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Research Article

Detecting semi-arid forest decline using time series of Landsat data

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Article: 2260549 | Received 11 May 2023, Accepted 14 Sep 2023, Published online: 25 Sep 2023

References

  • Ahmadi, R., Kiadaliri, H., Mataji, A., & Kafaki, S. (2014). Oak forest decline zonation using AHP model and GIS technique in Zagros forests of Ilam province. Journal of Biodiversity and Environmental Sciences, 4(3), 141–18. https://doi.org/10.2478/quageo-2021-0006
  • Anderegg, W. R. L., Hicke, J. A., Fisher, R. A., Allen, C. D., Aukema, J., Bentz, B., Hood, S., Lichstein, J. W., Macalady, A. K., McDowell, N., Pan, Y., Raffa, K., Sala, A. K., Shaw, J. D., Stephenson, N. L., Tague, C., & Zeppel, M. (2015). Tree mortality from drought, insects, and their interactions in a changing climate. New Phytologist, 208(3), 674–683. https://doi.org/10.1111/nph.13477
  • Attarod, P., Rostami, F., Dolatshahi, A., Sadeghi, S. M. M., Amiri, G. Z., & Bayramzadeh, V. (2016). Do changes in meteorological parameters and evapotranspiration affect the declining oak forests of Iran? Journal of Forest Science, 62(12), 553–561. https://doi.org/10.17221/83/2016-JFS
  • Bae, S., Müller, J., Förster, B., Hilmers, T., Hochrein, S., Jacobs, M., Leroy, B. M., Pretzsch, H., Weisser, W. W., & Mitesser, O. (2022). Tracking the temporal dynamics of insect defoliation by high-resolution radar satellite data. Methods in Ecology and Evolution, 13(1), 121–132. https://doi.org/10.1111/2041-210X.13726
  • Belgiu, M., & Dragut, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  • Buce Saleh, M., Surati Jaya, N., Santi, N. A., Sutrisno, D., Carolita, I., Yuxing, Z., Xuejun, W., & Qian, L. (2019). Algorithm for detecting deforestation and forest degradation using vegetation indices. Telkomnika, 17(5), 2335–2345. https://doi.org/10.12928/telkomnika.v17i5.12585
  • Cailleret, M., Dakos, V., Jansen, S., Robert, E. M. R., Aakala, T., Amoroso, M. M., Antos, J. A., Bigler, C., Bugmann, H., Caccianaga, M., Camarero, J.-J., Cherubini, P., Coyea, M. R., Cufar, K., Das, A. J., Davi, H., Gea-Izquierdo, G., Gillner, S. … Martínez-Vilalta, J. (2019). Early-warning signals of individual tree mortality based on annual radial growth. Frontiers in Plant Science, 9, 1964. https://doi.org/10.3389/fpls.2018.01964
  • Camarero, J. J., Franquesa, M., & Sangüesa-Barreda, G. (2015). Timing of drought triggers distinct growth responses in Holm Oak: Implications to predict warming-induced forest defoliation and growth decline. Forests, 6(12), 1576–1597. https://doi.org/10.3390/f6051576
  • Camps-Valls, G., Campos-Taberner, M., Moreno-Martínez, Á., Walther, S., Duveiller, G., Cescatti, A., Mahecha, M. D., Muñoz-Marí, J., García-Haro, F. J., Guanter, L., Jung, M., Gamon, J. A., Reichstein, M., & Running, S. W. (2021). A unified vegetation index for quantifying the terrestrial biosphere, Sci. Science Advances, 7(9), eabc7447. https://doi.org/10.1126/sciadv.abc7447
  • Correa‐Díaz, A., Silva, L. C. R., Horwath, W. R., Gómez‐Guerrero, A., Vargas‐Hernández, J., Villanueva‐Díaz, J., Velázquez-Martínez, A., & Suárez-Espinoza, J. (2019). Linking remote sensing and dendrochronology to quantify climate‐induced shifts in high‐elevation forests over space and time. Journal of Geophysical Research: Biogeosciences, 124(1), 166–183. https://doi.org/10.1029/2018JG004687
  • Croft, H., Chen, J. M., & Zhang, Y. (2014). The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures. Ecological Complexity, 17, 119–130. https://doi.org/10.1016/j.ecocom.2013.11.005
  • Daneshmand Parsa, R., Mirzaei, R., & Bihamtai Toosi, N., (2016). Predicting the changes of Chaharmahal and Bakhtiari province forests using landscape metrics and Markov chain model (1994-2035), The 2nd International Earth Surface Ecology Conference, 25th-30th October (in Persian), Isfahan University of Technology, Isfahan, Iran.
