Complex land cover classifications and physical properties retrieval of tropical forests using multi-source remote sensing

The work presented in this thesis mainly focuses on two subjects related to the application of remote sensing data: (1) for land cover classification combining optical sensor, texture features generated from spectral information and synthetic aperture radar (SAR) features, and (2) to develop a non-destructive approach for above ground biomass (AGB) and forest attributes estimation employing multi-source remote sensing data (i.e. optical data, SAR backscatter) combined with in-situ data. Information provided by reliable land cover map is useful for management of forest resources to support sustainable forest management, whereas the generation of the non-destructive approach to model forest biophysical properties (e.g. AGB and stem volume) is required to assess the forest resources more efficiently and cost-effective, and coupled with remote sensing data the model can be applied over large forest areas. This work considers study sites over tropical rain forest landscape in Indonesia characterized by different successional stages and complex vegetation structure including tropical peatland forests. The thesis begins with a brief introduction and the state of the art explaining recent trends on monitoring and modeling of forest resources using remote sensing data and approach. The research works on the integration of spectral information and texture features for forest cover mapping is presented subsequently, followed by development of a non-destructive approach for AGB and forest parameters predictions and modeling. Ultimately, this work evaluates the potential of mosaic SAR data for AGB modeling and the fusion of optical and SAR data for peatlands discrimination. The results show that the inclusion of geostatistics texture features improved the classification accuracy of optical Landsat ETM data. Moreover, the fusion of SAR and optical data enhanced the peatlands discrimination over tropical peat swamp forest. For forest stand parameters modeling, neural networks method resulted in lower error estimate than standard multi-linear regression technique, and the combination of non-destructive measurement (i.e. stem number) and remote sensing data improved the model accuracy. The up scaling of stem volume and biomass estimates using Kriging method and bi-temporal ETM image also provide favorable estimate results upon comparison with the land cover map.Die in dieser Dissertation präsentierten Ergebnisse konzentrieren sich hauptsächlich auf zwei Themen mit Bezug zur angewandten Fernerkundung: 1) Der Klassifizierung von Oberflächenbedeckung basierend auf der Verknüpfung von optischen Sensoren, Textureigenschaften erzeugt durch Spektraldaten und Synthetic-Aperture-Radar (SAR) features und 2) die Entwicklung eines nichtdestruktiven Verfahrens zur Bestimmung oberirdischer Biomasse (AGB) und weiterer Waldeigenschaften mittels multi-source Fernerkundungsdaten (optische Daten, SAR Rückstreuung) sowie in-situ Daten. Eine zuverlässige Karte der Landbedeckung dient der Unterstützung von nachhaltigem Waldmanagement, während eine nichtdestruktive Herangehensweise zur Modellierung von biophysikalischen Waldeigenschaften (z.B. AGB und Stammvolumen) für eine effiziente und kostengünstige Beurteilung der Waldressourcen notwendig ist. Durch die Kopplung mit Fernerkundungsdaten kann das Modell auf große Waldflächen übertragen werden. Die vorliegende Arbeit berücksichtigt Untersuchungsgebiete im tropischen Regenwald Indonesiens, welche durch verschiedene Regenerations- und Sukzessionsstadien sowie komplexe Vegetationsstrukturen, inklusive tropischer Torfwälder, gekennzeichnet sind. Am Anfang der Arbeit werden in einer kurzen Einleitung der Stand der Forschung und die neuesten Forschungstrends in der Überwachung und Modellierung von Waldressourcen mithilfe von Fernerkundungsdaten dargestellt. Anschließend werden die Forschungsergebnisse der Kombination von Spektraleigenschaften und Textureigenschaften zur Waldbedeckungskartierung erläutert. Desweiteren folgen Ergebnisse zur Entwicklung eines nichtdestruktiven Ansatzes zur Vorhersage und Modellierung von AGB und Waldeigenschaften, zur Auswertung von Mosaik- SAR Daten für die Modellierung von AGB, sowie zur Fusion optischer mit SAR Daten für die Identifizierung von Torfwäldern. Die Ergebnisse zeigen, dass die Einbeziehung von geostatistischen Textureigenschaften die Genauigkeit der Klassifikation von optischen Landsat ETM Daten gesteigert hat. Desweiteren führte die Fusion von SAR und optischen Daten zu einer Verbesserung der Unterscheidung zwischen Torfwäldern und tropischen Sumpfwäldern. Bei der Modellierung der Waldparameter führte die Neural-Network-Methode zu niedrigeren Fehlerschätzungen als die multiple Regressions. Die Kombination von nichtdestruktiven Messungen (z.B. Stammzahl) und Fernerkundungsdaten führte zu einer Steigerung der Modellgenauigkeit. Die Hochskalierung des Stammvolumens und Schätzungen der Biomasse mithilfe von Kriging und bi-temporalen ETM Daten lieferten positive Schätzergebnisse im Vergleich zur Landbedeckungskarte

