Climate Impacts on Sustainable Natural Resource Management E-Book
168,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
CLIMATE IMPACTS ON SUSTAINABLE NATURAL RESOURCE MANAGEMENT
Climate change has emerged as one of the predominant global concerns of the 21st century. Statistics show that the average surface temperature of the Earth has increased by about 1.18°C since the late 19th century and the sea levels are rising due to the melting of glaciers. Further rise in the global temperature will have dire consequences for the survival of humans on the planet Earth. There is a need to monitor climatic data and associated drivers of changes to develop sustainable planning. The anthropogenic activities that are linked to climate change need scientific evaluation and must be curtailed before it is too late.
This book contributes significantly in the field of sustainable natural resource management linked to climate change. Up to date research findings from developing and developed countries like India, Indonesia, Japan, Malaysia, Sri Lanka and the USA have been presented through selected case studies covering different thematic areas. The book has been organised into six major themes of sustainable natural resource management, determinants of forest productivity, agriculture and climate change, water resource management and riverine health, climate change threat on natural resources, and linkages between natural resources and biotic-abiotic stressors to develop the concept and to present the findings in a way that is useful for a wide range of readers. While the range of applications and innovative techniques is constantly increasing, this book provides a summary of findings to provide the updated information.
This book will be of interest to researchers and practitioners in the field of environmental sciences, remote sensing, geographical information system, meteorology, sociology and policy studies related to natural resource management and climate change.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 671
Veröffentlichungsjahr: 2021
Ähnliche
Table of Contents
Cover
Title Page
Copyright Page
About the Editors
List of Contributors
Foreword
Preface
Section 1: Sustainable Natural Resource Management
1 Impact of Local REDD+ Intervention on Greenhouse Gas Emissions in East Kalimantan Province, Indonesia
1.1 Introduction
1.2 Materials and Methods
1.3 Results
1.4 Discussion
1.5 Conclusions
Acknowledgement
Author Contribution
List of Appendix
References
2 Role of Geospatial Technologies in Natural Resource Management
2.1 Introduction
2.2 Applications of Geospatial Technology in Natural Resource Management
2.3 LiDAR Technology
2.4 Artificial Intelligence and Remote Sensing
2.5 Machine Learning Tools for Natural Resource Management
2.6 Applications of Unmanned Aerial Systems in Natural Resource Management
2.7 Google Earth Engine as a Platform for Environmental Monitoring and NRM
2.8 Conclusion
References
3 Estimation of Snow Cover Area Using MicrowaveSAR Dataset
3.1 Introduction
3.2 Classification Technique
3.3 Statistical Parameters
3.4 Error and Accuracy Assessment
3.5 Study Area
3.6 Methodology
3.7 Result and Discussion
3.8 Conclusion and Future Perspective
References
Section 2: Determinants of Forest Productivity
4 Forest Cover Change Detection Across Recent Three Decades in Persian Oak Forests Using Convolutional Neural Network
4.1 Introduction
4.2 Materials and Methods
4.3 Results and Discussion
4.4 Conclusion and Future Prospects
References
5 The Interlinked Mechanisms of Productivity for Developing Process‐Based Forest Growth Models
5.1 Introduction
5.2 Productivity: Definition and Associated Components
5.3 Various Processes and Components Driving Forest Productivity
5.4 Different Approaches to Productivity Assessment
5.5 Evolution of Process‐Based Models
5.6 Conclusion
References
6 Allometric Equations for the Estimation of Biomass and Carbon in the Sub‐tropical Pine Forests of India
6.1 Introduction
6.2 Chir Pine – a Boon or Bane?
6.3 Forest Carbon and Forest Biomass
6.4 Composition of Forest Biomass
6.5 Allometric Equations for Biomass Estimation
6.6 Biomass and Carbon Stock Estimation in Chir Pine Forests of India Using Allometric Equations
6.7 Conclusion
References
Section 3: Agriculture and Climate Change
7 Characterization of Stress‐Prone Areas for Dissemination of Suitable Rice Varieties and their Adoption in Eastern India:
7.1 Introduction
7.2 Materials and Method (for Submergence‐prone: Assam)
7.3 Results and Discussion
7.4 Conclusions
References
8 Farmers' Perspective and Adaptation Efforts to Tackle the Impacts of Climate Change
8.1 Introduction
8.2 Methodology
8.3 Results and Analysis
8.4 Understanding the Farmer's Perception of Climate Change
8.5 Adaptation Efforts
8.6 Conclusion
References
Section 4: Water Resource Management and Riverine Health
9 Multicriteria Drought Severity Analysis in Monaragala District Sri Lanka by Utilizing Remote Sensing and GIS
9.1 Introduction
9.2 Methodology
9.3 Meteorological Drought of Monaragala District
9.4 Agricultural Drought of Monaragala District
9.5 Hydrological Drought of Monaragala District
9.6 Drought Risk Area Map of Monaragala District
9.7 Conclusion and Recommendations
9.8 Conclusion
9.9 Recommendation
References
10 Comparative Evaluation of Predicted Hydrologic Response Under Two Extremities of Sustainability Using Transformed Landuse‐Landcover and CORDEX‐Based Climatic Scenarios
10.1 Introduction
10.2 A Brief Account of the Kangshabati River Basin, the Study Area
10.3 Data and Methodological Description
10.4 Results and Observations
10.5 Conclusion
References
11 Riverine Health a Function of Riverscape Variable: A Case Study of the River Ganga in Varanasi
11.1 Introduction
11.2 Material and Methods
11.3 Result and Discussion
11.4 Conclusions
References
Section 5: Climate Change Threat on Natural Resources
12 Socio‐Economic Impacts of Climate Change
12.1 Introduction
12.2 Trends in Climate Variables
12.3 Welfare Impact of Climate Change
12.4 Impact on Agriculture
12.5 Impact of Climate Change on Society
12.6 Conclusion
References
13 The Political Economy of Vulnerable Environment in the Age of Climate Change: A Kerala Experience
13.1 Introduction
13.2 Climate Change in Kerala
13.3 Climate and Sea Level Change Projections
13.4 Natural Disasters Associated with Climate Change
13.5 The Political Economy of Climate Change and Associated Disasters
13.6 Who Are the Affected?
13.7 Conclusion and Suggestions
References
14 Land Use/Land Cover (LULC) Changes in Cameron Highlands, Malaysia: Explore the Impact of the LULC Changes on Land Surface Temperature (LST)Using Remote Sensing
14.1 Introduction
14.2 Effectiveness of Usage of Satellite Imagery in Land Use/Land Cover (LULC) Change
14.3 The Impact of LULC Changes on Land Surface Temperature (LST)
14.4 Methodology
14.5 Land Use/Cover Changes in Cameron Highland from 2009 to 2019
14.6 Land Surface Temperature Analysis of Comparative Sensors between Landsat Satellite Data and MODIS
14.7 The LULC Effect on LST in Cameron Highlands
14.8 Conclusions
References
Section 6: Linkages between Natural Resources and Biotic‐Abiotic Stressors
15 Emerging Roles of Osmoprotectants in Alleviating Abiotic Stress Response Under Changing Climatic Conditions
15.1 Introduction
15.2 Role of Osmoprotectant Under Abiotic Stress
15.3 Role of Osmoprotectants Under Drought Stress
15.4 Role of Osmoprotectants Under Salinity Stress
15.5 Role of Osmoprotectants Under Cold Stress
15.6 Role of Osmoprotectants Under Submergence Stress
15.7 Role of Osmoprotectants Under Low Light Stress
15.8 Mechanisms of Osmoprotectants Under Multiple Abiotic Stress
15.9 Approaches to Improve Osmoprotectants to Confer Abiotic Stress Tolerance
15.10 Metabolic Engineering Approach
15.11 Future Prospect for Osmoprotectants Under Changing Climatic Conditions
References
16 Growth Variability of Conifers in Temperate Region of Western Himalayas
16.1 Introduction
16.2 Material and Methods
16.3 Results
16.4 Discussion
16.5 Conclusion
References
17 Process‐Based Carbon Sequestration Study with Reference to the Energy‐Water‐Carbon Flux in a Forest Ecosystem
17.1 Introduction
17.2 Concept of Soil‐Vegetation‐Atmosphere‐Transfer (SVAT)
17.3 History of Flux Measurements and Recent Advances‐Different Methods
17.4 Exchange Flux Measurements over Forest Ecosystems
17.5 Ecosystem Flux Measurements Network Worldwide and Indian Scenario
17.6 State of the Current Knowledge at Forest Research Institute, Dehradun
17.7 Research Gaps and Future Needs
17.8 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Land cover classes and their carbon stocks for estimating carbon...