  • Das, A. C., Shahriar, S. A., Chowdhury, M. A., Hossain, M. L., Mahmud, S., Tusar, M. K., Ahmed, R., & Salam, M. A. (2023). Assessment of remote sensing-based indices for drought monitoring in the north-western region of Bangladesh. Heliyon, 9(2), e13016. https://doi.org/10.1016/j.heliyon.2023.e13016
  • David, R. M., Rosser, N. J., & Donoghue, D. N. M. (2022). Remote sensing for monitoring tropical dryland forests: A review of current research, knowledge gaps and future directions for Southern Africa. Environmental Research Communications, 4(4), 042001. https://doi.org/10.1088/2515-7620/ac5b84
  • Diao, J., Feng, T., Li, M., Zhu, Z., Liu, L., Biging, G., Zheng, G., Shen, W., Wang, H., Wang, J., & Ji, B. (2020). Use of vegetation change tracker, spatial analysis, and random forest regression to assess the evolution of plantation stand age in Southeast China. Annals of Forest Science, 77(2), 27. https://doi.org/10.1007/s13595-020-0924-x
  • Dutrieux, L. P., Verbesselt, J., Kooistra, L., & Herold, M. (2015). Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia. ISPRS Journal of Photogrammetry and Remote Sensing, 107, 112–125. https://doi.org/10.1016/j.isprsjprs.2015.03.015
  • Eng, L. S., Ismail, R., Hashim, W., & Baharum, A. (2019). The use of VARI, GLI, and VIgreen formulas in detecting vegetation in aerial images. International Journal of Technology, 10(7), 1385–1394. https://doi.org/10.14716/ijtech.v10i7.3275
  • Erfanifard, Y., Khodaei, Z., & Shamsi, R. F. (2014). A robust approach to generate canopy cover maps using UltraCam-D derived ortho imagery classified by support vector machines in Zagros woodlands West Iran. European Journal of Remote Sensing, 47(1), 773–792. https://doi.org/10.5721/EuJRS20144744
  • Francini, S., & Chirici, G. (2022). A Sentinel-2 derived dataset of forest disturbances occurred in Italy between 2017 and 2020. Data in Brief, 42, 108297. https://doi.org/10.1016/j.dib.2022.108297
  • Genuer, R., Poggi, J. M., & Tuleau-Malot, C. (2015). VSURF: An R package for variable selection using random forests. The R Journal, 7(2), 19. https://doi.org/10.32614/RJ-2015-018
  • Ghanbari Motlagh, M., & Kiadaliri, M. (2021). Zoning of areas with susceptibility to oak decline in western Iran. Quaestiones Geographicae, 40(1), 76–83. https://doi.org/10.2478/quageo-2021-0006
  • Giannetti, F., Pegna, R., Francini, S., McRoberts, R. E., Travaglini, D., Marchetti, M., Mugnozza, G. S., & Chirici, G. (2020). A New method for automated clearcut disturbance detection in Mediterranean coppice forests using Landsat time series. Remote Sensing, 12(22), 3720. https://doi.org/10.3390/rs12223720
  • Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80(1), 76–87. https://doi.org/10.1016/S0034-4257(01)00289-9
  • Gitelson, A. A., Merzyak, M. N., & Lichtenthaler, H. K. (1996). Detection of red-edge position and chlorophyll content by reflectance measurements near 700 nm. Journal of Plant Physiology, 148(3–4), 501–508. https://doi.org/10.1016/S0176-1617(96)80285-9
  • Gitelson, A. A., Vina, A., Ciganda, V., Rundquist, D. C., & Arkebauer, T. J. (2005). Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32(8), L08403. https://doi.org/10.1029/2005GL022688
  • Goodarzi, N., Zargaran, M. R., Banj Shafiei, A., & Tavakoli, M. (2016). The effect of geographical directions and location on the dispersion of oak decline, Shurab forest area. Lorestan Province, Forest Research and Development, 2(3). https://sid.ir/paper/263763/fa. in Persian.
