Remote sensing of mangrove composition and structure in the Galapagos Islands

Mangroves are unique inter-tidal ecosystems that provide valuable ecosystem goods and services. This dissertation investigates new methods of characterizing mangrove forests using remote sensing with implications for mapping and modeling ecosystem goods and services. Specifically, species composition, leaf area, and canopy height are investigated for mangroves in the Galapagos Islands. The Galapagos Islands serve as an interesting case study where environmental conditions are highly variable over short distances producing a wide range of mangrove composition and structure to examine. This dissertation reviews previous mangrove remote sensing studies and seeks to address missing gaps. Specifically, this research seeks to examine pixel and object-based methods for mapping mangrove species, investigate the usefulness of spectral and spatial metrics to estimate leaf area, and compare existing global digital surface models with a digital surface model extracted from new very high resolution imagery. The major findings of this research include the following: 1) greater spectral separability between true mangrove and mangrove associate species using object-based image analysis compared to pixel-based analysis, but a lack of separability between individual mangrove species, 2) the demonstrated necessity for novel machine-learning classification techniques rather than traditional clustering classification algorithms, 3) significant but weak relationships between spectral vegetation indices and leaf area, 4) moderate to strong relationships between grey-level co-occurrence matrix image texture and leaf area at the individual species level, 5) similar accuracy between a very high resolution stereo optical digital surface model a coarse resolution InSAR product to estimate canopy height with improved accuracy using a hybrid model of these two products. The results demonstrate advancements in remote sensing technology and technique, but further challenges remain before these methods can be applied to monitoring and modeling applications. Based on these results, future research should focus on emerging technologies such as hyperspectral, very high resolution InSAR, and LiDAR to characterize mangrove forest composition and structure

Urban forest ecosystem analysis using fused airborne hyperspectral and lidar data

Urban trees are strategically important in a city's effort to mitigate their carbon footprint, heat island effects, air pollution, and stormwater runoff. Currently, the most common method for quantifying urban forest structure and ecosystem function is through field plot sampling. However, taking intensive structural measurements on private properties throughout a city is difficult, and the outputs from sample inventories are not spatially explicit. The overarching goal of this dissertation is to develop methods for mapping urban forest structure and function using fused hyperspectral imagery and waveform lidar data at the individual tree crown scale. Urban forest ecosystem services estimated using the USDA Forest Service’s i-Tree Eco (formerly UFORE) model are based largely on tree species and leaf area index (LAI). Accordingly, tree species were mapped in my Santa Barbara, California study area for 29 species comprising >80% of canopy. Crown-scale discriminant analysis methods were introduced for fusing Airborne Visible Infrared Imaging Spectrometry (AVIRIS) data with a suite of lidar structural metrics (e.g., tree height, crown porosity) to maximize classification accuracy in a complex environment. AVIRIS imagery was critical to achieving an overall species-level accuracy of 83.4% while lidar data was most useful for improving the discrimination of small and morphologically unique species. LAI was estimated at both the field-plot scale using laser penetration metrics and at the crown scale using allometry. Agreement of the former with photographic estimates of gap fraction and the latter with allometric estimates based on field measurements was examined. Results indicate that lidar may be used reasonably to measure LAI in an urban environment lacking in continuous canopy and characterized by high species diversity. Finally, urban ecosystem services such as carbon storage and building energy-use modification were analyzed through combination of aforementioned methods and the i-Tree Eco modeling framework. The remote sensing methods developed in this dissertation will allow researchers to more precisely constrain urban ecosystem spatial analyses and equip cities to better manage their urban forest resource

Understanding the measurement of forests with waveform lidar

The measurement of forests is essential for monitoring and predicting the role and response of the land surface to global climate change. Globally consistent and frequent measurements can only be made by satellites; unfortunately many current system’s measurements saturate at moderate canopy densities and are not directly related to forest properties, requiring tenuous empirical relationships that are insensitive to many of the Earth’s most important, Carbon rich forests. Lidar (laser radar) is a relatively new technology that offers the potential to make direct measurements of forest height, vertical density and, when ground based, explicit measurements of structure. In addition measurements do not saturate until much higher forest densities. In recent years there has been much interest in the measurement of forests by lidar, with a number of airborne and terrestrial and one spaceborne lidar developed. Measuring a forest leaf by leaf is impractical and very tedious, so more rapid ground based methods are needed to collect data to validate satellite derived estimates. These rapid methods are themselves not directly related to forest properties causing uncertainty in any validation of remotely sensed estimates. This thesis uses Monte Carlo ray tracing to simulate the measurement of forests by full waveform lidars over explicit geometric forest models for both above and below canopy instruments. Existing methods for deriving forest properties from measurements are tested against the known truth of these simulated forests, a process impossible in reality. Causes of disagreements are explored and new methods developed to attempt to overcome any shortcomings. These new methods include dual wavelength lidar for correcting satellite based measurements for topography and a voxel based method for more directly relating terrestrial lidar signals to forest properties

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Bridging Arctic pathways: integrating hydrology, geomorphology and remote sensing in the North

