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

Relationship between aquarius L-band active and passive multi-year observations over Australia

This PFC is focused in evaluating the feasibility of doing a combined changed algorithm to simplify the process of low resolution downscaling using high resolution.The aim of this Thesis is to further our understanding of the geophysical information that can be estimated from active and passive L-band sensors. All data was obtained from NASA's satellite Aquarius durin the period Sept. 2011- August 2014

Synergism of optical and radar data for forest structure and biomassSinergismo entre dados ópticos e de radar da estrutura da floresta e biomassa

AbstractThe structure of forests, the three-dimensional arrangement of individual trees, has a profound effect on how ecosystems function and carbon cycle, water and nutrients. Repeated optical satellite observations of vegetation patterns in two-dimensions have made significant contributions to our understanding of the state and dynamics of the global biosphere. Recent advances in Remote Sensing technology allow us to view the biosphere in three-dimensions and provide us with refined measurements of horizontal as well as vertical structure of forests. This paper provides an overview of the recent advances in fusion of optical and radar imagery in assessing terrestrial ecosystem structure and aboveground biomass. In particular, the paper will focus on radar and LIDAR sensors from recent and planned spaceborne missions and provide theoretical and practical applications of the measurements. Finally, the relevance of these measurements for reducing the uncertainties of terrestrial carbon cycle and the response of ecosystems to future climate will be discussed in details. ResumoA estrutura de florestas, o arranjo tridimensional de árvores individuais, tem um efeito profundo sobre o funcionamento dos ecossistemas e do ciclo do carbono, água e nutrientes. Repetidas observações de satélite óptico de padrões de vegetação em duas dimensões trouxeram contribuições significativas para a nossa compreensão do estado e da dinâmica da biosfera global. Recentes avanços na tecnologia de Sensoriamento Remoto nos permitem ver a biosfera em três dimensões e nos fornecer medições apuradas da estrutura horizontal, bem como a vertical das florestas. Esse artigo fornece uma visão geral dos recentes avanços na fusão de imagens ópticas e de radar para avaliar a estrutura do ecossistema terrestre e biomassa. Em particular, o trabalho concentra-se em sensores radar e LIDAR de recentes missões espaciais planejadas e fornece aplicações teóricas e praticas das medições. Por fim, a relevância dessas medidas para reduzir as incertezas do ciclo de carbono terrestre e de resposta dos ecossistemas ao clima no futuro será discutida em detalhes

