Spatial and temporal statistics of SAR and InSAR observations for providing indicators of tropical forest structural changes due to forest disturbance

Tropical forests are extremely important ecosystems which play a substantial role in the global carbon budget and are increasingly dominated by anthropogenic disturbance through deforestation and forest degradation, contributing to emissions of greenhouse gases to the atmosphere. There is an urgent need for forest monitoring over extensive and inaccessible tropical forest which can be best accomplished using spaceborne satellite data. Currently, two key processes are extremely challenging to monitor: forest degradation and post-disturbance re-growth. The thesis work focuses on these key processes by considering change indicators derived from radar remote sensing signal that arise from changes in forest structure. The problem is tackled by exploiting spaceborne Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) observations, which can provide forest structural information while simultaneously being able to collect data independently of cloud cover, haze and daylight conditions which is a great advantage over the tropics. The main principle of the work is that a connection can be established between the forest structure distribution in space and signal variation (spatial statistics) within backscatter and Digital Surface Models (DSMs) provided by SAR. In turn, forest structure spatial characteristics and changes are used to map forest condition (intact or degraded) or disturbance. The innovative approach focuses on looking for textural patterns (and their changes) in radar observations, then connecting these patterns to the forest state through supporting evidence from expert knowledge and auxiliary remote sensing observations (e.g. high resolution optical, aerial photography or LiDAR). These patterns are descriptors of the forest structural characteristics in a statistical sense, but are not estimates of physical properties, such as above-ground biomass or canopy height. The thesis tests and develops methods using novel remote sensing technology (e.g. single-pass spaceborne InSAR) and modern image statistical analysis methods (wavelet-based space-scale analysis). The work is developed on an experimental basis and articulated in three test cases, each addressing a particular observational setting, analytical method and thematic context. The first paper deals with textural backscatter patterns (C-band ENVISAT ASAR and L-band ALOS PALSAR) in semi-deciduous closed forest in Cameroon. Analysis concludes that intact forest and degraded forest (arising from selective logging) are significantly different based on canopy structural properties when measured by wavelet based space-scale analysis. In this case, C-band data are more effective than longer wavelength L-band data. Such a result could be explained by the lower wave penetration into the forest volume at shorter wavelength, with the mechanism driving the differences between the two forest states arising from upper canopy heterogeneity. In the second paper, wavelet based space-scale analysis is also used to provide information on upper canopy structure. A DSM derived from TanDEM-X acquired in 2014 was used to discriminate primary lowland Dipterocarp forest, secondary forest, mixed-scrub and grassland in the Sungai Wain Protection Forest (East Kalimantan, Indonesian Borneo) which was affected by the 1997/1998 El Niño Southern Oscillation (ENSO). The Jeffries- Matusita separability of wavelet spectral measures of InSAR DSMs between primary and secondary forest was in some cases comparable to results achieved by high resolution LiDAR data. The third test case introduces a temporal component, with change detection aimed at detecting forest structure changes provided by differencing TanDEM-X DSMs acquired at two dates separated by one year (2012-2013) in the Republic of Congo. The method enables cancelling out the component due to terrain elevation which is constant between the two dates, and therefore the signal related to the forest structure change is provided. Object-based change detection successfully mapped a gradient of forest volume loss (deforestation/forest degradation) and forest volume gain (post-disturbance re-growth). Results indicate that the combination of InSAR observations and wavelet based space-scale analysis is the most promising way to measure differences in forest structure arising from forest fires. Equally, the process of forest degradation due to shifting cultivation and post-disturbance re-growth can be best detected using multiple InSAR observations. From the experiments conducted, single-pass InSAR appears to be the most promising remote sensing technology to detect forest structure changes, as it provides three-dimensional information and with no temporal decorrelation. This type of information is not available in optical remote sensing and only partially available (through a 2D mapping) in SAR backscatter. It is advised that future research or operational endeavours aimed at mapping and monitoring forest degradation/regrowth should take advantage of the only currently available high resolution spaceborne single-pass InSAR mission (TanDEM-X). Moreover, the results contribute to increase knowledge related to the role of SAR and InSAR for monitoring degraded forest and tracking the process of forest degradation which is a priority but still highly challenging to detect. In the future the techniques developed in the thesis work could be used to some extent to support REDD+ initiatives

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

Editorial for the Special Issue "SAR for Forest Mapping II"

