Using earth observation satellites to explore forest dynamics across large areas

A third of the land on earth is covered by forests. Forests provide valuable resources and essential ecosystem services, including filtering air and water, harbouring biodiversity and managing the carbon cycle. Regular monitoring and reporting across various indicators is necessary to manage forests sustainably. Due to the vastness of forests, satellite Earth observation is one of the most practical and cost-effective ways to monitor forests. The regular and consistent measurements provided from space enable time series analysis, which can reveal trends over time. The temporal, spatial and radiometric depth of the Landsat archive, which extends back to 1972 in some cases, is one of the most useful resources for monitoring forest dynamics across large areas. Analysing forest disturbance and recovery trends using Landsat has recently become widespread, particularly since the opening of the image archive in 2008. However, deriving useful information from the data is challenging on many fronts, including overcoming cloud-cover, differentiating true changes from noise and relating spectral measurements to meaningful outputs. In addition, large data volumes create hurdles for processing and storage. This study presents new techniques for exploiting the Landsat archive in relation to monitoring and measuring forest disturbance and recovery across large areas. Landsat data were processed through a series of steps, analysed in time series, and combined with other data sources to produce mapped outputs and statistical summaries, which can be interpreted by non-experts. The spatial extent of the analysis expands across multiple scales - from local and regional to global (temperate and boreal forests). Firstly, eight Landsat spectral indices were assessed to determine their sensitivity to forest disturbance (caused by wildfire) and recovery in southeast Australian forests. Results indicated that indices making use of the shortwave infrared wavelengths were more reliable indicators of forest disturbance and recovery than indices using only the red and near-infrared wavelengths. Following this exploratory analysis, three indices and two change detection algorithms were evaluated in terms of their ability to detect forest disturbance. Results showed that the LandTrendr algorithm with the Normalised Burn Ratio (NBR) was the most accurate single algorithm/index combination (overall error 21%). However, results were greatly improved by using an ensemble approach. A Random Forests model combining several Landsat-derived metrics with multiple indices, trained with human interpreted reference data, had an overall error of 7%. A notable finding was that priming the training data with confusing cases (commission errors from the change detection algorithms) led to increased accuracy. One Random Forests model was used to create annual forest disturbance maps (1989-2017) across the state of Victoria, Australia. These maps, in conjunction with each pixel's temporal trajectory, were used to extract metrics for spectral disturbance magnitude and recovery length across 2 million ha of burned forest in southeast Australia. The association between disturbance magnitude and forest recovery length, as measured spectrally, was then explored. A novel patch-based technique was used to isolate the disturbance-recovery relationship from confounding factors such as climate, elevation and soil type. The results showed statistically significant differences across bioregions and forest types. The patch-based method demonstrated how Landsat time series can be harnessed to explore ecological changes. The methods developed above were then employed over a much larger area, to investigate trends in fire disturbance and forest recovery in temperate and boreal forests worldwide. This work used both MODIS and Landsat data, through the Google Earth Engine platform, to look at trends in burned area, fire severity and forest recovery across almost 2 billion ha of forests, over the last 18 years. Burned area results showed significant increasing trends in two cases: coniferous forests in Canada and Mediterranean forests in Chile. A significant decreasing trend was found in temperate mixed forests in China. An assessment of fire severity, as measured by Landsat spectral change, highlighted possible trends in a few cases; most notably, the Russian taiga, where increasing severity was observed. An analysis of forest recovery, based on Landsat time series, indicated recovery times were accelerating in many regions. However, given the relatively short time-period analysed, these results should be interpreted with caution. The results presented in this thesis demonstrate the power of Earth observation satellites in monitoring forests at the landscape scale. Although forests are complex systems that are influenced by a myriad of factors, the regular and consistent measurements provided by satellites can be analysed in time series to provide inter-comparable results across large areas. This can broaden our understanding of the dynamic nature of forests, and in doing so, help progress towards their sustainable management

Development of a global burned area mapping algorithm for moderate spatial resolution optical sensors

