In this thesis, I analyze the potential for a REDD+ program to succeed in Uganda at this time, and I explore why this may or may not be possible. REDD+, which stands for Reducing Deforestation and Forest Degradation, aims to enhance carbon stocks while also conserving and sustainably managing forests. In doing so, REDD+ not only works to offset carbon emission levels and conserve forests and forest biodiversity, but also provides financial benefits to REDD+ participant countries, thus improving the livelihoods of local people living adjacent to forests. This program is widely regarded as the most effective and least risky solution to deforestation and forest degradation in developing countries, however, I argue that this may not be the case. Government documents reveal a rather simplistic and idealistic view of the policy, its implementation process, and desired outcomes, while specific case studies in countries outside of Uganda in which the REDD+ program has been implemented or is in the process of being implemented reveal unaddressed concerns with the mechanism itself and within the surrounding communities. Based on my research, I believe that if the recommendations that I propose are not included in the REDD+ preparation and implementation phases, the REDD+ mechanism is not only predestined to fail, but also to harm the most at-risk stakeholders that it is meant to benefit.

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.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第20448号 / 農博第2233号 / 新制||農||1050(附属図書館) / 学位論文||H29||N5069(農学部図書室) / 京都大学大学院農学研究科森林科学専攻 / (主査)教授 神﨑 護, 教授 北島 薫, 教授 柴田 昌三 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

Indigenous Peoples and REDD+: A Critical Perspective / Indigenous Peoples' Biocultural Climate Change Assessment Initiative (IPCCA) / November 2014 / Public Political Ecology Lab / Executive Summary: REDD+ stands for Reducing Emissions from Deforestation and forest Degradation in developing countries (REDD) and includes conservation, sustainable forest management and the enhancement of carbon stocks (the +). An international initiative negotiated under the United Nations Framework Convention on Climate Change (UNFCCC), REDD+ has been proposed as a central strategy for mitigating climate change in forests. While advocates highlight the cost effectiveness and social and ecological co-benefits that can be generated through REDD+, many indigenous and forest dependent groups have expressed concerns about the potential effects of projects on their access to land and resources. This report identifies key issues facing indigenous and forest-dependent communities with respect to REDD, and is based on existing academic literature and more current reports by NGOs and indigenous organizations. We first lay out a brief history of REDD+, interrogate its key assumptions, and discuss major issues of concern. We then discuss REDD+ as it relates to indigenous peoples and forest-dependent communities. This is followed by a series of case studies of developing countries participating in REDD+. We conclude with a discussion of the principal elements for an alternative vision for REDD+ that takes seriously the rights of indigenous peoples. / / Resumen Ejecutivo REDD+ es acrónimo para Reducing Emissions from Deforestation and forest Degradation (Reducir las Emisiones por Deforestación y Degradación de bosques) (en países en desarrollo). Incluye acciones para la conservación, el manejo sustentable de bosques y el aumento en los suministros de carbono (el +). Como una iniciativa internacional negociada bajo el United Nations Framework Convention on Climate Change (UNFCCC) (Convención Marco de Naciones Unidas sobre el Cambio Climático), REDD+ ha sido propuesto como una estrategia principal para mitigar el cambio climático en bosques. Aunque sus defensores enfatizan la eficacia financiera y los co-beneficios sociales y ecológicos que se pueden generar a través del REDD+, muchos grupos indígenas y de gente quien depende de bosques para su sustento tienen preocupaciones acerca de los posibles efectos de los proyectos sobre el acceso a la tierra y los recursos forestales. Este informe identifica los problemas principales que enfrentan las comunidades indígenas y gente dependiente de bosques con REDD. Está basado en la literatura académica existente y otros reportes actuales escritos por organizaciones no gubernamentales (ONGs) y organizaciones indígenas. Primero proveeremos una historia breve de REDD+, evaluaremos las suposiciones principales y discutiremos los problemas de mayor preocupación. Luego describiremos la relación entre REDD+ y los pueblos indígenas y comunidades dependientes de bosques. Esto será seguido por una colección de casos de estudio en los países en desarrollo que participan en REDD+. Concluiremos con una discusión de los elementos principales para una visión alternativa de REDD+ que toma en cuenta los derechos de los pueblos indígenas.

