Estimation of Above-Ground Mangrove Biomass Using Landsat-8 Data- Derived Vegetation Indices: A Case Study in Quang Ninh Province, Vietnam
Additional Files
Deprecated: json_decode(): Passing null to parameter #1 ($json) of type string is deprecated in /home/journal33/public_html/plugins/generic/citations/CitationsPlugin.inc.php on line 49
This study aimed to map the status of mangrove forests over the coasts of Hai Ha District and Mong Cai City in Quang Ninh Province by using 2019 Landsat-8 imagery. It then developed the AGB estimation model of mangrove forests based on the AGB estimation-derived plots inventory and vegetation indices-derived from Landsat-8 data. As results, there were five land covers identified, including mangrove forests, other vegetation, wetlands, built-up, and water, with the overall accuracy assessments of 80.0% and Kappa coefficient of 0.74. The total extent of mangrove forests was estimated at 4291.2 ha. The best AGB estimation model that was selected to estimate the AGB and AGC of mangrove forests for the whole coasts of Hai Ha District and Mong Cai City is AGB= 30.38 + 911.95*SAVI (R2=0.924, PValue <0.001). The model validation assessment has confirmed that the selected AGB model can be applied to Hai Ha and Mong Cai coasts with the mean difference between AGB observed and AGB predicted at 16.0 %. This satisfactory AGB model also suggests a good potential for AGB and AGC mapping, which offer the carbon trading market in the study site. As the AGB model selected, the total AGB and AGC of mangrove forests were estimated at about 14,600,000 tons and 6,868,076 tons with a range of from 94.0 - 432.0 tons ha-1, from 44.2 - 203.02 tons ha-1, respectively. It also suggests that the newly-developed AGB model of mangrove forests can be used to estimate AGC stocks and carbon sequestration of mangrove forests for C-PFES in over the coasts of Hai Ha District and Mong Cai City, which is a very importantly financial source for mangrove forest managers, in particular for local mangrove protectors.
Ai, J., Zhang, C., Chen, L., & Li, D. (2020). Mapping annual land use and land cover changes in the Yangtze estuary region using objective-based classification framework and Landsat time series data. Sustainability, 12(2), 659. https://doi.org/10.3390/su12020659
Alongi, D. M. (2009). Introduction in the energetics of mangrove forests. Springer Science and Business Media.
Aksornkoae, S. (1993). Ecology and management of mangroves. Gland, Switzerland, IUCN, Wetlands and Water Resources Programme.
Bao, T. Q., & Hoa, L. S. (2018). Using Sentinel satellite image to estimate biomass of mangrove forest in Vinh Quang commune, Tien Lang district, Hai Phong city. Journal of Forestry Science and Technology, 5, 71-79.
Blasco, F., Gauquelin, T., Rasolofoharinoro, M., Denis, J., Aizpuru, M., & Caldairou, V. (1998). Recent advances in mangrove studies using remote sensing data. Marine and Freshwater Research, 49(4), 287-296. https://doi.org/10.1071/MF97153
Castillo, J. A. A., Apan, A. A., Maraseni, T. N., & Salmo, III. S. G. (2017). Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 134, 70-85. https://doi.org/10.1016/j.isprsjprs.2017.10.016
Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors. Remote Sensing of Environment, 113(5), 893-903. https://doi.org/10.1016/j.rse.2009.01.007
Chen, B., Xiao, X., Li, X., Pan, X., Doughty, R., Ma, J., Dong, J., Qin, Y., Zhao, B., Wu, Z., Sun., R., Lan, G., Xie, G., Clinton, N., & Giri, C. (2017). A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel 1A imagery in Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 131, 104-210. https://doi.org/10.1016/j.isprsjprs.2017.07.011
Cissell, J. R., Delgado, A. M., Sweetman, B. M., & Steinberg, M. K. (2018). Monitoring mangrove forest dynamics in Campeche, Mexico, using Landsat satellite data. Remote Sensing Applications: Society and Environment, 9, 60-68. https://doi.org/10.1016/j.rsase.2017.12.001
Dan, T. T., Chen, C. F., Chiang, S. H., & Ogawa, S. (2016). Mapping and change analysis in mangrove forest by using Landsat imagery. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Science, 8, 109-116. https://doi.org/10.5194/isprs-annals-III-8-109-2016
Donato, D. C., Kauffman, J. B., Murdiyarso, D., Kurnianto, S., Stidham, M., & Kanninen, M. (2011). Mangroves among the most carbon-rich forests in the tropics. Nature Geoscience, 4(5), 293-297. https://doi.org/10.1038/ngeo1123
Dube, T., Gara, T. W., Mutanga, O., Sibanda, M., Shoko, C., Murwira, A., ... & Hatendi, C. M. (2018). Estimating forest standing biomass in savanna woodlands as an indicator of forest productivity using the new generation WorldView-2 sensor. Geocarto International, 33(2), 178-188. https://doi.org/10.1080/10106049.2016.1240717
Eggleston, H. S., Buendia, L., Miwa, K., Ngara, T., & Tanabe, K. (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories. IPCC National Greenhouse Gas Inventories Programme. Retrieved from http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.htm
FAO. (2007). The World’s Mangroves 1980–2005. FAO Forestry Paper 153. FAO.
