Ecological impacts of deforestation and forest degradation in the peat swamp forests of northwestern Borneo

Tropical peatlands have some of the highest carbon densities of any ecosystem and are under enormous development pressure. This dissertation aimed to provide better estimates of the scales and trends of ecological impacts from tropical peatland deforestation and degradation across more than 7,000 hectares of both intact and disturbed peatlands in northwestern Borneo. We combined direct field sampling and airborne Light Detection And Ranging (LiDAR) data to empirically quantify forest structures and aboveground live biomass across a largely intact tropical peat dome. The observed biomass density of 217.7 ± 28.3 Mg C hectare-1 was very high, exceeding many other tropical rainforests. The canopy trees were ~65m in height, comprising 81% of the aboveground biomass. Stem density was observed to increase across the 4m elevational gradient from the dome margin to interior with decreasing stem height, crown area and crown roughness. We also developed and implemented a multi-temporal, Landsat resolution change detection algorithm for identify disturbance events and assessing forest trends in aseasonal tropical peatlands. The final map product achieved more than 92% user’s and producer’s accuracy, revealing that after more than 25 years of management and disturbances, only 40% of the area was intact forest. Using a chronosequence approach, with a space for time substitution, we then examined the temporal dynamics of peatlands and their recovery from disturbance. We observed widespread arrested succession in previously logged peatlands consistent with hydrological limits on regeneration and degraded peat quality following canopy removal. We showed that clear-cutting, selective logging and drainage could lead to different modes of regeneration and found that statistics of the Enhanced Vegetation Index and LiDAR height metrics could serve as indicators of harvesting intensity, impacts, and regeneration stage. Long-term, continuous monitoring of the hydrology and ecology of peatland can provide key insights regarding best management practices, restoration, and conservation priorities for this unique and rapidly disappearing ecosystem

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

Estimation of surface soil moisture by a multi-elevation UAV-based ground penetrating radar

The measurement of soil moisture is important for a wide range of applications, including ecosystem conservation and agricultural management. However, most traditional measurement methods, e.g., time-domain reflectometry (TDR), are unsuitable for mapping field scale variability. In this study, we propose a method that uses an unmanned aerial vehicle (UAV) to support a ground penetrating radar (GPR) system for spatial scanning investigation at different elevations above ground level. This method measures the surface reflectivity to estimate the soil moisture, exploiting the linear relationship between the ratio of the reflected and the direct wave amplitudes along with the reciprocal of GPR antenna height. This relationship is deduced in this study based on the point source assumptions of a transmitter antenna and ground reflections, which is confirmed by numerical simulation results using the gprMax software. Unlike previous air-launched GPR methods, the UAV-GPR method presented here removes the limitations of a steady transmitter power and a fixed GPR survey height and the need for calibration of antenna transfer functions and geophysical inversion calculations, and thus is simpler and more convenient for field applications. We test the method at field sites within the riparian zone and a river-island grassland adjacent to the Yangtze River. The results from the field study illustrate comparable measured soil moisture to those obtained invasively using TDR. The root mean square error (RMSE) of surface reflectivity and soil moisture values between UAV-GPR with 8 antenna height investigations and TDR in the grassland are 0.03 and 0.05 cm3/cm3, respectively

The fine-scale spatial and temporal variability of hydrologic attributes associated with the process of infiltration in \u27nano-catchments\u27 during a rainfall event.

The dynamism of a variety of hydrologic phenomena tied to the process of infiltration are studied here in relation to their spatial and temporal variability within subhectare bowl-like depressions, or \u27nano-catchments\u27. The process of infiltration is becoming increasingly important to understand as a result of anthropogenically driven changes to the near-surface soil matrix, which alters this process. Within the context of infiltration, the spatial variability of soil moisture is assessed under a changing hydrologic regime in south-central Ontario during a rainfall event. With an increase in soil moisture following precipitation events, the spatial auto-correlation increases for both samples that incorporate 15 cm and 30 cm samples. The pattern of soil moisture is influenced by local topographic shape; however this pattern is also altered by the effect of vegetation in the form of active photosynthesizing vegetation and leaf detritus. The effect of vegetation is such that the relationship between topographic gradient and soil moisture is enhanced under active vegetation, while this same relationship is muted under leaf litter. The variability of infiltration to the point of soil saturation is also assessed. A number of estimates of hydraulic conductivity are used, as well as differing estimates of soil moisture to evaluate the bias of using single point measures versus areal estimates in the modelling of infiltration within these nano-catchments. In conjunction with infiltration modelling, matric potential throughout two nano-catchments is assessed in relation to site characteristics including vegetation, macropores and topographic position. Conclusions support that in monitoring infiltration and soil moisture cannot be fully represented by single point measurements, even at a sub-hectare scale.Dept. of Earth Sciences. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .A537. Source: Masters Abstracts International, Volume: 45-01, page: 0259. Thesis (M.Sc.)--University of Windsor (Canada), 2006