Various biotic and abiotic agents are changing forests. Prolonged human activities substantially could cause not only different changes in forests but also could accelerate natural hazards in the Anthropocene. Despite several remote sensing-based research in forest changes, there is a need for a holistic study that could visualize different dimensions of anthropogenic-induced forest changes such as forest loss, forest fragmentation, and forest degradation. Besides, the effects of these changes require to be investigated in the natural hazards’ studies in forest regions. This research was accomplished for holistic assessing of long-term forest loss, forest fragmentation, and forest degradation induced by human activities such as sprawling residential areas and expanding road networks in northeast Iran. Moreover, it has investigated the significance of forest dynamics in analyzing of landslide susceptibility in the forest regions. The time series of Landsat data with the contribution of aerial photos were employed to investigate long-term forest changes in three spans from 1966 to 2016. The expansion of forest roads was extracted from a combination of satellite images and topographic maps. Both pixel– and object-based approaches were used for analyzing forest changes. The spatial autocorrelation indicators and spatial regression models were applied for visualizing patterns of forest changes and possible relationships between forest changes and the expansion of residential areas and road networks. Furthermore, the detection of old and new landslide events was accomplished through Sentinel-1 and -2 images and DEM derivatives using object-oriented random forest method. The significance of conditioning and triggering factors that control the susceptibility of protected and non-protected forests to landslides was explored using the object-based random forest approach as well. Key findings revealed that the expansion of residential areas and rural roads have increasingly heightened the rates of forest loss before 2000. However, the spatial patterns of forest dynamics were changed from forest loss to forest fragmentation and forest degradation– along with the expansion of forest and mine roads– since the 1980s. Although the topographic and hydrologic features were the top influential predictors that control the susceptibility of protected forests to landslides, the natural and anthropogenic triggers have obtained significant values in non-protected forests to the landslides as well; forest fragmentation and logging were the top features of anthropogenic triggers. This research verifies that influential variables are different either for detecting landslides or for assessing landslide susceptibility in different forest regions. The spatial-based regression models showed higher efficiency than the traditional regression model for modelling relationships between forest changes and anthropogenic- induced drivers; however, there was no priority between spatial models. Random forest algorithm demonstrated satisfactory accuracy for mapping of both old and new landslides and landslide susceptibility with higher accuracy in the protected forests. This research has investigated human-induced forest changes; however, other abiotic and biotic agents may cause these changes such as climate hazards, forest fires, insect outbreaks, pathogens, and other natural hazards that need to be explored in the future studies. / Verschiedene biotische und abiotische Faktoren verursachen Veränderungen im Wald. Dauerhafte menschliche Eingriffe im Anthropozän könnten nicht nur zu unterschiedlichen Typen von Veränderungen im Wald selbst führen, sondern auch bestehende Naturgefahren verstärken. Trotz verschiedener fernerkundungsgestützter Forschungsarbeiten zu Waldveränderungen besteht Bedarf an einer holistischen Studie, welche verschiedene Dimensionen anthropogen verursachter Waldveränderungen wie Waldverlust, Waldfragmentierung und Waldschädigung aufzeigen kann. Außerdem ist es notwendig, die Auswirkungen derartiger Veränderungen in Naturgefahrenstudien für Waldgebiete zu untersuchen. Ziel dieser Forschung war es, eine holistische Bewertung von langfristigem Waldverlust, Waldfragmentierung