Giri, C. (2016). Observation and monitoring of mangrove forests using remote sensing: Opportunities and challenges. Remote Sensing, 8(9), 783. https://doi.org/10.3390/rs8090783
Giri, C. (2021). Recent advancement in mangrove forests mapping and monitoring of the world using earth observation satellite data. Remote Sensing, 13(4), 563. https://doi.org/10.3390/rs13040563
Goessens, A., Satyanarayana, B., Van der Stocken, T., Zuniga, M. Q., Mohd-Lokman, H., Sulong, I., & Dahdouh-Guebas, F. (2014). Is Matang mangrove forest in Malaysia sustainably rejuvenating after more than a century of conservation and harvesting management? PloS one, 9, e105069. https://doi.org/10.1371/journal.pone.0105069
Gupta, K., Mukhopadhyay, A., Giri, S., Chanda, A., Majumdar, S.D., Samanta, S., & Hazra, S. (2018). An index for discrimination of mangroves from non-mangroves using LANDSAT 8 OLI imagery. MethodsX, 5, 1129-1139. https://doi.org/10.1016/j.mex.2018.09.011
Hai-Hoa, N., McAlpine, C., Pullar, D., Johansen, K., & Duke, N. C. (2013). The relationship of spatial-temporal changes in fringe mangrove extent and adjacent land-use: Case study of Kien Giang coast, Vietnam. Ocean and Coastal Management, 76, 12-32. doi.org/10.1016/j.ocecoaman.2013.01.003
Hai-Hoa, N. (2014). The relation of coastal mangrove changes and adjacent land-use: A review in Southeast Asia and Kien Giang, Vietnam. Ocean and Coastal Management, 90, 1-10. http://dx.doi.org/10.1016/j.ocecoaman.2013.12.016
Hai-Hoa, N., & Binh T. D. (2016). Using Landsat imagery and vegetation indices differencing to detect mangrove change: A case in Thai Thuy district, Thai Binh province. Journal of Forest Science and Technology, 5, 59-66.