  • Gu, Y., Hunt, E., Wardlow, B., Basara, J. B., Brown, J. F., & Verdin, J. P. (2008). Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data. Geophysical Research Letters, 35(22), L22401. https://doi.org/10.1029/2008GL035772
  • Hardisky, M. A., Klemas, V., & Smart, R. M. (1983). The influences of soil salinity, growth form, and leaf moisture on the spectral reflectance of Spartina alterniflora canopies. Photogrammetric Engineering and Remote Sensing, 49, 77–83. https://doi.org/10.0099/1112/83/4901-77$02.25/0
  • Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., & Neal, J. (2022). A 30m global map of elevation with forests and buildings removed. Environmental Research Letters, 17(2), 024016. https://doi.org/10.1088/1748-9326/ac4d4f
  • Higginbottom, T. P., Symeonakis, E., Meyer, H., & van der Linden, S. (2018). Mapping fractional woody cover in semi-arid savannahs using multi-seasonal composites from Landsat data. Isprs Journal of Photogrammetry & Remote Sensing, 139, 88–102. https://doi.org/10.1016/j.isprsjprs.2018.02.010
  • Hoekman, D., Kooij, B., Quiñones, M., Vellekoop, S., Carolita, I., Budhiman, S., Arief, R., & Roswintiarti, O. (2020). Wide-area Near-Real-time monitoring of tropical, forest degradation and deforestation using Sentinel-1. Remote Sensing, 12(19), 3263. https://doi.org/10.3390/rs12193263
  • Hosseini, A., Hosseini, S. M., & Linares, J. C. (2017). Site factors and stand conditions associated with Persian oak decline. Forest Systems, 26(3), e014. https://doi.org/10.5424/fs/2017263-11298
  • Hoyos, L. E., Cingolani, A. M., Zak, M. R., Vaieretti, M. V., Gorla, D. E., Cabido, M. R., & Henebry, G. (2013). Deforestation and precipitation patterns in the arid Chaco forests of central Argentina. Applied Vegetation Science, 16(2), 260–271. https://doi.org/10.1111/j.1654-109X.2012.01218.x
  • Jahanbazy Goujani, H., Iranmanesh, Y., Talebi, M., Shirmardi, H. A., Mehnatkesh, A., Pourhashemi, M., & Habibi, M. (2020). Effect of physiographic factors on the absorption of essential nutritional elements of the leaf in Brant`s oak (Quercus brantii Lindl.) forests of Helen, Chaharmahal & Bakhtiari province, affected by the decline, nutrient habitats. Functional Ecology. 15, 423–434. https://dorl.net/dor/20.1001.1.23832592.1399.33.3.18.2. in Persian.
  • Jin, Y., Sung, S., Lee, D. K., Biging, G. S., & Jeong, S. (2016). Mapping deforestation in North Korea using phenology-based multi-index and random forest. Remote Sensing, 8(12), 997. https://doi.org/10.3390/rs8120997
  • Jordan, C. F. (1969). Derivation of leaf area index from quality of light on the forest floor. Ecology, 50(4), 663–666. https://doi.org/10.2307/1936256
  • Kaufman, Y. J., & Tanre, D. (1992). Atmospherically resistant vegetation index (ARVI) for eos-modis. IEEE Transactions on Geoscience and Remote Sensing, 30(2), 261–270. https://doi.org/10.1109/36.134076
  • Kennedy, R. E., Yang, Z., & Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — temporal segmentation algorithms. LandTrendr - Temporal Segmentation Algorithms, Remote Sensing of Environment, 114(12), 2897–2910. https://doi.org/10.1016/j.rse.2010.07.008
  • Kooh Soltani, S., Alesheikh, A. A., Ghermezcheshmeh, B., & Mehri, S. (2018). An evaluation of the potential oak declines forest of the Zagros using GIS, RS, and FAHP methods. Iranian Journal of Ecohydrology, 5(2), 713–725. https://doi.org/10.22059/ije.2018.225917.448. in Persian.
  • Larsen, S. Ø., & Salberg, A.-B. (2010). Vehicle detection and roadside tree shadow removal in high-resolution satellite images, the International archives of the Photogrammetry. Remote Sensing & Spatial Information Sciences, XXXVIII–4/C7. https://scholar.google.com/scholar_lookup?title=Vehicle+detection+and+roadside+tree+shadow+removal+in+high+resolution+satellite+images&author=S.+O.+Y.+Larsen&author=A.+O.+R.+Salberg&conference=Proc.+GEOBIA&publication_year=2010. in Persian.