Table 1.2 Ten largest GHG emitters from land‐based sector from 2000 to 2016...
Chapter 3
Table 3.1 Confusion matrix for four class.
Table 3.2 Cerro Laukaru, Chile SIR‐C dataset specification.
Table 3.3 Unsupervised classification for Cerro Laukaru, Chile SIR‐C image.
Table 3.4 SAR statistical parameter for unsupervised classification of Cerro...
Table 3.5 Class population of supervised classification for Cerro Laukaru, C...
Table 3.6 Confusion matrix of Wishart supervised classification for Cerro La...
Table 3.7 Confusion matrix of SVM classification for Cerro Laukaru, Chile SI...
Table 3.8 Class population of Wishart classification with respective class f...
Table 3.9 Class population of SVM classification with respective class for C...
Table 3.10 Error and accuracy assessment of Wishart supervised classificatio...
Table 3.11 Error and accuracy assessment of SVM supervised classification f...
Chapter 4
Table 4.1 Change matrix of land use classes (area in hectares) during 1980–2...
Chapter 5
Table 5.1 Approaches for estimating forest productivity.
Chapter 6
Table 6.1 Taxonomic classification of
Pinus roxburghii
.
Table 6.2 Various methods for vegetation carbon estimation using inventory‐b...
Table 6.3 Equations and default values used in various biomass and carbon es...
Chapter 7
Table 7.1 Crop area and farm families affected due to flood in Assam (2007–2...
Table 7.2 Rice‐growing environments and productivity constraints.
Table 7.3 Sahabhagi dhan yield comparison with other adjacent variety (2012–...
Table 7.4 Targeted dissemination of STRVs in Assam and Uttar Pradesh during ...
Table 7.5 Targeted dissemination of STRVs in Odisha during 2016–2020.
Table 7.6 Yield advantage of sub 1 varieties over other traditionally grown ...
Table 7.7 Adoption of Sahbhagi Dhan in eastern India states.
Chapter 9
Table 9.1 Rainfall decile ranges and respective drought severity class.
Table 9.2 Agricultural drought risk classification using VCI.
Table 9.3 Drought severity parameters based on stream density in Monaragala ...
Table 9.4 Drought severity parameters based on water source in Monaragala Di...
Table 9.5 Percentage areas of Meteorological, Agricultural and Hydrological ...
Table 9.6 Drought severity type and the percentage that affects for each div...
Chapter 10
Table 10.1 Data used in SWAT model.
Table 10.2 List of driving models used in multi‐model ensemble.
Table 10.3 Historical and projected LULC maps used in the study.
Table 10.4 Trends in climatic parameter in response to different climatic s...
Table 10.5 Catchment specific trend in volumetric runoff (in cumecs) for di...
Table 10.6 Catchment specific trend in surface runoff depth (in mm) for dif...
Chapter 11
Table 11.1 Water quality parameters assessed, instruments and methods used,...
Table 11.2 Spearman correlation coefficients between land uses and water qu...
Chapter 12
Table 12.1 Welfare impact of climate change.
Table 12.2 Key climate change risks, its major driving elements, adaptation...
Chapter 14
Table 14.1 Description of land cover used in the study.
Table 14.2 The distribution of land cover/land use extent in 2009 and 2019 ...
Table 14.3 Non‐parametric ANOVA test of significance on land cover change.
Table 14.4 Confusion matrix of the year 2019 for land cover classification.
Table 14.5 The temperature in
o
C of LST obtained from Met Malaysia, MODIS, a...
Chapter 15
Table 15.1 List of transgenic plants engineered for stress tolerance using ...
Chapter 16
Table 16.1 The increment sampling sites of conifers in SFD Tangmar.
Table 16.2 Species‐wise average diameter increments (mm)
Table 16.3 Site‐wise average diameter increments (mm).
Table 16.4 Diameter class‐wise average diameter increments (mm).
List of Illustrations
Chapter 1
Figure 1.1 Annual GHG emissions (Mt CO
2
yr
–1
) from 2000 to 2016.
Figure 1.2 Percentage of GHG emissions from the land‐based sector (2000–2016...
Figure 1.3 The trend lines of annual GHG emissions for predicting future tra...
Figure 1.4 The percentage of REDD+ progress in East Kalimantan for 2030.
Sou
...
Chapter 2
Figure 2.1 Different components of remote sensing used for collecting a wide...
Figure 2.2 Applications of geospatial techniques for forest resource assessm...
Figure 2.3 Representation of multiple spatial layers that can be developed w...
Figure 2.4 Development of thematic maps and their integration for the priori...
Figure 2.5 Applications of geospatial techniques for crop monitoring and man...
Figure 2.6 Satellite and LiDAR data fusion for natural resource management....
Figure 2.7 Various applications of Google Earth Engine for natural resource ...
Chapter 3
Figure 3.1 Support vectors machine, (a) nonlinearly separable case, (b) line...
Figure 3.2 Location of study area (a) map of Chile, (b) selected area of Cer...
Figure 3.3 (a) Level 1 quad pol dataset for Cerro Laukaru, Chile SIR‐C SAR i...
Figure 3.4 (a) Entropy, (b) Anisotropy, (c) Alpha for Cerro Laukaru, Chile S...
Figure 3.5 Unsupervised classification for Cerro Laukaru, Chile SIR‐C SAR im...
Figure 3.6 Graph for comparison between unsupervised classifications for Cer...
Figure 3.7 Graph of SAR statistical parameter for unsupervised classificatio...
Figure 3.8 Supervised classification image for Cerro Laukaru, Chile SIR‐C SA...
Figure 3.9 Graph of omission error for Cerro Laukaru, Chile SIR‐C SAR image....
Figure 3.10 Graph of commission error for Cerro Laukaru, Chile SIR‐C SAR ima...
Figure 3.11 Graph of accuracy assessment Cerro Laukaru, Chile SIR‐C SAR imag...
Chapter 4
Figure 4.1 Map and landscapes of the study area: (a) Location of the study a...
Figure 4.2 Landsat scenes of study area which acquired on (a) 2000 and (b) 2...
Figure 4.3 The land cover maps generated by CNNs model for (a) Landsat‐5 TM ...
Figure 4.4 Change detection maps: (a) 1980–2000, (b) 2000–2020.