Multi-Temporal Remote-Sensing-based Mapping and Characterization of Landscape Evolution of a Meandering River Floodplain

Large meandering river floodplains are critical components of the Earth ecosystems for their high biodiversity and productivity. However, it is challenging to study these regions because of their complex land-covers and dynamic surface processes. This study applies soft classification and change-detection analysis to five Landsat 5 Thematic Mapper (TM) satellite images to examine long-term surface-cover composition and configuration change of the Rio Beni floodplain in Bolivia from 1987 to 2006. One hard/crisp classification algorithm (i.e., ISODATA) and two soft classification algorithms (i.e., Bayes classification and fuzzy classification) were applied to the study-area satellite images to examine the performances of classifying and mapping meandering river-floodplain environments between hard and soft classification approaches. In all five scenes, three algorithms achieved ~90% classification accuracy via hard classification outputs. However, the two soft algorithms were of more utility in this study because their results were less affected by “salt-and-pepper” noise and provided extra land-cover probability/membership layers. A novel change-detection algorithm was proposed in this study, namely Modified Change Vector Analysis (MCVA). The MCVA operated in fuzzy-membership space, considered change uncertainty during the thresholding stage, and utilized change-vector directions to modify the determination of change/no-change status for each pixel. A fuzzy Markov Random Field (FMRF) model was applied to further refine the change maps by incorporating spatial change uncertainty. A second thresholding stage was also applied to separate a type of change referred to as “transitional change,” which preserved fuzzy membership information and provided a concise map output. Compared with three traditional change-detection algorithms, the MCVA achieved higher change-detection accuracy and provided more detailed change dynamics regarding the land-surface change. Dynamics of major floodplain cover types (i.e., oxbow lakes, river, sand, forest, non-forest vegetation, and dry and wet soil) were investigated via multi-temporal analysis. Over the observing period of 1987 to 2006, 74.4% of pixels remained the same land-cover, 20% experienced clear land-cover change and 5.6% experienced transitional land-cover change. The riparian area experienced more dramatic change than other parts of the Rio Beni floodplain during this period. Additional analysis of landscape metrics provided information regarding the spatial patterns of the land-cover, but future work would be needed to further examine its utility in understanding floodplain dynamics. This study provides information on remote-sensing-based mapping and quantitative characterization methods for meandering river floodplains. The spatiotemporal patterns of landscape on Rio Beni floodplain can be used in sustainable management and protection of floodplain ecosystems

Mapping natural forest cover, tree species diversity and carbon stocks of a subtropical Afromontane forest using remote sensing.

Doctoral Degree. University of KwaZulu-Natal, Durban.Natural forests cover about a third of terrestrial landmass and provides benefits such as carbon sequestration, and regulation of biogeochemical cycles. It is essential that adequate information is available to support forest management. Remote Sensing imageries provide data for mapping natural forests. Hence, our study aimed at mapping the Nkandla Forest Reserve attributes with Remote Sensing imageries. Quantitative information on the forest attributes is non-existent for many of these forests, including the sub-tropical Afromontane Nkandla Forest Reserve. This does not support scientific and evidence based natural forest management. A review of literature revealed that progress has been made in Remote Sensing monitoring of natural forest attributes. The Random Forest (RF) and Support Vector Machine (SVM) were applied to Landsat 8 in classifying the land use land cover (LULC) classes of the forest. Each of the algorithms produced higher accuracy of above 95% with the SVM performing slightly better than the RF. The SVM, Markov Chain and Multi-Layer Perceptron Neural Network (MLPNN) were adopted for a spatiotemporal change detection over the last 30 years at decadal interval for the forest. There were consistent changes in each of the four LULC classes. The study further conducted a forecasting of LULC distribution for 2029. Aboveground carbon (AGC) estimation was carried out using Sentinel 2 imagery and RF modelling. Four models made up smade of Sentinel 2 products could successfully map the AGC with high accuracies. The last two studies focused on tree species diversity with the first evaluating the influence of spatial and spectral resolution on prediction accuracies by comparing the PlanetScope, RapidEye, Sentinel 2 and Landsat 8. Both the spatial and spectral resolution were found to influence accuracies with the Sentinel 2 emerging as the best imagery. The second aspect focused on identifying the best season for the prediction of tree species diversity. Summer imagery emerged as the best season and the winter being the least performer. Overall, our study indicates that Remote Sensing imageries could be used for successful mapping of natural forest attributes. The outputs of our studies could also be of interest to forest managers and Remote Sensing experts.Author's Publications and Manuscripts can be found on page iii

