Preparation of Distribution and Health map of Afforestation in Langroud County using Sentinel-2 Sensor Images and Field Data

Document Type : Original Article

Authors

1 Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, I.R. Iran

2 Forest Expert, Department of Natural Resources of Langroud, Guilan Natural Resources and Watershed Administration, Rasht, Iran.

3 Researcher Expert, Research Division of Natural Resources, Guilan Agricultural and Natural Resources Research and Education Center, AREEO, Rasht, Iran

4 Forest Expert, Guilan Natural Resources and Watershed Administration, Rasht, Iran.

Abstract

Background and aim: Monitoring the current status of existing afforestation’s in management decisions is very important for the development of afforestation’s in the future. This study was conducted in order to monitor the area, distribution and health of afforestation’s in Langroud County, Guilan province. Sustainable management of current and future annual afforestation’s requires studies on the status of the afforestation’s with focusing on their health, and healthy afforestation’s can have greater environmental performance compared to unhealthy ones. The aim of this study is to prepare a map and survey of the areas of afforestation’s in Langroud County using ground surveys, Android GPS software (GFAMP), and Google Earth for the protection, enrichment, and development of afforestation’s in the future. The health of the stands is also assessed using Sentinel 2 sensor images and vegetation cover indices of SAVI, TNDVI, NDVI, and RVI.
Materials and methods: For this purpose, first, field surveys were done in the form of land points from the existing afforestation, and the distribution map of afforestation was prepared using the land surveys, GPS Fields Area Measure PRO application (GFAMP), and Google Earth system. Then the Sentinel 2 sensor image related to the growing season in Langroud County was prepared from the Copernicus site. From the Sentinel 2 sensor images, various vegetation indices such as NDVI, TNDVI, SAVI and RVI related to the growing season were extracted and their maps were prepared in the area of afforestation. In the following, the amount of each vegetation index was extracted at the points of land harvesting and the correlation of the values of each vegetation index (resulting from images of the growing season of Sentinel 2 sensor) with the health of afforestation (resulting from field harvesting) was investigated. For this purpose, Pearson's correlation coefficient was used. Then, the index that showed the highest correlation with the health of afforestation in Langroud County (NDVI) was selected as the most important index to estimate the health of afforestation and its regression relationship with the health of afforestation was obtained. In the following, using the map of the most favorable vegetation index, information of field survey and the relationship between these two cases, a health map of afforestation in Langroud County was prepared.
Results and discussion: Based on the findings of this study, a total of 66 afforestation plots were identified in this county that the total area of these plots was obtained using the 100% inventory method and ground control as 746.2 ha, which are mainly distributed in the southwest of the county. In addition, the results showed that the NDVI Index is the most favorable vegetation index for estimating the health of afforestation in Langroud County, which indicates the ability of this index to assess the health of trees during the growing season. After that, the SAVI index showed the highest correlation with tree health, which also shows the good ability of this index in monitoring tree health. While the two indices TNDVI and RVI showed a much lower correlation with tree health within the Langroud afforestation area, their use for assessing tree health in future studies is not recommended. Of the current total area of afforestation, 400.56 ha are in full health status, 305.60 ha are in medium health status, and 40.04 ha are in unhealthy status. The overall accuracy (OA) of the afforestation’s health map in this study was 80 %, the producer accuracy was 79 %, the user accuracy was 78 %, and the kappa coefficient was 0.73. These results indicate the optimal classification of the afforestation’s health map into different tree health and vigor classes. In general, the afforestation’s in this county are mostly in perfect health, but some areas of Kachlebon, Larzian, Gandom Bijaran, Ghazi Dasht, Khorsh Sara, and Chaksar (Chakdasht) are less successful due to the lack of conservation operations and the lack of guards in the early years.
Conclusion: The results of this research will help the forest managers for quantitative and qualitative monitoring of afforestation and its continuity in certain time periods as well as future afforestation development plans. The presented regression model, based on the high correlation of the NDVI index with the health of afforestation, allows for rapid estimation, quantitative, low-cost, affordable, and economic assessment of the health status of afforestation on a large scale and in inaccessible areas. The results of this research, by understanding the current status of afforestation, provide a good view of the capacity and potential of Langroud County for afforestation, considering the environmental and ecological conditions of this county.