First Paragraph: As vital natural resources, forests are of extreme importance for all living beings on our planet. They play a key role in controlling climate change; represent essential sources of energy (e.g., biomass), food, jobs, and livelihoods; and serve as a natural habitat to a large variety of animal species, which is essential for biodiversity preservation. Forest ecosystems are constantly shaped and changed by physical and biological disturbances, and eventual regeneration processes. As an example, forest degradation is currently occurring at an alarming rate, and it often occurs due to illegal anthropogenic activities such as logging and fires. Sensitive environments have been irreversibly damaged, with critical environmental and economic consequences at regional as well as global scales. Precise and efficient assessment and monitoring of forest resources, treatments, and recreational opportunities are therefore of crucial importance in order to develop early warning systems. In this scenario, synthetic aperture radar (SAR) remote sensing represents a unique technique to provide high-resolution images independently of daylight and in almost any weather conditions. In the past few decades, SAR imaging has demonstrated its suitability for forest mapping applications. The combination of polarimetric, interferometric, and/or tomographic information further increases its capabilities and its products’ accuracy

The Changing Matrix: Reforestation and Connectivity in a Tropical Habitat Corridor

In the last two decades, export-oriented crops and timber and fruit plantations have joined small-scale cultivation and pasture as important causes of tropical deforestation. Widespread conversion of tropical forest to agriculture threatens to isolate protected areas, which has led to efforts to maintain functional connectivity in landscapes between protected areas. Relatively few "landscape conservation" efforts have been assessed for their effect on deforestation, but advances in remote sensing now permit detailed monitoring of tropical land uses over time, including mapping of tree crops and plantations. This dissertation evaluates the long-term impact of forest conservation and reforestation policies on tropical forests in a habitat corridor. The following chapters test the capability of remote sensing to monitor tropical conservation efforts and assess whether landscape conservation policies can maintain forest cover and connectivity in the face of rapid agricultural expansion. Costa Rica has one of the most comprehensive landscape conservation policies in the tropics: a 1996 Forest Law banned deforestation and expanded payments for environmental services (PES) to protect forests and plant trees, prioritizing designated habitat corridors between protected areas. The long-term effect of the program on land-use transitions is not well known. To take advantage of this regional policy experiment, I used a time-series of five moderate-resolution Landsat images to track land-use change from 1986 to 2011in the oldest habitat corridor, the San Juan-La Selva Biological Corridor (SJLSBC). Forest conservation policies were associated with a 40% decline in deforestation after 1996 despite a doubling in the area of cropland in the last decade. The proportion of cropland derived from mature forest dropped from 16.4% to 1.9% after 1996, while one fifth of pasture expansion continued to be derived from mature forest. These results suggest that forest conservation policies can successfully lower deforestation, and that they can be more effective with large export producers than small-scale cattle producers. Tree plantations are an important component of Costa Rican PES, but knowledge of their distribution and contribution to connectivity in the corridor region is poor. After reviewing the remote sensing literature, I employed a novel integration of hyperspectral images and a Landsat time-series to create the first regional map of tropical tree plantation species. Including multitemporal data significantly improved overall hyperspectral map accuracy to 91%; the six tree plantation species were classified with 83% mean producer's accuracy. Non-native species made up 89% of tree plantations, and they were cleared more rapidly than native tree plantations and secondary forests. I combined existing land cover maps, field behavioral experiments, and a graph connectivity model to estimate whether landscape conservation policies increased connectivity for understory insectivorous birds, a representative forest-dependent group. The field playback experiments indicated both native and exotic tree plantations with a dense shrubby understory were acceptable dispersal habitat for all species, and that birds traveled readily near secondary forest edges but rarely into forested pasture. Graph model parameters were informed by these results. For all of these bird species, functional connectivity declined by 14-21% with only a 4.9% decline in forest area over time, implying that conservation policies have not caused a net increase in functional connectivity in the SJLSBC region. Despite making up 2% of the region, tree plantations had little effect on regional connectivity because of their placement in the landscape; we demonstrate that spatially-targeted reforestation of 0.1% of the region could increase connectivity by 1.8%. Collectively, the results presented in these chapters underline the potential and limitations of landscape conservation policies and corridor plans in the tropics; combining regulations and PES can lower deforestation over the medium-term, but increased enforcement, improved monitoring with remote sensing, and targeted conservation effort is needed to combat illegal deforestation and restore functional connectivity. Given numerous new tropical corridor and PES programs and the qualified successes of landscape conservation policies in Costa Rica and other tropical countries, our approach to the analysis can be applied to monitor and evaluate connectivity across the tropics

