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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Table of Contents
Cover
Title page
Copyright Page
Dedication Page
List of Contributors
Foreword
Preface
List of Abbreviations
Editors
Section I: General Introduction to Forest Monitoring
1 Introduction to Forest Monitoring Using Advanced Remote Sensing Technology – An Editorial Message
1.1 Introduction
1.2 Forest Monitoring: Importance and Trends
1.3 Advances in Remote Sensing Technology for Forest Monitoring
1.4 Summary
References
2 Geospatial Perspectives of Sustainable Forest Management to Enhance Ecosystem Services and Livelihood Security
2.1 Introduction and Background
2.2 Major Ecological Disturbances of Forests
2.3 Forest Fires
2.4 Invasive Plant Species (IPS)
2.5 Climate Change
2.6 Forest Ecosystem Services (FESs)
2.7 Sustainable Uses of Forests and Their Contributions to Livelihood Security
2.8 Landscape Based Approach (LbA) and Ecosystem‐Based Approach (EbA) of Sustainable Forests Management (SFM)
2.9 Conclusions
References
Section II: Forest Parameters – Biochemical and Biophysical Parameters
3 Distinguishing Carotene and Xanthophyll Contents in the Leaves of Riparian Forest Species by Applying Machine Learning Algorithms to Field Reflectance Data
3.1 Introduction
3.2 Study Area
3.3 Data
3.4 Methodology
3.5 Results
3.6 Discussion
3.7 Conclusion
Acknowledgments
Funding
References
Supporting Information
4 Modeling of Abiotic Stress of Conifers with Remote Sensing Data
4.1 Introduction
4.2 Natural Factors
4.3 Anthropogenic Factors
4.4 Thresholds and Critical Loads
4.5 Conclusions
References
5 Retrieval of Mangrove Forest Properties Using Synthetic Aperture Radar
5.1 Introduction
5.2 Microwave Remote Sensing
5.3 Conclusions
References
6 Photosynthetic Variables Estimation in a Mangrove Forest
6.1 Introduction
6.2 Materials and Methodology
6.3 Results
6.4 Discussion
6.5 Conclusions
References
7 Quantifying Carbon Stock Variability of Species Within a Reforested Urban Landscape Using Texture Measures Derived from Remotely Sensed Imagery
7.1 Introduction
7.2 Materials and Methods
7.3 Results
7.4 Discussion
7.5 Conclusion
Acknowledgments
References
8 Mapping Oil Palm Plantations in the Fringe of Sebangau National Park, Central Kalimantan, Indonesia
8.1 Introduction
8.2 Methodology
8.3 Results and Discussion
8.4 Conclusion
Acknowledgments
References
Section III: Remote Sensing Technology for Forest Fire Monitoring
9 Forest Fire Susceptibility Mapping by Integrating Remote Sensing and Machine Learning Algorithms
9.1 Introduction
9.2 Study Area
9.3 Materials and Methods
9.4 Results
9.5 Discussion
9.6 Conclusion
Acknowledgements
References
10 Leveraging Google Earth Engine (GEE) and Landsat Images to Assess Bushfire Severity and Postfire Short‐Term Vegetation Recovery
10.1 Introduction
10.2 Materials and Methods
10.3 Results
10.4 Discussion
10.5 Conclusions
Acknowledgements
References
Section IV: Advancement in RS‐Drones and Multi‐Sensors Multi‐Source for Forest Monitoring
11 Recent Advancement and Role of Drones in Forest Monitoring
11.1 Introduction
11.2 Field Sampling Methods in Forest Application: Traditional to Present
11.3 Biophysical Parameters Assessment UsingRemote Sensing
11.4 Biochemical Parameter Assessment Using Remote Sensing
11.5 UAV‐Based Remote Sensing
11.6 Other Important Forest Research Applications and Practices
11.7 Conclusions
References
12 Applications of Multi‐Source and Multi‐Sensor Data Fusion of Remote Sensing for Forest Species Mapping
12.1 Introduction
12.2 Forest Mapping Process
12.3 Data Fusion
12.4 Discussion
12.5 Conclusion and Future Trends
Acknowledgments
References
Section V: Opportunities, Challenges, and Future Aspects in Forest Monitoring
13 Challenges and Monitoring Methods of Forest Management Through Geospatial Application: A Review
13.1 Introduction
13.2 Importance of Forest Cover
13.3 Challenges in the Sustainability of Forest Management
13.4 Summary
References
14 Challenges and Future Possibilities Toward Himalayan Forest Monitoring
14.1 Introduction
14.2 Component of Forest Monitoring
14.3 Challenges in Satellite Monitoring
14.4 Challenges in Ground Survey and Inventory
14.5 Future Possibilities in Forest Monitoring
14.6 Conclusion
References
Web Sources
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Remote sensing sensors and its applications in sustainable forest...
Chapter 3
Table 3.1 Statistics of the six carotenoid contents analyzed in plant leave...
Table 3.2 Normalized Root Mean Square Error (NRMSE) obtained by comparing t...
Table S3.1 Calibrated parameters of the machine learning algorithms retained...
Chapter 4
Table 4.1 List of the used vegetation indices in the chapter.
Chapter 5
Table 5.1 Details of microwave frequency bands and their suitability for ty...
Table 5.2 Detail of free and open data from past, present, and forthcoming ...
Table 5.3 Studies done on the mangroves forest using SAR data.
Table 5.4 SAR backscatter values before and after degradation.
Chapter 6
Table 6.1 Description of satellite datasets that were used in the study.
Table 6.2 Vegetation indices used to estimate LAI and chlorophyll concentra...
Chapter 7
Table 7.1 Image‐texture metrics derived from Sentinel‐2 MSIs and their form...
Table 7.2 Selection of optimal bands texture measures at the best moving wi...
Table 7.3 Performance of optimal texture measures in predicting carbon stoc...
Chapter 8
Table 8.1 Accuracies obtained from conventional backscatter coefficients an...
Chapter 9
Table 9.1 Details of the ignition factors used for forest fire modeling.
Table 9.2 Computation of multicollinearity analysis among the ignition cond...
Table 9.3 Calculated area under the forest fire susceptible zones.
Chapter 10
Table 10.1 Specifications of Landsat 5 TM and Landsat 8 OLI images used in ...
Table 10.2 Earth Engine Data Catalog for used data in Google Earth Engine....
Table 10.3 Description of climatic and topographic variables used in this s...
Table 10.4 Severity classification threshold for dNBR and RdNBR classificat...
Table 10.5 Error matrices of bushfire severity classification map.
Chapter 11
Table 11.1 Common multispectral sensors available for UAV in market.
Table 11.2 Hyperspectral sensors and their specifications.
Table 11.3 Thermal sensors and their specifications.
Table 11.4 LiDAR sensors and their specifications.
Table 11.5 List of widely used drone type along with their pros, cons, and ...
Chapter 12
Table 12.1 Commonly used vegetation indices available in this Chapter, by r...
Chapter 13
Table 13.1 Major classification of forest and other vegetation.
Table 13.2 World distribution of forests.
Table 13.3 Challenges in sustainability of forest.
Table 13.4 Advantages of using Geospatial tools in forest management.
Table 13.5 Major applications of GIS in management of forests.