La tesis doctoral titulada “Development of a global burned area mapping algorithm for moderate spatial resolution optical sensors” propone el desarrollo de un algoritmo de detección de área quemada global para sensores ópticos de resolución espacial moderada. El trabajo ha sido financiado y desarrollado bajo los proyectos Fire Disturbance (FireCCI) del programa Climate Change Initiative (CCI) de la European Space Agency (ESA) y el Copernicus Climate Change Service (C3S) de la European Commission (EC). El autor de este trabajo también ha recibido financiación del Ministerio de Ciencia, Innovación y Universidades, a través de una beca FPU. Cuando se propuso esta tesis solo había un único producto global de área quemada que ofrecía una serie temporal larga y consistente. Se trataba del producto MCD64A1 de la National Aeronautics and Space Administration (NASA) que se generaba operacionalmente y que proveía información de área quemada a nivel global a 500 m desde noviembre del 2000. Por la parte europea solo había dos productos, el FireCCI41 y el GIO_GL1_BA, pero se trataba de productos que o bien ofrecían una serie temporal demasiado reducida (FireCCI41) o bien una serie con baja fiabilidad. En cualquier caso, los tres productos, incluido el MCD64A1, presentaban limitaciones que les hacían estar lejos de cumplir los requerimientos establecidos por los usuarios en términos de errores de comisión y omisión. Es en este contexto donde se plantea esta tesis que pretende avanzar en el conocimiento de los algoritmos de área quemada globales y la generación de productos globales que cumplan o se acerquen de forma más significativa a las expectativas de los usuarios. Para este propósito, se ha utilizado información proveniente de sensores que no se habían utilizado hasta el momento para generar productos de área quemada globales. Esta información incluye las bandas de alta resolución a 250 m del Moderate Resolution Imaging Spectroradiometer (MODIS), las bandas del Ocean and Land Colour Instrument (OLCI) y del SYNERGY, así como fuegos activos de MODIS y del Visible Infrared Imaging Radiometer Suite (VIIRS). En este último caso, ha sido la primera vez que se utilizan globalmente para generar este tipo de productos. Así, se han desarrollado cuatro algoritmos y se han generado sus respectivos productos de área quemada a escala global. Cada uno de ellos ha jugado un papel complementario al resto, ya sea a modo de versión mejorada o como adaptación de un mismo algoritmo a distintos sensores. Todos los productos derivados han sido validados globalmente y se han llevado a cabo comparaciones exhaustivas con otros productos existentes. Además, para confirmar la estabilidad de los patrones espacio temporales, los productos se han aplicado para dar respuesta a distintas preguntas científicas relacionadas con las anomalías en las tendencias del área quemada en distintas partes del mundo. Para explicar todo este proceso la tesis se ha estructurado en ocho capítulos: introducción, seis publicaciones en revistas internacionales y unas conclusiones

A review of carbon monitoring in wet carbon systems using remote sensing

Carbon monitoring is critical for the reporting and verification of carbon stocks and change. Remote sensing is a tool increasingly used to estimate the spatial heterogeneity, extent and change of carbon stocks within and across various systems. We designate the use of the term wet carbon system to the interconnected wetlands, ocean, river and streams, lakes and ponds, and permafrost, which are carbon-dense and vital conduits for carbon throughout the terrestrial and aquatic sections of the carbon cycle. We reviewed wet carbon monitoring studies that utilize earth observation to improve our knowledge of data gaps, methods, and future research recommendations. To achieve this, we conducted a systematic review collecting 1622 references and screening them with a combination of text matching and a panel of three experts. The search found 496 references, with an additional 78 references added by experts. Our study found considerable variability of the utilization of remote sensing and global wet carbon monitoring progress across the nine systems analyzed. The review highlighted that remote sensing is routinely used to globally map carbon in mangroves and oceans, whereas seagrass, terrestrial wetlands, tidal marshes, rivers, and permafrost would benefit from more accurate and comprehensive global maps of extent. We identified three critical gaps and twelve recommendations to continue progressing wet carbon systems and increase cross system scientific inquiry

Hybrid modeling of aboveground biomass carbon using disturbance history over large areas of boreal forest in eastern Canada