Hai-Hoa, N., Nghia, N. H., Hien, N. T. T., An, L. T., Lan, T. T., Linh, D., Simone, B., & Michael, F. (2020a). Classification Methods for Mapping Mangrove Extents and Drivers of Change in Thanh Hoa Province, Vietnam during 2005-2018. Forest and Society, 4(1), 225-242. https://doi.org/10.24259/fs.v4i1.9295
Hai-Hoa, N., Lan, T. T. N., An, L. T., Nghia, N. H., Linh, D. V. K., Hien, N. T. T., Bohm, S., & Premnath, C. F. S (2020b). Monitoring changes in coastal mangrove extents using multi-temporal satellite data in selected communes, Hai Phong city, Vietnam. Forest and Society, 4(1), 256-720. https://doi.org/10.24259/fs.v4i1.8486
Hamdan, O., Khairunnisa, M. R., Ammar, A. A., Hasmadi, I. M., Aziz, H. K. (2013). Mangrove carbon stock assessment by optical satellite imagery. Journal of Tropical Forest Science, 25(4), 554-565. https://www.jstor.org/stable/23616997
Hamdan, O., Azi, H. K., & Hasmadi, I. M. (2014). L-band ALOS PALSAR for biomass estimation of Matang Mangroves, Malaysia. Remote Sensing of Environment, 155, 69-78. https://doi.org/10.1016/j.rse.2014.04.029
Hashim, T. M. Z. T., Suratman, M. N., Singh, H. R., Jaafar, J., & Bakar, A. N. (2020, July). Predictive Model of Mangroves Carbon Stocks in Kedah, Malaysia using Remote Sensing. In IOP Conference Series: Earth and Environmental Science, 540(1), 012033. http://dx.doi.org/10.1088/1755-1315/540/1
Heumann, B. W. (2011). Satellite remote sensing of mangrove forests: Recent advances and future opportunities. Progress in Physical Geography, 35, 87-108. https://doi.org/10.1177%2F0309133310385371
Hohne, N., Warnecke, C., Day, T., & Roser, F. (2015). Carbon market mechanisms - Role
in future international cooperation on climate change. New climate institute.
Howard, J., Hoyt, S., Isensee, K., Telszewski, M., & Pidgeon, E. (2014). Coastal blue carbon: methods for assessing carbon stocks and emissions factors in mangroves, tidal salt marshes, and seagrasses. Conservation International, Intergovernmental Oceanographic Commission of UNESCO, International Union for Conservation of Nature, Arlington.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2
Hu, T., Zhang, Y., Su, Y., Zheng, Y., Lin, G., & Guo, Q. (2020). Mapping the global mangrove forest aboveground biomass using multisource remote sensing data. Remote Sensing, 12(10), 1690. https://doi.org/10.3390/rs12101690
Nguyen, H. T. T., Hardy, G. E., Le, T. V., Nguyen, H. Q., Nguyen, H. H., Nguyen, T. V., & Dell, B. (2021). Mangrove Forest Landcover Changes in Coastal Vietnam: A Case Study from 1973 to 2020 in Thanh Hoa and Nghe An Provinces. Forests, 12(5), 637. https://doi.org/10.3390/f12050637
Hutchison, J., Manica, A., Swetnam, R., Balmford, A., & Spalding, M. (2014). Predicting global patterns in mangrove forest biomass. Conservation Letters, 7(3), 233-240. https://doi.org/10.3390/rs12101690
Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., & Zhu, Y.H. (2000). Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128, 109-120. https://doi.org/10.1016/S0378-1127(99)00278-9
Jachowski, N. R., Quak, M. S., Friess, D. A., Duangnamon, D., Webb, E. L., & Ziegler, A. D. (2013). Mangrove biomass estimation in Southwest Thailand using machine learning. Applied Geography, 45, 311-321. https://doi.org/10.1016/j.apgeog.2013.09.024
Kaufman, J. B., & Donato, D. C. (2012). Protocols for the measurement, monitoring and reporting of structure, biomass and carbon stocks in mangrove forests. Working Paper 86. CIFOR. https://doi.org/10.17528/cifor/003749
Kauffman, J. B., Adame, M. F., Arifanti, V. B., Schile‐Beers, L. M., Bernardino, A. F., Bhomia, R. K., ... & Hernandez Trejo, H. (2020). Total ecosystem carbon stocks of mangroves across broad global environmental and physical gradients. Ecological Monographs, 90(2), e01405. https://doi.org/10.1002/ecm.1405
Komiyama, A., Poungparn, S., & Kato, S. (2005). Common allometric equations for estimating the tree weight of mangroves. Journal of Tropical Ecology, 21(4), 471-477. https://doi.org/10.1017/S0266467405002476