Chapter 14
Table 14.1 Distribution of Himalayan forest types along the elevation gradi...
List of Illustrations
Chapter 2
Figure 2.1 Cattle are on the way to the nearby forest for grazing at the sam...
Figure 2.2 Traditional and scientific approaches that should be practiced by...
Figure 2.3 Freshly collected mango and jackfruit rachis from the local fores...
Figure 2.4 Minor forest products like Sal leaves, twig brush (dattun), fuelw...
Figure 2.5 Five‐year cumulative forest fire frequency based on Gi‐Bin optimi...
Figure 2.6 Graphical representation of forest fire hotspot during the period...
Figure 2.7 Dominant IPS of Indian Tropical Forests: (a)
Lantana camara
; (b)
Figure 2.8 Ecosystem services.
Chapter 3
Figure 3.1 (a) ‐carotene and xanthophyll transformation pathways. Violaxanth...
Figure 3.2 (left figures) Original spectral signatures of leaves from the fi...
Figure 3.3 Relationship between the six carotenoid pigments (in μg cm
−2
...
Figure 3.4 Comparison between the measured and predicted carotenoid contents...
Figure 3.5 Variable importance metrics obtained for the different machine le...
Figure S3.1 Example of the
continuum
removal algorithm applied to the spectr...
Figure S3.2 Correlation maps obtained between all possible pairs of waveleng...
Figure S3.3 Correlation maps obtained between all possible pairs of waveleng...
Figure S3.4 Samples selected for the calibration and validation datasets usi...
Figure S3.5 Pearson coefficient of correlation (
r
) obtained between continuu...
Figure S3.6 Suitability of the different machine learning algorithms to pred...
Figure S3.7 Comparison between the measured and predicted carotenoid content...
Figure S3.8 Regression residuals for the six predicted carotenoid contents i...
Chapter 4
Figure 4.1 An extract of a burned area mapping product generated by CIMA Fou...
Figure 4.2 Map of geochemical test sites at Teyna watershed.
Figure 4.3 Environmental interactions between an individual plant and other ...
Chapter 5
Figure 5.1 Sketch of scattering types.
Figure 5.2 Penetration capability of different SAR band into forest.
Figure 5.3 Global PALSAR‐2 fnf (forest/non‐forest) image of L‐HH band of the...
Figure 5.4 Images of the Tapi estuary, Gujarat, India: (a–d) represent ALOS‐...
Figure 5.5 (a, c, i, k) shows the mangrove area; (b, d, j, l) shows the mang...
Figure 5.6 Mangrove forest, Khajod village, Gujarat, India; (a) before degra...
Chapter 6
Figure 6.1 (a) A true color composite (TCC, Sentinel‐2, March 2019) image of...
Figure 6.2 Seasonal trend analysis of (a) LAI, (b) SIF, and (c) GPP, during ...
Figure 6.3 Relationship between satellite‐derived (a) GPP and LAI, (b) GPP a...
Figure 6.4 Relationship between DHP‐based field LAI and VIs; (a) NDVI, (b) N...
Figure 6.5 (a) Predicted LAI image using RF algorithm wherein four VIs (NDVI...
Figure 6.6 Relationship between leaf chlorophyll content and Vis; (a) NDVI, ...
Figure 6.7 (a) Chlorophyll concentration image generated using RF algorithm ...
Figure 6.8 Temporal pattern of (a) MODIS‐based LAI, (b) MODIS‐based GPP, and...
Chapter 7
Figure 7.1 The study area and field sample points.
Figure 7.2 Descriptive statistics of the aboveground measured carbon stock v...
Figure 7.3 Relationship between predicted versus measured carbon stock of (a...
Figure 7.4 Total mean carbon stock variability between different reforested ...
Figure 7.5 Spatial distribution of aboveground carbon stock of (a)
Acacia ro
...
Chapter 8
Figure 8.1 Site map and the coverage of PALSAR‐2 data. Sampit city, the capi...
Figure 8.2 Data sources to construct the reference dataset: (a) point‐wise g...
Figure 8.3 Histogram of backscatter coefficients grouped by land cover class...
Figure 8.4 Distribution of entropy‐alpha angle dual polarimetric decompositi...
Figure 8.5 Classified map, overlaid on top of Open Street Map (OSM) data. Ma...
Chapter 9
Figure 9.1 Locational and forest fire inventory map of the study area.
Figure 9.2 Forest fire ignition parameters of the study area; (a) elevation,...
Figure 9.3 Forest fire susceptibility mapping using (a) LR, (b) RF, (c) SVM,...
Figure 9.4 Validation of the forest fire susceptibility models; (a) LR, (b) ...
Chapter 10
Figure 10.1 Map of the study area, Kilmore East – Murrindindi region of Vict...
Figure 10.2 Overall workflow of the study. The first stage presents bushfire...
Figure 10.3 Bushfire severity map showing low, moderate, high, and very high...
Figure 10.4 Histogram map of dNBR and RdNBR indices showing the frequency di...
Figure 10.5 Accuracy assessment of bushfire severity classification using dN...
Figure 10.6 Normalized Difference Vegetation Index (NDVI) maps of the study ...
Figure 10.7 Mean NDVI time‐series line graph showing how the mean NDVI value...
Figure 10.8 Climate anomaly diagram of climatic variables used in this study...
Figure 10.9 Relative importance of climatic and topographical variables in s...
Chapter 11
Figure 11.1 (a) The UAV orthophotoquad image of the spatial distribution of
Figure 11.2 Aerial images of the genetic trial of
Pinus halepensis
considere...
Figure 11.3 Examples of (a) the octocopter UAV LiDAR point cloud data in a f...
Figure 11.4 Automatic isolation of stems of isolated trees and taper extract...
Figure 11.5 Airborne laser scanning (ALS) data examples. (a) Airborne point ...
Figure 11.6 Unmanned aerial vehicle (UAV) remote sensing system of the Finni...
Figure 11.7 Typical photogrammetric processing pipeline. The acquired UAVs i...
Chapter 12
Figure 12.1 Commonly used machine learning methods.
Figure 12.2 Accuracy assessment and model evaluation metrics.
Figure 12.3 (a) Overview of Single‐stage and Two‐stage approaches for calibr...
Figure 12.4 Above‐ground biomass maps of HWF generated using decision tree‐b...
Figure 12.5 An overview of the land cover classification process.
Chapter 13
Figure 13.1 The most intact forests by global ecological zone, 2015.
Figure 13.2 Proportion and distribution of global forest area by climatic do...
Figure 13.3 Application of remote sensing and GIS in forest data management....
Figure 13.4 Linking different approaches (high frequency wireless sensor net...
Chapter 14
Figure 14.1 Map of the Himalayan biodiversity hotspot and Greater Hindu‐Kush...
Figure 14.2 The stages of forest data collection and database generation....
Figure 14.3 Components of forests montoring.
Figure 14.4 Fuel type classification obtained from the EO‐1 Hyperion; (a) Qu...
Figure 14.5 Examples of imagery gathered by small drones that show the extre...