Keywords

References

Ahmadi Malakut, E., Soltani, A. & Hasanzad Navrodi, I., 2011, A Comparison between Understory Phytodiversity of a Natural Forest and Forest Plantations (Case Study: Langerud – Guilan), Iranian Journal of Forest, 3(2), PP. 157-167.
Bagheri, R. & Erfanifard, Y., 2020, Spatial Distribution of Persian Oak Decline Using a Combination of Geostatistical Techniques and Remote Sensing (Case Study: Barm Plain, Fars Province), Journal of RS and GIS for Natural Resources, 11(1), PP. 104-120, https://doi.org/10.30495/girs.2020.672379.
Barazmand, S., Shataee Joybari, S. & Abdi, O., 2012, Recognition Possibility of Trees Canopy Die Back Using High Resolution Satellite Image of Quick Bird (Case Study: Shastkolate Forest, Gorgan), Iranian Journal of Forest and Poplar Research, 19(4), PP. 477-466, https://doi.org/10.22092/ijfpr.2011.107504.
Barka, I., Lukeš, P., Bucha, T., Hlásny, T., Strejček, R., Mlčoušek, M. & Křístek, Š., 2018, Remote Sensing-Based Forest Health Monitoring Systems-Case Studies from Czechia and Slovakia, Central European Forestry Journal, 64(3-4), PP. 259-275, https://doi.org/10.1515/forj-2017-0051.
Barkey, R.A. & Nursaputra, M., 2017, The Detection of Forest Health Level as an Effort to Protecting Main Ecosystem in the Term of Watershed Management in Maros Watershed, South Sulawesi, The 1st Biennial Conference on Tropical Biodiversity, IOP Conference Series: Earth and Environmental Science, 270(1), P. 012006, https://doi.org/10.1088/1755-1315/270/1/012006.
Bihamta, M.R. & Zare Chahouki, M.A., 2015, Principles of Statistics for the Natural Resources Science, University of Tehran Press, Tehran.
Cavalli, A., Francini, S., McRoberts, R.E., Falanga, V., Congedo, L., De Fioravante, P., Maesano, M., Munafò, M., Chirici, G. & Scarascia Mugnozza, G., 2023, Estimating Afforestation Area Using Landsat Time Series and Photointerpreted Datasets, Remote Sensing, 15(4), PP. 1-26, https://doi.org/10.3390/rs15040923.
Congalton, R.G. & Green, K., 2008, Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, 2nd ed.; CRC Press: Boca Raton, FL, USA, https://doi.org/ 10.1201/9781420055139.
Cotrozzi, L., 2022, Spectroscopic Detection of Forest Diseases: A Review (1970–2020), Journal of Forestry Research, 33(1), PP. 21-38, https://doi.org/10.1007/s11676-021-01378-w.
Deering, D.W., Rouse, J.W., Haas, R.H. & Schell, J.A., 1975, Measuring Forage Production of Grazing Units from Landsat MSS Data, In Proceedings of the Tenth International Symposium on Remote Sensing of Environment; Cook, J.J. (Ed.); Ann Arbor, Michigan, USA, PP. 1169-1178.
Deputy of Statistic and Information of Guilan Province, 2021, Data and Statistical Information, The Statistical Yearbook, Publications of Management and Planning Organization of Guilan Province, Rasht, P. 56, https://www.amar.org.ir/Portals/0/PropertyAgent/3909/Files/26995/400-01-01.
 
