerados Áreas de Preservação Permanente, porém, não há um inventário e tampouco um delineamento desses ambientes. Deste modo, o objetivo destatese é comparar diferentes métodos baseados em sensoriamento remoto ativo e passivo e aprendizado de máquina(AP)para o delineamento de Banhados. Para isto, utilizamos três abordagens: i) aplicação de índices espectrais de sensoriamento remoto e árvore de decisão; ii) integração de imagens SAR de dupla e quádrupla polarização em bandas C e L e árvore de decisão; e, iii) análise multisensor (ativo e passivo), Geobia e diferentes classificadores. Nossos resultados mostram que os índices espectrais de sensoriamento remoto apresentaram acurácias entre 77,9% e 95,9%; a aplicação de imagens SAR resultou em acurácias entre 56,1% e 72,9%, ambos pelo algoritmo Árvore de Decisão. Para a abordagem multisensor utilizando Geobia e diferentes classificadores, as acurácias variaram entre 95,5% e 98,5%, sendo que, o k-NN foi o algoritmo que apresentou maior acurácia entre os modelos avaliados, demonstrando o potencial da análise multisensor (ativo e passivo) e doaprendizado de máquinapara o delineamento e classificação de Banhados. Adotamos como estudo de caso um Banhado localizado no Sul do Brasil, porém recomendamos que devido as semelhanças hidrológicas, estruturais e espectrais desses ambientes, essas metodologias possam ser aplicadas em outras áreas de Banhados (marshes).Wetlands are ecosystems of global importance, with high levels of ecological diversity and primary and secondary productivity.Marshes are a type of wetland characteristic of the southernBrazil, Uruguay and Argentina.The delineationand classification of these ecosystems is an arduous task, given the hydrological structure, soil, vegetation and spectral characteristics.In the Rio Grande do Sul state, marshesare considered Permanent Preservation Areas, however, there is no inventory and no delineationof these environments.Thus, the aim of this thesis is to compare different active and passive remote sensing based methodsand machine learningfor the delineationof marshes. For this, we use three approaches: i) application of spectral indices of remote sensing and decision tree; ii) integration of dual and quad-poll SAR images in C and L-bands and decision tree, and iii) multisensor analysis (active and passive), Geobia and different classification methods. Our results show that the spectral indexes of remote sensing presented accuracy between 77.9% and 95.9%; the application of SAR images resulted in accuracy between 56.1% and 72.9%, both using the Decision Tree algorithm. For the multisensor approach using Geobia and different classifiers, the accuracy varied between 95.5% to 98.5%, k-NN was the algorithm that showed greater accuracy among the models evaluated, demonstrating the potential of the multisensor analysis (activeand passive) and machine learningfor marshesdelineation and classification. Our study was carried out in a marsh located in the southernBrazil, however due to the hydrological, structural and spectral similarities of these environments, the methodologies can be applied in other marshes area

Using satellite remote sensing to quantify woody cover and biomass across Africa

The goal of quantifying the woody cover and biomass of tropical savannas, woodlands and forests using satellite data is becoming increasingly important, but limitations in current scientific understanding reduce the utility of the considerable quantity of satellite data currently being collected. The work contained in this thesis reduces this knowledgegap, using new field data and analysis methods to quantify changes using optical, radar and LiDAR data. The first paper shows that high-resolution optical data (Landsat & ASTER) can be used to track changes in woody vegetation in the Mbam Djerem National Park in Cameroon. The method correlates a satellite-derived vegetation index with field-measured canopy cover, and the paper concludes that forest encroached rapidly into savanna in the region from 1986-2006. Using the same study area, but with radar remote sensing data from 1996 and 2007 (ALOS PALSAR & JERS-1), the second paper shows that radar backscatter correlates well with field-measured aboveground biomass (AGB). This dataset confirms the woody encroachment within the park; however, in a larger area around the park, deforestation dominates. The AGB-radar relationships described above are expanded in the next paper to include field plots from Budongo Forest (Uganda), the Niassa Reserve (north Mozambique), and the Nhambita Community Project (central Mozambique). A consistent AGB-radar relationship is found in the combined dataset, with the RMSE for predicted AGB values for a site increasing by <30 %, compared with a site-specific equation, when using an AGB-radar equation derived from the three other sites. The study of the Nhambita site is extended in the following paper to assess the ability of radar to detect change over short time periods in this environment, as will be needed for REDD (Reducing Emissions from Deforestation and Degradation). Using radar mosaics from 2007 and 2009, areas known (from detailed ground data) to have been degraded decreased in AGB in the radar change detection, whereas areas of agroforestry and forest protection showed small increases