Guide
Cover Page
Title Page
Copyright Page
Dedication Page
List of Contributors
Preface
Preface
List of Abbreviations
Editors
Table of Contents
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Advances in Remote Sensing for Forest Monitoring
Edited by Prem C. Pandey and Paul Arellano
This edition first published 2023© 2023 John Wiley & Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
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Library of Congress Cataloging‐in‐Publication Data
Names: Pandey, Prem C., 1985‐ editor. | Arellano, Paul, 1967– editor.Title: Advances in remote sensing for forest monitoring / edited by Prem C. Pandey & Paul Arellano.Description: Hoboken, NJ : Wiley, 2023. | Includes bibliographical references and index.Identifiers: LCCN 2022040573 (print) | LCCN 2022040574 (ebook) | ISBN 9781119788126 (cloth) | ISBN 9781119788133 (adobe pdf) | ISBN 9781119788140 (epub)Subjects: LCSH: Forests and forestry–Remote sensing. | Forest monitoring.Classification: LCC SD387.R4 A36 2023 (print) | LCC SD387.R4 (ebook) | DDC 634.9–dc23/eng/20220829LC record available at https://lccn.loc.gov/2022040573LC ebook record available at https://lccn.loc.gov/2022040574
Cover Design: WileyCover Image: © Vonkara1/Getty Images
Dedicated to the loving memory of my father, 1941–2019, and mother, 1946–2013.who inspired me to follow the dreams for success.
— Dr. Prem C. Pandey
This book is dedicated to my six‐year‐old beloved son Paul Nicolás, the sweetest little sunshine of my life.
— Dr. Paul Arellano
List of Contributors
Zulyan Afif
Regional Hazard Mitigation Agency (BPBD), Kotawaringin Timur, Sampit, Indonesia.
Paul Arellano
College of Forest Resources and Environmental Sciences, Michigan Technological University, Houghton, Michigan, USA.
Jagannath Aryal
Department of Infrastructure Engineering, The University of Melbourne; Centre for Spatial Data Infrastructures and Land Administration (CSDILA), The University of Melbourne; and Centre for Disaster Management and Public Safety (CDMPS), The University of Melbourne, Melbourne, Parkville, Victoria, Australia.
Surbhi Barnwal
Centre for Oceans, Rivers, Atmosphere, and Land Sciences (CORAL), Indian Institute of Technology Kharagpur, Kharagpur, India.
Mukunda D. Behera
Centre for Oceans, Rivers, Atmosphere, and Land Sciences (CORAL), Indian Institute of Technology Kharagpur, Kharagpur, India.
Soumit K. Behera
Plant Ecology and Climate Change Science Division, CSIR–National Botanical Research Institute, Lucknow, India.
Bimal K. Bhattyacharya
Space Applications Centre,ISRO, Ahmedabad, India.
Aashri Chauhan
Center for Environmental Sciences& Engineering, School of Natural Sciences, Shiv Nadar Institution of Eminence (Deemed to be University), Greater Noida, Uttar Pradesh, India.
Anup Kumar Das
Space Application Center, ISRO, Ahmedabad, Gujarat, India.
Tanmoy Das
Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India.
Soumesh K. Dash
Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.
Pawan Ekka
Department of Environmental Sciences, Central University of Jharkhand, Brambe, Ranchi, Jharkhand, India.
Sophie Fabre
Office National d’Études et de Recherches Aérospatiales (ONERA), Toulouse, France.
Lachezar Filchev
Space Research and Technology Institute, Bulgarian Academy of Sciences, Bulgaria.
Ayushi Gupta
Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
Yephi Haptadi
Regional Hazard Mitigation Agency (BPBD), Kotawaringin Timur, Sampit, Indonesia.
Bodi S.P.C. Kishore
Department of Geoinformatics, Central University of Jharkhand, Brambe, Ranchi, Jharkhand, India.
Amit Kumar
Department of Geoinformatics, Central University of Jharkhand, Brambe, Ranchi, Jharkhand, India.
Gajendra Kumar
Department of Geoinformatics, Central University of Jharkhand, Brambe, Ranchi, Jharkhand, India.
Rahul Kumar
ICFRE‐Institute of Forest Productivity, Lalgutwa, Ranchi, Jharkhand, India.
Neeta Kumari
Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.
Rina Kumari
School of Environment Science and Sustainable Development, Central University of Gujarat, Gujarat, India.
Guillaume Lassalle
Office National d’Études et de Recherches Aérospatiales (ONERA), Toulouse, France.
Salim Lamine
Faculty of Biological Sciences, University of Sciences and Technology Houari Boumediene (USTHB), Algiers, Algeria; and Department of Geography and Earth Sciences, University of Aberystwyth, Ceredigion, Wales, UK.
Nikhil Lele
Space Applications Centre, ISRO, Ahmedabad Gujarat, India.
Neeraj K. Maurya
Center for Environmental Sciences & Engineering, School of Natural Sciences, Shiv Nadar Institution of Eminence (Deemed to be University), Greater Noida, Uttar Pradesh, India.
Parul Maurya
School of Environment Science and Sustainable Development, Central University of Gujarat, Gujarat, India.
Mthembeni Mngadi
School of Agricultural, Earth, and Environmental Sciences, Discipline of Geography, University of KwaZulu‐Natal, Pietermaritzburg, South Africa.
Pegah Mohammadpour
University of Coimbra, ADAI, Department of Mechanical Engineering, Pólo II, Coimbra, Portugal; and Universidad de Alcalá, Department of Geology, Geography and Environment Science, Alcala de Henares, Spain.
Onisimo Mutanga
School of Agricultural, Earth, and Environmental Sciences, Discipline of Geography, University of KwaZulu‐Natal, Pietermaritzburg, South Africa.
Mohd Waseem Naikoo
Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India.
Shah Al Nawajish
Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.
John Odindi
School of Agricultural, Earth, and Environmental Sciences, Discipline of Geography, University of KwaZulu‐Natal, Pietermaritzburg, South Africa.
Prem C. Pandey
Center for Environmental Sciences & Engineering, School of Natural Sciences, Shiv Nadar Institution of Eminence (Deemed to be University), Greater Noida, Uttar Pradesh, India.
Soumya Pandey
Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.
Dyah R. Panuju
Geospatial Information and Technologies for the Integrative and Intelligent Agriculture (GITIIA), Center for Regional Systems Analysis, Planning and Development (CrestPent), Bogor Agricultural University, Bogor, Indonesia; and Department of Soil Science and Land Resources, Bogor Agricultural University, Dramaga, Bogor, Indonesia.
Somnath Paramanik
Centre for Ocean, River, Atmosphere and Land Sciences (CORAL), IndianInstitute of Technology Kharagpur, Kharagpur, India.
Subhashree Patra
Department of Environmental Sciences, Central University of Jharkhand, Brambe, Ranchi, Jharkhand, India.
Atiqur Rahman
Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India.
Abbas Rajabifard
Department of Infrastructure Engineering, The University of Melbourne; Centre for Spatial Data Infrastructures and Land Administration (CSDILA), The University of Melbourne; and Centre for Disaster Management and Public Safety (CDMPS), The University of Melbourne, Melbourne, Parkville, Victoria, Australia.
Mohd Rihan
Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India.
Purabi Saikia
Department of Environmental Sciences, Central University of Jharkhand, Brambe, Ranchi, Jharkhand, India.
