Land-Cover and Elevation-Based Mapping of Aboveground Carbon in a Tropical Mixed-Shrub Forest Area in West Java, Indonesia
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Sampling Method
2.3. Aboveground Carbon Estimation
2.4. Mapping Methods and Validation
2.4.1. Lookup Table
2.4.2. Regression Modeling
2.4.3. Geostatistical Interpolation
3. Results
3.1. Lookup Table Based on Land Cover
3.2. Lookup Table Based on a Combination of Land Cover and Elevation
3.3. Regression Modeling
3.4. Geostatistical Interpolation without Stratification
3.5. Stratified Geostatistical Interpolation Based on Land Cover
3.6. Stratified Geostatistical Interpolation Based on Elevation
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Plot no | Aboveground Carbon (ton C/ha) | Land Cover Types | Elevation (Meter above Mean Sea Level) |
---|---|---|---|
1 | 98.0 | Others | 836 |
2 | 87.4 | Mix forest | 853 |
3 | 61.9 | Mix forest | 810 |
4 | 1.3 | Others | 904 |
5 | 15.1 | Others | 824 |
6 | 107.2 | Mix forest | 815 |
7 | 19.2 | Mix forest | 903 |
8 | 31.6 | Others | 938 |
9 | 31.6 | Pine-dominated forest | 938 |
10 | 2.5 | Others | 907 |
11 | 165.1 | Mahogany-dominated forest | 808 |
12 | 75.0 | Pine-dominated forest | 936 |
13 | 3.9 | Others | 877 |
14 | 111.1 | Mahogany-dominated forest | 803 |
15 | 77.7 | Mix forest | 850 |
16 | 3.8 | Others | 897 |
17 | 32.4 | Pine-dominated forest | 990 |
18 | 87.8 | Mahogany-dominated forest | 853 |
19 | 22.2 | Pine-dominated forest | 890 |
20 | 22.5 | Others | 1010 |
21 | 49.3 | Mahogany-dominated forest | 895 |
22 | 7.6 | Calliandra-dominated shrub | 983 |
23 | 32.8 | Mix forest | 908 |
24 | 56.6 | Calliandra-dominated shrub | 957 |
25 | 70.3 | Mahogany-dominated forest | 956 |
26 | 101.5 | Pine-dominated forest | 907 |
27 | 15.5 | Calliandra-dominated shrub | 1056 |
28 | 62.1 | Calliandra-dominated shrub | 981 |
29 | 142.1 | Calliandra-dominated shrub | 965 |
30 | 11.8 | Calliandra-dominated shrub | 1067 |
31 | 40.1 | Calliandra-dominated shrub | 986 |
32 | 35.8 | Mix forest | 918 |
33 | 40.6 | Mix forest | 998 |
34 | 52.1 | Mix forest | 994 |
35 | 64.3 | Mix forest | 1079 |
36 | 29.8 | Calliandra-dominated shrub | 1024 |
37 | 45.4 | Calliandra-dominated shrub | 1088 |
38 | 37.1 | Calliandra-dominated shrub | 1072 |
39 | 35.3 | Calliandra-dominated shrub | 1092 |
40 | 35.3 | Others | 1092 |
41 | 33.9 | Calliandra-dominated shrub | 1097 |
42 | 25.2 | Others | 1016 |
43 | 27.4 | Calliandra-dominated shrub | 1123 |
44 | 17.7 | Calliandra-dominated shrub | 1045 |
45 | 9.9 | Calliandra-dominated shrub | 1169 |