und Waldschädigung durchzuführen, die durch menschliche Aktivitäten wie Ausbreitung von Siedlungsgebieten und Ausbau von Straßennetzen im Nordosten des Iran verursacht werden. Darüber hinaus hat diese Forschungsarbeit die Bedeutung der Walddynamik in der Analyse von Rutschungsneigung innerhalb von Waldgebieten untersucht. Um langfristige Waldveränderungen in drei Intervallen zwischen 1966 und 2016 zu untersuchen, wurden Zeitreihen von Landsat-Daten und zusätzlich von Luftbildern verwendet. Die Erweiterung der Waldwege wurde aus einer Kombination von Satellitenbildern und topographischen Karten extrahiert. Für die Analyse von Waldveränderungen wurden sowohl pixel- als auch objektbasierte Ansätze verwendet. Räumliche Autokorrelationsindikatoren und räumliche Regressionsmodelle wurden eingesetzt, um Muster von Waldveränderungen und Zusammenhänge zwischen Waldveränderungen und der Erweiterung von Wohngebieten und Straßennetzen zu visualisieren. Darüber hinaus wurde die Erkennung alter und neuer Erdrutsche aus Sentinel-1 und -2 Bildern und DEM-Derivaten unter Verwendung der objektorientierten „Random Forest “-Methode durchgeführt. Ebenfalls mit dem objektbasierten „Random Forest “-Ansatz wurde die Bedeutung von Konditionierungs- und Auslösefaktoren untersucht, welche die Suszeptibilität einer Fläche für Erdrutsche in geschützten und nicht geschützten Wäldern kontrollieren. Die zentralen Erkenntnisse sind, dass die Ausbreitung von Siedlungsflächen und der Ausbau von Landstraßen die Waldverluste vor dem Jahr 2000 zunehmend erhöht haben. Mit dem Ausbau von Wald- und Bergbaustraßen änderten sich jedoch seit den 1980er Jahren die räumlichen Muster der Walddynamik von Waldverlust hin zu Waldfragmentierung und Walddegradierung. Obwohl die orographischen und hydrologischen Merkmale die wichtigsten Einflussfaktoren hinsichtlich Suszeptibilität für das Auftreten von Erdrutschen in Schutzwäldern waren, haben weitere natürliche und anthropogene Auslöser Signifikanz erreicht innerhalb der nicht geschützten Waldflächen: Holzeinschlag und Waldfragmentierung waren dabei die dominierenden anthropogenen Auslöser. Die Studie bestätigt zudem, dass die Parameter für die Erkennung von Erdrutschen und für die Beurteilung der Rutschungs-Suszeptibilität an die verschiedenen Waldgebiete anzupassen sind. In der Modellierung der Beziehung zwischen Waldveränderungen und anthropogenen Einflüssen zeigten räumlich basierte Regressionsmodelle eine höhere Effizienz als das traditionelle Regressionsmodell; allerdings gab es keine klare Priorität innerhalb der räumlichen Modelle. Der „Random Forest “-Ansatz zeigte eine zufriedenstellende Genauigkeit sowohl bei der Kartierung historischer Erdrutsche als auch in der Bestimmung der Rutschungs-Suszeptibilität. Dabei lag die erreichte Genauigkeit in den geschützten Waldgebieten höher. Diese Forschungsarbeit hat vom Menschen verursachte Waldveränderungen untersucht. Da jedoch auch andere abiotische und biotische Faktoren die geschilderten Veränderungen verursachen können, sind weitere Studien notwendig. Diese könnten z. B. Klimaparameter, Waldbrände, Insektenschädigung und andere Naturgefahren einschließen.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第19775号 / 農博第2171号 / 新制||農||1041(附属図書館) / 学位論文||H28||N4991(農学部図書室) / 32811 / 京都大学大学院農学研究科森林科学専攻 / (主査)教授 神﨑 護, 教授 柴田 昌三, 教授 大澤 晃, 外国人教師 鄭 克聲 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

Forest degradation is a significant yet underestimated source of carbon emissions. Traditionally, monitoring forest degradation has been difficult due to a lack of sufficiently frequent satellite observations and reliable analysis methods. Recent advancements in satellite remote sensing provide new opportunities to monitor, attribute and analyze forest degradation. This dissertation develops methods to monitor and attribute forest degradation and analyzes the spatial-temporal patterns of forest degradation and associated carbon emissions. A new method, Continuous Change Detection and Classification - Spectral Mixture Analysis (CCDC-SMA), was developed on Google Earth Engine (GEE) to monitor abrupt and gradual forest degradation in temperate climate zones using Landsat time