Monitoring and quantifying forest degradation: remote sensing approaches for applied conservation in the Congo Basin

Wälder spielen global eine entscheidende Rolle bei der Regulierung des Weltklimas, da sie aktiv Kohlenstoff speichern und binden. Trotz der Bemühungen durch internationale Programme nehmen die Waldschäden weiter zu. Entwaldung und Walddegradierung sind zwei unterschiedliche Prozesse, die sich auf die globalen Wälder auswirken. Entwaldung ist eine klar definierte Umwandlung oder Abholzung der Waldflächen, während Degradierung subtiler, vorübergehend und variabel sein kann und daher schwer zu detektieren ist. Walddegradierung wird im Allgemeinen als eine funktionale Verringerung der Fähigkeit von Wäldern Ökosystemleistungen zu erbringen identifiziert. Sie wird nicht als Veränderung der Landbedeckung oder Entwaldung klassifiziert. Daraus folgt keine deutliche Verringerung der Waldfläche, sondern eher eine Abnahme der Qualität und des Zustands. Diese Veränderung kann, wie die Entwaldung dennoch mit einer signifikanten Verringerung der oberirdischen Biomasse und damit miterheblichen Treibhausgasemissionen verbunden sein. Die Schätzungen der Kohlenstoffemissionen aus Waldstörungen liegen zwischen 12 und 20 % aller weltweit emittierten Emissionen. Durch eine fehlende einheitliche Definition oder Methode zur Quantifizierung der Degradation, der Vielzahl an Einflussfaktoren und der Unsicherheit bei der Schätzung der Biomasse variieren die Werte stark. Die von der Walddegradierung betroffene Fläche könnte in der Tat viel größer sein als die der Entwaldung, die ohnehin jedes Jahr auf eine Fläche von etwa der Größe Islands geschätzt wird. Die REDD+-Mechanismen zur Finanzierung von Emissionsreduktionen zur Minderung des Klimawandels erfordern robuste, transparente und skalierbare Methoden zur Quantifizierung der Walddegradierung, zusammen mit der Erfassung der damit verbundenen Treibern. Da die Degradierung oft der Entwaldung vorausgeht, kann ein schnelles Monitoring mit einer Beurteilung der Waldschäden und ihren Treibern ein wichtiges Frühwarnsystem sein. Nur so können Maßnahmen frühzeitig ergriffen werden, die die Wälder schützen und sowohl der Natur und der Biodiversität als auch dem Lebensunterhalt, der Gesundheit und dem Wohlbefinden von Millionen von Menschen auf der ganzen Welt zugute kommen. In dieser Arbeit werden Methoden für konsistente, reproduzierbare, skalierbare und satellitengestützte Indikatoren zur Identifizierung und Quantifizierung verschiedener Arten von Walddegradation um zukünftige Risiko- und Politikszenarien zu unterstützen.Global forests play a crucial role in regulating global climate by actively storing and sequestering carbon. Despite efforts to mitigate climate through international efforts, human-caused forest disturbance and forest-related greenhouse gas emissions continue to rise. Deforestation and forest degradation are two different processes affecting global forests. Deforestation is a clearly defined conversion or removal of forest cover, while degradation can be more subtle, temporary, variable, and therefore difficult to detect. Forest degradation is generally identified as a functional reduction in the capacity of forests to provide ecosystem services, that does not qualify as a change in land cover or forest clearing. That means no clear reduction of the forest area, but rather a decrease in quality and condition. This change, like deforestation can still be associated with significant reductions in above-ground biomass and therefore considerable greenhouse gas emissions. Estimates of carbon emissions from forest degradation and disturbance range anywhere from 12-20% of all emissions emitted globally with values varying widely because of a lack of uniform definition or method for quantifying degradation, the broad number of influencing factors, and uncertainty in biomass estimates. The area affected by forest degradation could in fact be much larger than that of deforestation, which is already estimated to be an area about the size of Iceland every year. The REDD+ mechanisms of financing emissions reductions to mitigate climate change require robust, transparent and scalable methods for quantifying degradation, along with a quantification of associated direct drivers. Furthermore, as degradation often precedes deforestation, timely monitoring and assessment of forest degradation and changes in drivers can provide crucial early warning to engage interventions to keep forests intact, benefitting nature and biodiversity as well as the livelihoods, health and well-being of millions of people around the world. This research proposes methods for consistent, repeatable and scalable satellite-derived indicators for identifying and quantifying different types of forest degradation and its causes to inform future risk and policy scenarios