Le feu joue un rôle important dans la succession de la forêt boréale du nord-est de l’Amérique et le temps depuis le dernier feu (TDF) devrait être utile pour prédire la distribution spatiale du carbone. Les deux premiers objectifs de cette thèse sont: (1) la spatialisation du TDF pour une vaste région de forêt boréale de l'est du Canada (217,000 km2) et (2) la prédiction du carbone de la biomasse aérienne (CBA) à l’aide du TDF à une échelle liée aux perturbations par le feu. Un modèle non paramétrique a d’abord été développé pour prédire le TDF à partir d’historiques de feu, des données d'inventaire et climatiques à une échelle de 2 km2. Cette échelle correspond à la superficie minimale d’un feu pour être inclus dans la base de données canadienne des grands feux. Nous avons trouvé un ajustement substantiel à l’échelle de la région d’étude et à celle de paysages régionaux, mais la précision est restée faible à l’échelle de cellules individuelles de 2 km2. Une modélisation hiérarchique a ensuite été développée pour spatialiser le CBA des placettes d’inventaire à la même échelle de 2 km2. Les proportions des classes de densité du couvert étaient les variables les plus importantes pour prédire le CBA. Le CBA co-variait également avec la vitesse de récupération du couvert au travers de laquelle le TDF intervient indirectement. Finalement, nous avons comparé des estimations de CBA obtenues par télédétection satellitaire avec celles obtenues précédemment. Les résultats indiquent que les proportions des classes de densité du couvert et des types de dépôts ainsi que le TDF pourraient servir comme variables auxiliaires pour augmenter substantiellement la précision des estimés de CBA par télédétection. Les résultats de cette étude ont montré: 1) l'importance d’allonger la profondeur temporelle des historiques de feu pour donner une meilleure perspective des changements actuels du régime de feu; 2) l'importance d'intégrer l’information sur la reprise du couvert après feu aux courbes de rendement de CBA dans les modèles de bilan de carbone; et 3) l'importance de l'historique des feux et de la récupération de la végétation pour améliorer la précision de la cartographie de la biomasse à partir de la télédétection.Fire is as a main succession driver in northeastern American boreal forests and time since last fire (TSLF) is seen as a useful covariate to infer the spatial variation of carbon. The first two objectives of this thesis are: (1) to elaborate a TSLF map over an extensive region in boreal forests of eastern Canada (217,000 km2) and (2) to predict aboveground carbon biomass (ABC) as a function of TSLF at a scale related to fire disturbances. A non-parametric model was first developed to predict TSLF using historical records of fire, forest inventory data and climate data at a 2-km2 scale. Two kilometer square is the minimum size for fires to be considered important enough and included in the Canadian large fire database. Overall, we found a substantial agreement at the scale of both the study area and landscape units, but the accuracy remained fairly low at the scale of individual 2-km2 cells. A hierarchical modeling approach is then presented for scaling-up ABC from inventory plots to the same 2 km2 scale. The proportions of cover density classes were the most important variables to predict ABC. ABC was also related to the speed of post-fire canopy recovery through which TSLF acts indirectly upon ABC. Finally, we compared remote sensing based aboveground biomass estimates with our inventory based estimates to provide insights on improving their accuracy. The results indicated again that abundances of canopy cover density classes of surficial deposits, and TSLF may serve as ancillary variables for improving substantially the accuracy of remotely sensed biomass estimates. The study results have shown: 1) the importance of lengthening the historical records of fire records to provide a better perspective of the actual changes of fire regime; 2) the importance of incorporating post-fire canopy recovery information together with ABC yield curves in carbon budget models at a spatial scale related to fire disturbances; 3) the importance of adding disturbance history and vegetation recovery trends with remote sensing reflectance data to improve accuracy for biomass mapping

Global Forest Monitoring from Earth Observation

Covering recent developments in satellite observation data undertaken for monitoring forest areas from global to national levels, this book highlights operational tools and systems for monitoring forest ecosystems. It also tackles the technical issues surrounding the ability to produce accurate and consistent estimates of forest area changes, which are needed to report greenhouse gas emissions and removals from land use changes. Written by leading global experts in the field, this book offers a launch point for future advances in satellite-based monitoring of global forest resources. It gives readers a deeper understanding of monitoring methods and shows how state-of-art technologies may soon provide key data for creating more balanced policies

Effects of large fires on boreal forests of China : historical reconstruction and future prediction through landscape modeling

Includes vita.Boreal forests of China store about 350 Tg tree biomass carbon, which is approximately 24–31 [percent] of the total forest carbon storage in China, and thus, play an important role in maintain national carbon balance. Long-term fire exclusion and climate warming have foster larger and more severe fires. On 1987 May 6, a catastrophic fire, known as the Black Dragon Fire, occurred in this region, and burned 1.3 million ha. This fire is among the top five of such megafires ever recorded in the world, resulting in high degree of tree mortality and reset forest succession stage for most burned stands. Forests have grown back since, with much more homogeneous age classes and composition, which post new ecological risks and challenges. It is predicted that the warming will continue in the next century, and thus uncertainties exist in future fire regimes and vegetation response under novel climate. Chapter II estimate the burn severity and carbon emissions from the Black Dragon fire. I combined field and remote sensing data to map four burn severity classes and calculated combustion efficiency in terms of the biomass immediately consumed in the fire. Results of this chapter showed that 1.30 million hectares burned and 52 [percent] of that area burned with high severity. The emitted carbon dioxide equivalents (CO2e), accounted for approximately 10 [percent] of total fossil fuel emissions from China in 1987, along with CO (2 [percent] - 3 [percent] of annual anthropogenic CO emissions from China) and non-methane hydrocarbons (NMHC) contributing to the atmospheric pollutants. This study provides an important basis for carbon emission estimation and understanding the impacts of megafires. Chapter III developed a novel framework to spatially reconstruct the post-fire time-series of forest conditions after the 1987 Black Dragon fire of China by integrating a forest landscape model (LANDIS) with remote sensing and inventory data. I derived pre-fire (1985) forest composition and the megafire perimeter and severity using remote sensing and inventory data. I simulated the megafire and the post-megafire forest recovery from 1985-2015 using the LANDIS model. I calibrated the model and validated the simulation results using inventory data. I demonstrated that the framework was effective in reconstructing the post-fire stand dynamics and that it is applicable to other types of disturbances. Chapter IV investigated the effects of future fire regimes on boreal forests of China under a warming climate. I simulated species composition and distribution changes to the year 2100 using a coupled forest dynamic model (LANDIS PRO) and ecosystem process model (LINKAGES). I focused on two possible fire regimes (frequent small fires and infrequent large fires). Results of this chapter showed that climate warming and fires strongly affected tree species composition and distribution in the boreal forests of China. Climate warming promoted transitions from boreal species to pioneer and temperate species. Fire effects acted in the same direction as climate change effects on species occurrences, thereby catalyzing climate-induced transitions. Frequent small fires exerted stronger effects on the species composition shifts than infrequent large fires. The combined effects of climate warming and fire on the shifts in species composition will accumulate through time and space and can induce a complete transition of forest type, and alter forest dynamics and functions.Includes bibliographical reference