ERDAS Imagine, 2014, Hexagon Geospatial, Peachtree Corners Circle Norcross, US.
ESA (European Space Agency), 2018, Sentinle Online, Level-1C, Available at: https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/product-types/level-1c (accessed on 17 June 2018).
Eskandari, S., Jaafari, M.R., Oliva, P., Ghorbanzadeh, O. & Blaschke, Th., 2020, Mapping Land Cover and Tree Canopy Cover in Zagros Forests of Iran: Application of Sentinel-2, Google Earth, and Field Data, Remote Sensing, 12(12), PP. 1-32, https://doi.org/10.3390/rs12121912.
Esmaeili Varaki, M., Zamani, A. & Kazemirad, M., 2012, Numerical Simulation of Various Cut-Offs on Meandering Rivers, A Case Study: Shalman Rood River in Guilan Province, 11th Iranian Hydraulic Conference, Urmia University, Urmia, Iran, November 6-8th.
Ghasemi, M., Latifi, H., Shafeian, E., Naghavi, H. & Pourhashemi, M., 2024, A Novel Linear Spectral Unmixing-Based Method for Tree Decline Monitoring by Fusing UAV-RGB and Optical Space-Borne Data, International Journal of Remote Sensing, 45(4), PP. 1079-1109, https://doi.org/ 10.1080/01431161.2024.2305630.
Haywood, A. & Stone, C., 2011, Mapping Eucalypt Forest Susceptible to Dieback Associated with Bell Miners (Manorina melanophys) Using Laser Scanning, SPOT 5 and Ancillary Topographical Data, Ecological Modelling, 222(5), PP. 1174-1184, https://doi.org/10.1016/j.ecolmodel. 2010.12.012.
Hedayati, M.A., 2001, The Stages of Afforestation in Iran, Range and Forest Magazine, 52, PP. 13-19.
Heikkilä, J., Nevalainen, S. & Tokola, T., 2002, Estimating Defoliation in Boreal Coniferous Forests by Combining Landsat TM, Aerial Photographs and Field Data, Forest Ecology and Management, 158(1-3), PP. 9-23, https://doi.org/10.1016/S0378-1127(00)00671-X.
Hoseinpour, A., Oladi, J., Akbari, H. & Sarajian, M., 2019, Recognizing Plant Tension in Plantations by Use of UAVs Visible Light Detector, (Case Study: Nekazalemrood Forestry Plan), Ecology of Iranian Forests, 7 (13), PP. 20-28, https://doi.org/10.29252/ ifej.7.13.20.
Hosseini, Z., Naghavi, H., Latifi, H. & Bakhtiari Bakhtiarvand, S., 2019, Estimating Biomass and Carbon Sequestration of Plantations around Industrial Areas Using Very High Resolution Stereo Satellite Imagery, iForest, 12(6), PP. 533-541, https://doi.org/10.3832/ifor3155-012.
Hosseini, Z., Latifi, H., Naghavi, H., Bakhtiarvand Bakhtiari, S. & Fassnacht, F.E., 2021, Influence of Plot and Sample Sizes on Aboveground Biomass Estimations in Plantation Forests Using Very High Resolution Stereo Satellite Imagery, Forestry: An International Journal of Forest Research, 94(2), PP. 278-291, https://doi.org/ 10.1093/forestry/cpaa028.
Huete, A.R., 1988, A Soil-Adjusted Vegetation Index (SAVI), Remote Sensing of Environment, 25(3), PP. 295-309, https://doi.org/10.1016/0034-4257(88)90106-X.
Islamzadeh, N., Mikaeili Tabrizi, A.R., Mahiny, A.S. & Ghorbani, R., 2022, Landscape Health Mapping by Landsat Images, Journal of Environmental Studies, 47(4), PP. 395-411, https://doi.org/10.22059/JES.2021. 332755.1008242.
Jafari, M., Hosseini, A., Asgari, S., Najafifar, A. & Tahmasebi, M., 2021, Evaluation of Oak Forest Drying in Physiographic and Landuse Units Ilam Province Using Landsat Satellite Images 8, Ecology of Iranian Forests, 9(18), PP. 1-9, https://doi.org/ 10.52547/ifej.9.18.1.
Jahdi, R. & Hazbavi, Z., 2024, Evaluation of Watershed Scale Forest Ecosystem Health by Remote Sensing and Forest Health Monitoring (FHM) Method, Journal of Environmental Science Studies, 8(4), PP. 7612-7627, https://doi.org/10. 22034/JESS.2023.387287.1980.
Jordan, C.F., 1969, Derivation of Leaf Area Index from Quality of Light on Forest Floor, Ecology, 50(4), PP. 663-666, https://doi.org/10.2307/1936256.
Karlson, M., Ostwald, M., Reese, H., Sanou, J., Tankoano, B. & Mattsson, E., 2015, Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest, Remote Sensing, 7(8), PP. 10017-10041, https://doi.org/10.3390/rs70810017.
 
