This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques.
This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images' classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques.
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.
This edited book is a collection of information on recent advancements in remote sensing for forestry application. The main focus of this book is to address the novel applications in remote sensing in terms of recent techniques, sensors, methods and data collection. Remote sensing has been used for many decades. Today, remote sensing is heavily utilized in forest management, which is acquired from airborne and space-borne platforms using satellite data. In recent years, there have been rapid advances in the new types of sensors. The new generations of satellite sensors are introduced not only to provide important information on forest ecosystems, but also to improve the techniques and accuracies obtained by the traditional approaches. Researchers have become increasingly aware of the potential of remote sensing to address important forestry issues and challenges. The number of forestry publications using remote sensing has grown very rapidly, and this is noticeable with many recent technologies and applications. This book provides valuable source of reference to foresters, researchers, ecologists, climate change scientists and scholars who use remote sensing in their work. Furthermore, it serves as an academic book for undergraduate and graduate students of forestry, agriculture, natural resources and environmental sciences. The book demonstrates and engages the students how the new advancements in remote sensing technology and theory relate to the many real-world forestry applications. It gives readers deeper understanding on how remote sensing has come a long way and evolved into a mature science in the past five decades.
Forests provide a large range of beneficial services, including tangible ones such as timber and recreation, and intangible services such as climate regulation, biodiversity, and watershed protection. On the other hand, forests can also be considered roadblocks to progress that occupy space more productively used for agriculture, making consideration of their regulating services crucial for balancing land use and forest loss. Monitoring forest cover and loss is critical for obtaining the data necessary to help define what is needed to maintain the varying forest service requirements in different parts of the world. There is an increasing need for timely and accurate forest change information, and consequently a greater interest in monitoring those changes. Global Forest Monitoring from Earth Observation covers the very recent developments undertaken for monitoring forest areas from global to national levels using Earth observation satellite data. It describes operational tools and systems for monitoring forest ecosystems, discussing why and how researchers currently use remotely sensed data to study forest cover and loss over large areas. The book introduces the role of forests in providing ecosystem services and the need for monitoring their change over time, followed by an overview of the use of earth observation data to support forest monitoring. It discusses general methodological differences, including wall-to-wall mapping and sampling approaches, as well as data availability. This book provides excellent coverage of the research and applications of forest monitoring, indicator mapping at coarse spatial resolution, sample-based assessments, and wall-to-wall mapping at medium spatial resolution using optical remote sensing datasets, such as MODIS and Landsat. It examines the use of radar imagery in forest monitoring and presents a number of operational systems, from Brazil’s PRODES and DETER products to Australia’s NCAS system. Written by leading global experts in the field, this book offers a launch point for future advances in satellite-based monitoring of global forest resources. It gives readers a deeper understanding of global forest monitoring methods and shows how state-of-the-art technologies may soon provide key data for creating more balanced policies.
Advances in Remote Sensing for Forest Monitoring An expert overview of remote sensing as applied to forests and other vegetation In Advances in Remote Sensing for Forest Monitoring, a team of distinguished researchers delivers an expansive and insightful discussion of the latest research on remote sensing technologies as they relate to the monitoring of forests, plantations, and other vegetation. The authors also explore the use of unmanned aerial vehicles and drones, as well as multisource and multi-sensor data – such as optical, SAR, LIDAR, and hyperspectral data. The book draws on the latest data and research to show how remote sensing solutions are being used in real-world settings. It offers contributions from researchers and practitioners from a wide variety of backgrounds and geographical regions to provide a diverse and global set of perspectives on the subject. Readers will also find: A thorough introduction to forest monitoring using remote sensing including recent advances in remote sensing technology Comprehensive explorations of sustainable forest management to enhance ecosystem services and livelihood security using a geospatial approach Case studies of monitoring the biochemical and biophysical parameters of forests, including carotene and xanthophyll content Practical advice on how to apply machine learning tools to remote sensing data Perfect for postgraduates, lecturers, and researchers in the fields of environmental science, forestry, and natural resource management, Advances in Remote Sensing for Forest Monitoring will also earn a place in the libraries of professionals and researchers working with remote sensing technology.
Proceedings of a Seminar on the Practical Applications of Remote Sensing in Forestry, Jönköping, May 1985, under the Auspices of the Joint FAO/ECE Working Party on Forest Economics and Statistics and the Swedish National Board of Forestry under the Ministry of Agriculture
Improved monitoring of forest biomass is needed to quantify natural and anthropogenic effects on the terrestrial carbon cycle. Landsat's temporal and spatial coverage, fine spatial grain, and long history of earth observations provide a unique opportunity for measuring biophysical properties of vegetation across large areas and long time scales. However, like other multi-spectral data, the relationship between single-date reflectance and forest biomass weakens under certain canopy conditions. Because the structure and composition of a forest stand at any point in time is linked to the stand's disturbance history, one potential means of enhancing Landsat's spectral relationships with biomass is by including information on vegetation trends prior to the date for which estimates are desired. The purpose of this research was to develop and assess a method that links field data, airborne lidar, and Landsat-derived disturbance and recovery history for mapping of forest biomass and biomass change. Our study area is located in eastern Oregon (US), an area dominated by mixed conifer and single species forests. In Chapter 2, we test and demonstrate the utility of Landsat-derived disturbance and recovery metrics to predict current forest structure (live and dead biomass, basal area, and stand height) for 51 field plots, and compare the results with estimates from airborne lidar and single-date Landsat imagery. To characterize the complex nature of long-term (insect, growth) and short-term (fire, harvest) vegetation changes found in this area, we use annual Landsat time series between 1972 and 2010. This required integrating Landsat data from MSS (1972-1992) and TM/ETM+ (1982-present) sensors. In Chapter 2, we describe a method to bridge spectral differences between Landsat sensors, and therefore extent Landsat time-series analyses back to 1972. In Chapter 3, we extend and automate our approach and develop maps of current (2009) and historic (1993-2009) live forest biomass. We use lidar data for model training and evaluate the results with forest inventory data. We further conduct a sensitivity analysis to determine the effects of forest structure, time-series length, terrain and sampling design on model predictions. Our research showed that including disturbance and recovery trends in empirical models significantly improved predictions of forest biomass, and that the approach can be applied across a larger landscape and across time for estimating biomass change.
A review of stem volume and biomass equations for tree species growing in Europe is presented. The mathematical forms of the empirical models, the associated statistical parameters and information about the size of the trees and the country of origin were collated from scientific articles and from technical reports. The collected information provides a basic tool for estimation of carbon stocks and nutrient balance of forest ecosystems across Europe as well as for validation of theoretical models of biomass allocation.
The accurate measurement of ecosystem biomass is of great importance in scientific, resource management and energy sectors. In particular, biomass is a direct measurement of carbon storage within an ecosystem and of great importance for carbon cycle science and carbon emission mitigation. Remote Sensing is the most accurate tool for global biomass measurements because of the ability to measure large areas. Current biomass estimates are derived primarily from ground-based samples, as compiled and reported in inventories and ecosystem samples. By using remote sensing technologies, we are able to scale up the sample values and supply wall to wall mapping of biomass. Three separate remote sensing technologies are available today to measure ecosystem biomass: passive optical, radar, and lidar. There are many measurement methodologies that range from the application driven to the most technologically cutting-edge. The goal of this book is to address the newest developments in biomass measurements, sensor development, field measurements and modeling. The chapters in this book are separated into five main sections.