Data driven estimation of soil and vegetation attributes using airborne remote sensing

Airborne remote sensing using imaging spectroscopy and LiDAR (Light Detection and Ranging) measurements enable us to quantify ecosystem and land surface attributes. In this study we use high resolution airborne remote sensing to characterize soil attributes and the structure of vegetation canopy. Soil texture, organic matter, and chemical constituents are critical to ecosystem functioning, plant growth, and food security. However, most of the soil data available globally are of coarse resolutions at scales of 1:5 million and lack quantitative information for modeling and land management decisions at field or catchment scales. Thus the need for a spatially contiguous quantitative soil information is of immense scientific merit which can be obtained using airborne and space-borne imaging spectroscopy. Towards this goal we systematically explore the feasibility of characterizing soil properties from imaging spectroscopy using data driven modeling approaches. We have developed a modeling framework for quantitative prediction of different soil attributes using airborne imaging spectroscopy and limited field soil grab sample datasets. The results of our analysis using fine resolution (7.6m) Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data collected over midwestern United States immediately after the large 2011 Mississippi River flood indicate the feasibility of using the developed models for quantitative spatial prediction of soil attributes over large areas (> 700 sq. km) of the landscape. The quantitative predictions reveal coherent spatial correlations of the difference in constituent concentrations with legacy landscape features, and immediate disturbances on the landscape due to extreme events. Further for model validation using independent test data, we demonstrate that the results are better represented as a probability density function compared to a single validation subset. We have simulated up-scaled datasets at multiple spatial resolutions ranging from 10m to 90m from the AVIRIS data, including future space based Hyperspectral Infrared Imager (HyspIRI) like observations. These datasets are used to investigate the applicability of the developed modeling framework over increasing spatial resolutions on the characterization of soil constituents. We have outlined an evaluation framework with a set of metrics that considers the point-scale model performance as well as the consistency of cross-scale spatial predictions. The results indicate that the ensemble quantification method is scalable over the entire range of airborne to space-borne spatial resolutions and establishes the feasibility of quantification of soil constituents from space- based observations. Further, we develop a retrieval framework from satellites, which combines the developed modeling framework and spectral similarity measures for global scale characterization of soils using a weighted constrained optimization framework. The retrieval algorithm takes advantage of the potential of repeat temporal satellite measurements to evolve a dynamic spectral library and improve soil characterization. Finally, we demonstrate that in addition to soil constituents, hyperspectral data can add value to characterizations of leaf area density (LAD) estimations for dense overlapping canopies. We develop a method for the estimation of the vertical distribution of foliage or LAD using a combination of airborne LiDAR and hyperspectral data using a feature based data fusion approach. Tree species classification from hyperspectral data is used to develop a novel ellipsoidal ‘tree shaped’ voxel approach for characterizing the LAD of individual trees in a riparian forest setting. We found that the tree shaped voxels represents a more realistic characterization of the upper and middle parts of the tree canopy in terms of higher LAD values, for trees of different heights in a forest stand

Remote Sensing of Precipitation: Part II

Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products

Protected Area Management

Until recently, values and benefits from protected areas have often been underestimated as well as taken for granted. Protected Area Management - Recent Advances demonstrates that there are deep necessities in how the wider scientific, environmental, socioeconomic, and cultural values that these natural ecosystems provide should increasingly be recognized. The book highlights various approaches for managing and conserving protected areas to respond to some pressing global challenges such as climate change, demand for food and energy, overexploitation, and habitat change. It addresses these issues in five main sections that cover biodiversity and genetic resources; protected marine areas; community, ecotourism, and protected areas; and protected area conservation and monitoring

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

Hydrological uncertainty analysis and scenario-based streamflow modelling for the Congo River Basin