Köhl, M., Lasco, R., Cifuentes, M., Jonsson, Ö., Korhonen, K.T., Mundhenk, P., Jesus Navar, J. & Stinson, G., 2015, Changes in Forest Production, Biomass and Carbon: Results from the 2015 UN FAO Global Forest Resource Assessment, Forest Ecology and Management, 352(7), PP. 21-34, https://doi.org/10.1016/j.foreco.2015.05.036.
Lago, A.A.C.d., 2009, Stockholm, Rio, Johannesburg: Brazil and the Three United Nations Conferences on the Environment, Fundação Alexandre de Gusmão, Brazil.
Levesque, J. & King, D., 2003, Spatial Analysis of Radiometric Fractions from High-Resolution Multispectral Imagery for Modelling Individual Tree Crown and Forest Canopy Structure and Health, Remote Sensing of Environment, 84(4), PP. 589-602, https://doi.org/10.1016/S0034-4257(02)00182-7.
Liu, D., Kelly, M. & Gong, P., 2006, A Spatialtemporal Approach to Monitoring Forest Disease Spread Using Multi-Temporal High Spatial Resolution Imagery, Remote Sensing of Environment, 101(2), PP. 167-180, https://doi.org/10.1016/ j.rse.2005.12.012.
Meng, J., Li, S., Wang, W., Liu, Q., Xie, S. & Ma, W., 2016, Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images, Remote Sensing 8(9), PP. 1-20, https://doi.org/10.3390/rs8090719.
Mosayeb Neghad, I., Rostami Shahraji, T., Kahneh, E. & Porbabaii, H., 2007, Evaluation of Native Broadleaved Forest Plantations in East of Guilan Province, Iranian Journal of Forest and Poplar Research, 15(4), PP. 311-319.
Moskal, L.M. & Franklin, S.E., 2004, Relationship between Airborne Multispectral Image Texture and Aspen Defoliation, International Journal of Remote Sensing, 25(14), PP. 2701-2711, https://doi.org/10. 1080/01431160310001642304.
Ogaya, R., Barbeta, A., Başnou, C. & Peñuelas, J., 2015, Satellite Data as Indicators of Tree Biomass Growth and Forest Dieback in a Mediterranean Holm Oak Forest, Annals of Forest Science, 72(1), PP. 135-144, https://doi.org/10.1007/s13595-014-0408-y.
Rouse, J.W., Haas, R.H., Schell, J.A. & Deering, W.D., 1973, Monitoring Vegetation Systems in the Great Plains with ERTS, In Proceedings of the Third ERTS Symposium, NASA SP–351, NASA. Washington, DC, USA, PP. 309-317.
Saba, F., Latifi, H., Valadan Zoej, M.J. & Heipke, C., 2024, Analysis of the Spatio-Temporal Dynamics of Buxus hyrcana Pojark Defoliation Using Spaceborne Satellite Data, Forestry: An International Journal of Forest Research, cpae005, PP. 1-15, https://doi.org/10.1093/forestry/cpae005.
Schultz, M., Clevers, J.G.P.W., Carter, S., Verbesselt, J., Avitabile, V., Quang, H.V. & Herold, M., 2016, Performance of Vegetation Indices from Landsat Time Series in Deforestation Monitoring, International Journal of Applied Earth Observation and Geoinformation, 52, PP. 318-327, https://doi.org/10.1016/j.jag.2016.06.020.
Shafeian, E., Fassnacht, F.E. & Latifi, H., 2023, Detecting Semi-Arid Forest Decline Using Time Series of Landsat Data, European Journal of Remote Sensing, 56(1), P. 2260549, https://doi.org/10.1080/22797254. 2023.2260549.
Soares, C., Príncipe, A., Köbel, M., Nunes, A., Branquinho, C. & Pinho, P., 2018, Tracking Tree Canopy Cover Changes in Space and Time in High Nature Value Farmland to Prioritize Reforestation Efforts, International Journal of Remote Sensing, 39(14), PP. 4714-4726, https://doi.org/ 10.1080/01431161.2018.1475777.
Thales Alenia Space, 2017, Sentinel-2 Products Specification Documents, Eesa Publication, P. 487.
Recanatesi, F., Giuliani, C. & Ripa, M.N., 2018, Monitoring Mediterranean Oak Decline in a Peri-Urban Protected Area Using the NDVI and Sentinel-2 Images: The Case Study of Castelporziano State Natural Reserve, Sustainability, 10(9), PP. 1-10, https://doi.org/10.3390/su10093308.
Waser, L.T., Küchler, M., Jütte, K. & Stampfer, T., 2014, Evaluating the Potential of WorldView-2 Data to Classify Tree Species and Different Levels of Ash Mortality, Remote Sensing, 6(5), PP. 4515-4545, https://doi.org/10.3390/rs6054515.
Zhang, J., Cong, S., Zhang, G., Ma, Y., Zhang, Y. & Huang, J., 2022, Detecting Pest-Infested Forest Damage through Multispectral Satellite Imagery and Improved UNet++, Sensors (Basel), 22(19), PP. 1-21, https://doi.org/10.3390/s22197440.

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