Use Of The Normalized Difference Vegetation Index Ndvi To Assess Land Degradation At Multiple Scales

This report examines the scientific basis for the use of remotely sensed data, particularly Normalized Difference Vegetation Index (NDVI), primarily for the assessment of land degradation at different scales and for a range of applications, including resilience of agro-ecosystems. Evidence is drawn from a wide range of investigations, primarily from the scientific peer-reviewed literature but also non-journal sources. The literature review has been corroborated by interviews with leading specialists in the field. The report reviews the use of NDVI for a range of themes related to land degradation, including land cover change, drought monitoring and early warning systems, desertification processes, greening trends, soil erosion and salinization, vegetation burning and recovery after fire, biodiversity loss, and soil carbon. This SpringerBrief also discusses the limits of the use of NDVI for land degradation assessment and potential for future directions of use. A substantial body of peer-reviewed research lends unequivocal support for the use of coarse-resolution time series of NDVI data for studying vegetation dynamics at global, continental and sub-continental levels. There is compelling evidence that these data are highly correlated with biophysically meaningful vegetation characteristics such as photosynthetic capacity and primary production that are closely related to land degradation and to agroecosystem resilience.

This book provides a coherent review of NDVI including its origin, its availability, its associated advantages and disadvantages, and its possible applications in ecology, environmental monitoring, wildlife management, and conservation.

This unique book focuses on environmental degradation in Asian countries including land degradation and soil erosion. The land degradation covers assessing environmental degradation using geospatial technology, land use land cover mapping, environmental and anthropogenic degradation, assessment of land degradation vulnerability, evaluation of the impact of earthquake and the environmental control of the sand dunes. It also addresses the soil degradation and environmental pollution and presents several case studies such as tectonic activity and erosion, assessment of aircraft sound, soil degradation assessment for the arid territories, soil pollution, waste engine oil contamination, soil degradation, soil erosion modelling, land use and land cover change and its effect on soil erosion changes. Additionally, the book discusses the impact of climate change, and human activities including urban environmental quality, air pollution and the impact of armed conflict on the environment. Moreover, topics such as vegetation degradation including forest changes, hydrological and agricultural drought are presented. The book includes authors and scientists from Egypt, Iraq, Iran, India, Mongolia, United Arab Emirates, Uzbekistan, Republic of Kazakhstan, USA, Turkey, South Africa, Italy, China, Malaysia, Poland and Russia. Graduate students, researchers, engineers, policy planners, policymakers and stockholders could benefit from the information and the knowledge in this book.

This volume deals with land degradation, which is occurring in almost all terrestrial biomes and agro-ecologies, in both low and high income countries and is stretching to about 30% of the total global land area. About three billion people reside in these degraded lands. However, the impact of land degradation is especially severe on livelihoods of the poor who heavily depend on natural resources. The annual global cost of land degradation due to land use and cover change (LUCC) and lower cropland and rangeland productivity is estimated to be about 300 billion USD. Sub-Saharan Africa (SSA) accounts for the largest share (22%) of the total global cost of land degradation. Only about 38% of the cost of land degradation due to LUCC - which accounts for 78% of the US$300 billion loss – is borne by land users and the remaining share (62%) is borne by consumers of ecosystem services off the farm. The results in this volume indicate that reversing land degradation trends makes both economic sense, and has multiple social and environmental benefits. On average, one US dollar investment into restoration of degraded land returns five US dollars. The findings of the country case studies call for increased investments into the rehabilitation and restoration of degraded lands, including through such institutional and policy measures as strengthening community participation for sustainable land management, enhancing government effectiveness and rule of law, improving access to markets and rural services, and securing land tenure. The assessment in this volume has been conducted at a time when there is an elevated interest in private land investments and when global efforts to achieve sustainable development objectives have intensified. In this regard, the results of this volume can contribute significantly to the ongoing policy debate and efforts to design strategies for achieving sustainable development goals and related efforts to address land degradation and halt biodiversity loss.

Land degradation neutrality (LDN) has been defined by the Parties to the Convention as: A state whereby the amount and quality of land resources, necessary to support ecosystem functions and services and enhance food security, remains stable or increases within specified temporal and spatial scales and ecosystems. The present report is an overview of the status of land degradation neutrality (LDN) in the region including a national overview for the 17 programme countries.

Above ground biomass has been listed by the Intergovernmental Panel on Climate Change as one of the five most prominent, visible, and dynamic terrestrial carbon pools. The increased awareness of the impacts of climate change has seen a burgeoning need to consistently assess carbon stocks to combat carbon sequestration. An accurate estimation of carbon stocks and an understanding of the carbon sources and sinks can aid the improvement and accuracy of carbon flux models, an important pre-requisite of climate change impact projections. Based on 15 research topics, this book demonstrates the role of remote sensing in quantifying above ground biomass (forest, grass, woodlands) across varying spatial and temporal scales. The innovative application areas of the book include algorithm development and implementation, accuracy assessment, scaling issues (local–regional–global biomass mapping), and the integration of microwaves (i.e. LiDAR), along with optical sensors, forest biomass mapping, rangeland productivity and abundance (grass biomass, density, cover), bush encroachment biomass, and seasonal and long-term biomass monitoring.

In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.

There has been a recent surge of interest in remote sensing and its use in ecology and conservation but this is the first book to focus explicitly on the NDVI (Normalised Difference Vegetation Index), a simple numerical indicator and powerful tool that can be used to assess spatio-temporal changes in green vegetation. The NDVI opens the possibility of addressing questions on scales inaccessible to ground-based methods alone; it is mostly freely available with global coverage over several decades. This novel text provides an authoritative overview of the principles and possible applications of the NDVI in ecology, environmental and wildlife management, and conservation. NDVI data can provide valuable information about temporal and spatial changes in vegetation distribution, productivity, and dynamics; allowing monitoring of habitat degradation and fragmentation, or assessment of the ecological effects of climatic disasters such as drought or fire. The NDVI has also provided ecologists with a promising way to couple vegetation with animal distribution, abundance, movement, survival and reproductive parameters. Over the last few decades, numerous studies have highlighted the potential key role of satellite data and the NDVI in macroecology, plant ecology, animal population dynamics, environmental monitoring, habitat selection and habitat use studies, and paleoecology. The chapters are organised around two sections: the first detailing vegetation indices and the NDVI, the principles behind the NDVI, its correlation with climate, the available NDVI datasets, and the possible complications and errors associated with the use of this satellite-based vegetation index. The second section discusses the possible applications of the NDVI in ecology, environmental and wildlife management, and conservation.