Download or read book Quantifying and Mapping Soil Organic Carbon in Mali West Africa Using Spatiotemporal Methods written by Antonio Luis Evora Ferreira Querido and published by . This book was released on 2008 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Kyoto protocol recognized the importance of the terrestrial sink of carbon and proposed schemes that allow countries to treat sequestered carbon as a commodity that can be traded for global environmental benefit. Carbon sequestration can be a win-win scenario because it also introduces a set of new benefits into dryland farming communities particularly in Sub-Saharan Africa. The possibility, however, for agricultural producers to participate in the emerging market for tradable carbon-credits requires a reliable verification mechanism. Soil carbon inventories of many developing nations rely on a broad scale assessment. These approaches do not account for the spatial and temporal variability of soil carbon nor do they provide a measure of uncertainty associated with these assessments. This study proposed the use of Bayesian Maximum Entropy (BME) to quantify and map soil organic carbon at field scale in four agroecological zones of Mali, Sub-Saharan Africa. The prediction model comparisons using the mean error (ME) indicated that BME performed better than did the kriging methods (0.033, 0.41, respectively). BME prediction also provided a lower MSE representing a 25% reduction compared with Kriging, and 10% compared with cokriging. This study also demonstrated potential use of space---time covariances as tools to improve our understanding of spatial and temporal variability of soil organic carbon. Based on the temporal and spatial models maps were generated to predict mean trends. The estimation of tree biomass in Sub-Saharan Africa is important for an accurate assessment of the potential of these systems to capture and store carbon. The results show that tree carbon represented as much as 34% of the amount of organic carbon stored in soil surface (0-20 cm). Data from 2000 to 2006 indicated a net increase of soil organic carbon, which varied between 2.6 to 13.9 Mg ha-1. Despite the complexities that characterize the spatial and temporal distribution of most environmental processes, BME provides a framework to analyze both space and time components.
Download or read book Soil Organic Carbon Mapping Cookbook written by Food and Agriculture Organization of the United Nations and published by Food & Agriculture Org.. This book was released on 2018-05-21 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Soil Organic Carbon Mapping cookbook provides a step-by-step guidance for developing 1 km grids for soil carbon stocks. It includes the preparation of local soil data, the compilation and pre-processing of ancillary spatial data sets, upscaling methodologies, and uncertainty assessments. Guidance is mainly specific to soil carbon data, but also contains many generic sections on soil grid development, as it is relevant for other soil properties. This second edition of the cookbook provides generic methodologies and technical steps to produce SOC maps and has been updated with knowledge and practical experiences gained during the implementation process of GSOCmap V1.0 throughout 2017. Guidance is mainly specific to SOC data, but as this cookbook contains generic sections on soil grid development it can be applicable to map various soil properties.
Download or read book Methodological Developments for Mapping Soil Constituents Using Imaging Spectroscopy written by and published by . This book was released on 2013 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: Climatic variations and human activity now and increasingly in the future cause land cover changes and introduce perturbations in the terrestrial carbon reservoirs in vegetation, soil and detritus. Optical remote sensing and in particular Imaging Spectroscopy has shown the potential to quantify land surface parameters over large areas, which is accomplished by taking advantage of the characteristic interactions of incident radiation and the physico-chemical properties of a material. The objective of this thesis is to quantify key soil parameters, including soil organic carbon, using field and Imaging Spectroscopy. Organic carbon, iron oxides and clay content are selected to be analyzed to provide indicators for ecosystem function in relation to land degradation, and additionally to facilitate a quantification of carbon inventories in semiarid soils. The semiarid Albany Thicket Biome in the Eastern Cape Province of South Africa is chosen as study site. It provides a regional example for a semiarid ecosystem that currently undergoes land changes due to unadapted management practices and furthermore has to face climate change induced land changes in the future. The thesis is divided in three methodical steps. Based on reflectance spectra measured in the field and chemically determined constituents of the upper topsoil, physically based models are developed to quantify soil organic carbon, iron oxides and clay content. Taking account of the benefits limitations of existing methods, the approach is based on the direct application of known diagnostic spectral features and their combination with multivariate statistical approaches. It benefits from the collinearity of several diagnostic features and a number of their properties to reduce signal disturbances by influences of other spectral features. In a following step, the acquired hyperspectral image data are prepared for an analysis of soil constituents. The data show a large spatial heterogeneity that is caused by the patchiness of the natural vegetation in the study area that is inherent to most semiarid landscapes. Spectral mixture analysis is performed and used to deconvolve non-homogenous pixels into their constituent components. For soil dominated pixels, the subpixel information is used to remove the spectral influence of vegetation and to approximate the pure spectral signature coming from the soil. This step