series. CCDC-SMA was applied to the Republic of Georgia from 1987-2019. Results show that forest degradation affected a much larger area than deforestation. In addition, CCDC-SMA was extended to monitor forest degradation in the tropics and applied in Laos. Attribution of the drivers of forest degradation was based on a combination of CCDC-SMA results, post-disturbance land cover classification and object-based image analysis. Shifting cultivation is the largest kind of forest disturbance in Laos, affecting 32.9% ± 1.9% of Laos during 1991-2020. The results show that shifting cultivation has been expanding and intensifying in Laos, especially in the last five years. Furthermore, the length of fallow periods has been continuously declining, which indicates that shifting cultivation is becoming increasingly unsustainable. Combining biomass estimates from the Global Ecosystem Dynamics Investigation (GEDI) and area estimates of shifting cultivation, the net carbon emissions from shifting cultivation during 1991-2020 in Laos are 1.28 ± 0.12 petagrams of CO2 equivalent (Pg CO2 eq). Tree canopy height and aboveground biomass density are strongly correlated with the years of regrowth since the latest year of slash-and-burn activities, which can be expressed using logarithmic models. It takes 131 years for the biomass to recover to pre-disturbed levels based on the logarithmic models. In addition to advancements in remote sensing of forest degradation, the results of this dissertation provide valuable information for policy related to forest management and reduction of carbon emissions.

Forest degradation causes environmental damage and carbon emissions, but its extent and magnitude are not well understood. New methods for monitoring forest degradation and deforestation show that more disturbance has occurred in the Amazon in recent decades than previously realized, indicating an unaccounted for source of carbon emissions and damage to Amazon ecosystems. Forest degradation and natural disturbance change a landscape, but the visible damage apparent in satellite images may be temporary and difficult to differentiate from undisturbed forests. Time series analysis of Landsat data used in a spectral mixture analysis improves monitoring of forest degradation and natural disturbance. In addition, the use of statistical inference accounts for classification bias and provides an estimate of uncertainty. Application of the methodology developed in this dissertation to the Amazon Ecoregion found that forest degradation and natural disturbance were more prevalent than deforestation from 1995 to 2017. Of consequence, the total area of forest in the Amazon that has been recently disturbed is greater than previously known. Overall, deforestation affected 327,900 km2 (±15,500) of previously undisturbed forest in the Amazon while degradation and natural disturbance affected 434,500 km2 (±22,100). Forest degradation and natural disturbance occur more frequently during drought years, which have increased in frequency and severity in recent years. Deforestation has largely decreased since 2004, while forest degradation and natural disturbance have remained consistent. Previously disturbed forests are lower in biomass than undisturbed forests, yet regeneration after disturbance gradually sequesters carbon. A carbon flux model shows that gross aboveground carbon loss from forest degradation and natural disturbance and deforestation from 1996 to 2017 in the Amazon were 2.2-2.8 Pg C and 3.3-4.3 Pg C, respectively. Since 2008, however, carbon loss from degradation and natural disturbance has been approximately the same as from deforestation. The methodologies developed in this dissertation are useful for monitoring deforestation and degradation throughout the world’s forest ecosystems. By leveraging dense data time series, statistical inference, and carbon modeling it is possible to quantify areas of deforestation and forest degradation in addition to the resulting carbon emissions. The results of this dissertation stress the importance of degradation and natural disturbance in the global carbon cycle and information valuable for climate science and conservation initiatives.