Biomass Representation in Synthetic Aperture Radar Interferometry Data Sets

This work makes an attempt to explain the origin, features and potential applications of the elevation bias of the synthetic aperture radar interferometry (InSAR) datasets over areas covered by vegetation. The rapid development of radar-based remote sensing methods, such as synthetic aperture radar (SAR) and InSAR, has provided an alternative to the photogrammetry and LiDAR for determining the third dimension of topographic surfaces. The InSAR method has proved to be so effective and productive that it allowed, within eleven days of the space shuttle mission, for acquisition of data to develop a three-dimensional model of almost the entire land surface of our planet. This mission is known as the Shuttle Radar Topography Mission (SRTM). Scientists across the geosciences were able to access the great benefits of uniformity, high resolution and the most precise digital elevation model (DEM) of the Earth like never before for their a wide variety of scientific and practical inquiries. Unfortunately, InSAR elevations misrepresent the surface of the Earth in places where there is substantial vegetation cover. This is a systematic error of unknown, yet limited (by the vertical extension of vegetation) magnitude. Up to now, only a limited number of attempts to model this error source have been made. However, none offer a robust remedy, but rather partial or case-based solutions. More work in this area of research is needed as the number of airborne and space-based InSAR elevation models has been steadily increasing over the last few years, despite strong competition from LiDAR and optical methods. From another perspective, however, this elevation bias, termed here as the “biomass impenetrability”, creates a great opportunity to learn about the biomass. This may be achieved due to the fact that the impenetrability can be considered a collective response to a few factors originating in 3D space that encompass the outermost boundaries of vegetation. The biomass, presence in InSAR datasets or simply the biomass impenetrability, is the focus of this research. The report, presented in a sequence of sections, gradually introduces terminology, physical and mathematical fundamentals commonly used in describing the propagation of electromagnetic waves, including the Maxwell equations. The synthetic aperture radar (SAR) and InSAR as active remote sensing methods are summarised. In subsequent steps, the major InSAR data sources and data acquisition systems, past and present, are outlined. Various examples of the InSAR datasets, including the SRTM C- and X-band elevation products and INTERMAP Inc. IFSAR digital terrain/surface models (DTM/DSM), representing diverse test sites in the world are used to demonstrate the presence and/or magnitude of the biomass impenetrability in the context of different types of vegetation – usually forest. Also, results of investigations carried out by selected researchers on the elevation bias in InSAR datasets and their attempts at mathematical modelling are reviewed. In recent years, a few researchers have suggested that the magnitude of the biomass impenetrability is linked to gaps in the vegetation cover. Based on these hints, a mathematical model of the tree and the forest has been developed. Three types of gaps were identified; gaps in the landscape-scale forest areas (Type 1), e.g. forest fire scares and logging areas; a gap between three trees forming a triangle (Type 2), e.g. depending on the shape of tree crowns; and gaps within a tree itself (Type 3). Experiments have demonstrated that Type 1 gaps follow the power-law density distribution function. One of the most useful features of the power-law distributed phenomena is their scale-independent property. This property was also used to model Type 3 gaps (within the tree crown) by assuming that these gaps follow the same distribution as the Type 1 gaps. A hypothesis was formulated regarding the penetration depth of the radar waves within the canopy. It claims that the depth of penetration is simply related to the quantisation level of the radar backscattered signal. A higher level of bits per pixels allows for capturing weaker signals arriving from the lower levels of the tree crown. Assuming certain generic and simplified shapes of tree crowns including cone, paraboloid, sphere and spherical cap, it was possible to model analytically Type 2 gaps. The Monte Carlo simulation method was used to investigate relationships between the impenetrability and various configurations of a modelled forest. One of the most important findings is that impenetrability is largely explainable by the gaps between trees. A much less important role is played by the penetrability into the crown cover. Another important finding is that the impenetrability strongly correlates with the vegetation density. Using this feature, a method for vegetation density mapping called the mean maximum impenetrability (MMI) method is proposed. Unlike the traditional methods of forest inventories, the MMI method allows for a much more realistic inventory of vegetation cover, because it is able to capture an in situ or current situation on the ground, but not for areas that are nominally classified as a “forest-to-be”. The MMI method also allows for the mapping of landscape variation in the forest or vegetation density, which is a novel and exciting feature of the new 3D remote sensing (3DRS) technique. Besides the inventory-type applications, the MMI method can be used as a forest change detection method. For maximum effectiveness of the MMI method, an object-based change detection approach is preferred. A minimum requirement for the MMI method is a time-lapsed reference dataset in the form, for example, of an existing forest map of the area of interest, or a vegetation density map prepared using InSAR datasets. Preliminary tests aimed at finding a degree of correlation between the impenetrability and other types of passive and active remote sensing data sources, including TerraSAR-X, NDVI and PALSAR, proved that the method most sensitive to vegetation density was the Japanese PALSAR - L-band SAR system. Unfortunately, PALSAR backscattered signals become very noisy for impenetrability below 15 m. This means that PALSAR has severe limitations for low loadings of the biomass per unit area. The proposed applications of the InSAR data will remain indispensable wherever cloud cover obscures the sky in a persistent manner, which makes suitable optical data acquisition extremely time-consuming or nearly impossible. A limitation of the MMI method is due to the fact that the impenetrability is calculated using a reference DTM, which must be available beforehand. In many countries around the world, appropriate quality DTMs are still unavailable. A possible solution to this obstacle is to use a DEM that was derived using P-band InSAR elevations or LiDAR. It must be noted, however, that in many cases, two InSAR datasets separated by time of the same area are sufficient for forest change detection or similar applications