K.V. Satish
Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
Abdul R.M. Shariff
Faculty of Engineering, University Putra Malaysia, UPM, Serdang, Selangor Darul Ehsan, Malaysia; Smart Farming Technology Research Centre, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia; and Institute of Plantation Studies (IKP), Universiti Putra Malaysia, Serdang, Malaysia.
Jigarkumar B. Solanki
School of Environment Science and Sustainable Development, Central University of Gujarat, Gujarat, India.
Prashant K. Srivastava
Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
Shahfahad
Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India.
Saroj K. Sharma
Department of Infrastructure Engineering, The University of Melbourne; Centre for Spatial Data Infrastructures and Land Administration (CSDILA), The University of Melbourne; and Centre for Disaster Management and Public Safety (CDMPS), The University of Melbourne, Melbourne, Parkville, Victoria, Australia.
Swapan Talukdar
Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India.
Amit K. Tripathi
Center for Environmental Sciences & Engineering, School of Natural Sciences, Shiv Nadar Institution of Eminence (Deemed to be University), Greater Noida, Uttar Pradesh, India.
Bambang H. Trisasongko
Geospatial Information and Technologies for the Integrative and Intelligent Agriculture (GITIIA), Center for Regional Systems Analysis, Planning and Development (CrestPent), Bogor Agricultural University, Jalan Pajajaran, Bogor, Indonesia; and Department of Soil Science and Land Resources, Bogor Agricultural University, Dramaga, Bogor, Indonesia.
Roma Varghese
Centre for Oceans, Rivers, Atmosphere, and Land Sciences (CORAL), Indian Institute of Technology Kharagpur, Kharagpur, India.
Carlos Viegas
University of Coimbra, ADAI, Department of Mechanical Engineering, Pólo II, Coimbra, Portugal.
Foreword
Forest ecosystems are a vital part of our biosphere, and forest ecosystem services and resources are the foundations of our civilized societies. The need to monitor and manage global forest resources is becoming increasingly important and urgent as the deleterious impacts of human activities on our planet's ecosystems are becoming widespread at an alarming rate.
Traditionally, forest resource monitoring and management are accomplished using conventional methods; however, advanced remote sensing technologies have emerged as indispensable tools for forest resource monitoring and management over the past several decades. More recently, the advancement and availability of remote sensing data obtained from various sensors including air‐borne, space‐borne, and terrestrial‐handheld instruments, along with a wide range and improved spectral, spatial, and temporal resolutions have significantly added to our capabilities for monitoring and managing forest resources.
The editors and authors of this book have strived to provide an extensive discussion of the use of various advanced techniques in remote sensing that are relevant for forest resource management. In addition they have expertly synthesized various case studies to provide insights into biophysical and biochemical characteristics of forest ecosystems and their utilization in a sustainable manner.
I commend the editors Dr. Prem C. Pandey from the Center for Environmental Sciences & Engineering, Shiv Nadar Institution of Eminence (Deemed to be University), India, and Dr. Paul Arellano at Michigan Technological University, in the US for this outstanding publication. The book contains 14 thoughtfully organized chapters that cover various aspects of forest monitoring and their sustainable management utilizing remote sensing data and techniques to illustrate the relevant advancements made in recent years.
The editors are also to be commended for including an impressive international list of contributors from several countries: Algeria, Australia, Bulgaria, Ecuador, France, Greece, India, Indonesia, Malaysia, Portugal, Thailand, South Africa, and the United Kingdom.
I consider this comprehensive publication on remote sensing technology for forest ecosystems a very valuable contribution to both the remote sensing and forest management literature.
I am confident that this book will be an excellent resource for the students and researchers alike.
I congratulate Dr. Pandey and Dr. Arellano for undertaking an important task of editing this book, which will serve a wide range of research and professional communities.
Kamlesh Lulla, Ph.D.
NASA Medalist
Dr. Kamlesh Lulla serves as Director, Research Collaboration, and Partnership at NASA Johnson Space Centre, Houston Texas. Dr. Lulla served as Chief Scientist for Earth Observation at NASA Johnson for over 25 years.
March 2022
Preface
After the success of the first edited book on “Advances in Remote Sensing for Natural Resource Monitoring” in 2021, we were encouraged to continue the Series for other individual topics. In order to highlight the importance of remote sensing and its advancement in the different research themes, we have selected individual topics in upcoming editions. This is the second volume dealing with the advancement in remote sensing for forest monitoring. A careful attempt is taken in this volume to ensure the coverage of all topics related to forest monitoring. To achieve sustainable forest management across the globe, information of all parameters to be observed from remotely sensed images, and an urgent review of the present and future scenario is required.
Recent developments in Sustainable Development Goals (SDGs) have gathered people collectively to manage forest resources, by implementing long‐term plans so that forests may fulfill their expected functions through forest management based on the inherent resilience of a healthy forest ecosystem. The forests function in several ways, a few major ways which are directly linked to goals of SDGs are water resource quantification, climate actions, forest carbon stock assessment, biodiversity conservation, and forest management for timber production at a large scale. SDGs No. 6 (Clean Water and Sanitation), 12 (Responsible Consumption and Production), 13 (Climate Actions), and 14 (Life on Land) will be directly and indirectly linked to the forest functions, and thus monitoring will help to achieve these goals through forest monitoring through Earth Observation (EO) datasets. Thus, the curiosity and contribution toward the environment may be achieved by anyone, and will help in achieving SDGs for future generations. This is supported by the Earth observation technology at local, regional, and global scales with high spatial/spectral and temporal datasets.
Therefore, an attempt has been made to deliver both the basic and advanced methods that are very much awaited from end‐users to understand technology supporting SDGs. Moreover, how the EO technologies are employed in forest resource monitoring and management, and how they play an effective role in policy implementation at different scales; root level to regional to global scale. EO provides a clear concept in different fields, such as vegetation, water, soil, and disasters related to them, with advanced techniques implemented and their outcome will help to understand the feasibility of remote sensing in the future in terms of reliability, accuracy, and cost‐effective applications. The Editors believe that this effort will help readers in understanding the advancement of remote sensing and offer practical guidance toward their research. The Editors hope that the present book will be a valuable asset for researchers working toward the sustainable and judicious use of forest resources using RS technology. Moreover, taking advantage of the methods and technologies, researchers will be able to scientifically address the issues involved with forest research.
The updated knowledge from countries including Algeria, Australia, Bulgaria, Ecuador, France, Greece, India, Indonesia, Malaysia, Portugal, The Netherlands, Thailand, South Africa, and The United Kingdom is demonstrated in this edited book through research and selected case studies for monitoring forests, to elaborating the scientific understanding of advancement in remote sensing and forest parameters assessment. This book is primarily focused on the Advances in Remote Sensing for Forest Monitoring; it provides a detailed overview of the potential applications of advanced satellite data including spaceborne, airborne, and handheld instruments employed in the assessment of forest parameters for their monitoring. Further, this book determines how environmental–ecological knowledge and satellite‐based–drone‐based information could be effectively combined to address a wide array of current management needs and requirements. Each chapter covers different aspects of remote sensing approaches to monitor the forest parameters effectively, to provide a platform for conservation and planning.