Forest degradation and deforestation have been pervasive problems in Indonesia and the country is placed second in terms of a high rate of tropical deforestation. Different definitions and different techniques have been used to quantify forest degradation and deforestation in Indonesia, and different factors have been detected as direct and underlying causes. However, almost no quantitative studies have been conducted to relate deforestation and forest degradation to the causes. This study quantifies the rate of forest degradation and deforestation between 2000 and 2009 in three provinces of Indonesia, South Kalimantan, East Kalimantan and South East Sulawesi, as a case study. A geographic information system (GIS) was used to generate geographic datasets to allow quantification; accompanied by a descriptive statistical analysis. Land cover in 2000 and 2009 was used as the basis of analysis. A national land use classification was aggregated into 10 different land use classes. Changes in land use between 2000 and 2009 allowed quantification of the rates of forest degradation and deforestation as well as the association between degradation and deforestation and potential causes. Overall, 95% of primary forest degradation in South Kalimantan, 65% in East Kalimantan, and 46% in SE Sulawesi were associated with direct causes such as forest concessions, mining activities and the government-sponsored transmigration program. The selected direct causes explain 56% of secondary forest deforestation in South Kalimantan, 44% in East Kalimantan and 55% in SE Sulawesi. Results of this study also show that forest degradation and deforestation occurred across the official forest areas. Almost 40% of forest degradation in South Kalimantan and East Kalimantan occurred in conservation forests and production forests respectively; while 57% of forest degradation in SE Sulawesi occurred in protected forests. Deforestation occurred 29% and 34% in the production forests of South Kalimantan and East Kalimantan respectively. In SE Sulawesi, 34% of deforestation occurred in watershed protection forests. This study suggests that current practice in the forest resources management in Indonesia is not effective and efficient enough to mitigate and to halt forest degradation and deforestation. This is due to the lack of consistency in implementing forest land use policy, mistaken policy interventions, and non-synchronised policy between central and local governments. Policy reform is needed to conserve the remaining forest resources and mega-diversity of Indonesia.

La dégradation de la forêt est un changement de sa structure et de la composition floristique et faunistique, ce qui conduit à une perte de biodiversité, de production de biens et de services et à un accroissement de la vulnérabilité aux aléas climatiques et aux incendies. Elle concerne de vastes espaces en zone tropicale particulièrement dans les régions de fronts pionniers plus ou moins consolidés où la forêt primaire est soumise à l’extraction de bois, aux incendies et à la fragmentation. Pour évaluer son ampleur et son intensité, il est nécessaire de recourir à la télédétection. Mais les méthodologies disponibles restent encore insuffisantes.L’enjeu scientifique est de développer des méthodes adaptées à de grandes surfaces afin d’analyser l’effet de différentes perturbations sur les trajectoires suivies par le couvert forestier. Il s’agit également de distinguer différentes intensités de dégradation suite à l’accumulation de perturbations. C’est un préalable indispensable pour définir et mettre en œuvre des plans de gestion adaptés. Le premier axe de ce travail a pour objectif de cartographier annuellement l’ampleur des perturbations, d’identifier les principaux types de perturbations et de caractériser la trajectoire de restauration de l’activité photosynthétique. Il est réalisé à partir de séries temporelles d’images Landsat traitées au moyen du progiciel CLASlite. L’agrégation des couvertures annuelles résultant des traitements avec CLASlite a également permis de constituer un indicateur de dégradation résultant du cumul de processus de perturbations sur plusieurs années. / The forest degradation is a change of the structure and the composition of flora and fauna, which leads to a loss of biodiversity, of production of goods and services and an increased vulnerability to weather hazards and fires. This process concerns large areas in the tropics, particularly in agricultural frontier where primary forest is subject to timber extraction, fire and fragmentation. Remote sensing is used to assess the magnitude and the extent of forest degradation. However, the methodologies available are still insufficient. The scientific challenge is to develop methods adapted to large areas to analyze the effect of different disturbances on the trajectories followed by the forest cover. It is also to identify different intensities of degradation following disturbances events. This is a prerequisite for defining and implementing appropriate management plans. The first axis of this work aims to map annually the extent of the disturbances, to identify the main types of disturbances and to characterize the restoration trajectory of the photosynthetic activity. This work is based on time series of Landsat images processed using CLASlite software. The aggregation of the annual coverages resulting from treatments with CLASlite also made it possible to constitute an indicator of degradation resulting from the accumulation of disturbance processes over several years. The second axis aims to evaluate the evolution of the forest sensitivity to drought as a function of its degradation and to build a degradation indicator. The approach uses MODIS images and TRMM precipitation data. This work is implemented in the municipality of Paragominas (state of Pará, Brazil).