Chapter 5
Figure 5.1 Interlinkages of plant traits and abiotic factors with the growth...
Figure 5.2 Processes and elements controlling plant productivity.
Chapter 6
Figure 6.1 Map showing distribution of Indian Himalayan subtropical pine for...
Figure 6.2 Indigenous Pine species found in India. (
Pinus roxburghii
,
Pinus
...
Figure 6.3 The upright forest biomass pyramid indicating that the primary pr...
Figure 6.4 Approximate distribution of biomass in different components of a ...
Figure 6.5 Various conversion factors used in biomass estimation studies....
Figure 6.6 Methodological steps adopted for estimating forest biomass and ca...
Chapter 7
Figure 7.1 Location map of study area.
Figure 7.2 Methodology for targeted dissemination of submergence‐tolerant ri...
Figure 7.3 Methodology for targeted dissemination for drought‐tolerant rice ...
Figure 7.4 Potential sites for the dissemination of Sub 1 rice varieties.
Figure 7.5 Methodology for targeted dissemination for drought‐tolerant rice ...
Figure 7.6 Yield of Sub 1 rice varieties in submergence condition, Assam.
Figure 7.7 Yield advantage of STRVs in drought‐prone ecology in Assam.
Figure 7.8 Submergence tolerance of BINA dhan 11 vs other adjacent varieties...
Figure 7.9 Increasing seed sale of Bina dhan 11 in Odisha from 2016 to 2019....
Chapter 8
Figure 8.1 Study area. Map data: Google, DigitalGlobe.
Figure 8.2 Average monthly rainfall (mm) – May.
Figure 8.3 Average monthly rainfall (mm) – June.
Figure 8.4 Average monthly rainfall (mm) – July.
Figure 8.5 Average monthly rainfall (mm) – August.
Figure 8.6 Average monthly rainfall (mm) – September.
Figure 8.7 Average monthly rainfall (mm) – October.
Figure 8.8 Average monthly rainfall (mm) – November.
Figure 8.9 Average annual rainfall (mm) – May to November.
Figure 8.10 Number of rainy days in May.
Figure 8.11 Number of rainy days in June.
Figure 8.12 Number of rainy days in July.
Figure 8.13 Number of rainy days in August.
Figure 8.14 Number of rainy days in September.
Figure 8.15 Number of rainy days in October.
Figure 8.16 Number of rainy days in November.
Figure 8.17 Average rainy days (annual).
Figure 8.18 Percentage change in rainfall – May.
Figure 8.19 Percentage change in rainfall – June.
Figure 8.20 Percentage change in rainfall – July.
Figure 8.21 Percentage change in rainfall – August.
Figure 8.22 Percentage change in rainfall – September.
Figure 8.23 Percentage change in rainfall – October.
Figure 8.24 Percentage change in rainfall – November.
Figure 8.25 Percentage change in rainfall – Annual.
Figure 8.26 Annual household income.
Figure 8.27 Understanding farmers' (respondents') perception of climate chan...
Figure 8.28 Scale used to represent confidence level of the respondents.
Chapter 9
Figure 9.1 Study area, Monaragala District, Uva Province of Sri Lanka;
Figure 9.2 The complete workflow of the experiments conducted under the stud...
Figure 9.3 Rainfall data‐based Meteorological drought analysis map of Monara...
Figure 9.4 MODIS satellite image data‐based Agricultural drought analysis ma...
Figure 9.5 Stream density‐based drought severity classification map of Monar...
Figure 9.6 Irrigated area and water source‐based drought severity classifica...
Figure 9.7 Stream density, irrigated area, and water source‐based Hydrologic...
Figure 9.8 Drought risk area map (combination of Meteorological, Agricultura...
Figure 9.9 Spatial percentage of coverage of the drought risk area (combinat...
Figure 9.10 The percentage of the drought level in each divisional sectarian...
Chapter 10
Figure 10.1 A study map of
Kangsabati River Basin
(KRB) showing elevation an...
Figure 10.2 Taylor diagram for deviation of RCMs (historical run) from IMD o...
Figure 10.3 Flowchart of the methodology followed in the study.
Figure 10.4 Spatial trends in climatic parameter (a) Precipitation (b) Maxim...
Figure 10.5 Temporal variation in areal extent of different land use types L...
Figure 10.6 Maps depicting LULC scenario for (a) Historical time period. 198...
Figure 10.7 Trends in volumetric runoff for (a) LULC scenario 1 representing...
Figure 10.8 Intra‐seasonal comparison in volumetric runoff for different LUL...
Figure 10.9 Trends in surface runoff depth for (a) LULC scenario 1 represent...
Figure 10.10 Intra‐seasonal comparison in surface runoff depth for different...
Chapter 11
Figure 11.1 Riverine ecosystem as affected by various landscape variables. B...
Figure 11.2 Location Map representing sampling and survey sites.
Figure 11.3 LULC statistics of study region.
Figure 11.4 Percent distribution of Cyanophycean, Bacillariophycean, and Chl...
Figure 11.5 Respondents' dependency on the River Ganga for their livelihoods...
Figure 11.6 Tourists' perceptions of different reasons of pollution in the r...
Figure 11.7 Percentage of (a) locals and tourists (b) Indians and foreigners...
Figure 11.8 Tourists' perceptions of different reasons for pollution in the ...
Chapter 12
Figure 12.1 Global land‐ocean temperature index.
Figure 12.2 Emission entity contribution in top atmosphere radiative forcing...
Figure 12.3 The modeling chain of CGETemp – Temperature, Prec is precipi...
Figure 12.4 Socio‐economic impacts of climate change.
Chapter 14
Figure 14.1 Cameron Highlands captured from satellite imagery. A map of Peni...
Figure 14.2 Satellite imageries from Landsat 7 ETM+ and 8 (OLI) TIRS: From l...
Figure 14.3 Several land use/land cover types in Cameron Highlands: Primary ...
Figure 14.4 The land use/cover changes maps from classified satellite images...
Figure 14.5 Relative change in land use/cover in Cameron Highlands: generall...
Figure 14.6 Land surface temperature (LST) for the year 2009 in Cameron High...
Figure 14.7 Images are showing a land surface temperature series for the yea...
Figure 14.8 Mean maximum value of Met Malaysia air temperature data.
Figure 14.9 Mean minimum value of Met Malaysia air temperature data.
Figure 14.10 Highest recorded values of Met Malaysia air temperature data....
Chapter 15
Figure 15.1 Schematic representation of mechanism of osmoprotectants under m...
Chapter 16
Figure 16.1 Study area map with sampling locations.
Figure 16.2 Species‐wise average trend of diameter increments (mm).
Figure 16.3 Site‐wise average trend of diameter increment (mm).
Figure 16.4 Diameter class‐wise average trend of diameter increment (mm).
Chapter 17
Figure 17.1 Schematic representation of the inter‐connectedness of water, ca...
Figure 17.2 Worldwide flux measurement.
