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Cum laude graduation (with distinction).
Forests provide a large range of beneficial services, including tangible ones such as timber and recreation, and intangible services such as climate regulation, biodiversity, and watershed protection. On the other hand, forests can also be considered roadblocks to progress that occupy space more productively used for agriculture, making consideration of their regulating services crucial for balancing land use and forest loss. Monitoring forest cover and loss is critical for obtaining the data necessary to help define what is needed to maintain the varying forest service requirements in different parts of the world. There is an increasing need for timely and accurate forest change information, and consequently a greater interest in monitoring those changes. Global Forest Monitoring from Earth Observation covers the very recent developments undertaken for monitoring forest areas from global to national levels using Earth observation satellite data. It describes operational tools and systems for monitoring forest ecosystems, discussing why and how researchers currently use remotely sensed data to study forest cover and loss over large areas. The book introduces the role of forests in providing ecosystem services and the need for monitoring their change over time, followed by an overview of the use of earth observation data to support forest monitoring. It discusses general methodological differences, including wall-to-wall mapping and sampling approaches, as well as data availability. This book provides excellent coverage of the research and applications of forest monitoring, indicator mapping at coarse spatial resolution, sample-based assessments, and wall-to-wall mapping at medium spatial resolution using optical remote sensing datasets, such as MODIS and Landsat. It examines the use of radar imagery in forest monitoring and presents a number of operational systems, from Brazil’s PRODES and DETER products to Australia’s NCAS system. 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 global forest monitoring methods and shows how state-of-the-art technologies may soon provide key data for creating more balanced policies.
Improved monitoring of forest biomass is needed to quantify natural and anthropogenic effects on the terrestrial carbon cycle. Landsat's temporal and spatial coverage, fine spatial grain, and long history of earth observations provide a unique opportunity for measuring biophysical properties of vegetation across large areas and long time scales. However, like other multi-spectral data, the relationship between single-date reflectance and forest biomass weakens under certain canopy conditions. Because the structure and composition of a forest stand at any point in time is linked to the stand's disturbance history, one potential means of enhancing Landsat's spectral relationships with biomass is by including information on vegetation trends prior to the date for which estimates are desired. The purpose of this research was to develop and assess a method that links field data, airborne lidar, and Landsat-derived disturbance and recovery history for mapping of forest biomass and biomass change. Our study area is located in eastern Oregon (US), an area dominated by mixed conifer and single species forests. In Chapter 2, we test and demonstrate the utility of Landsat-derived disturbance and recovery metrics to predict current forest structure (live and dead biomass, basal area, and stand height) for 51 field plots, and compare the results with estimates from airborne lidar and single-date Landsat imagery. To characterize the complex nature of long-term (insect, growth) and short-term (fire, harvest) vegetation changes found in this area, we use annual Landsat time series between 1972 and 2010. This required integrating Landsat data from MSS (1972-1992) and TM/ETM+ (1982-present) sensors. In Chapter 2, we describe a method to bridge spectral differences between Landsat sensors, and therefore extent Landsat time-series analyses back to 1972. In Chapter 3, we extend and automate our approach and develop maps of current (2009) and historic (1993-2009) live forest biomass. We use lidar data for model training and evaluate the results with forest inventory data. We further conduct a sensitivity analysis to determine the effects of forest structure, time-series length, terrain and sampling design on model predictions. Our research showed that including disturbance and recovery trends in empirical models significantly improved predictions of forest biomass, and that the approach can be applied across a larger landscape and across time for estimating biomass change.
The growth of trees is a key ecological parameter of forests and thus of high importance as an indicator of forest condition in long-term forest monitoring. Forest growth can be easily and fairly inexpensively assessed on both intensive monitoring plots and large-scale plots. For intensive monitoring plots, we propose a hierarchical system of stem diameter measurements with simple manual growth assessments in regular multiannual intervals on all trees (periodic measurements), annual to weekly readings of permanently installed girth bands (permanent measurements) on a subset of trees, and electronically recorded dendrometer measurements with high-time resolution on a few selected trees (continuous measurements) for physiological measurements. For this, we describe possible plot layouts, sampling protocols for the trees, and measuring methods and instruments used, and give suggestions for data Quality Assurance, forest growth calculations, and data evaluations.
The demand for comparable, long-term, high quality data on forest ecosystems' status and changes is increasing at the international and global level. Yet, sources for such data are limited and in many case it is not possible to compare data from different monitoring initiatives across space and time because of methodological differences. Apart from technical manuals, there is no comprehensive multidisciplinary, scientific, peer-reviewed reference for forest monitoring methods that can serve and support the user community. This book provides in a single reference the state-of-the-art of monitoring methods as applied at the international level. The book present scientific concepts and methods that form the basis of the transnational, long-term forest monitoring in Europe and looks at other initiatives at the global level. Standardized methods that have been developed over two decades in international forest monitoring projects are presented. Emphasis is put on trans-nationally harmonized methods, related data quality issues, current achievements and on remaining open questions. A comprehensive overview of needs, requirements, organization and possible outcomes of an integrated monitoring program Tested and quality assured, internationally harmonized methodologies based on a complete revision of existing methods carried out in 2009-2011 Connection with monitoring results allows assessment of the potential of the monitoring method
Forest covers about 40 percent of the Earth's total land surface and is of tremendous ecological and economic value. Spatially explicit knowledge of forest composition and biophysical attributes is very important for monitoring forest development and for informing decisions for sustainable development. Landsat images have been widely used to map forest composition and estimate biophysical attributes but the accuracy is not yet satisfactory. This dissertation seeks to develop new approaches to produce high-quality seasonal Landsat time-series and then classify detailed forest types and model forest aboveground biomass (AGB). First, a new method for removing thick clouds was developed based on a modified neighborhood similar pixel interpolator (NSPI) approach. Second, a new Geostatistical Neighborhood Similar Pixel Interpolator (GNSPI) was developed for gap-filling. After cloud removal and gap filling processes, we can obtain high-quality Landsat time-series data. Third, a hierarchical classification method was proposed to get detailed forest types from dense Landsat time-series to improve forest mapping accuracy. This method integrates a feature selection and an iterative training-sample-adding procedure into a hierarchical classification framework. The proposed method has been tested in Vinton County of southeastern Ohio. The accuracy of these forest types reaches 90%. Last, NDVI time-series derived from six Landsat images across different seasons was used to estimate AGB in southeast Ohio by empirical modeling approaches. Results clearly show that NDVI in the fall season has a stronger correlation to AGB than that in the peak season, and using seasonal NDVI time-series can obtain more accurate AGB estimations and less saturation than using a single NDVI. This study demonstrates the value of multi-seasonal Landsat images for improving forest studies.