Integrated global assessment of the natural forest carbon potential

Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system 1. Remote-sensing estimates to quantify carbon losses from global forests 2–5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced 6 and satellite-derived approaches 2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea 2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets

Integrated global assessment of the natural forest carbon potential

Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system1. Remote-sensing estimates to quantify carbon losses from global forests2–5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced6 and satellitederived approaches2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.EEA Santa CruzFil: Mo, Lidong. Institute of Integrative Biology. ETH Zurich (Swiss Federal Institute of Technology); SuizaFil: Zohner, Constantin M. Institute of Integrative Biology. ETH Zurich (Swiss Federal Institute of Technology); SuizaFil: Reich, Peter B. University of Minnesota. Department of Forest Resources; Estados UnidosFil: Reich, Peter B. Western Sydney University. Hawkesbury Institute for the Environment; Australia.Fil: Reich, Peter B. University of Michigan. Institute for Global Change Biology; Estados UnidosFil: Liang, Jingjing. Purdue University. Department of Forestry and Natural Resources; Estados UnidosFil: de-Miguel, Sergio. University of Lleida. Department of Agricultural and Forest Sciences and Engineering; EspañaFil: de-Miguel, Sergio. Joint Research Unit CTFC - AGROTECNIO – CERCA; EspañaFil: Nabuurs, Gert-Jan. Wageningen University and Research; Países BajosFil: Renner, Susanne S. Washington University. Department of Biology; Estados UnidosFil: van den Hoogen, Johan. Institute of Integrative Biology. ETH Zurich (Swiss Federal Institute of Technology); SuizaFil: Araza, Arnan. Wageningen University and Research; Países BajosFil: Herold, Martin. Helmholtz GFZ German Research Centre for Geosciences. Remote Sensing and Geoinformatics Section; Alemania.Fil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral.; Argentina.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Crowther, Thomas W. Institute of Integrative Biology. ETH Zurich (Swiss Federal Institute of Technology); Suiz

Carbon Consequences of Forest Disturbance and Recovery Across the Conterminous United States

Forests of North America are thought to constitute a significant long term sink for atmospheric carbon. The United States Forest Service Forest Inventory and Analysis (FIA) program has developed a large data base of stock changes derived from consecutive estimates of growing stock volume in the US. These data reveal a large and relatively stable increase in forest carbon stocks over the last two decades or more. The mechanisms underlying this national increase in forest stocks may include recovery of forests from past disturbances, net increases in forest area, and growth enhancement driven by climate or fertilization by CO2 and Nitrogen. Here we estimate the forest recovery component of the observed stock changes using FIA data on the age structure of US forests and carbon stocks as a function of age. The latter are used to parameterize forest disturbance and recovery processes in a carbon cycle model. We then apply resulting disturbance/recovery dynamics to landscapes and regions based on the forest age distributions. The analysis centers on 28 representative climate settings spread about forested regions of the conterminous US. We estimate carbon fluxes for each region and propagate uncertainties in calibration data through to the predicted fluxes. The largest recovery-driven carbon sinks are found in the South central, Pacific Northwest, and Pacific Southwest regions, with spatially averaged net ecosystem productivity (NEP) of about 100 g C / square m / a driven by forest age structure. Carbon sinks from recovery in the Northeast and Northern Lake States remain moderate to large owing to the legacy of historical clearing and relatively low modern disturbance rates from harvest and fire. At the continental scale, we find a conterminous U.S. forest NEP of only 0.16 Pg C/a from age structure in 2005, or only 0.047 Pg C/a of forest stock change after accounting for fire emissions and harvest transfers. Recent estimates of NEP derived from inventory stock change, harvest, and fire data show twice the NEP sink we derive from forest age distributions. We discuss possible reasons for the discrepancies including modeling errors and the possibility of climate and/or fertilization (CO2 or N) growth enhancements