is an integral part when working in natural non-agricultural areas where pure bare soil pixels are rare. It is identified as the largest benefit within the multi-stage methodology, providing the basis for a successful and unbiased prediction of soil constituents from hyperspectral imagery. With the proposed approach it is possible (1) to significantly increase the spatial extent of derived information of soil constituents to areas with about 40 % vegetation coverage and (2) to reduce the influence of materials such as vegetation on the quantification of soil constituents to a minimum. Subsequently, soil parameter quantities are predicted by the application of the feature-based soil prediction models to the maps of locally approximated soil signatures. Thematic maps showing the spatial distribution of the three considered soil parameters in October 2009 are produced for the Albany Thicket Biome of South Africa. The maps are evaluated for their potential to detect erosion affected areas as effects of land changes and to identify degradation hot spots in regard to support local restoration efforts. A regional validation, carried out using available ground truth sites, suggests remaining factors disturbing the correlation of spectral characteristics and chemical soil constituents. The approach is developed for semiarid areas in general and not adapted to specific conditions in the study area. All processing steps of the developed methodology are implemented in software modules, where crucial steps of the workflow are fully automated. The transferability of the methodology is shown for simulated data of the future EnMAP hyperspectral satellite. Soil parameters are successfully predicted from these data despite intense spectral mixing within the lower spatial resolution EnMAP pixels. This study shows an innovative approach to use Imaging Spectroscopy for mapping of key soil constituents, including soil organic carbon, for large areas in a non-agricultural ecosystem and under consideration of a partially vegetation coverage. It can contribute to a better assessment of soil constituents that describe ecosystem processes relevant to detect and monitor land changes. The maps further provide an assessment of the current carbon inventory in soils, valuable for carbon balances and carbon mitigation products
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2008 with total page 906 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Adapting Loss on ignition and Visible Near Infrared Methods of Measuring Soil Organic Carbon to Sahelian Soils of West Africa written by Hamidou Konare and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Country to Global Prediction of Soil Organic Carbon and Soil Moisture Using Digital Soil Mapping written by Mario Guevara and published by . This book was released on 2020 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: The largest carbon pool in terrestrial ecosystems is contained in soils and it plays a key role regulating hydrological processes, such as the spatial variability of soil moisture dynamics. Specifically, soil moisture and soil organic carbon are variables directly linked to ecosystem services such as food production and water storage. However, there are important knowledge gaps in the spatial representation (e.g., maps) of soil moisture and soil organic information from the country specific to the global scales. There is a pressing need to update the spatial detail of soil moisture estimates and the accuracy of digital soil carbon maps for improved land management, improved Earth system modeling and improved strategies (i.e., public policy) to combat land degradation. From the country specific to the global scale, the overreaching goal of this PhD research is to develop a reproducible digital soil mapping framework to increase the statistical accuracy of spatially continuous information on soil moisture and soil organic carbon across different scales of data availability (e.g., country-specific, regional, global). Chapter 1 provides a general introduction. Chapters 2 and 3 are focused on up-scaling soil organic carbon from the country-specific scale to the continental scale. Chapter 2 provides a country-specific and multi-modeling approach for soil organic carbon mapping across Latin America, where I identify key predictors and conclude that there is no best modeling method in a quantifiable basis across all the analyzed countries. In Chapter 3, I compare and test different methods and combinations of prediction factors to model the variability of soil organic carbon across Mexico and conterminous United States (CONUS). I describe soil organic carbon stocks across different land covers across the region, quantify the model uncertainty and discuss estimates derived from previous studies. Chapters 4 and 5 are devoted to improving the statistical detail and accuracy of satellite soil moisture from the country to the global scale. Chapter 4 describes how the machine learning fusion of satellite soil moisture with Geomorphometry increase the statistical accuracy and spatial detail of current soil moisture estimates across CONUS. Chapter 5 extends the previous chapter to the global scale and identifies global soil moisture trends. I provide a novel (gap-free) soil moisture global estimate that could be potentially used to predict the global feedback between primary productivity and long-term soil moisture trends. Chapters 4 and 5 reveal evidence of soil moisture decline across large areas of the world. Finally, chapter 6 summarizes the main findings of this research, the key conclusions, emergent challenges and future steps. The results of this research were useful to generate benchmarks against which to assess the impact of climate and land cover changes on soil organic carbon stocks and soil moisture trends. This research provides a framework (including high quality data and novel methodologies) to generate environmentally relevant science that can be used for the formulation of public policy around soil and water conservation efforts.