This book is structured as a set of 14 contributed chapters addressing the advancement of remote sensing to monitor and manage forest related research themes. This book is divided into five major sections; each section has specific chapters to present the knowledge and concepts of forest monitoring, and delivers the findings in a vivid way that is useful for a wide range of readers including undergraduate, graduate, and researchers. While a wide range of applications and the latest innovative technologies are constantly emerging, this book provides a crisp summary of key case studies to provide the most recent and widely used methods and information.
Section I, General‐Editorial Message – Introduction to Forest Monitoring, includes an Editorial message which provides an insight to forest monitoring; importance, needs, and requirements, along with the use of advanced remote sensing technology for assessment of forest parameters discussed in chapters contributed by several authors and scientists. This Section also discusses sustainable forest management to enhance Ecosystem Services and livelihood security. There is a brief discussion about the ecological disturbances of forests, Forest ecosystem services (FESs), as well as detailed information about the Landscape‐based Approach (LbA) and Ecosystem‐based Approach (EbA) of Sustainable Forests Management (SFM). This section provides traditional knowledge in forest management, along with how forest resources help in the livelihood of the people.
Section II: Forest Parameters – Biochemical and Biophysical Parameters, contains three chapters that acclaim the virtue of Machine Learning (ML) methods and algorithms to assess biochemical and biophysical parameters of forests. This section employed different datasets; Multispectral, Synthetic Aperture Radar, MODIS, Global orbiting carbon observatory‐2 SIF (GOSIF), and handheld instruments such as spectroradiometers, and SPAD for retrieval of parameters and their assessment. Section II also presents Mapping Oil Palm Plantations in naturally protected areas, to illustrate the importance of monitoring vegetation even in the protected areas. This section presents several machine learning methods to distinguish carotene and xanthophyll contents in the leaves of riparian forest, review on modeling of abiotic stress of conifers, Retrieval of Mangrove Forest Properties, and photosynthetic variables estimation in a mangrove forest using advanced remote sensing datasets. This section also discusses strategies for evaluating and quantifying carbon stock allocation and variability across different species, structural components, and age groups of reforested trees.
Section III: Remote Sensing Technology for Forest Fire Monitoring, illustrates forest fire susceptibility mapping and Landscape‐Level Bushfire Severity assessment using remote sensing data. ML methods such as Random Forest (RF) and Support Vector Machines (SVMs) were incorporated for fire susceptibility mapping and validation. This section presents cloud‐based computing of remotely sensed datasets for Landscape‐Level Bushfire Severity assessment. It also incorporates the outcomes on temporal assessment of vegetation recovery in Australian bushfires. This section demonstrates the response of multi‐sensors data and effectiveness in estimating the forest fires and bushfire recovery/post fire damage assessment.
Section IV: Advancement in RS – Drones and Multi‐Sensors–Multi‐Source for Forest Monitoring exemplifies recent advances in remote sensing technology for forest parameters estimation. This section incorporates the widely used drones/UAVs for this purpose. There are opportunities in drone‐based studies, which can be mounted with RGB camera, multispectral sensors (MICA sense), thermal sensors, and LiDAR sensors for easy access to data and temporal studies as compared to spaceborne datasets, in term of spatial resolution up to 10 cm, and temporal resolution as and when required. This section exemplifies recent advances in drones and data fusion approaches for forest monitoring utilizing earth observation datasets.
Section V: Opportunities, Challenges and Future Aspects in Forest Monitoring provides a discussion on the current issues and reviews challenges in sustainability of forest and vegetation management due to natural and anthropogenic activities. This section deals with the forest components in brief, and presents literature on the challenges faced during ground/field surveys, and satellite use for forest monitoring. The main components of forests, which are experiencing challenges with monitoring are species distribution modeling and mapping, Land Use and Land Cover (LULC) and changes, climate monitoring, wildlife monitoring, biodiversity, livelihood forest production monitoring and others. This section provides a discussion on the current status, future trends, and prospects of remote sensing methods in forest monitoring, and underlines the scientific challenges that need to be addressed. It also discusses the future possibilities in forest monitoring with high‐resolution satellites and cameras, drones and aircraft, LiDAR, and carbon credits while attracting funds for nations. There are scope of development and additions of new technologies, algorithms, and methods for future advancement of remote sensing for forest monitoring. Finally, it discusses the importance of RS technology, how it has evolved with time and spread its wings in the research domains, and is still evolving and emerging to its global height.
The book attempts to match user needs with the level of technology required for forest monitoring, management, and planning. We believe that this endeavor shall provide a valuable scientific basis to students and researchers to address future challenges in forestry research. We further hope this book will be a valuable reference and provide practical guidance for all who work toward the themes mentioned.
We thank all the authors for their enthusiastic efforts in completing the book with the quality of their chapters. We are deeply indebted and thankful to the reviewers who took pains to review the chapter manuscripts, and for their voluntary support. On behalf of the team of authors, we express our gratitude to the entire crew of Wiley (Andrew, Frank, Merryl, and Athira) for all sorts of assistance to make this a successful endeavor. We are thankful to Shiv Nadar Institution of Eminence (Deemed to be University), and our colleagues for their support and help throughout the progress of the work. Last, but not the least, the editors are heartily thankful to our publisher, Wiley, for providing an opportunity to gather the thoughts of several contributors into a book. Dr. Pandey is grateful to his beloved wife and daughter Adele for all their support and emotions during the development of this book at the final stage. We are grateful to all persons and individuals who overcame the great challenges faced during the world wide pandemic of COVID‐19 for more than two years, among those are a few authors who lost their lives, and could not contribute in this volume/series.
We hope this Preface has successfully provided some insight into the breadth of the advancement of remote sensing applications and related topics covered in this book. Users of this book are encouraged to adapt to it and use it in the way it best fits their own needs to help them in understanding the capabilities and potentials of natural resources monitoring and its applications, of which this book is concerned. Users of this book can inform the editor of any errors, suggestions, or comments at [email protected] or [email protected] and [email protected].