Komiyama, A., Ogino, K., Aksornkoae, S., & Sabhasri, S. (1987). Root biomass of a mangrove forest in southern Thailand. 1. Estimation by the trench method and the zonal structure of root biomass. Journal of Tropical Ecology, 3(2), 97-108. https://doi.org/10.1017/S0266467400001826
Kongwongjan, J., Suwanprasit, C., & Thongchumnum, P. (2012). Comparison of vegetation indices for mangrove mapping using THEOS data. Proceedings of the Asia-Pacific Advanced Network, 33, 56-64. http://dx.doi.org/10.7125/APAN.33.6
Kovacs, J.M., Santiago, F. F., Bastien, J., & Lafrance, P. (2010). An Assessment of Mangroves in Guinea, West Africa, using a Field and Remote Sensing Based Approach. Wetlands, 30, 773-782. https://doi.org/10.1007/s13157-010-0065-3
Kristensen, E., Holmer, M., Banta, G. T., Jensen, M. H., & Hansen, K. (1995). Carbon, nitrogen and sulfur cycling in sediments of the Ao Nam Bor mangrove forest, Phuket, Thailand: a review. Phuket Marine Biological Center Research Bulletin, 60, 37-64
Kuenzer, C., Bluemel, A., Gebhardt, S., Tuan V. Q., & Dech, S. (2011). Remote Sensing of Mangrove Ecosystems: A Review. Remote Sensing, 3(5), 878-928. https://doi.org/10.3390/rs3050878
Kumar, L., & Mutanga, O. (2017). Remote sensing of above-ground biomass. Remote Sensing, 9(9), 935. https://doi.org/10.3390/rs9090935
Kusmana, C., & Sabiham, S., (1992). An estimation of above ground tree biomass of a mangrove forest in East Sumatra, Indonesia. Tropics, 1, 243–257. https://doi.org/10.3759/tropics.1.243
Li, X., Gar-on Yeh, A., Wang, S., Liu, K., Liu, X., Qian, J., & Chen, X. (2007). Regression and analytical models for estimating mangrove wetland biomass in South China using Radarsat images. International Journal of Remote Sensing, 28, 5567-5582. https://doi.org/10.1080/01431160701227638
Long, D. N., Cuong, T. N., Hoa, S. L., & Bao, Q. T. (2019). Mangrove mapping and above-ground biomass change detection using satellite iamges in coastal areas of Thai Binh province, Vietnam. Forest and Society, 3(2), 248-261. https://doi.org/10.24259/fs.v3i2.7326
Long, J. B., & Giri, C. (2011). Mapping the Philippines’ mangrove forests using Landsat imagery. Sensors, 11(3), 2972-2981. https://doi.org/10.3390/s110302972
Lu, X. T., Yin, J. X., Jepsen, M. R., & Tang, J. W. (2010). Ecosystem carbon storage and partitioning in a tropical seasonal forest in Southwestern China. Forest Ecology and Management, 260, 1798-1803. https://doi.org/10.1016/j.foreco.2010.08.024
Lugo, A. E., & Snedaker, S. C. (1975). Properties of a mangrove forest in southern Florida. In G.E. Walsh, S.C. Snedaker & M.J. Teas, eds. Proceedings of the International Symposium on Biology and Management of Mangroves (pp. 170–212). Gainesville, Florida, USA, University of Florida.
Manandhar, R., Odeh, I. O., & Ancev, T. (2009). Improving the accuracy of land use and land cover classification of Landsat data using post-classification enhancement. Remote Sensing, 1(3), 330-344. https://doi.org/10.3390/rs1030330
Malarvizhi, K., Kumar, S. V., & Porchelvan, P. (2016). Use of High-Resolution Google Earth Satellite Imagery in Land use Map Preparation for Urban Related Applications. Procedia Technology, 24, 1835-1842. https://doi.org/10.1016/j.protcy.2016.05.231
Nguyen, V. D., Hai-Hoa, N., Quyet, N., & Duy Quang, P., (2021). Land surface temperature responses to vegetation and soil moisture index using Landsat-8 data in Luong Son District, Hoa Binh Province. Journal of Forest Science and Technology, 11, 1-13.
Norjamaki, I., & Tokola, T. (2007). Comparison of atmospheric correction methods in mapping timber volume with multi-temporal Landsat images in Kainuu, Finland. Photogrammetric Engineering & Remote Sensing, 73(2), 155-163. https://doi.org/10.14358/PERS.73.2.155
Ong, J. E., Malaysia, U. S., Gong, W. K., & Wong, C. H. (1982). Studies on Nutrient Levels in Standing Biomass, Litter and Slash in a Mangrove Forest. School of Biological Sciences, Universiti Sains Malaysia.