Constraints on tree growth, impacts of tropical cyclones and outcomes of community management in the Miombo woodlands

The Miombo woodlands of southern Africa are a globally significant store of carbon (C) and biodiversity. They also provide services for more than 150M people across several of the world’s most economically impoverished countries. The Miombo woodlands are dynamic, with extensive resource loss accompanied by areas of regrowth and increase. Disturbance processes, both from natural processes and widespread anthropogenic activities, are critical in maintaining woody biomass in these ecosystems, although intensity of disturbance varies widely. Increase in woody biomass has been observed in the Miombo, though the drivers of this trend are uncertain and the fundamental constraints on trees and woodlands not well understood. Ultimately, both losses and gains can be difficult to detect and hard to attribute to a particular cause. The aim of this thesis is to use field data and remote sensing to add to understanding of the constraints on tree populations in the Miombo and the impacts of severe environmental disturbances and management interventions on woodland structure. Tree growth is a crucial demographic rate in African woodlands and plays a key role in shaping woodland structure and C cycling. However, observations of tree growth are relatively lacking in the Miombo and the determinants of tree growth rates are poorly known. In Chapter 2 I use data collected from long-term monitoring of permanent sample plots in Mozambique and Tanzania and linear mixed modelling to estimate tree growth increments and assess the relative importance of different determinants of tree growth. The estimated growth (diameter increment) in these plots was 1.8 ± 0.17 mm/yr. Climate and edaphic factors explained little variation in tree growth. Tree-tree competition was found to be a significant constraint on growth (trees in experiencing competition levels in the top 5% of values grew 1.24 ± 0.08 mm/yr slower on average than those in the bottom 5%) as was stem wounding (wounded trees grew 0.84 ± 0.04 mm/yr slower). Root symbioses (both fungal and bacterial symbionts) which aid in the uptake of nutrients were found to have a strong positive impact on growth, particularly ectomycorrhizal associations which are common to dominant species in the Miombo. The impacts of tree-tree competition and nutrient symbioses are poorly represented in biogeochemical models in these ecosystems but this analysis suggests they are critical, whilst the subtle impacts of human interaction with trees (through wounding) are also possibly underappreciated. Tropical Cyclones can have substantial long-term impacts on woodland structure in affected areas and projections indicate that the impacts of Cyclones will increase in southeastern Africa over the coming century. There are few studies which have documented the immediate impacts or long-term responses of woodland ecosystems to this damage. In Chapter 3 I analyse data from a survey of eight permanent sample plots setup explicitly to assess the damage caused by Cyclone Idai to in woodlands in Gorongosa National Park, central Mozambique. It is found that Cyclone Idai caused damage primarily to large trees, thus whilst only 2% of trees were felled these individuals represented 8.5% of overall basal area. The implications of this damage are discussed in context of the constraints on trees in these woodlands, and whilst the damage is severe it is concluded that the outcomes are highly uncertain. Whilst damage from the cyclone is substantial, detecting change in woodland structure is challenging in these ecosystems. In Chapter 4 I explore the possibility of upscaling field observations of treefall occurrence using data from a small unmanned aerial vehicle (drone) and satellite radar. Drone survey produced comparable estimates of treefall intensity to the PSP observations (in terms of fallen number of stems, fallen basal area and carbon) and allowed survey of 155 ha, capturing widespread damage across the study area. In the study area radar backscattering intensity in C-Band radar reduced in the two years after the cyclone relative to the two years before whilst interferometric coherence increased - both in agreement with radar theory - although backscattering intensity in L-Band radar increased. Whilst significant relationships were identified between change in radar data and the intensity of damage in drone surveys, there appears to be limited ability to map variations in treefall intensity across the wider landscape using this method, or to determine areal impacts on above ground C thereafter. It is concluded that repeat analysis may yield better results however. In Tanzania, Village Land Forest Reserves (VLFRs, a form of participatory forest management) aim to promote sustainable profit from woodland resources, although the impact of VLFRs on land cover change rates is uncertain. In Chapter 5 I use satellite radar to map deforestation and a degradation across an area of southern Tanzania from 2010-2018 and statistical matching to compare rates of land cover change within a sample of VLFRs to woodlands under comparable resource pressure outside VLFRs or other protection status. It is found that VLFRs in the majority of cases were very effective in reducing deforestation (with five of seven having rates close to zero) and also reduced degradation rates (though to a lesser degree). Increasing density of woody biomass in forested areas was observed in all VLFRs, but varied widely across the sample (from 0.2 - 1.5 tC ha yr-1) and was in five of seven VLFRs below that observed in woodland areas with no protection status in this region (+0.7 tC ha yr-1). Whilst it appears that VLFR establishment achieved its intended goal of sustainable profit from woodlands resources from 2010-2018, further work is required to understand variation in outcome across the observed sample. This methodology however shows promise in continued assessment of VLFR performance for this purpose