46 | 80.9 | Mix forest | 992 |
47 | 33.1 | Calliandra-dominated shrub | 1246 |
48 | 71.6 | Mahogany-dominated forest | 1103 |
49 | 19.1 | Calliandra-dominated shrub | 1160 |
50 | 16.8 | Mix forest | 1040 |
51 | 56.7 | Mahogany-dominated forest | 1102 |
52 | 107.7 | Mahogany-dominated forest | 1021 |
53 | 3.8 | Calliandra-dominated shrub | 1196 |
54 | 97.2 | Mix forest | 1145 |
55 | 74.5 | Mahogany-dominated forest | 1090 |
56 | 6.2 | Calliandra-dominated shrub | 1143 |
57 | 90.1 | Mahogany-dominated forest | 1048 |
58 | 54.3 | Mahogany-dominated forest | 1089 |
59 | 100.8 | Mahogany-dominated forest | 1070 |
60 | 19.4 | Calliandra-dominated shrub | 1136 |
61 | 57.2 | Mahogany-dominated forest | 1063 |
62 | 45.3 | Calliandra-dominated shrub | 1078 |
63 | 64.0 | Pine-dominated forest | 1004 |
64 | 24.8 | Calliandra-dominated shrub | 1066 |
65 | 57.7 | Pine-dominated forest | 1015 |
66 | 54.8 | Mahogany dominated forest | 1087 |
67 | 4.1 | Calliandra-dominated shrub | 1069 |
68 | 18.5 | Calliandra-dominated shrub | 1054 |
69 | 24.0 | Calliandra-dominated shrub | 1074 |
70 | 25.0 | Mahogany dominated forest | 1004 |
71 | 23.6 | Calliandra-dominated shrub | 1031 |
72 | 4.5 | Calliandra-dominated shrub | 1067 |
73 | 23.5 | Pine-dominated forest | 910 |
74 | 95.5 | Calliandra-dominated shrub | 1054 |
75 | 27.5 | Calliandra-dominated shrub | 1023 |
76 | 6.9 | Calliandra-dominated shrub | 1015 |
77 | 35.8 | Calliandra-dominated shrub | 964 |
78 | 17.9 | Calliandra-dominated shrub | 978 |
79 | 16.6 | Pine-dominated forest | 1086 |
80 | 39.5 | Pine-dominated forest | 964 |
81 | 3.8 | Pine-dominated forest | 850 |
82 | 45.5 | Mahogany-dominated forest | 954 |
83 | 6.3 | Calliandra-dominated shrub | 1130 |
84 | 5.6 | Calliandra-dominated shrub | 940 |
85 | 30.8 | Calliandra-dominated shrub | 1051 |
86 | 36.2 | Others | 895 |
87 | 7.4 | Calliandra-dominated shrub | 1117 |
88 | 16.7 | Calliandra-dominated shrub | 899 |
89 | 14.2 | Calliandra-dominated shrub | 993 |
90 | 12.9 | Calliandra-dominated shrub | 1147 |
91 | 11.1 | Calliandra-dominated shrub | 1035 |
92 | 41.1 | Calliandra-dominated shrub | 1039 |
93 | 5.5 | Calliandra-dominated shrub | 1094 |
94 | 4.5 | Calliandra-dominated shrub | 998 |
95 | 12.5 | Calliandra-dominated shrub | 954 |
References
- United Nations; European Commissions; Food and Agriculture Organization of the United Nations; International Monetary Fund; Organisation for Economic Co-operation and Development; The World Bank. System of Environmental-Economic Accounting 2012 Central Framework; United Nations: New York, NY, USA, 2014.