Guide
Cover Page
Title Page
Copyright Page
About the Editors
List of Contributors
Foreword
Preface
Table of Contents
Begin Reading
Index
Wiley End User License Agreement
Pages
iii
iv
xiii
xiv
xv
xvi
xvii
xviii
xix
xx
xxi
xxii
xxiii
1
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
57
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
109
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
237
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
303
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
Climate Impacts on Sustainable Natural Resource Management
Edited by
Pavan Kumar
College of Horticulture and Forestry
Rani Lakshmi Bai Central Agricultural University
Gwalior Road, Jhansi‐284003, Jhansi, India
Ram Kumar Singh
Hexagon Geospatial
Gurgaon, India
Manoj Kumar
GIS Centre
Forest Research Institute, PO: New Forest
Dehradun ‐ 248006, Uttarakhand, India
Meenu Rani
Department of Geography
Kumaun University
Nainital‐263001, Uttarakhand, India
Pardeep Sharma
Department of Geography,
Government College for Women, Hisar‐125001, Haryana, India
This edition first published 2022© 2022 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.
The right of Pavan Kumar, Ram Kumar Singh, Manoj Kumar, Meenu Rani, and Pardeep Sharma to be identified as the authors of the editorial material in this work has been asserted in accordance with law.
Registered OfficesJohn Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USAJohn Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK
Editorial Office9600 Garsington Road, Oxford, OX4 2DQ, UK
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Wiley also publishes its books in a variety of electronic formats and by print‐on‐demand. Some content that appears in standard print versions of this book may not be available in other formats.
Limit of Liability/Disclaimer of WarrantyThe contents of this work are intended to further general scientific research, understanding, and discussion only and are not intended and should not be relied upon as recommending or promoting scientific method, diagnosis, or treatment by physicians for any particular patient. In view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of medicines, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each medicine, equipment, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
Library of Congress Cataloging‐in‐Publication Data
Names: Kumar, Pavan (Professor of forestry); Singh, Ram Kumar; Kumar, Manoj; Rani, Meenu; Sharma, Pardeep editors.Title: Climate impacts on sustainable natural resource management / edited by Pavan Kumar, Ram Kumar Singh, Manoj Kumar, Meenu Rani, Pardeep Sharma.Description: Hoboken, NJ : John Wiley & Sons, Ltd, 2022. | Includes bibliographical references and index.Identifiers: LCCN 2021038152 (print) | LCCN 2021038153 (ebook) | ISBN 9781119793373 (cloth) | ISBN 9781119793380 (adobe pdf) | ISBN 9781119793397 (epub)Subjects: LCSH: Natural resources–Management–Case studies. | Renewable natural resources–Case studies. | Climatic changes–Case studies.Classification: LCC HC85 .C56 2022 (print) | LCC HC85 (ebook) | DDC 333.7–dc23LC record available at https://lccn.loc.gov/2021038152LC ebook record available at https://lccn.loc.gov/2021038153
Cover Design: WileyCover Images: © Josephine Julian/Shutterstock, Ratnakorn Piyasirisorost/Getty, japatino/Getty, Patrick Orton/Getty
About the Editors
Dr. Pavan Kumar is a faculty member at the College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, U.P., India. He obtained his Ph.D degree from the Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi. He did BSc (Botany) and MSc (Environmental Science) at Banaras Hindu University, Varanasi, India and subsequently obtained a Master's degree in Remote Sensing (M. Tech) from Birla Institute of Technology, Mesra Ranchi, India. His current research interests include resilient agriculture and climate change studies. He is a recipient of an Innovation China National Academy Award for Remote Sensing. Dr. Kumar has published 50 research papers in international journals and authored several books. He has visited countries like USA, France, the Netherlands, Italy, China, Indonesia, Brazil, and Malaysia for various academic/scientific assignments, workshops, and conferences. Dr. Kumar is a member of the International Association for Vegetation Science, USA and the Institution of Geospatial and Remote Sensing, Malaysia.
Dr. Ram Kumar Singh works with Hexagon Geospatial to provide innovative geospatial solutions in the capacity of Deputy Manager. Dr. Singh is also affiliated to the TERI School of Advanced Studies New Delhi as a senior researcher. He earned a Ph.D in Natural Resources from the Department of Natural Resources, TERI School of Advanced Studies New Delhi. He obtained M.Sc. in Remote Sensing and GIS from the Symbiosis Institute of Geoinformatics, Pune, India and an Engineering in Electronics and Instrumentation degree from Samrat Ashok Technological Institute, Vidisha, India. Dr. Singh has worked on remote sensing applications as a Research Fellow in the National Informatics Centre, New Delhi. Dr. Singh has authored several peer‐reviewed scientific research papers and presented works at many national and international conferences. Dr. Singh is an adjunct faculty for teaching M.Sc. Environment Management and Forestry, along with the Ph.D course at the Forest Research Institute Deemed to be University, Dehradun, India. Dr. Singh is a member of the American Geophysical Union, Washington, D.C., United States and the European Geosciences Union, Munich, Germany.
Dr. Manoj Kumar is a senior scientist working as In‐charge of GIS Centre at the Forest Research Institute (FRI), Dehradun, India. The FRI is a premier research institute of the Government of India credited with initiating forestry research in the Asia Pacific region after its establishment in 1905 as the Imperial Forest Research Institute. Dr. Kumar primarily works in the field of forestry, environment, climate change, and related interdisciplinary fields with wider applications of Information Technology, Remote Sensing and GIS tools with a working experience of more than 15 years. He has initiated work on developing forest growth simulation model to study functional relationship of plants with the environment that could be used for climate change impact studies and has published high‐impact international papers on various themes of agriculture, forestry, environment and climate change. He has successfully implemented more than 20 research projects funded by national and international agencies.
Dr. Meenu Rani is a senior research fellow in the Department of Geography, Kumaun University, Nainital, Uttarakhand, India. Dr. Rani received her M. Tech degree in Remote Sensing from Birla Institute of Technology, Ranchi, India. She has working experience in the major disciplines of agriculture and forestry while working with Haryana Space Application Centre, Indian Council of Agricultural Research and GB Pant National Institute of Himalayan Environment and Sustainable Development. Dr. Rani has authored several peer‐reviewed scientific research papers and presented works at many national and international conferences in the USA, Italy, and China. She has been awarded with various fellowships from the International Association for Ecology, Future Earth Coast, and the SCAR Scientific Research Programme. She was awarded early career scientists achievement in 2017 at Columbia University, New York, USA.
Dr. Pardeep Sharma is a Assistant Professor in Department of Geography, GCW, Hisar (Haryana) India. Dr. Sharma completed his Bachelors in Geography and subsequently obtained his Master's degree in Geography. He specialized in remote sensing and GIS and hydrological studies. His area of interest includes coastal ecosystem conservation and management, climate change, and disaster management. He has made a remarkable contribution in water‐related researches such as coastal landscape vulnerability and flood vulnerability. He has presented his research at various national and international conferences.