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

Remote Sensing of Plant Biodiversity

At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally. This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution. The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity. Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely. Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON). This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing

SAR (Synthetic Aperture Radar). Earth observing system. Volume 2F: Instrument panel report

The scientific and engineering requirements for the Earth Observing System (EOS) imaging radar are provided. The radar is based on Shuttle Imaging Radar-C (SIR-C), and would include three frequencies: 1.25 GHz, 5.3 GHz, and 9.6 GHz; selectable polarizations for both transmit and receive channels; and selectable incidence angles from 15 to 55 deg. There would be three main viewing modes: a local high-resolution mode with typically 25 m resolution and 50 km swath width; a regional mapping mode with 100 m resolution and up to 200 km swath width; and a global mapping mode with typically 500 m resolution and up to 700 km swath width. The last mode allows global coverage in three days. The EOS SAR will be the first orbital imaging radar to provide multifrequency, multipolarization, multiple incidence angle observations of the entire Earth. Combined with Canadian and Japanese satellites, continuous radar observation capability will be possible. Major applications in the areas of glaciology, hydrology, vegetation science, oceanography, geology, and data and information systems are described

Optimized ground penetrating radar methods can account for landscape variance in properties informing soil carbon distribution in boreal forest hillslopes

This thesis entailed developing optimized ground penetrating radar (GPR) methods for estimating soil horizon thickness and bulk density to determine soil carbon (C) distribution across forest hillslopes. A review of forest GPR studies was conducted to synthesize optimized system settings, survey parameters, and data processing steps. Recommended GPR survey settings (> 500 MHz antenna frequency, > 32 stacks, 5 cm sampling interval) and data processing tools were compiled for forest soil surveys and demonstrated to improve the interpretability of specific soil targets (ex. soil horizon boundaries, rock, and root content) in forest soil radargrams. Physical soil sampling and GPR surveying methods were conducted across a boreal forest hillslope in Pynn’s Brook, Newfoundland to collect small (1 m² soil pits) and large (80 m GPR survey lines) spatial scale soil horizon thickness and bulk density estimates. This allowed for comparisons between physical soil sampling and GPR estimates of soil horizon thickness, soil bulk density and resulting soil C distribution calculated using soil C stocks. Furthermore, large spatial scale GPR surveying revealed landscape trends in soil bulk density, such as increasing density downslope and high variability across the slope, which informs our understanding of forest soil C distribution and its landscape controls

Biophysical parameter retrieval from satellite laser altimetry.

Quantifying and monitoring vegetation distribution and change are fundamental to carbon accounting and requirements of national forest inventories. This research explores the potential of the Geoscience Laser Altimeter System (GLAS), launched in 2003 by NASA as the first global Earth surface satellite LiDAR mission. The project study site is the Forest of Dean, Gloucestershire, UK, a highly mixed, temperate forest with varied topography. Methods are developed to distinguish the regions within waveforms returned from vegetation and ground. When compared with field measurements, estimation of canopy height gives a correlation of R2=0.92; RMSE=2.81m. Waveform indices are determined and evaluated with respect to their potential to estimate biophysical parameters. Heights of cumulative energy percentiles within the waveform prove to be significant estimators. When compared to calculations from independent yield models, results show correlations with stand- level top height (R2=0.76; RMSE 3.9m) and stemwood volume (mixed composition stands dominated by broadleaves: R2=0.47, RMSE=75.6m3/ha; conifers: R2=0.66, RMSE=82.5m3/ha). Uncertainty analysis is undertaken of both waveform and yield model estimates. Canopy cover is estimated for the area beneath GLAS waveforms, corrected for differences in reflectance for ground and canopy surfaces. These are assessed against airborne LiDAR estimates, validated using hemispherical photography. The method produces results with R2=0.63; RMSE=11% for stands with greatest coverage by broadleaves and R2=0.41; RMSE 16% for conifer-dominated stands. Small footprint airborne LiDAR (AL) is widely accepted to offer valuable data regarding forest parameters. An evaluation of AL and GLAS results demonstrate that the broad GLAS footprint dimensions allow similar estimation of stand-level parameters (e.g. AL/yield model Top Height: R2=0.73, RMSE=4.5m). Direct comparison of GLAS with AL shows ground surface identification with mean difference of 0.32m and that elevation profiles correspond well (98th percentiles R2=0.76, RMSE=3.4m). Finally, prospects for use of LiDAR in carbon accounting, assimilation within models and for forestry applications are discussed