- Shimamoto, C.Y.; Botosso, P.; Marques, M.C.M. How much carbon is sequestered during the restoration of tropical forests? Estimates from tree species in the Brazilian Atlantic forest. For. Ecol. Manag. 2014, 329, 1–9. [Google Scholar] [CrossRef]
- Wheeler, C.E.; Omeja, P.A.; Chapman, C.A.; Glipin, M.; Tumwesigye, C.; Lewis, S.L. Carbon sequestration and biodiversity following 18 years of active tropical forest restoration. For. Ecol. Manag. 2016, 373, 44–55. [Google Scholar] [CrossRef]
- Fernández-Amador, O.; Francois, J.F.; Tomberger, P. Carbon dioxide emissions and international trade at the turn of the millennium. Ecol. Econ. 2016, 125, 14–26. [Google Scholar] [CrossRef]
- Putman, W.M.; Ott, L.; Darmenov, A.; da Silva, A. A global perspective of atmospheric carbon dioxide concentrations. Parallel Comput. 2016, 55, 2–8. [Google Scholar] [CrossRef]
- Hummel, C.; Poursanidis, D.; Orenstein, D.; Elliott, M.; Adamescu, M.C.; Cazacu, C.; Ziv, G.; Chrysoulakis, N.; van der Meer, J.; Hummel, H. Protected Area management: Fusion and confusion with the ecosystem services approach. Sci. Total Environ. 2019, 651, 2432–2443. [Google Scholar] [CrossRef] [PubMed]
- Scolforo, H.F.; Scolforo, J.R.S.; de Mello, J.M.; de Mello, C.R.; Morais, V.A. Spatial interpolators for improving the mapping of carbon stock of the arboreal vegetation in Brazilian biomes of Atlantic forest and Savanna. For. Ecol. Manag. 2016, 376, 24–35. [Google Scholar] [CrossRef]
- Shen, W.; Li, M.; Huang, C.; Tao, X.; Wei, A. Annual forest aboveground biomass changes mapped using ICESat/GLAS measurements, historical inventory data, and time-series optical and radar imagery for Guangdong province, China. Agric. For. Meteorol. 2018, 259, 23–38. [Google Scholar] [CrossRef] [Green Version]
- Fayad, I.; Baghdadi, N.; Guitet, S.; Bailly, J.S.; Hérault, B.; Gond, V.; El Hajj, M.; Minh, D.H.T. Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data. Int. J. Appl. Earth Obs. Geoinf. 2016, 52, 502–514. [Google Scholar] [CrossRef] [Green Version]
- Temgoua, L.F.; Momo Solefack, M.C.; Nguimdo Voufo, V.; Tagne Belibi, C.; Tanougong, A. Spatial and temporal dynamic of land-cover/land-use and carbon stocks in Eastern Cameroon: A case study of the teaching and research forest of the University of Dschang. For. Sci. Technol. 2018, 14, 181–191. [Google Scholar] [CrossRef] [Green Version]
- Zhu, J.; Huang, Z.; Sun, H.; Wang, G. Mapping forest ecosystem biomass density for Xiangjiang River Basin by combining plot and remote sensing data and comparing spatial extrapolation methods. Remote Sens. 2017, 9, 241. [Google Scholar] [CrossRef] [Green Version]
- Li, L.; Zhou, X.; Chen, L.; Chen, L.; Zhang, Y.; Liu, Y. Estimating urban vegetation biomass from Sentinel- 2A image data. Forest 2020, 11, 125. [Google Scholar] [CrossRef] [Green Version]
- Castillo-Santiago, M.Á.; Ghilardi, A.; Oyama, K.; Hernández-Stefanoni, J.L.; Torres, I.; Flamenco-Sandoval, A.; Fernández, A.; Mas, J.F. Estimating the spatial distribution of woody biomass suitable for charcoal making from remote sensing and geostatistics in central Mexico. Energy Sustain. Dev. 2013, 17, 177–188. [Google Scholar] [CrossRef]
- Jubanski, J.; Ballhorn, U.; Kronseder, K.; Franke, J.; Siegert, F. Detection of large above-ground biomass variability in lowland forest ecosystems by airborne LiDAR. Biogeosciences 2013, 10, 3917–3930. [Google Scholar] [CrossRef] [Green Version]
- Bazezew, M.N.; Hussin, Y.A.; Kloosterman, E.H. Integrating airborne LiDAR and terrestrial laser scanner forest parameters for accurate above-ground biomass/carbon estimation in Ayer Hitam tropical forest, Malaysia. Int. J. Appl. Earth Obs. Geoinf. 2018, 73, 638–652. [Google Scholar] [CrossRef]