List of Contributors
Madhoolika AgarwalInstitute of ScienceDepartment of BotanyBanaras Hindu UniversityVaranasi ‐ 221005, IndiaEmail: [email protected]
Mohammd Ajaz‐ul‐IslamDivision of Natural Resource ManagementFaculty of ForestrySher‐e‐Kashmir University of Agricultural Sciences and Technology of KashmirBenhama Ganderbal, J&K ‐ 191201, India
Mohamad Azani AliasDepartment of Forestry Science & BiodiversityFaculty of Forestry and EnvironmentUniversiti Putra, MalaysiaEmail: [email protected]
K.A.M. ChathurangaDepartment of Spatial SciencesFaculty of Built Environment and Spatial SciencesGeneral Sir John Kotelawala Defense UniversitySouthern CampusSooriyawewa, Sri Lanka
Goutam Kumar DashDivision of Crop Physiology and BiochemistryICAR‐National Rice Research InstituteCuttackOdisha, IndiaDeepali DashDepartment of Plant PhysiologyCollege of AgricultureOUATBhubaneswarOdisha, India
Anwesha DeyDepartment of Agricultural EconomicsInstitute of Agricultural SciencesBanaras Hindu UniversityVaranasi ‐ 221005, IndiaEmail: [email protected]
Prajjal DeyFaculty of AgricultureSri Sri UniversityCuttackOdisha, India
Amir FarooqDivision of Wildlife SciencesFaculty of ForestrySher‐e‐Kashmir University of Agricultural Sciences and Technology of KashmirBenhama Ganderbal, J&K ‐ 191201, India
Shilan FelegariDepartment of Soil ScienceFaculty of AgricultureUniversity of ZanjanZanjan, IranEmail: [email protected]
Aasif Ali GatooDivision of Natural Resource ManagementFaculty of ForestrySher‐e‐Kashmir University of Agricultural Sciences and Technology of KashmirBenhama Ganderbal, J&K ‐ 191201, India
Sk Mosharaf HossainInternational Rice Research InstitutePlot No. 340/CSaheed NagarBhubaneswar ‐ 751 001, IndiaCorresponding author: [email protected]
Mohd Hasmadi IsmailDepartment of Forestry Science & BiodiversityFaculty of Forestry and EnvironmentUniversiti Putra, MalaysiaEmail: [email protected]
Harshi JainGIS CentreForest Research Institute (FRI)PO: New ForestDehradun ‐ 248006, IndiaEmail: [email protected]
Darren How Jin AikDepartment of Forestry Science & BiodiversityFaculty of Forestry and EnvironmentUniversiti Putra, MalaysiaEmail: [email protected]
Abhishek K. KalaAdvanced Environmental Research InstituteUniversity of North TexasDenton, TX, USAEmail: [email protected]
KiswantoFaculty of ForestryMulawarman UniversityIndonesiaCampus of Gunung KeluaPenajam StreetSamarindaEast Kalimantan ‐ 75116, IndonesiaEmail: [email protected]
Akhilesh KumarDepartment of Civil EngineeringHaldia Institute of TechnologyP.O HIT, HIT College RdKshudiram NagarHaldiaWest Bengal ‐ 721657, IndiaEmail: [email protected]
Manoj KumarGIS CentreForest Research Institute (FRI)PO: New ForestDehradun ‐ 248006, IndiaEmail: [email protected]
Pavan KumarCollege of Horticulture and ForestryRani Lakshmi Bai Central Agricultural UniversityJhansi ‐ 284003, IndiaEmail: [email protected]
B.A.S.C. KumaraDepartment of GeographyUniversity of Sri JayewardenepuraNugegoda, Sri Lanka
Tariq Hussain MasoodiFaculty of ForestrySher‐e‐Kashmir University of Agricultural Sciences and Technology of KashmirBenhama Ganderbal, J&K ‐ 191201, India
Shivani MehtaState ConsultantRedR IndiaMaharashtra, IndiaEmail: [email protected]
Ufaid MehrajDivision of Natural Resource ManagementFaculty of ForestrySher‐e‐Kashmir University of Agricultural Sciences and Technology of KashmirBenhama Ganderbal, J&K ‐ 191201, India
Ankita MishraDepartment of Plant Breeding and GeneticsCollege of AgricultureOUATBhubaneswarOdisha, India
Farrah Melissa MuharamDepartment of Agriculture TechnologyFaculty of AgricultureUniversiti Putra, MalaysiaEmail: [email protected]
Shah Murtaza MushtaqDivision of Natural Resource ManagementFaculty of ForestrySher‐e‐Kashmir University of Agricultural Sciences and Technology of KashmirBenhama Ganderbal, J&K ‐ 191201, India
Swati NayakInternational Rice Research Institute1st FloorCG BlocksNASC ComplexPusaNew Delhi ‐ 110 012, India
Aditya Kiran PadhiaryKrishi Vigyan KendraOUATSambalpurOdisha, India
Shubhi PatelDepartment of Agricultural EconomicsInstitute of Agricultural SciencesBanaras Hindu UniversityVaranasi ‐ 221005, IndiaEmail: [email protected]
Debasish PattnaikDepartment of Plant PhysiologyCollege of AgricultureOUATBhubaneswarOdisha, IndiaEmail: [email protected]
Akshay PaygudeGIS CentreForest Research Institute (FRI)PO: New ForestDehradun ‐ 248006, IndiaEmail: [email protected]
Sweta Nisha PhukonGIS Centre, Forest Research Institute (FRI)PO: New Forest, Dehradun ‐ 248006, IndiaEmail: [email protected]
Harshith Clifford PrinceDisaster Management Studies DepartmentIndian Institute of Remote SensingISROKalidas RoadDehradun ‐ 248001, India
D.K.D.A. RanaweeraDepartment of GeographyUniversity of Sri JayewardenepuraNugegoda, Sri Lanka
Abhishek RanjanGIS Centre, Forest Research Institute (FRI)PO: New Forest, Dehradun ‐ 248006, IndiaEmail: [email protected]
P. RatheeshMonHSST (Jr) at Department of General EducationKerala, IndiaEmail: [email protected]
Shidharth RouthDepartment of Civil EngineeringHaldia Institute of TechnologyP.O HIT, HIT College RdKshudiram NagarHaldiaWest Bengal ‐ 721657, IndiaEmail: [email protected]
Arijit RoyDisaster Management Studies DepartmentIndian Institute of Remote SensingISROKalidas RoadDehradun ‐ 248001, India
Shridhar SamantAssistant ProfessorSchool of Rural DevelopmentTata Institute of Social SciencesTuljapur, IndiaEmail: [email protected]
K.U.J. SandamaliDepartment of Spatial SciencesFaculty of Built Environment and Spatial SciencesGeneral Sir John Kotelawala Defense UniversitySouthern CampusSooriyawewa, Sri LankaEmail: [email protected]
Abhisek SantraDepartment of Civil EngineeringHaldia Institute of TechnologyP.O HIT, HIT College RdKshudiram NagarHaldiaWest Bengal ‐ 721657, IndiaEmail: [email protected]
Shreyashi Santra MitraDepartment of Civil EngineeringHaldia Institute of TechnologyP.O HIT, HIT College RdKshudiram NagarHaldiaWest Bengal ‐ 721657, IndiaEmail: [email protected]
Shafiyoddin B. SayyadMicrowave and Imaging Spectroscopy Research LaboratoryDepartment of Physics and Computer ScienceMilliya Arts, Science and Management Science CollegeBeedMaharashtra ‐ 431122, IndiaEmail: [email protected]
Martiwi Diah SetiawatiInstitute for Future InitiativesThe University of Tokyo7‐3‐1 HongoBunkyo‐kuTokyo ‐ 113‐8654, JapanEmail: [email protected]: +81358411541andResearch Center for OceanographyIndonesian Institute of SciencesJl. PasirPutih IEast AncolJakarta ‐ 14430, IndonesiaEmail: [email protected]
Saima SiddiquiDepartment of GeographyUniversity of the PunjabLahore, Pakistan
Saima SiddiquiDepartment of GeographyUniversity of the PunjabLahore, Pakistan
Devi Dayal SinhaInternational Rice Research InstitutePlot No. 340/CSaheed NagarBhubaneswar ‐ 751 001, India
Immad Ahmad ShahDivision of Agricultural StatisticsSher‐e‐Kashmir University of Agricultural Sciences and Technology of KashmirShalimar SrinagarJ&K ‐ 190025, India
Mudassar A. ShaikhDepartment of Electronic ScienceNew ArtsCommerce and Science CollegeAhmednagar (Autonomous)Maharashtra ‐ 414001, IndiaEmail: [email protected]
Alireza SharifiDepartment of Surveying EngineeringFaculty of Civil EngineeringShahid Rajaee Teacher Training UniversityTehran ‐ 16785 ‐136, IranEmail: [email protected]
Shikha SharmaInstitute of ScienceDepartment of BotanyBanaras Hindu UniversityVaranasi ‐ 221005, IndiaandDisaster Management Studies DepartmentIndian Institute of Remote SensingISROKalidas RoadDehradun ‐ 248001, IndiaEmail: [email protected]
H.P. SinghDepartment of Agricultural EconomicsInstitute of Agricultural SciencesBanaras Hindu UniversityVaranasi ‐ 221005, IndiaEmail: [email protected]
Hukum SinghForest Ecology and Climate Change DivisionForest Research InstituteDehradunUttarakhand, IndiaEmail: [email protected]
Rakesh SinghDepartment of Agricultural EconomicsInstitute of Agricultural SciencesBanaras Hindu UniversityVaranasi ‐ 221005, IndiaEmail: [email protected]