EditorsGreater Noida, IndiaQuito, EcuadorMarch 2022
Prem C. PandeyPaul Arellano
List of Abbreviations
Acronym
Description
2D
Two‐Dimensional
3D
Three‐Dimensional
AATSR
Advanced Along‐Track Scanning Radiometer
ADCM
Ancillary Data Classification Model
AE
Assimilation Efficiency
AECL
Atomic Energy Canada Limited
AF
Absorbed Part of Internal Irradiation
AGB
Above Ground Biomass
AI
Artificial Intelligence
AIRSAR
Airborne synthetic aperture radar
AISA
Airborne Imaging Spectrometer for Applications
ALOS‐1/2
Advanced Land Observing Satellite‐1/2
ALS
Airborne Laser Scanning
ANN
Artificial Neural Network
ANOVA
Statistical Analysis of Variance
AO
Avicennia officinalis
APAR
Absorbed Photosynthetically Active Radiation
AR–CDM
Afforestation–Reforestation Clean Development Mechanism
Arc‐GIS
Aeronautical Reconnaissance Coverage Geographic Information System
ARI2
Anthocyanin Reflectance Index2
ARIES
Australian Resource Information and Environment Satellite
ARVI
Atmospherically Resistant Vegetation Index
ASAR
Advanced Synthetic Aperture Radar
ASI
Italian Space Agency
ASM
Angular Second Moment
ASTER
Advanced Spaceborne Thermal Emission and Reflection Radiometer
ATSR
Along Track Scanning Radiometer
AUC
Area Under Curve
AVHRR
Advanced Very High‐Resolution Radiometer
AVIRIS
Airborne Visible/Infrared Imaging Spectrometer
AWiFS
Advanced Wide Field Sensor
BCC
Biochemical Content Classifier
BWG
Biota Working Group
BWS
Bhitarkanika Wildlife Sanctuary
C
Carbon
C&Is
Criteria and Indicators
CASI
Canadian Aeronautics and Space Institute (
https://casi.ca/
)
CASI
The Compact Airborne Spectrographic Imager
CBI
Composite Burn Index
C‐cycle
Carbon Cycle
CCC
Canopy Chlorophyll Content
CCM
Chlorophyll Content Meter
CDM
Clean Development Mechanism
CER's
Certified Emission Reductions
CFCs
Chlorofluorocarbons
Chl‐a
Chlorophyll a
Chl‐b
Chlorophyll b
CHM
Canopy Height Model
CHRIS
Compact High Resolution Imaging Spectrometer
CNES
Centre National d'études Spatiales ‐ French Space Agency
CNN
Convolutional Neural Network
CO
2
Carbon dioxide
COIS
Coastal Ocean Imaging Spectroradiometer
CR
Continuum Removal
CRI2
Carotenoid Reflectance Index 2
CRR
Continuum‐Removed Reflectance
CWC
Canopy Water Content
DART
Discrete anisotropic radiative transfer
dB
Decibel
DBH
Diameter at Breast Height
DELWP
Department of Environment, Land, Water, and Planning
DEM
Digital Elevation Model
DHP
Digital Hemispherical Photography
dNBR
Difference Normalized Burn Ratio
dNDVI
Differenced Normalized Difference Vegetation Index
DNN
Deep Neural Networks
DOS
Dark Object Subtraction
DRAP
Durban Research Action Partnership
DSM
Digital Surface Model
DT
Decision Tree
DTM
Digital Terrain Model
EA
Excoecaria agallocha
EbA
Ecosystem‐based Approach
EBV
Essential Biodiversity Variable
EC
European Commission
ECVs
Essential Climate Variables
EIA
Environmental Impact Assessment
ELVIS
Elevation and Depth – Foundation Spatial Data
EMR
ElectroMagnetic Radiation
EMRAS
Environmental Modeling for Radiation Safety
ENET
Elastic net
EO
Earth Observation
EOS
End of the Season
ERICA
Environmental Risk from Ionising Contaminants – Assessment and Management
ERTS
Earth Resources Technology Satellite
ESA
European Space Agency
ESD
Ecosystem Syndrome Distress
ESRI
Environmental Systems Research Institute
ESs
Ecosystem Services
ESU
Elementary Sampling Unit
ET
Evapotranspiration
ETM
Enchanced Thematic Mapper
ETM+
Enhanced Thematic Mapper Plus
EUFORGEN
European Forest Genetic Resource Program
EVI
Enhanced Vegetation Index
EWT
Equivalent Water Thickness
FAO
Food and Agriculture Organization
FAPAR
Fraction of Photosynthetically Active Radiation
f
APAR
Fraction of Absorbed Photosynthetically Active Radiation
FAR
False Alarm Rate
FBD
Fine beam dual polarization
FBP
Canadian Fire Behavior Prediction
FBS
Fine beam single polarization
f
COVER
Fraction of Vegetation Cover
FESs
Forest Ecosystem Services
FnF
Forest/non‐Forest
FORTRAN
FORmula TRANslation
f
PAR
Fraction of Absorbed Photosynthetically Active Radiation
FSDAF
Flexible Spatiotemporal Data‐Fusion
FSI
Forest Survey of India
FTHSI
Fourier Transform Hyperspectral Imager
GBH
Girth at Breast Height
GCOM
Global Change Observation Mission
GCOS
Global Climate Observing System
GCP
Ground Control Points
GDP
Gross Domestic Product
GEE
Google Earth Engine
GEOV1
Geoland2/Bio Par version 1
GHG/s
Greenhouse Gas/es
GLCF
Global Land Cover Facility
GLCM
Grey Level Co‐occurrence Matrix
GOSAT
Greenhouse gases Observing SATellite
GOSIF
Global Orbiting Carbon Observatory‐2 SIF
GPP
Gross Primary Productivity
GPM (10)
Global Precipitation Measurement
GPS
Global Positioning Systems
GRD
Ground Range Detected
GSD
Ground Sampling Distance
HERO
Hyperspectral Environment and Resource Observer
HF
Heritiera fomes
HH
Horizontal transmit, Horizontal receive
HIRIS
High Resolution Imaging Spectrometer
HKH
Hindu Kush Himalaya
HPLC
High‐Pressure Liquid Chromatography
HRG
High Resolution Geometric
HRV
High Resolution Visible
HSI
Hyperspectral Imager
HSI transformation
Hue–Saturation–Intensity transform
HSR
High Spatial Resolution
HV
Horizontal transmit, Vertical receive
IAEA
International Atomic Energy Agency
ICP “Forest”
International Co‐operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests
IHR
Indian Himalayan Region
IMD
India Meteorological Department
INS
Inertial Navigation System
InSAR
Interferometric Synthetic Aperture Radar
IPCC
Intergovernmental Panel on Climate Change
IPS
Invasive Plant Species
IR
Infra‐Red
IRS
Indian Remote Sensing
IRS‐1B
Indian Remote Sensing satellite‐1B
IRS‐1C
Indian Remote Sensing satellite‐1C
IRS‐1D
Indian Remote Sensing Satellite‐1D
IRS‐P6
Indian Remote‐Sensing Satellite‐P6
ISFR
India State of Forest Report
ISRIC
International Soil Reference and Information Centre
IUCN
The International Union for Conservation of Nature
IW
Interferometric wide swath
JAXA
Japan Aerospace Exploration Agency
JERS‐1
Japanese earth resources satellite‐1
JF
January and February
JFM
Joint Forest Management (India)
K
Kappa Coefficient
L1TP
Level‐1 Precision Terrain
L8SR
Landsat 8 Surface Reflectance
LAI
Leaf Area Index
Landsat
Land Remote‐Sensing Satellite
Landsat TM
Land remote‐sensing satellite Thematic mapper
Landsat ETM+
Landsat 7 Enhanced Thematic Mapper‐Plus
LaSRC
Landsat Surface Reflectance Code
LbA
Landscape based Approach
LCC
Leaf Chlorophyll Content
LiDAR
Light Detection and Ranging data
LISS
Linear Imaging Self‐Scanning Sensor
LISS III/IV
Linear Imaging Self‐Scanning Sensor‐III
LN
Linear Kernel
LPM
Laser Penetration Matrices
LR
Logistic regression
LSD
Landlord Simile Dynamic link
LST
Land Surface Temperature
LSU
Linear Spectral Unmixing
LUE
Light Use Efficiency
LULC
Land Use and Land Cover
LVIS
Land, Vegetation, and Ice Sensor
MAM
March, April, and May
MARS
Multivariate Adaptive Regression Splines
MCARI
Modified Chlorophyll Absorption Ratio Index
MCC
Means Clustering Classifier
MERIS
Medium Resolution Imaging Spectrometer
MFPs
Minor Forest Products
Mha
Million hectares
MISR
Multi‐angle Imaging SpectroRadiometer
ML
Machine Learning
MLA
Machine learning algorithm
MLC
Maximum Likelihood Classifier
MLP
Multi‐Layer Perceptron