  • Latifi, H. (2023). Potentials, and pitfalls of applying consumer-grade unmanned aerial vehicles for the inventory of Zagros forests. Iranian Journal of Forest and Poplar Research, 30(3), 299–307. https://dorl.net/dor/20.1001.1.17350883.1401.30.3.7.5
  • Lausch, A., Erasmi, S., King, D. J., Magdon, P., & Heurich, M. (2016). Understanding forest Health with Remote Sensing -part I - A review of spectral traits, processes, and Remote-Sensing characteristics. Remote Sensing, 8(12), 1029. https://doi.org/10.3390/rs8121029
  • Le Polain de Waroux, Y., & Lambin, E. F. (2012). Monitoring degradation in arid and semi-arid forests and woodlands: The case of the argan woodlands (Morocco. Applied Geography, 32(2), 777–786. https://doi.org/10.1016/j.apgeog.2011.08.005
  • Li, X., Chen, Y., Jian, S., Wan, C., Weng, S., & Rao, D. (2022). Methods for mapping forest disturbance and degradation from optical Earth observation data. A Review, Sustainability, 14(16), 10312. https://doi.org/10.3390/su141610312
  • Li, M., Huang, C., Zhu, Z., Wen, W., Xu, D., & Liu, A. (2009). Use of remote sensing coupled with a vegetation change tracker model to assess rates of forest change and fragmentation in Mississippi, USA. International Journal of Remote Sensing, 30(24), 6559–6574. https://doi.org/10.1080/01431160903241999
  • Liu, H. Q., & Huete, A. R. (1995). A feedback-based modification of the NDV I to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience & Remote Sensing, 33, 457–465. https://doi.org/10.1109/TGRS.1995.8746027
  • Lymburner, L., Beggs, P. J., & Jacobson, C. R. (2000). Estimation of canopy-average surface-specific leaf area using Landsat TM data. Photogrammetric Engineering and Remote Sensing, 66, 183–191. https://doi.org/10.0099/1112/00/6602/183$3.00/0
  • Madonsela, S., Cho, M. A., Ramoelo, A., Mutanga, O., & Naidoo, L. (2018). Estimating tree species diversity in the savannah using NDVI and woody canopy cover. International Journal of Applied Earth Observation and Geoinformation, 66, 106–115. https://doi.org/10.1016/j.jag.2017.11.005
  • Maier, P., Fassnacht, F. E., & Schmidtlein, S., (2022). Detection and Explanation of Vegetation Degradation Patterns on the Tibetan Plateau via Historic and Current Satellite Data (Bsc. thesis, KIT) (in German).
  • Martín-Ortega, P., García-Montero, L. G., & Sibelet, N. (2020). Temporal patterns in illumination conditions and its effect on vegetation indices using landsat on Google Earth Engine, remote sens. 12, 211. https://doi.org/10.3390/rs12020211
  • Marusig, D., Petruzzellis, F., Tomasella, M., Napolitano, R., Altobelli, A., & Nardini, A. (2020). Correlation of field-measured and remotely sensed Plant water status as a tool to monitor the risk of drought-induced forest decline. Forests, 11(1), 77. https://doi.org/10.3390/f11010077
  • Meyer, L. H., Heurich, M., Beudert, B., Premier, J., & Pflugmacher, D. (2019). Comparison of Landsat-8 and Sentinel-2 data for estimation of leaf area index in temperate forests. Remote Sensing, 11(10), 1160. https://doi.org/10.3390/rs11101160
  • Moradi, M. J., Kiadaliri, H., Babaie Kafaky, S., & Bakhoda, H. (2021). Detection of high potential areas of Persian oak forests declines in Zagros, using topics method. Cerne, 27, e–102640. https://doi.org/10.1590/01047760202127012640
  • Moreno-Fernández, D., Ledo, A., Martin-Benito, D., Canellas, A., & Gea-Izquierdo, G. (2019). Negative synergistic effects of land-use legacies and climate drive widespread oak decline in evergreen Mediterranean open woodlands. Forest Ecology, and Management, 432, 884–894. https://doi.org/10.1016/j.foreco.2018.10.023
  • Moreno-Fernández, D., Viana-Soto, A., Camarero, J. J., Zavala, M. A., Tijerín, J., & García, M. (2021). Using spectral indices as early warning signals of forest dieback: The case of drought-prone Pinus pinaster forests. Science of the Total Environment, 793, 148578. https://doi.org/10.1016/j.scitotenv.2021.148578
  • Reygadas, Y., Jensen, J. L. R., & Moisen, G. G. (2019). Forest degradation assessment based on trend analysis of MODIS-Leaf area index: A case study in Mexico. Remote Sensing, 11(21), 2503. https://doi.org/10.3390/rs11212503
  • Rodman, K. C., Andrus, R. A., Veblen, T. T., & Hart, S. J. (2021). Disturbance detection in Landsat time series is influenced by tree mortality agent and severity, not by prior disturbance. Remote Sensing of Environment, 254, 112244. https://doi.org/10.1016/j.rse.2020.112244
  • Rouse, J. W., Haas, R. H., & Schell, J. A. (1973). Deering D.W. Proceedings of the third Earth Resources Technology satellite-1 symposium. NASASP-351. Monitoring vegetation systems in the Great Plains with ERTS.