Download or read book Mapping Soil Organic Carbon SOC in a Semi arid Mountainous Watershed Using Variables from Hyperspectral Lidar and Traditional Datasets written by Ryan Matthew Will and published by . This book was released on 2017 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Quantifying soil organic carbon (SOC) in complex terrain is challenging due to its high spatial variability. Generally, limited discrete observations of SOC data are used to develop spatially distributed maps of SOC by developing quantitative relationships between SOC and available spatially distributed variables. In many ecosystems, remotely sensed information on aboveground vegetation can be used to predict belowground carbon stocks. In this research, we developed maps of SOC across a semi-arid watershed based on discrete field observations and modeling using a suite of variables inclusive of hyperspectral and lidar datasets; these observations provide insights into the controls on soil carbon in this environment. The Reynolds Creek Experimental Watershed (RCEW), in SW Idaho, has a strong elevation gradient that controls precipitation and vegetation. Soil samples were collected to 30 cm depth using a nested sampling approach, across the watershed (samples, 279 data points, in 28 plots, discretized with depth, total n=1344) and analyzed for SOC content. Point SOC data was combined with a suite of predictor variables from traditional, lidar and hyperspectral datasets to calibrate Random Forest and Stepwise Multiple Linear Regression models that predict SOC distribution across RCEW. In this study, SOC generally increased along the precipitation-elevation gradient corresponding with an increase in the diversity and abundance of vegetation. We found that variable soil bulk densities and areas of high rock content strongly influenced mass/unit area SOC values. Interestingly, rock content was also negatively correlated with percent SOC. Local variability of SOC in this study was high with the variability at the plot scale about 1/3 of that observed at the watershed scale. Our research suggests that vegetation indices calculated from spectral data are the best predictors of SOC storage in this system. Roughly 60% of the variance in SOC data is explained using Normalized Difference Vegetation Index while two hyperspectral vegetation indices, Modified Red Edge Simple Ratio and Modified Red Edge Normalized Difference Vegetation Index explain over 70%. The addition of Lidar variables modestly improved SOC prediction, explaining 75% of variability in SOC."--Boise State University ScholarWorks.
Download or read book Measuring Soil Organic Carbon Using Reflectance based Models in the Intermountain West written by Stuart Chod Stephens and published by . This book was released on 2006 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: The quantification of soil organic carbon across various landscapes is necessary in order to assess and manage terrestrial carbon sequestration efforts in agriculture and natural environments. Because soils rich in organic carbon are most often identified by their dark appearance, most research has looked at reflectance within visible bands to quantify and map carbon variability.
Download or read book Mapping Soil Organic Carbon Using Convolutional Neural Networks and Global Data written by B.J.M. van Heumen and published by . This book was released on 2019 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Processing of Spatial Information for Mapping of Soil Organic Carbon written by Gregorio C. Simbahan and published by . This book was released on 2004 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Approaches for Mapping Forest Soil Organic Carbon written by Brian J. Clough and published by . This book was released on 2014 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forest soil organic carbon (SOC) is the largest terrestrial pool of carbon, and its management plays a significant role in global efforts to mitigate atmospheric carbon concentrations. Despite its importance, much of the world is still lacking good baseline data of forest soil carbon stocks. In the past, broad scale stocks of forest SOC have been derived from soil surveys based on a small number of sampling units, and the resulting estimates are highly uncertain. More recently, predictive statistical models have received attention as an approach for mapping soil carbon at scales relevant to climate change policy and research. However, in order for these models to be useful they must provide full and accurate accounting of uncertainty, in addition to accurate predictions. This dissertation aims to improve prediction of forest SOC by incorporating two potentially important sources of uncertainty into the modeling process: (1) spatial dependence in soil inventory data; and (2) error associated with assuming a single model to be "true". In order to address these issues, we turn to well established techniques in the Bayesian statistics literature. Our primary focus is on exploring the application of spatial Bayesian hierarchical regression models for improving estimates of forest carbon. This line of research involves both characterizing the spatial dependence in forest SOC inventories at regional, national, and continental scales (the focus of chapters 1 and 3), and applying spatial hierarchical models for mapping SOC and validating this method against non-spatial approaches (chapter 4). Additionally, in chapter 2 we compare methods for model selection and weighting, as well as the effect of model averaging to account for model uncertainty, through rigorous predictive checks. This work is conducted with both forest SOC data as well as other ecological datasets. Taken together, these studies highlight the need for a consistent statistical framework in order to generate reproducible estimates of forest SOC stocks across the globe. Our results argue for hierarchical models, and especially spatial hierarchical models, as a reasonable way forward for predictive mapping of SOC. However, they also highlight significant methodological development that will be necessary in order to obtain predictively accurate models.