MNDWI
Modified Normalized Difference Water Index
MODFLOW
MODular finite‐difference FLOW model
MODIS
Moderate Resolution Imaging Spectroradiometer
MOS
Modular Optoelectronic Scanner
MSAVI
Modified Soil Adjusted Vegetation Index
MSI
Multispectral Image
MSS
Multispectral Scanner
MSS
Multispectral Scanner System
mtry
number of explanatory variables
MTVI
Modified Triangular Vegetation Index
MVC
Mapping Vegetation Community
NASA
National Aeronautics and Space Administration
NBR
Normalized Burn Ratio
NDCI
Normalized Difference Chlorophyll Index
NDREI
Normalized Difference Red‐Edge Index
NDVI
Normalized Difference Vegetation Index
NDWI
Normalized Difference Water Index
NEMO
Naval Earth Map Observer
NetCDF
Network Common Data Form
NFFL
Northern Forest Fire Laboratory
NFI
National Forest Inventory
NIR
Near‐Infra Red
NISAR
NASA‐ISRO Synthetic aperture radar
NMP
New Millennium Program
NN
Neural Network
NOAA
National Oceanic and Atmospheric Administration
NPP
Nuclear Power Plant
NPV
Non‐Photosynthetic Vegetation
NRMSE
Normalized Root Mean Square Error
NTFPs
Non‐Timber Forest Products
ntree
number of trees
OA
Overall Accuracy
OCO‐2
Orbiting Carbon Observatory‐2
OLI
Operational Land Imager
OND
October, November, and December
OSAVI
Optimized‐Soil‐Adjusted Vegetation Index
PA
Producer Accuracy
PAI
Plant Area Index
PALSAR
Phased Arraytype L‐band Synthetic Aperture Radar
PAN
peroxyacetyl nitrate
PAR
Photosynthetically Active Radiation
PB
propyl‐peroxybutylenetrate
PCA
Principal Component Analysis
PEM
Production Efficiency Models
PL
Polynomial kernel
PLSR
Partial Least Square Regression
PPP
peroxypropionyltrate
PRI
Photochemical Reflectance Index
PROBA
Project for On‐Board Autonomy
PROSAIL
PROSPECT and SAIL radiative transfer models
PRVI
Polarimetric radar vegetation index
PSRI
Plant Sense Reflectance Index
PVI
Perpendicular Vegetation Index
QC
Quality Control
QGIS
Quantum Geographic Information System
R
Coefficient of correlation
R
2
Coefficient of Determination
RADAR
Radio Detection and Ranging
RBF
Radial Basis Function
RdNBR
Relative Difference Normalized Burn Ratio
RDVI
Renormalized Difference Vegetation Index
REDD
Reducing Emissions from Deforestation and forest Degradation
REDD+
Reduce Emissions from Deforestation and Forest Degradation +
RF
Random Forest
RFR
Random Forest Regression
RGB
Red‐Green‐Blue
RMSE
Root Mean Square Error
ROC
Receiver operating characteristic
RPD
Residual Predictive Deviation
RS
Remote Sensing
RRF
Regularized Random Forest
RSADU
Remote Sensing Applications Development Unit
RSDP
Remote Sensing Data Policy
RST
Remote Sensing Technology
RT
Regression Tree
RTMs
Radiative Transfer Models
RUSLE
Revised Universal Soil Loss Equation
RVI
Ratio Vegetation Index
SAC
Spectral Angle Classifiers
SAIL
Scattering by Arbitrary Inclined Leaves
SAM
Spectral Angle Mapper
SAR
Synthetic Aperture Radar
SAVI
Soil Adjusted Vegetation Index
ScanSAR
Scanning synthetic aperture radar
SCOPE
Soil Canopy Observation, Photochemistry and Energy fluxes
SDGs
Sustainable Development Goals
SDM
Species Distribution Model
SDS
Scientific Data Sets
SENTINEL
Copernicus Programme satellite constellation conducted by the European Space Agency
SFM
Sustainable Forest Management
S
f
M
Structure from Motion
SGLI
Second Generation Global Imager
SI
Separability Index
SIF
Solar‐Induced (chlorophyll) Fluorescence
SIG
Sigmoid kernel
SIP
Subpixel Inundation Percentage
SIPI
Structure Insensitive Pigment Index
SIR‐B/C
Shuttle imaging radar‐B/C
SLAR
Side Looking Airborne Radar
SLC
Scan Line Corrector
SLC
Single Look Complex
SLS
Spaceborne Laser Scanning
SNAP
Sentinel application platform
SNC
Sebangau National Park
SOS
Start of the Season
SPAD
Soil Plant Analysis Development
SPOT
Satellite for observation of Earth
SRTM
Shuttle radar topography mission
SVM
Support Vector Machines
SVR
Support Vector Regression
SWIR
Short Wave InfraRed
SWR
Stepwise Regression
TCI
Temperature Condition Index
TIS
Thermal Infrared Sensor
TLS
Terrestrial laser scanning
TM
Thematic Mapper
UA
User Accuracy
UAS
Unmanned Aircraft Systems
UAV
Unmanned Aerial vehicles
UAV/UAS
Unmanned Aerial Vehicle/System
UHF
Ultra‐High Frequency
UK
United Kingdom
UML
Unified Modeling Language
UN
The United Nations
UNEP
United Nations Environment Programme
UNEP‐WCMC
Environment Programme World Conservation Monitoring Centre
UNFCCC
United Nations Framework Convention on Climate Change
UNSDGs
United Nations Sustainable Development Group
USA
United States of America
USDOE
U.S. Department of Energy
USGS
United States Geological Survey
USSR
Union of Soviet Socialist Republics
VH
Vertical transmit, Horizontal receive
VH
Vertical transmit, Horizontal receive (Cross‐polarization‐Horizontal)
VI/s
Vegetation Index/Indices
VIF
Variance Inflation Factor
VIP
Variable Importance Projection
VIS
Visible
VNIR
Visible and Near‐Infrared
VNIS
Visible and Near‐infrared Imaging Spectrometer
VPD
Vapor Pressure Deficit
VTOL
Vertical Take‐Off and Landing
VV
Vertical transmit, Vertical receive
VV
Vertical transmit, Vertical receive (Co‐polarization‐Vertical)
WAVI
Water‐Adjusted Vegetation Index
WBI
Water Band Index
WRS‐2
Wordwide Reference System‐2
WUI
Wildland–Urban Interfaces
WV
WorldView
XGB
Extreme Gradient Boosting
Editors
Dr. Prem C. Pandey received his PhD from CLCR, University of Leicester, UK, and completed his Postdoctoral from Tel Aviv University, Israel. Currently he is working as Assistant Professor at the Center for Environmental Sciences & Engineering, School of Natural Sciences, Shiv Nadar Institution of Eminence (Deemed to be University), formerly known as Shiv Nadar University, Greater Noida, India. Previously, he had been associated with IESD, Banaras Hindu University India. He received his M.Sc in Environmental Sciences and M.Tech in Remote Sensing. He worked on remote sensing applications as Professional Research funded by NRSC Government of India. He has been a recipient of several awards including The Commonwealth award UK, INSPIRE fellowship, MHRD UGC fellowships, and SERB‐NPDF from GoI India. He has published more than 45 peer reviewed journal papers, has edited five books, a few book chapters, and presented his work in several conferences. Additionally, he is a life member of the Indian Society of Geomatics, and the Indian Society of Remote Sensing, as well as a IUCN CEM Member (2017–2021; 2022–2025), and the Society of Wetland Scientists (2021–2022). He is serving as an associate editor for the Journal‐Geocarto International, Taylor & Francis, and associated with Remote Sensing MDPI as guest Editor. Dr. Pandey is working in three projects related to Monitoring of wetlands/chilika lake mainly focusing on Ramsar sites along with other natural resources based research work funded by NGP and SERB Government of India. Dr. Pandey is also working with science‐collaborators for real time Disaster monitoring at Himalayan regions. He has expertise in remote sensing of environment; his research interests include natural resources monitoring such as forestry, agriculture, urban studies, and atmospheric pollutant monitoring, mapping and modeling.