Surface Soil Moisture Retrievals from Remote Sensing:Current Status, Products & Future Trends

Advances in Earth Observation (EO) technology, particularly over the last two decades, have shown that soil moisture content (SMC) can be measured to some degree or other by all regions of the electromagnetic spectrum, and a variety of techniques have been proposed to facilitate this purpose. In this review we provide a synthesis of the efforts made during the last 20 years or so towards the estimation of surface SMC exploiting EO imagery, with a particular emphasis on retrievals from microwave sensors. Rather than replicating previous overview works, we provide a comprehensive and critical exploration of all the major approaches employed for retrieving SMC in a range of different global ecosystems. In this framework, we consider the newest techniques developed within optical and thermal infrared remote sensing, active and passive microwave domains, as well as assimilation or synergistic approaches. Future trends and prospects of EO for the accurate determination of SMC from space are subject to key challenges, some of which are identified and discussed within. It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments. Given the importance of SMC in Earth’s land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today’s world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space

Estimating tropical forest above-ground biomass at the local scale using multi-source space-borne remote sensing data

Although forest biomass estimation has attracted a great number of studies using remote sensing data, its usage still contains high uncertainties. After transitioning from deforestation to reforestation under the development of Payments for Environmental Services (PES) programmes, young forests that are dominated by numerous small regenerating understory trees are found in many areas of many developing countries. However, the lack of analysis on the effect of this understory vegetation on total AGB is one the limitations of biomass studies. Moreover, it is always challenging to estimate the biomass of tropical forest due to its complex structure, high diversity of species, and dense canopy of understory trees. Taking into account these factors, this study, therefore, aims to investigate the effect of including understory trees in accuracy of AGB estimation in complex tropical heterogeneous forest at the local scale. The research conducted three consecutive experiments, using different remote sensing data sources, being: optical data, synthetic aperture radar (SAR) data and the integration of optical and SAR data, across various forest types in different test site locations. The results provide comprehensive insights into the impact of small regenerating trees on improving AGB estimation. This major finding alone demonstrates that the role of small regenerating trees should not be automatically discounted, especially for tropical forest where a number of different tree layers is common. This is especially important in areas with a large number of small regenerating trees and where open canopy layers are young. The thesis reveals that the level of influence of small regenerating trees on each forest type is different. Therefore, the study recommends an approach to including small regenerating trees for each forest type. This thesis argues there is a need to develop local-specific allometric equations for both overstory and understory layers to improve the accuracy of biomass models. Methods required for collecting field data and calculating biomass for small regenerating trees should be considered carefully in terms of evaluating cost-effective biomass estimation for each ecological region and each species. This requirement is most critical for young forest sites where there are mixtures of mature trees and young regenerating trees

Earth resources: A continuing bibliography with indexes (issue 59)

This bibliography lists 518 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1988. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, oceanography and marine resources, hydrology and water management, data processing and distribution systems, and instrumentation and sensors