- Solberg, S.; Hansen, E.H.; Gobakken, T.; Næssset, E.; Zahabu, E. Biomass and InSAR height relationship in a dense tropical forest. Remote Sens. Environ. 2017, 192, 166–175. [Google Scholar] [CrossRef]
- Schröter, M.; Remme, R.P.; Sumarga, E.; Barton, D.; Hein, L. Lessons learned for spatial modelling of ecosystem services in support of ecosystem accounting. Ecosyst. Serv. 2015, 13, 64–69. [Google Scholar] [CrossRef]
- Su, Y.; Guo, Q.; Xue, B.; Hu, T.; Alvarez, O.; Tao, S.; Fang, J. Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data. Remote Sens. Environ. 2016, 173, 187–199. [Google Scholar] [CrossRef] [Green Version]
- Hlatshwayo, S.T.; Mutanga, O.; Lottering, R.T.; Kiala, Z.; Ismail, R. Mapping forest aboveground biomass in the reforested Buffelsdraai landfill site using texture combinations computed from SPOT-6 pan-sharpened imagery. Int. J. Appl. Earth Obs. Geoinf. 2019, 74, 65–77. [Google Scholar] [CrossRef]
- Ketterings, Q.M.; Coe, R.; van Noordwijk, M.; Ambagau’, Y.; Palm, C.A. Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests. For. Ecol. Manag. 2001, 146, 199–209. [Google Scholar] [CrossRef]
- Aalde, H.; Gonzalez, P.; Gytarsky, M.; Krug, T.; Kurz, W.A.; Ogle, S.; Raison, J.; Schoene, D.; Ravindranath, N.H.; Elhassan, N.G.; et al. Chapter 4: Forest land. In 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Available online: https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_04_Ch4_Forest_Land.pdf (accessed on 25 January 2019).
- Sya’bani, Z.S. Allometric Equations for Estimating above Ground Biomass of Pine Stand in Lawu Mountain. Bachelor’s Thesis, Bogor Agricultural University, Bogor, Indonesia, 2017. [Google Scholar]
- Adinugroho, W.C.; Sidiyasa, K. Model for estimating above ground biomass of mahogany tree. J. Penelit. Hutan Dan Konserv. Alam 2006, 3, 103–117. [Google Scholar] [CrossRef] [Green Version]
- Alhamd, L.; Rahajoe, J.S. Species composition and above ground biomass of a pine forest at Bodogol, Gunung Gede Pangrango National Park, West Java. J. Trop. Biol. Conserv. 2013, 10, 43–49. [Google Scholar]
- Malmoud, E. Accuracy Measures and the Evaluation of Forecasts; The University of Michigan: Ann Arbor, MI, USA, 1986. [Google Scholar]
- R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; R Core Team: Vienna, Austria, 2019. [Google Scholar]
- Pebesma, E.J. Multivariable geostatistics in S: The gstat package. Comput. Geosci. 2004, 30, 683–691. [Google Scholar] [CrossRef]
- Li, W.; Niu, Z.; Liang, X.; Li, Z.; Huang, N.; Gao, S.; Wang, C.; Muhammad, S. Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling. Int. J. Appl. Earth Obs. Geoinf. 2015, 41, 88–98. [Google Scholar] [CrossRef]
- Spanò, M.; Leronni, V.; Lafortezza, R.; Gentile, F. Are ecosystem service hotspots located in protected areas? Results from a study in Southern Italy. Environ. Sci. Policy 2017, 73, 52–60. [Google Scholar] [CrossRef]
- Castro, A.J.; Martín-López, B.; López, E.; Plieninger, T.; Alcaraz-Segura, D.; Vaughn, C.C.; Cabello, J. Do protected areas networks ensure the supply of ecosystem services? Spatial patterns of two nature reserve systems in semi-arid Spain. Appl. Geogr. 2015, 60, 1–9. [Google Scholar] [CrossRef]
- Siregar, U.J.; Narendra, B.H.; Suryana, J.; Siregar, C.A.; Weston, C. Evaluation on community tree plantations as sustainable source for rural bioenergy in Indonesia. In Proceedings of the IOP Conference Series: Earth and Environmental Science, Bogor, Indonesia, 10–11 October 2016. [Google Scholar]
- Masripatin, N.; Ginoga, K.; Pari, G.; Dharmawan, W.S.; Siregar, C.A. Carbon Stocks on Various Types of Forest and Vegetation in Indonesia; Forest Research and Development Agency, Ministry of Forestry of Indonesia: Jakarta, Indonesia, 2010.