Ram Kumar SinghDepartment of Natural Resources,TERI School of Advanced Studies, 10 Institutional AreaVasant KunjNew Delhi ‐ 110070, IndiaEmail: [email protected]
Shani Kumar SinghDepartment of Extension EducationInstitute of Agricultural SciencesBanaras Hindu UniversityVaranasi ‐ 221005, IndiaEmail: [email protected]
Satoshi TsuyukiGraduate School of Agricultural and Life SciencesThe University of Tokyo, 1‐1‐1 YayoiBunkyo‐kuTokyo ‐ 113‐8657, JapanEmail: [email protected]‐tokyo.ac.jp
Aqil TariqState key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (LIESMARS)Wuhan UniversityWuhan, China
Keshav TyagiGIS Centre, Forest Research Institute (FRI)PO: New ForestDehradun ‐ 248006, IndiaEmail: [email protected]
Akhlaq Amin Wani (✉) (Corresponding Author)Division of Natural Resource ManagementFaculty of ForestrySher‐e‐Kashmir University of Agricultural Sciences and Technology of KashmirBenhama Ganderbal, J&K ‐ 191201, IndiaEmail: [email protected].
Pakhriazad Hassan ZakiDepartment of Forestry Science & BiodiversityFaculty of Forestry and EnvironmentUniversiti Putra, MalaysiaEmail: [email protected]
Foreword
Climate change refers to any distinct behavior in measures of climate such as temperature, rainfall, snow, or wind patterns lasting for decades. Over the past several years, the international and national research communities have developed a progressively clearer picture of how and why Earth's climate is changing and of the impacts of climate change on a wide range of human and environmental systems. Natural resources and their management form a critical interface between climate change and development. The impacts of climate change can affect the quality and reliability of many of the services that natural resources provide. On the other hand, natural resources play an important role in greenhouse gas mitigation and also serve as a first line of defense against climate change.
This book covers a wide range of strategies that can be applied to various sectors, from forest productivity to climate change threat on natural resources. Its aim, as with resource management itself, is to combine economics, policy, and science to help rehabilitate and preserve our natural resources. This book provides a comprehensive compilation of sustainable natural resource management, determinants of forest productivity, agriculture and climate change impact, water management and control of salinity, climate change threat on natural resources, and vulnerability due to climate change. I believe that this book serves as an opportunity for scientists who are internationally renowned in their fields, addressing issues and interests of academics and other stakeholders engaged in applied science.
I congratulate the editors, the contributors across the country, and the publisher for bringing out timely publication depicting climate impact on natural resource management and hope that this important book shall serve as a reference for different institutions working in this area.
Dr. Arvind Kumar
Vice‐Chancellor
Rani Lakshmi Bai Central Agricultural University
Gwalior Road, Jhansi 284 003 (UP) India
Preface
Climate change is one of the major global concerns in today's world. Assessment of the relationship between natural resources and climatic drivers along with the assessment of probable impacts of climate change on natural resources is essentially required to develop mitigation and adaptation strategies. Statistics show that the average surface temperature of planet Earth has increased by about 1.18 °C since the late nineteenth century. Apart from this, the sea level has also increased by about 8 inches since the last century. Any further increase in the global temperature and sea‐level rise would have a significant negative impact on the majority of natural resources and human well‐being. Global climate change that is mostly driven by human‐induced activities needs to be understood clearly so that strategies can be planned for an early action before it is too late. Thus it is quite evident that we need to be serious when dealing with the consequences of climate change. Updated and timely information on the changing flux of solar radiation, atmospheric warming, patterns and variations in precipitation, trends of increasing CO2 concentration, etc. are essential to keep track of changing climate. The pathways that are linked to climate change such as different socio‐economic activities, burning of fossil fuels, land‐use change, forest cover change dynamics, health estimates of agriculture and forested lands, the productivity of agriculture and forestry systems, etc. provide essential insight to develop a sustainable plan for the management of natural resources.
The IPCC announced that the increase in the recorded Earth's temperature during the last 50 years is the result of various human activities. Climate change has become an important driving force in regulating mechanisms of the physical and biological world. It is now well established that climate change influences a vast number of sectors and resources, either directly or indirectly. The governments of the world have responded to the threats associated with climate change to formulate actions for mitigation. For a better understanding, long‐term observation is desirable to continuously monitor precipitation, temperature, daily radiation, etc. along with the monitoring of the spatial extent and health of natural resources to conclusively establish site‐specific impact assessment. Throughout the world, several attempts have been made to minimize the impact of climate change or to initiate necessary steps to retard the speed of climate change. Natural resources not only contribute to the overall growth of the economy of a nation but also reduces poverty when adequate management and sustainability are ensured. Natural resource management has emerged as one of the most inclusive growth sectors for supporting the economy of Asian countries. Besides, the forward and backward linkages of natural resources to climate change require a comprehensive understanding of a sustainable world. Global weather and climate studies are increasingly being considered as a vital source of information to understand the Earth's environment, especially human‐induced climate studies and factors affecting natural resources.
This book covers significant and updated contributions in the field of sustainable natural resources management linked to climate change. The updated knowledge from countries like India, Indonesia, Japan, Malaysia, Sri Lanka, and the USA is presented in this book through selected case studies for major thematic areas that have basic preliminary concepts and elaborates the scientific understanding of the relationship between natural resources and climatic drivers, influence of climate change on agriculture, forest, water resources, etc. The book has been separated into six major themes, each having subject‐specific chapters to develop the concept and to present the findings in a lucid way that is useful for a wide range of readers. While the range of applications and innovative techniques is constantly increasing, this book provides a summary of key case studies to provide the most updated information. Chapters incorporate multi‐source data and information that offer critical understanding to explain the causes and effects of environmental changes linked to natural resource management. This book will be of interest to researchers and practitioners in the field of environmental sciences, remote sensing, geographical information, meteorology, sociology and policy studies, etc. related to natural resource management and climate change. Also, scientists and graduate and post‐graduate level students of various disciplines will find valuable information in this book. We believe that the book would be read by people with a common interest in sustainable development and other diverse backgrounds within earth observation.