Pham, T. D., & Yoshino, K. (2017). Aboveground biomass estimation of mangrove species using ALOS-2 PALSAR imagery in Hai Phong City, Vietnam. Journal of Applied Remote Sensing, 11(2), 026010. https://doi.org/10.1117/1.JRS.11.026010
Pham. T. D., Yoshino, K., Le, N. N., & Bui, D. T. (2018). Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data. International Journal of Remote Sensing, 39(22), 7761-7788. https://doi.org/10.1080/01431161.2018.1471544
Pham, T. D., Le, N. N., Ha, N. T., Nguyen, L. V., Xia, J., Yokoya, N., To, T. T., Trinh, H. X., Kieu, L. Q., & Takeuchi, W. (2020). Estimating Mangrove Above-Ground Biomass Using Extreme Gradient Boosting Decision Trees Algorithm with Fused Sentinel-2 and ALOS-2 PALSAR-2 Data in Can Gio Biosphere Reserve, Vietnam. Remote Sensing 12(5) 777. https://doi.org/10.3390/rs12050777
Pham, T. T., Phuong, V. T., Chien, P. D., Trang, D. L. H., Truong, N. V., Hoa, H. N. V., Long, H. T., Chi, D. T. T., & Tien, N. D., (2019). Opportunities and challenges for mangrove management in Vietnam: Lessons learned from Thanh Hoa, Thai Binh and Quang Ninh provinces. Occasional Paper 197. CIFOR. https://doi.org/10.17528/cifor/007404
Phuong, V. T. (2006). Environmental value and environmental services. Journal of Agriculture and Rural Development, 15, 7-11.
Putz, F. E., & Chan, H. T. (1986). Tree growth, dynamics, and productivity in a mature mangrove forest in Malaysia. Forest Ecology and Management, 17(2-3), 211-230. https://doi.org/10.1016/0378-1127(86)90113-1
Quang Ninh FPD (Quang Ninh Forest Protection Department). (2018). http://kiemlamqni.org.vn
Ramdani, F., Rahman, S., & Giri, C. (2018). Principal polar spectral indices for mapping mangroves forest in South East Asia: study case Indonesia. International Journal of Digital Earth, 12(10), 1103-1117. https://doi.org/10.1080/17538947.2018.1454516
Saleh, M. (2007). Mangrove vegetation on Abu Minqar Island of the Red Sea. International Journal of Remote Sensing, 28(23), 5191-5194. https://doi.org/10.1080/01431160500391932
Sharma, S., MacKenzie, R. A., Tieng, T., Soben, K., Tulyasuwan, N., Resanond, A., ... & Litton, C. M. (2020). The impacts of degradation, deforestation and restoration on mangrove ecosystem carbon stocks across Cambodia. Science of the Total Environment, 706, 135416. https://doi.org/10.1016/j.scitotenv.2019.135416
Sinha, S., Jeganathan, C., Sharma, L. K., Nathawat, M. S. (2015). A review of radar remote sensing for biomass estimation. International Journal of Environmental Science and Technology, 12, 1779-1792. https://doi.org/10.1007/s13762-015-0750-0
Steininger, M. (2000). Satellite estimation of tropical secondary forest above-ground biomass data from Brazil and Bolivia. International Journal of Remote Sensing, 21, 1139-1157. https://doi.org/10.1080/014311600210119
Stringer, C. E., Trettin, C. C., Zarnoch, S. J., & Tang, W. (2015). Carbon stocks of mangroves within the Zambezi River Delta, Mozambique. Forest Ecology and Management, 354, 139-148. https://doi.org/10.1016/j.foreco.2015.06.027
Tinh, P. H., Hanh, N. T. H., Thanh, V. V., Tuan, M. S., Quang, P. V., Sharma, S., & Mackenzie, A. (2020). A comparison of soil carbon stocks of intact and restored mangrove extents in Norhern Vietnam. Forests, 11(6), 660. https://doi.org/10.3390/f11060660
Tieng, T., Sharma, S., Mackenzie, R. A., Venkattappa, M., Sasaki, N. K., & Collin, A. (2019, April). Mapping mangrove forest cover using Landsat-8 imagery, Sentinel-2, very high resolution images and Google Earth Engine Algorithm for entire Cambodia. IOP Conference Series: Earth and Environmental Science, 266(1), 012010. http://dx.doi.org/10.1088/1755-1315/266/1/012010
Timothy, D., Onisimo, M., Cletah, S., Adelabu, S., & Tsitsi, B. (2016). Remote sensing of aboveground forest biomass: A review. Tropical Ecology, 57(2), 125-132.