The effects of climate and environmental change are likely to exacerbate water stress in Africa over the next five decades. It appears obvious, therefore, that large river basins with considerable total renewable water resources will play a prominent role in regional cooperation to alleviate the pressure of water scarcity within Africa. However, managing water resources in the large river basins of Africa involves problems of data paucity, lack of technical resources and the sheer scale of the problem. These river basins are located in regions that are characterized by poverty, low levels of economic development and little food security. The rivers provide multiple goods and services that include hydro-power, water supply, fisheries, agriculture, transportation, and maintenance of aquatic ecosystems. Sustainable water resources management is a critical issue, but there is almost always insufficient data available to formulate adequate management strategies. These basins therefore represent some of the best test cases for the practical application of the science associated with the Predictions in Ungauged Basins (PUB). The thesis presents the results of a process-based hydrological modelling study in the Congo Basin. One of the primary objectives of this study was to establish a hydrological model for the whole Congo Basin, using available historical data. The secondary objective of the study was to use the model and assess the impacts of future environmental change on water resources of the Congo Basin. Given the lack of adequate data on the basin physical characteristics, the preliminary work consisted of assessing available global datasets and building a database of the basin physical characteristics. The database was used for both assessing relationships of similarities between features of physiographic settings in the basin (Chapters 3 and 4), and establishing models that adequately represent the basin hydrology (Chapters 5, 6, and 7). The representative model of the Congo Basin hydrology was then used to assess the impacts of future environmental changes on water resources availability of the Congo Basin (Chapter 8). Through assessment of the physical characteristics of the basin, relationships of similarities were used to determine homogenous regions with regard to rainfall variability, physiographic settings, and hydrological responses. The first observation that comes from this study is that these three categories of regional groups of homogenous characteristics are sensible with regards to their geographical settings, but the overlap and apparent relationships between them are weak. An explanation of this observation is that there are insufficient data, particularly associated with defining sub-surface processes, and it is possible that additional data would have assisted in the discrimination of more homogenous groups and better links between the different datasets. The model application in this study consisted of two phases: model calibration, using a manual approach, and the application of a physically-based a priori parameter estimation approach. While the first approach was designed to assess the general applicability of the model and identify major errors with regard to input data and model structure, the second approach aimed to establish an understanding of the processes and identify useful relationships between the model parameters and the variations in real hydrological processes. The second approach was also designed to quantify the sensitivity of the model outputs to the parameters of the model and to encompass information sharing between the basin physical characteristics and quantifying the parameters of the model. Collectively, the study’s findings show that these two approaches work well and are appropriate to represent the real hydrological processes of Congo Basin. The secondary objective of this study was achieved by forcing the hydrological model developed for the Congo Basin with downscaled Global Climate Model (GCMs) data in order to assess scenarios of change and future possible impacts on water resources availability within the basin. The results provide useful lessons in terms of basin-wide adaptation measures to future climates. The lessons suggest that there is a risk of developing inappropriate adaptation measures to future climate change based on large scale hydrological response, as the response at small scales shows a completely different picture from that which is based on large scale predictions. While the study has concluded that the application of the hydrological model has been successful and can be used with some degree of confidence for enhanced decision making, there remain a number of uncertainties and opportunities to improve the methods used for water resources assessment within the basin. The focus of future activities from the perspective of practical application should be on improved access to data collection to increase confidence in model predictions, on dissemination of the knowledge generated by this study, and on training in the use of the developed water resources assessment techniques

Dipterocarps protected by Jering local wisdom in Jering Menduyung Nature Recreational Park, Bangka Island, Indonesia

Apart of the oil palm plantation expansion, the Jering Menduyung Nature Recreational Park has relatively diverse plants. The 3,538 ha park is located at the north west of Bangka Island, Indonesia. The minimum species-area curve was 0.82 ha which is just below Dalil conservation forest that is 1.2 ha, but it is much higher than measurements of several secondary forests in the Island that are 0.2 ha. The plot is inhabited by more than 50 plant species. Of 22 tree species, there are 40 individual poles with the average diameter of 15.3 cm, and 64 individual trees with the average diameter of 48.9 cm. The density of Dipterocarpus grandiflorus (Blanco) Blanco or kruing, is 20.7 individual/ha with the diameter ranges of 12.1 – 212.7 cm or with the average diameter of 69.0 cm. The relatively intact park is supported by the local wisdom of Jering tribe, one of indigenous tribes in the island. People has regulated in cutting trees especially in the cape. The conservation agency designates the park as one of the kruing propagules sources in the province. The growing oil palm plantation and the less adoption of local wisdom among the youth is a challenge to forest conservation in the province where tin mining activities have been the economic driver for decades. More socialization from the conservation agency and the involvement of university students in raising environmental awareness is important to be done