Download or read book Integrated Soil Fertility Management in Africa written by Nteranya Sanginga and published by CIAT. This book was released on 2009 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forward. A call for integrated soil fertility management in Africa. Introduction. ISFM and the African farmer. Part I. The principles of ISFM: ISFM as a strategic goal, Fertilizer management within ISFM, Agro-minerals in ISFM, Organic resource management, ISFM, soil biota and soil health. Part II. ISFM practices: ISFM products and fields practices, ISFM practice in drylands, ISFM practice in savannas and woodlands, ISFM practice in the humid forest zone, Conservation Agriculture. Part III. The process of implementing ISFM: soil fertility diagnosis, soil fertility management advice, Dissemination of ISFM technologies, Designing an ISFM adoption project, ISFM at farm and landscape scales. Part IV. The social dimensions of ISFM: The role of ISFM in gender empowerment, ISFM and household nutrition, Capacity building in ISFM, ISFM in the policy arena, Marketing support for ISFM, Advancing ISFM in Africa. Appendices: Mineral nutrient contents of some common organic resources.
Download or read book Digital Soil Mapping written by Janis L. Boettinger and published by Springer Science & Business Media. This book was released on 2010-06-28 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital Soil Mapping is the creation and the population of a geographically referenced soil database. It is generated at a given resolution by using field and laboratory observation methods coupled with environmental data through quantitative relationships. Digital soil mapping is advancing on different fronts at different rates all across the world. This book presents the state-of-the art and explores strategies for bridging research, production, and environmental application of digital soil mapping.It includes examples from North America, South America, Europe, Asia, and Australia. The chapters address the following topics: - evaluating and using legacy soil data - exploring new environmental covariates and sampling schemes - using integrated sensors to infer soil properties or status - innovative inference systems predicting soil classes, properties, and estimating their uncertainties - using digital soil mapping and techniques for soil assessment and environmental application - protocol and capacity building for making digital soil mapping operational around the globe.
Download or read book Google Earth Engine Applications written by Lalit Kumar and published by MDPI. This book was released on 2019-04-23 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.
Download or read book Lamto written by Luc Abbadie and published by Springer Science & Business Media. This book was released on 2006-11-22 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: Synthesizing 40 years of ongoing ecological research, this book examines the structure, function, and dynamics of the Lamto humid savanna. From the history of the Lamto ecology station, to an overview of enivronmental conditions of the site, and examining the integrative view of energy and nutrient fluxes relative to the dynamics of the region's vegetation, this exacting work is as unique and treasured as Lamto itself.
Download or read book The Dry Forests and Woodlands of Africa written by Emmanuel N. Chidumayo and published by Routledge. This book was released on 2010-09-23 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: The dry forests and woodlands of Sub-Saharan Africa are major ecosystems, with a broad range of strong economic and cultural incentives for keeping them intact. However, few people are aware of their importance, compared to tropical rainforests, despite them being home to more than half of the continent's population. This unique book brings together scientific knowledge on this topic from East, West, and Southern Africa and describes the relationships between forests, woodlands, people and their livelihoods. Dry forest is defined as vegetation dominated by woody plants, primarily trees, the canopy of which covers more than 10 per cent of the ground surface, occurring in climates with a dry season of three months or more. This broad definition - wider than those used by many authors - incorporates vegetation types commonly termed woodland, shrubland, thicket, savanna, wooded grassland, as well as dry forest in its strict sense. The book provides a comparative analysis of management experiences from the different geographic regions, emphasizing the need to balance the utilization of dry forests and woodland products between current and future human needs. Further, the book explores the techniques and strategies that can be deployed to improve the management of African dry forests and woodlands for the benefit of all, but more importantly, the communities that live off these vegetation formations. Thus, the book lays a foundation for improving the management of dry forests and woodlands for the wide range of products and services they provide.
Download or read book Measuring Vulnerability to Natural Hazards written by Birkmann and published by The Energy and Resources Institute (TERI). This book was released on 2007-01-01 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt: Measuring Vulnerability to Natural Hazards presents a broad range of current approaches to measuring vulnerability. It provides a comprehensive overview of different concepts at the global, regional, national, and local levels, and explores various schools of thought. More than 40 distinguished academics and practitioners analyse quantitative and qualitative approaches, and examine their strengths and limitations. This book contains concrete experiences and examples from Africa, Asia, the Americas and Europe to illustrate the theoretical analyses.The authors provide answers to some of the key questions on how to measure vulnerability and they draw attention to issues with insufficient coverage, such as the environmental and institutional dimensions of vulnerability and methods to combine different methodologies.This book is a unique compilation of state-of-the-art vulnerability assessment and is essential reading for academics, students, policy makers, practitioners, and anybody else interested in understanding the fundamentals of measuring vulnerability. It is a critical review that provides important conclusions which can serve as an orientation for future research towards more disaster resilient communities.