- Toma, T.; Ishida, A.; Matius, P. Long-term monitoring of post-fire aboveground biomass recovery in a lowland dipterocarp forest in East Kalimantan, Indonesia. Nutr. Cycl. Agroecosyst. 2005, 71, 63–72. [Google Scholar] [CrossRef]
- Krisnawati, H.; Wahjono, D.; Imanuddin, R. Changes in the species composition, stand structure and aboveground biomass of a lowland dipterocarp forest in Samboja, East Kalimantan. J. For. Res. 2011, 8, 1–16. [Google Scholar] [CrossRef] [Green Version]
- Waring, B.G.; Powers, J.F. Overlooking what is underground: Root:shoot ratios and coarse root allometric equations for tropical forests. For. Ecol. Manag. 2019, 385, 10–15. [Google Scholar] [CrossRef] [Green Version]
- Medrilzam, M.; Dargusch, P.; Herbohn, J.; Smith, C. The socio-ecological drivers of forest degradation in part of the tropical peatlands of Central Kalimantan, Indonesia. Forestry 2013, 87, 335–345. [Google Scholar] [CrossRef]
- Sumarga, E. Spatial indicators for human activities may explain the 2015 fire hotspot distribution in Central Kalimantan, Indonesia. Trop. Conserv. Sci. 2017, 10, 1940082917706168. [Google Scholar] [CrossRef] [Green Version]
- Chen, X.; Liu, S.; Zhu, Z.; Vogelmann, J.; Li, Z.; Ohlen, D. Estimating aboveground forest biomass carbon and fire consumption in the U.S. Utah High Plateaus using data from the Forest Inventory and Analysis Program, Landsat, and LANDFIRE. Ecol. Indic. 2011, 11, 140–148. [Google Scholar] [CrossRef]
- Wu, X.; Wang, S.; Fu, B.; Liu, Y.; Zhu, Y. Land use optimization based on ecosystem service assessment: A case study in the Yanhe watershed. Land Use Policy 2018, 72, 303–312. [Google Scholar] [CrossRef]
- Sumarga, E.; Hein, L. Mapping ecosystem services for land use planning, the case of Central Kalimantan. Environ. Manag. 2014, 54, 84–97. [Google Scholar] [CrossRef] [PubMed]
- Tammi, I.; Mustajärvi, K.; Rasinmäki, J. Integrating spatial valuation of ecosystem services into regional planning and development. Ecosyst. Serv. 2017, 26 Pt B, 329–344. [Google Scholar] [CrossRef] [Green Version]
Tree Species | Allometric Equations | Sources |
---|---|---|
Pine (Pinus merkusii) | B = 0.066 D2.51 | Sya’bani [22]. |
Mahogany (Swietenia macrophylla) | B = 0.048 D2.68 | Adinugroho and Sidiyasa [23]. |
Calliandra calothyrsus | B = 0.047 D2.493 | Alhamd and Rahajoe [24]. |
Other tree species | B = 0.066 D2.59 | Ketterings et al. [20]. |
Land Cover Types | Number of Plots | Range (ton C/ha) | Mean (ton C/ha) | Standard Deviation (ton C/ha) | Standard Error (ton C/ha) | Relative Standard Error (%) |
---|---|---|---|---|---|---|
Pine-dominated forest | 11 | 3.8–101.5 | 42.5 | 29.0 | 8.7 | 21 |
Mahogany-dominated forest | 17 | 11.1–165.1 | 72.5 | 36.3 | 8.8 | 12 |