Dissertation (Ph.D.) University of Alaska Fairbanks, 2015This work presents improved approaches for integrating patterns and processes within hydrology, geomorphology, ecology and permafrost on Arctic landscapes. Emphasis was placed on addressing fundamental interdisciplinary questions using robust, repeatable methods. Water tracks were examined in the foothills of the Brooks Range to ascertain their role within the range of features that transport water in Arctic regions. Classes of water tracks were developed using multiple factor analysis based on their geomorphic, soil and vegetation characteristics. These classes were validated to verify that they were repeatable. Water tracks represented a broad spectrum of patterns and processes primarily driven by surficial geology. This research demonstrated a new approach to better understanding regional hydrological patterns. The locations of the water track classes were mapped using a combination method where intermediate processing of spectral classifications, texture and topography were fed into random forests to identify the water track classes. Overall, the water track classes were best visualized where they were the most discrete from the background landscape in terms of both shape and content. Issues with overlapping and imbalances between water track classes were the biggest challenges. Resolving the spatial locations of different water tracks represents a significant step forward for understanding periglacial landscape dynamics. Leaf area index (LAI) calculations using the gap-method were optimized using normalized difference vegetation index (NDVI) as input for both WorldView-2 and Landsat-7 imagery. The study design used groups to separate the effects of surficial drainage networks and the relative magnitude of change in NDVI over time. LAI values were higher for the WorldView-2 data and for each sensor and group combination the distribution of LAI values was unique. This study indicated that there are tradeoffs between increased spatial resolution and the ability to differentiate landscape features versus the increase in variability when using NDVI for LAI calculations. The application of geophysical methods for permafrost characterization in Arctic road design and engineering was explored for a range of conditions including gravel river bars, burned tussock tundra and ice-wedge polygons. Interpretations were based on a combination of Directcurrent resistivity - electrical resistivity tomography (DCR-ERT), cryostratigraphic information via boreholes and geospatial (aerial photographs & digital elevation models) data. The resistivity data indicated the presence/absence of permafrost; location and depth of massive ground ice; and in some conditions changes in ice content. The placement of the boreholes strongly influenced how geophysical data can be interpreted for permafrost conditions and should be carefully considered during data collection strategies

The global tree carrying capacity (keynote)

editorial reviewe

Changes in Tall Shrub Abundance on the North Slope of Alaska, 2000-2010

The observed greening of Arctic vegetation and the expansion of shrubs in the last few decades has likely had profound implications for the tundra ecosystem, including feedbacks to climate. Uncertainty surrounding the magnitude, direction, and implications of this vegetation shift calls for monitoring of vegetation structural parameters, such as fractional cover of shrubs. Due to the extent of the North Slope of Alaska and its extreme environments, remote sensing may be the most suitable tool to produce wall-to-wall fractional shrub cover maps for the entire region, however, most regional maps have relied on vegetation indices or needed many years worth of data to cover the whole region. Here, a new mapping approach is presented that uses satellite imagery from the Multi-angle Imaging SpectroRadiometer (MISR) sensor and some landscape variables to predict tall shrub (\u3e 0.5 m) cover with the ultimate goal of evaluating temporal changes in tall shrub fractional cover during the period of 2010-2000. Specifically, we: 1) undertook two field surveys in the North Slope of Alaska to obtain estimates of tall shrub cover, canopy height, crown radius, and total number of shrubs at 26 sites (250 m × 250 m each); 2) evaluated the ability of the semi-automated image interpretation algorithm CANAPI - CANopy Analysis from Panchromatic Imagery, to derive structural data for tall (\u3e 0.5 m) shrubs in the Arctic; 3) constructed a robust reference database with estimates of shrub structural parameters; 4) trained and validated the boosted regression tree model to predict tall shrub fractional cover from moderate resolution imagery; 5) created the 2000 and the 2010 tall shrub fractional cover map for the North Slope of Alaska; and 6) evaluated the changes in shrub abundance during the period 2010-2000 in the North Slope of Alaska. Results from the field surveys suggested that tall shrub fractional cover was less than 5% at 250 m scales. The evaluation of the CANAPI algorithm showed that CANAPI could successfully retrieve fractional cover (R2 = 0.83, P \u3c 0.001), mean crown radius (R2 = 0.81, P \u3c 0.001), and total number of shrubs (R2 = 0.54, P \u3c 0.001) from very-high resolution imagery. As a result, a robust reference database was constructed with estimates of tall shrub fractional cover, canopy radius, and total number of shrubs for 1,039 sites across the domain of the North Slope. After the training and validation of the Boosted Regression Tree (BRT), the best model used 14 predictor variables and explained 52% of the variation in the response variable, fractional cover. The red reflectance, slope, nadir Bidirectional Reflectance Distribution Function (BRDF) adjusted weight of determination, and isotropic scattering kernel were the variables more often used to generate the regression trees, and therefore they contributed the most to the model. The trained BRT model was used to construct the tall shrub fractional cover map for the year 2000 and 2010 using moderate resolution imagery. The maps revealed that cover ranged from 0.00 to 0.21 and about 75% of the sites had a fractional cover less than 0.013. High cover values were predicted along floodplains, creeks, and sloped terrain. The 2000 MISR-derived fractional cover map presented here outperformed the 2000 Landsat-derived tall shrub fractional cover map when compared to the robust validation data set (R2= 0.38, Root Mean Square Error (RMSE) = 0.08). Temporal comparisons of tall shrub abundance in the MISR-derived maps suggested that shrubs expanded during the period 2000-2010. The extent of the area that unequivocally experienced a robust change in tall shrub cover was less than 1 % (1,487 km2) of the total area of the North Slope of Alaska (213,090 km2). It is possible that tall shrubs may have expanded throughout a larger area but there is insufficient precision in the MISR-based estimates to make an unequivocal determination. Nevertheless, it seems that there was a positive trend toward an increase in shrub cover considering that 95% of the locations that had a robust change saw an increase. The tall shrub cover expansion rate varied between 0.006 yr-1 and 0.017 yr-1, being higher along the forest-tundra ecotone, north of the Brooks Range. More research is necessary to determine if the increase in cover corresponded to the advance of the tree line, or to the expansion of the tall shrubs, or both

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