  • Sagheb-Talebi, K., Sajedi, T., & Pourhashemi, M. (2014). Forests of Iran (A treasure from the past, a hope for the future). Springer Netherlands. https://doi.org/10.1007/978-94-007-7371-4
  • Sánchez-Pinillos, D’Orangeville, L., Boulanger, Y., Comeau, P., Wang, J., Taylor, A. R., Kneeshaw, D., & Sánchez‐Pinillos, M. (2021). Sequential droughts: A silent trigger of boreal forest mortality. Global Change Biology, 28(2), 542–556. https://doi.org/10.1111/gcb.15913
  • Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s TAU. Journal of the American Statistical Association, 63(324), 1379–1389. https://doi.org/10.1080/01621459.1968.10480934
  • Senf, C., Buras, A., Zang, C. S., Rammig, A., & Seidl, R. (2020). Excess forest mortality is consistently linked to drought across Europe. Nature Communications, 11(1), 6200. https://doi.org/10.1038/s41467-020-19924-1
  • Shafeian, E., Fassnacht, F. E., & Latifi, H. (2021). Mapping fractional woody cover in an extensive semi-arid woodland area at different spatial grains with Sentinel-2 and very high-resolution data. International Journal of Applied Earth Observation and Geo-Information, 105, 10262. https://doi.org/10.1016/j.jag.2021.102621
  • Sulla-Menashe, D., Kennedy, R. E., Yang, Z., Braaten, J., Krankina, O. N., & Friedl, M. A. (2014). Detecting forest disturbance in the pacific northwest from MODIS time series using temporal segmentation. Remote Sensing of Environment, 151, 114–123. https://doi.org/10.1016/j.rse.2013.07.042
  • Symeonakis, E., Higginbottom, T. P., Petroulaki, K., & Rabe, A. (2018). Optimization of savannah land cover characterization with optical and SAR data. Remote Sensing, 10(4), 499. https://doi.org/10.3390/rs10040499
  • van Deventer, A. P., Ward, A. D., Gowda, P. H., & Lyon, J. G. (1997). Using thematic mapper data to identify contrasting soil plains and tillage practices. Photogrammetric Engineering & Remote Sensing, 63(1), 87–93. https://doi.org/10.0099/1112/97/6301/087$3.00/0
  • Vásquez-Grandón, A., Donoso, P. J., & Gerding, V. (2018). Forest degradation: When is a forest degraded? Forests, 9(11), 726. https://doi.org/10.3390/f9110726
  • Verbesselt, J., Hyndman, R., Zeileis, A., & Culvenor, D. (2010). Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sensing of Environment, 114(12), 2970–2980. https://doi.org/10.1016/j.rse.2010.08.003
  • Wang, Z., Lyu, L., Liu, W., Liang, H., Huang, J., & Zhang, Q. B. (2020). Topographic patterns of forest decline as detected from tree rings and NDVI. Catena, 198, 105011. https://doi.org/10.1016/j.catena.2020.105011
  • Wang, H., Muller, J. D., Tatarinov, F., Yakir, D., & Rotenberg, E. (2022). Disentangling soil, shade, and tree canopy contributions to mixed satellite vegetation indices in a sparse dry forest. Remote Sensing, 14(15), 3681. https://doi.org/10.3390/rs14153681
  • Yu, T., Liu, P., Zhang, Q., Ren, Y., & Yao, J. (2021). Detecting forest degradation in the three-North Forest Shelterbelt in China from multi-scale satellite images. Remote Sensing, 13(6), 1131. https://doi.org/10.3390/rs13061131
  • Zhang, J., Yang, G., Yang, L., Li, Z., Gao, M., Yu, C., Gong, E., Long, H., & Hu, H. (2022). Dynamic monitoring of environmental quality in the Loess Plateau from 2000 to 2020 using the Google Earth Engine Platform and the Remote Sensing Ecological index. Remote Sensing, 14(20), 5094. https://doi.org/10.3390/rs14205094
  • Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152–171. https://doi.org/10.1016/j.rse.2014.01.011
  • Zhu, Z., Zhang, J., Yang, Z., Aljaddani, A. H., Cohen, W. B., Qiu, S., & Zhou, C. (2020). Continuous monitoring of land disturbance based on Landsat time series. Remote Sensing of Environment, 238, 111116. https://doi.org/10.1016/j.rse.2019.03.009