Dr. Paul Arellano received his Ph.D. from the Department of Geography, Geology, and Environment of the University of Leicester, UK. Currently, he is a Post‐doctoral Researcher at Michigan Technological University in the US, where he is focused on developing new remote sensing algorithms to detect, map, and monitor peatlands and greenhouse gases (GHG) in the Andean and Amazon regions of South America. He was the Dean of the School of the Earth Sciences, Energy, and Environment from 2015 until 2019, and a member of the Board of Trustees of Yachay Tech University in Ecuador. He was an Honorary Research Visitor from the University of Leicester‐UK and an Adjunct Professor at Arizona State University‐US. He has been a recipient of several scientific awards including the NUFFIC Scholarship from the Netherlands Government, the National Recognition for a scientific contribution in Ecuador, and best papers awards in several scientific conferences. Dr. Arellano focuses his research on remote sensing for vegetation monitoring, natural disasters, climate change, greenhouse gases monitoring, pollution detection, and fire monitoring.
Section IGeneral Introduction to Forest Monitoring
1Introduction to Forest Monitoring Using Advanced Remote Sensing Technology – An Editorial Message
Prem C. Pandey*,1and Paul Arellano2
1 Center for Environmental Sciences & Engineering, School of Natural Sciences, Shiv Nadar Institution of Eminence (Deemed to be University), Greater Noida, Uttar Pradesh, 201314, India,
2 College of Forest Resources and Environmental Sciences, Michigan Technological University, Houghton, Michigan, USA
1.1 Introduction
Remote sensing has a wide range of applications in forest monitoring at local, regional as well as global scale in all ecosystems on Earth. Remotely sensed images acquired through optical (multispectral–hyperspectral), radar, thermal, microwave, and LiDAR (Light Detection And Ranging) sensors are being employed to study and assess natural resources for their monitoring and management across the globe (Pandey and Sharma 2021a, Pandey et al. 2020a). Pandey and Sharma (2021b) have already extolled remote sensing technology and its advancement in natural resource monitoring. In this edited volume, editors have focused on forest monitoring using remote sensing technology as well as traditional methods. This volume discusses several machine learning (ML) methods, spectral indices, and spaceborne–airborne–(unmanned aerial vehicle) UAV data that support research, algorithm development, monitoring, and management of forestry resources. Of interest here is the fact that several remotely sensed data sets acquired from different platforms for forest regions provide sufficient and reasonable cues in the form of representative databases. This will allow the support of monitoring, surveillance, and management at local, regional, and global levels in the context of an ecosystem approach for conservation and protection. Further, advancement in upcoming satellite missions will add to the advantages of remote sensing for forest monitoring. Detailed information about the future mission are available from Sharma et al. (2021) and Pandey et al. (2020b).
1.2 Forest Monitoring: Importance and Trends
Over the past decade, tremendous progress has been made in demonstrating the potentials and limitations of the applications of remote sensing in forestry. In the case of forest research, this is mainly focused on the estimation of its biophysical and biochemical parameters through several remotely sensed images, and validated through field measurements. Remote sensing has been widely used for mapping the distribution of forest types, landscape, and global changes in plant productivity for seasonal, annual, and 3D forest structures, such as canopy height and canopy cover. The range and diversity of remote sensing systems, as well as the variety of applications, have evolved greatly over the last few decades.
Forest monitoring is the foremost requirement for the assessment of forest parameters and related research work. Forest monitoring includes traditional field survey as well as advanced earth observation with space‐borne, airborne, drone‐based, or terrestrial mounted sensors or scanners. Forest monitoring includes the capturing, measurement, and reporting of several forest parameters using field measurements and employing these measurements for verification and validation for models and algorithms developed using remotely sensed datasets. Monitoring also includes other estimation, assessment, and analysis such as forest disturbance arising from logging, burning, disease, or insect infestations, which can be monitored by remote sensing approaches. The concept behind forest monitoring is to identify the conservation priorities in global hotspots, rate of deforestation, and quantification of overall forest regions for its biophysical and biochemical parameters. Forest monitoring such as degradation monitoring is possible due to canopy gap analysis with temporal data, and clearing dense forest that changes the status from dense to open types of canopy cover. This is seen from datasets acquired from spaceborne, airborne, and UAVs, which illustrate the changes caused by natural and anthropogenic factors such as high‐intensity logging causing fragmentation of canopy cover in dense forest. Forest monitoring involves other applications such as wildlife management, species mapping, and distribution at multiple scales; local, regional, and global. Recently, data acquisition by users are facilitated by the Remote Sensing Data Policy (RSDP) 2022 and Drone (Amendment) Rules, 2022 in India. The government of India has relaxed policy over RS data for the civilians and researchers, so that they may acquire and distribute the RS data from India and foreign satellites easily, and experience a smoother procurement process. Similarly, other countries are also planning for easy data delivery to users.
These forest monitoring systems with advanced earth observation help to generate high quality maps, and reliable digital data to be shared with or transferred to the end users and agencies for work plan and future actions. These measurements include different forest types, species mapping, estimating pigments, tree heights, crown density, basal cover, degradation area, biomass, and carbon mapping of the forest. Moreover, these parameters are assessed and estimated with different algorithms and models, with the use of single data or multiple data, such as multispectral data, hyperspectral data, microwave data, or a combination of other earth observation datasets. Remotely sensed digital outputs are accurate and highly reliable data on forests, which are very important in forest monitoring because of the advantages over traditional field methods. Remotely sensed data are acquired for a large area, hilly, or inaccessible regions with synoptic view, which makes this technology preferable over traditional methods. Forest monitoring is required for the point of view of conservation and protection as well as these derived outputs and information, which are imperative to assist policies, make decisions, and provide sustainable management plans for different forest types. Based on the results and outcome, management may plan policies and actions accordingly to each type of forests, and positive policies will have many benefits, such as non‐carbon benefits, food security, forest health, and biodiversity richness and regularity, poverty alleviation, and improved restoration and land use land cover practices