Calliandra-dominated shrubs | 43 | 3.8–142.1 | 26.5 | 25.9 | 4.0 | 15 |
Mixed forest | 13 | 16.8–107.2 | 59.5 | 29.5 | 8.2 | 14 |
Others | 11 | 1.3–98.0 | 25.0 | 27.7 | 8.4 | 33 |
Types of Lookup Table | Classes | Mean Aboveground Carbon (ton C/ha) |
---|---|---|
Based on land-cover types | Pine-dominated forest | 44.4 |
Mahogany-dominated forest | 72.8 | |
Calliandra-dominated shrubs | 27.3 | |
Mixed forest | 57.4 | |
Others | 27.3 | |
Based on a combination of elevation and land-cover types | Pine-dominated forest, high elevation | 16.6 |
Pine-dominated forest, low elevation | 47.5 | |
Mahogany-dominated forest, high elevation | 61.1 | |
Mahogany-dominated forest, low elevation | 93.1 | |
Calliandra-dominated shrubs, high elevation | 23.4 | |
Calliandra-dominated shrubs, low elevation | 32.9 | |
Mixed forest, high elevation | 59.4 | |
Mixed forest, low elevation | 56.6 | |
Others, high elevation | 35.3 | |
Others, low elevation | 26.4 |
Variables | Coefficients | p Values |
---|---|---|
Intercept | 120,329.49 | 0.0108 * |
Elevation | −88.59 | 0.0452 * |
Land cover (mahogany-dominated forest) | 40,906.18 | 0.0003 ** |
Land cover (mix forest) | 21,717.33 | 0.0583 |
Land cover (pine-dominated forest) | 9397.80 | 0.4181 |
Land cover (others) | −10,497.10 | 0.3867 |
Geostatistical Interpolation Technique | Strata | CV of RMSE | Overall CV of RMSE |
---|---|---|---|
Geostatistical interpolation without stratification | No strata | 0.44 | 0.44 |
Geostatistical interpolation based on land cover | Pine-dominated forest | 0.26 | 0.44 |
Mahogany-dominated forest | 0.35 | ||
Calliandra-dominated shrubs | 0.52 | ||
Mixed forest | 0.48 | ||
Other types | 0.34 | ||
Geostatistical interpolation based on elevation | High elevation (>1025 m) | 0.46 | 0.56 |
Low elevation (<1025 m) | 0.57 |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Sumarga, E.; Nurudin, N.; Suwandhi, I. Land-Cover and Elevation-Based Mapping of Aboveground Carbon in a Tropical Mixed-Shrub Forest Area in West Java, Indonesia. Forests 2020, 11, 636. https://doi.org/10.3390/f11060636
Sumarga E, Nurudin N, Suwandhi I. Land-Cover and Elevation-Based Mapping of Aboveground Carbon in a Tropical Mixed-Shrub Forest Area in West Java, Indonesia. Forests. 2020; 11(6):636. https://doi.org/10.3390/f11060636
Chicago/Turabian StyleSumarga, Elham, Nuruddin Nurudin, and Ichsan Suwandhi. 2020. "Land-Cover and Elevation-Based Mapping of Aboveground Carbon in a Tropical Mixed-Shrub Forest Area in West Java, Indonesia" Forests 11, no. 6: 636. https://doi.org/10.3390/f11060636
APA StyleSumarga, E., Nurudin, N., & Suwandhi, I. (2020). Land-Cover and Elevation-Based Mapping of Aboveground Carbon in a Tropical Mixed-Shrub Forest Area in West Java, Indonesia. Forests, 11(6), 636. https://doi.org/10.3390/f11060636