Thuy, H. L. T., Tan, M. T., Van, T. T. T., Bien, L. B., Ha, N. M., & Nhung, N. T. (2020). Using Sentinel image data and plot survey for the assessment of biomass and carbon stocks in coastal forests of Thai Binh Province, Vietnam. Applied Ecology and Environmental Research, 18(6), 7499- 7514. http://dx.doi.org/10.15666/aeer/1806_74997514
Bui, T. D., Maier, S. W., & Austin, C. M. (2014). Land cover and land use change related to shrimp farming in coastal areas of Quang Ninh, Vietnam using remotely sensed data. Environmental Earth Sciences, 72(2), 441-455. https://doi.org/10.1007/s12665-013-2964-0
Thom, B. G. (1984). The mangrove ecosystem: research methods. The mangrove ecosystem: research methods. In S.C. Snedaker and J.G. Snedaker (Eds.) Monographs on oceanographic methodology (pp. 3–15). UNESCO.
Tue, N. T., Dung, L. V., Nhuan, M. T., & Omori, K. (2014). Carbon storage of a tropical mangrove forest in Mui Ca Mau National Park, Vietnam. Catena, 121, 119-126. https://doi.org/10.1016/j.catena.2014.05.008
Tomlinson, P. B. (1986). The botany of mangroves. Cambridge University Press.
Nam, V. N., Sasmito, S. D., Murdiyarso, D., Purbopuspito, J., & MacKenzie, R. A. (2016). Carbon stocks in artificially and naturally regenerated mangrove ecosystems in the Mekong Delta. Wetlands Ecology and Management, 24(2), 231-244. https://doi.org/10.1007/s11273-015-9479-2
Van Vinh, T., Marchand, C., Linh, T. V. K., Vinh, D. D., & Allenbach, M. (2019). Allometric models to estimate above-ground biomass and carbon stocks in Rhizophora apiculata tropical managed mangrove forests (Southern Viet Nam). Forest Ecology and Management, 434, 131-141. https://doi.org/10.1016/j.foreco.2018.12.017
Wang, D., Wan, B., Liu, J., Su, Y., Guo, Q., Qiu, P., & Wu, X. (2020). Estimating aboveground biomass of mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery. International Journal of Applied Earth Observation and Geoinformation, 85. https://doi.org/10.1016/j.jag.2019.101986
Zhang, K., Dong, X., Liu, Z., Gao, W., Hu, Z., & Wu. G. (2019). Mapping tidal flats with Landsat-8 images and Google Earth Engine: A case study of the China’s Eastern coastal zone circa 2015. Remote Sensing, 11(8), 924. https://doi.org/10.3390/rs11080924
Copyright (c) 2021 Forest and Society
This work is licensed under a Creative Commons Attribution 4.0 International License.
This is an open access journal which means that all contents is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access.
Submission of an article implies that the work described has not been published previously (except in the form of an abstract or as part of a published lecture or academic thesis), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher. An article based on a section from a completed graduate dissertation may be published in Forest and Society, but only if this is allowed by author's(s') university rules. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication.
Forest and Society operates a CC-BY 4.0 © license for journal papers. Copyright remains with the author, but Forest and Society is licensed to publish the paper, and the author agrees to make the article available with the CC-BY 4.0 license. Reproduction as another journal article in whole or in part would be plagiarism. Forest and Society reserves all rights except those granted in this copyright notice