The scientific quality of the book was ensured by a rigorous review process where leading researchers from Australia, Canada, India, Indonesia, Japan, Malaysia, Sri Lanka, and the USA participated to provide constructive comments to improve the chapters. Due to the confidentiality of the review process, we are unable to provide their names; however, we are deeply indebted and thankful for their voluntary support. On behalf of the team of authors, we express our gratitude to the entire crew of Wiley for all kind of assistance to make this a successful endeavor.
Pavan Kumar
Ram Kumar Singh
Manoj Kumar
Meenu Rani
Pardeep Sharma
Section 1Sustainable Natural Resource Management
1Impact of Local REDD+ Intervention on Greenhouse Gas Emissions in East Kalimantan Province, Indonesia
Kiswanto1, Martiwi Diah Setiawati2,3, and Satoshi Tsuyuki4
1 Faculty of Forestry, Mulawarman University, Indonesia, Campus of Gunung Kelua, Penajam Street, Samarinda, East Kalimantan 75116 Indonesia
2 Institute for Future Initiatives, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654 Japan
3 Research Center for Oceanography, Indonesian Institute of Sciences, Jl. PasirPutih I, East Ancol, Jakarta 14430 Indonesia
4 Graduate School of Agricultural and Life Sciences, TheUniversity of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657 Japan
1.1 Introduction
1.1.1 Tropical Deforestation
Initially, carbon was stored in the forests (Houghton 2012), but once the forests were logged and cleared, carbon (i.e. both above and below ground) was released into the atmosphere (Baccini et al. 2012) mostly in the for of carbon dioxide (CO2). However, the decomposition or burning of the forest may also release small amounts of methane (CH4) and carbon monoxide (CO) (Achard et al. 2014; Rosa et al. 2016; Bebber and Butt 2017; Brinck et al. 2017; Pearson et al. 2017). Thus deforestation received high attention from the scientific community (Rosa et al. 2016; Sierra et al. 2012; Zarin 2012) on carbon emissions (Numata et al. 2011; Houghton 2012; Le Quéré et al. 2015), especially in the tropics, where deforestation is responsible for 17%–25% of carbon dioxide (CO2) emissions into the atmosphere (Le Quéré et al. 2015). So tropical deforestation is one of the leading causes of global carbon emissions and biodiversity loss (Brun et al. 2015). Therefore, understanding its drivers is crucial for improving policies and measuring current forest trends toward a more climate‐ and biodiversity‐friendly outcome (Hosonuma et al. 2012). Also, the Forest Resources Assessment (FRA) of the Food and Agriculture Organization of the United Nations (FAO) provides a complete measurement of above‐ground carbon stocks for tropical forests (FAO 2006, 2010, 2015).
1.1.2 REDD+
Reducing emissions from deforestation and forest degradation (REDD+) now constitutes the international convention for mitigating climate change, particularly in forest‐rich developing countries (Gullison et al. 2007; Arima et al. 2014). The “+” after REDD comes from more recent dialogs that have broadened the mechanism's scope to recognize carbon benefits of forest conservation, sustainable forest management (SFM), and the sequestration potential of afforestation and reforestation (Venter and Koh 2012). The REDD+ mechanism is the most prominent of recent attempts to mitigate climate change (Agrawal et al. 2011). Furthermore, leakage policies of REDD+ should be monitored, measured, and mitigated to guarantee their effectiveness (Atmadja and Verchot 2012).
Reducing emissions from deforestation by 2020 could bring the international community nearer to the goal of less than 2 degree increase in global average temperature change (Zarin et al. 2016). Furthermore, more than 180 governments, private companies, indigenous peoples, and non‐governmental organizations have signed the New York Declaration on Forests (NYDF) in September 2014 (UN Climate Summit 2014). Within the REDD+ policy framework, developing countries might develop national systems for carbon accounting (Angelsen 2009; Logan‐Hines et al. 2012).
Developing countries are encouraged to develop national strategies and action plans for REDD+ by identifying the drivers of deforestation (Hosonuma et al. 2012). In the past three decades, satellite‐based observations of forest cover change provide an alternative to estimate deforestation rates regularly across space and time (Zhuravleva et al. 2013; Kuenzer et al. 2014; Kamaruddin et al. 2015). At continental to global scales, forest cover maps and change in cover are increasingly being generated from various satellite data sources (Potapov et al. 2012; Kim et al. 2014; Margono et al. 2014). In the latest development, Landsat images have been used to determine tropical deforestation rates (Broich et al. 2011; FAO 2011; Lehmann et al. 2014; Estavillo et al. 2013; Zhuravleva et al. 2013; Potapov et al. 2012). Also, previous studies of forest cover change datasets have been integrated with satellite‐based forest biomass information to quantify changes in forest carbon stocks (Baccini et al. 2012; Achard et al. 2014; Tyukavina et al. 2015). However, they might show diverse results due to their different methods for mapping and analyzing.
1.1.3 REDD+ in Indonesia
At the COP 15–2009 of the United Nations Framework Convention on Climate Change (UNFCCC), Indonesia voluntarily agreed to reduce emissions by 26% and up to 41% with international support by 2020. This commitment was submitted as Indonesia's Nationally Appropriate Mitigation Actions (NAMA) in 2010 (Indonesia 2013). Since the commitment, Indonesia made some policies, including Presidential Regulation No. 61 of 2011 (Indonesia 2011b) on the national action plan of REDD+ and Presidential Regulation No. 71 of 2011 on the implementation of the National GHG inventory (Indonesia 2011c). Those regulations mandate different government bodies to provide national, local, and corporate GHG inventories annually. Based on its nationally determined contribution (NDC) submitted to the UNFCCC on September 24, 2015 (Indonesia 2016), Indonesia committed to reducing GHG emissions by 29% under BAU (business as usual) scenario by 2030 unconditionally, and up to 41% conditionally. To meet the objective, Indonesia recognizes the requirement for consolidating both methods and data sources to guarantee a high degree of precision.
The study area, East Kalimantan, is one of the target provinces for REDD+ initiatives in Indonesia. This provincial government has also developed an action plan for reducing emissions (East Kalimantan 2013), REDD+ strategy (East Kalimantan 2012), and part of Forest Carbon Partnership Facility (FCPF). FCPF is a global partnership of governments, businesses, civil society, and indigenous peoples (IP), focused on reducing GHG emissions from deforestation and forest degradation, forest carbon stock conservation, SFM, and the enhancement of forest carbon stocks in developing countries (FCPF 2017). Furthermore, this province is also working through close support from civil society and the private sector, which have joined with the government to launch a Green Growth Compact (GGC) by the end of 2017. This initiative has two interrelated targets: to reduce deforestation by at least 80% by 2025 and to increase economic growth by 8% by 2030 (TNC 2016). Also, East Kalimantan was hosting some REDD+ demonstration projects managed by international NGOs and donors. However, further effort should be integrated within official government climate mitigation measures (East Kalimantan 2011a).
This study therefore aimed to estimate annual GHG emissions in East Kalimantan based on the yearly land cover maps derived from satellite data between 2000 and 2016, to determine the historical (2000–2010) and the REDD+ progress (2010–2016) baseline of GHG emissions, and to predict the future trajectories of GHG emissions for 2020 and 2030. Furthermore, 2010 was chosen as the base year for comparing emissions before and after the REDD+ commitment. Also, Indonesia's NDC target in 2030 was selected as the end period of future trajectories.
1.2 Materials and Methods
1.2.1 Spatial Dataset
Annual land cover maps in East Kalimantan from 2000 to 2016 from Landsat satellite images were used to estimate GHG emissions in each land cover map. The detailed data set and methodology used in this paper were explained in Kiswanto et al. 2018. These spatial datasets were used for calculating the total changed areas in each period using the transition matrix (Appendix 1.A) and multiplying with the carbon stock changes in each period.
1.2.2 Carbon Stock in Each Land Cover Class
The emission factor for land cover changes is defined as the stock difference in carbon between two land cover classes (Santosa et al. 2014). The reference for carbon stock estimation for each land cover class was required to calculate carbon stock differences and GHG emissions from the land‐based sector at a specific location. For each land cover class, the reference was generated from research related to above‐ground biomass for specific sites. For forest cover classes, data references were developed from the average of above‐ground biomass in the forest areas (Hairiah et al. 2011). For cropland and agricultural land covered with regular cycles of planting and harvesting, carbon stock references were developed from the time average of above‐ground biomass (Agus et al. 2013a).
The provincial and district are supported to develop the highest carbon stock estimation that accurately illustrated the circumstances. If reference data is available at the province level, the carbon stock in the various districts within the province can be used to represent the emission factor. If it is not available, data can be used from the national level (Santosa et al. 2014). Table 1.1 shows land cover classes and their carbon stocks for estimating GHG emissions that were used for devising the local action plan for REDD+ in East Kalimantan.
Table 1.1 Land cover classes and their carbon stocks for estimating carbon emissions from land‐based sectors.
Land cover type
Carbon stock (tC ha
–1
)
Reference; remarks
Forest
Dryland
Primary
195
MoF
2008
; Agus et al.
2013b
Secondary
169
Mangrove
Primary
170
MoF
2008
; Agus et al.
2013b
; Krisnawati et al.
2014
Secondary
120
Swamp
Primary
196
MoF
2008
, Agus et al.
2013a
,
b
Secondary
155
Artificial/Plantation forest
64
Agus et al.
2013b
; MoF
2008
; Verstegen et al.
2019
Non‐forest
Agriculture
Pure dry
8
East Kalimantan
2013
Mixed dry
10
East Kalimantan
2013
Rice field
5
Rahayu et al.
2005
; East Kalimantan
2013
,
Estate cropland
63
Agus et al.
2013b
; Verstegen et al.
2019
Aquaculture
0
Agus et al.
2013b
Shrubland
Dry
15
Prasetyo and Saito
2000
; East Kalimantan
2013
Wet
15
Prasetyo and Saito
2000
; East Kalimantan
2013
Savanna and grasses
4.5
Rahayu et al.
2005
; Agus et al.
2013b
Open swamp
0
Agus et al.
2013b
Open water
0
East Kalimantan
2013
Transmigration areas
10
Agus et al.
2013b
– assuming 30% of the area was used for agriculture
Settlement areas
1
East Kalimantan
2013
–assuming less vegetation
Port and harbor
5
East Kalimantan
2013
Mining areas
0
Agus et al.
2013b
Bare ground
0
Prasetyo and Saito
2000
; East Kalimantan
2013
Cloud
0
East Kalimantan
2013
1.2.3 Change in Carbon Stock and CO2 Emission
Based on the yearly land cover maps in the study area from 2000 to 2016 referred to in Kiswanto et al. 2018, the change in carbon stock for each period was estimated using the IPCC guideline for national GHG inventories (IPCC, 2006) for agriculture, forestry, and other land use (AFOLU) by calculating carbon stock difference before (B) and after (A), multiplying by the total changed area, and divided by interval year periods (Eq. (1.1)):
where:
ΔCBA‐1
CB‐1
CA‐1
ƩA=total amount of the changed area of the land cover, ha
t1
t2
The sign (–/+) of the calculation result represents the C stock difference. The positive (+) stock difference represents the increase in C stocks known as negative emission (sequestration). The negative (–) stock changes represent the decreases in C stocks known as positive emission (emission). The remaining unchanged land cover class during the analysis period or among similar carbon stock would be estimated as no emission. For example, Table 1.1 showed similar carbon stock for paddy/rice field and port and harbor (5 tC yr–1), transmigration areas and mixed dry agriculture (10 tC yr–1), and bare ground, mining area, open swamp, open water, fish pond/aquaculture, and cloud/no data (0 tC yr–1). The CO2 emission based on the total C stock difference was estimated for each land cover class every year by multiplying the total C stock change by 44/12.
1.2.4 Historical Baselines and Future Trajectories
Annual GHG emissions from 2000 to 2016 were divided into two periods for developing the baseline and REDD+ progress. The period to estimate a historical baseline of GHG emission trend before the commitment of REDD+ was from 2000 to 2010. The period to estimate the REDD+ progress of GHG emission trend after the commitment was from 2010 to 2016. The selection of 2010 as the base year was based on the official submission of Indonesia's commitment to the UNFCCC in 2010 (Indonesia 2013).
Both GHG emission baselines were then projected to estimate the future trajectories of GHG emissions in the target period of commitment. The target of Indonesia's commitment in 2030 (Indonesia, 2016) was considered to determine the final projection in this study. Some analytical tools can be used to predict the future trendlines in a possible downturn or upturn data by connecting many points on a graph. Understanding how to use the trendlines for predicting the trend in the future could help to reveal what might happen in the future. Both future trajectories of GHG emission were compared to measure the achievement of REDD+ progress in East Kalimantan for 2030.
1.3 Results
1.3.1 Annual GHG Emissions
Figure 1.1 shows the annual GHG emissions in East Kalimantan between 2000 and 2016. The figure shows that a growing increase in GHG emissions occurred every year. Moreover, during the study period, the increase reached 31 Mt CO2 with an increment rate of 2.1 Mt CO2 yr–1. Although the annual GHG emissions showed an increasing trend every year, the increment rate before REDD+ commitment (2000–2010) was larger (2.3 Mt CO2 yr–1) than the increment rate after the commitment (1.5 Mt CO2yr–1). So this result was able to illustrate the implication of the REDD+ strategies in East Kalimantan in reducing GHG emissions.
Figure 1.2 shows size of the contribution of land cover change to GHG emission in the study area. According to the figure, deforestation (i.e. the changes of forest cover to non‐forest cover) and forest degradation (the changes of dense forest to less dense forest) gave a significant contribution to GHG emission in East Kalimantan. The deforestation caused the annual GHG emission increase of 25 Mt CO2 by 2016 with an increment rate of 1.7 Mt CO2yr–1 and it contributed about 80% of GHG emission in the study area, respectively. Forest degradation affected the annual GHG emission increase of 6 Mt CO2 from 2000 to 2016, with an increment rate of 0.3 Mt CO2yr–1 and it contributed about 20% of GHG emission. Moreover, the changes within non‐forest cover only contributed to the annual GHG emission increase of 0.7 Mt CO2 from 2000 to 2016, with an increment rate of 0.05 Mt CO2yr–1.
Table 1.2 shows the 10 largest emitters of GHG of land cover change over the 17 years. As shown in Table 1.2, most of the top 10 largest emitters were classified as the deforestation process. The changes of secondary dryland forest to dry shrubland, estate cropland, and plantation forest were the three largest GHG emitters in East Kalimantan (Table 1.2), represented by 48.63%, 12.68%, and 10.04% of the total GHG emissions from 2000 to 2016, respectively. Furthermore, the conversion of secondary mangrove forest into wet shrubland and fish pond/aquaculture contributed 2.48% and 1.85% of total
