Assessment of forest dieback on the Moroccan Central Plateau using spectral vegetation indices

doi:10.1007/s11676-022-01525-x

Abstract

Abstract:

Cork oak forests in Morocco are rich in resources and services thanks to their great biological diversity, playing an important ecological and socioeconomic role. Considerable degradation of the forests has been accentuated in recent years by significant human pressure and effects of climate change; hence, the health of the stands needs to be monitored. In this study, the Google Engine Earth platform was leveraged to extract the normalized difference vegetation index (NDVI) and soil-adjusted vegetation index, from Landsat 8 OLI/TIRS satellite images between 2015 and 2017 to assess the health of the Sibara Forest in Morocco. Our results highlight the importance of interannual variations in NDVI in forest monitoring; the variations had a significantly high relationship (p < 0.001) with dieback severity. NDVI was positively and negatively correlated with mean annual precipitation and mean annual temperature with respective coefficients of 0.49 and − 0.67, highlighting its ability to predict phenotypic changes in forest species. Monthly interannual variation in NDVI between 2016 and 2017 seemed to confirm field observations of cork oak dieback in 2018, with the largest decreases in NDVI (up to − 38%) in December in the most-affected plots. Analysis of the influence of ecological factors on dieback highlighted the role of substrate as a driver of dieback, with the most severely affected plots characterized by granite-granodiorite substrates.

Key words: Forest health monitoring, Remote sensing, Dieback, Vegetation indices, Sibara forest

Youssef Dallahi, Amal Boujraf, Modeste Meliho, Collins Ashianga Orlando. Assessment of forest dieback on the Moroccan Central Plateau using spectral vegetation indices[J]. JOURNAL OF FORESTRY RESEARCH, 2023, 34(3): 793-808.

Youssef Dallahi, Amal Boujraf, Modeste Meliho, Collins Ashianga Orlando. [J]. 林业研究(英文版), 2023, 34(3): 793-808.

References 45

1	Adole T, Dash J, Atkinson P. A systematic review of vegetation phenology in Africa. Ecol Inform, 2016, DOI
2	Anees A, Olivier JC, O’Rielly M, Aryal J (2013) Detecting beetle infestations in pine forests using MODIS NDVI time-series data 2013 In: IEEE International geoscience and remote sensing symposium—IGARSS, pp 3329–3332. doi https://doi.org/10.1109/IGARSS.2013.6723540
3	Barka I, Bucha T, Molnár T, Móricz N, Somogyi Z, Koreň M. Suitability of MODIS-based NDVI index for forest monitoring and its seasonal applications in central Europe. Cent Eur for J, 2019, 66: 206-217, DOI
4	Bolton DK, Gray JM, Melaas EK, Moon M, Eklundh L, Friedl MA. Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery. Remote Sens Environ, 2020, 240: 16, DOI
5	Chen JM, Deng F, Chen MZ. Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter. IEEE Trans Geosci Remote Sens, 2006, 44(8): 2230-2238, DOI
6	Deng LZ, Fei K, Sun TY, Zhang LP, Fan XJ, Ni L. Characteristics of run off processes and nitrogen loss via surface flow and interflow from weathered granite slopes of Southeast China. J Mt Sci, 2019, 16: 1048-1064, DOI
7	Duan H, Yan C, Tsunekawa A, Song X, Li S, Xie JL. Assessing vegetation dynamics in the three-north shelter forest region of China using AVHRR NDVI data. Environ Earth Sci, 2011, 64: 1011-1020, DOI
8	Ebinne E, Apeh O, Ndukwu R, Abah E. Assessing the health of Akamkpa forest reserves in southeastern part of Nigeria using remote sensing techniques. Int J for Res, 2020, DOI
9	Elhag M, Boteva S, Al-Amri N. Forest cover assessment using remote-sensing techniques in crete Island Greece. Open Geosci, 2021, 13(1): 345-358, DOI
10	Gauthier S, Bernier P, Kuuluvainen T, Shvidenko AZ, Schepaschenko DG. Boreal forest health and global change. Science, 2015, 349: 819-822, DOI
11	Gitelson AA, Kaufman YJ, Stark R, Rundquist D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens Environ, 2002, 80: 76-87, DOI
12	Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ, 2017, DOI
13	Heumann WB, Seaquist WJ, Eklundh L, Jonsson P. AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982–2005. Remote Sens Environ, 2007, 108: 385-392, DOI
14	Hmimina G, Dufrêne E, Pontailler JY, Delpierre N, Aubinet M, Caquet B. Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: an investigation using ground-based NDVI measurements. Remote Sens Environ, 2013, 132: 145-158, DOI
15	Huang S, Tang LN, Hupy JP, Wang Y, Shao GF. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J for Res, 2021, 32: 1-6, DOI
16	Huete AR. A soil-adjusted vegetation index (SAVI). Remote Sens Environ, 1988, 25: 295-309, DOI
17	Keenan RJ, Reams GA, Achard F, de Freitas JV, Grainger A, Lindquist E. Dynamics of global forest area: results from the FAO global forest resources assessment. For Ecol Manag, 2015, 352: 9-20, DOI
18	Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. J Am Stat Assoc, 1952, 47(260): 583-662, DOI
19	Larson AJ, Franklin JF. The tree mortality regime in temperate old-growth coniferous forests: the role of physical damage. Can J Forest Res, 2010, 40: 2091-2103, DOI
20	Liu F, Liu HY, Xu CY, Shi L, Zhu XR, Qi Y, He WQ. Old-growth forests show low canopy resilience to droughts at the southern edge of the taiga. Glob Chang Biol, 2021, 27(11): 2392-2402, DOI
21	Machouri N. Les subéraies marocaines face aux changements climatiques et actions anthropiques. Actes du colloque international de Niamey (Niger), 2009. Changement climatique et évaluation environnementale. Publication de la Secrétariat International Francophone pour l’Evaluation Environnementale (SIFEE) et l’Institut de l’Energie et de l’Environnement de la Francophonie (IEPF). Collect Éval Environ, 2010, 9: 297-312
22	Marini L, Økland B, Jönsson AM, Bentz B, Carroll A, Forster B, Grégoire J-C, Hurling R, Nageleisen LM, Netherer S, Ravn HP, Weed A, Schroeder M. Climate drivers of bark beetle outbreak dynamics in Norway spruce forests. Ecography, 2017, 40: 1426-1435, DOI
23	Mate AR, Deshmukh RR. Analysis of effects of air pollution on chlorophyll, water, carotenoid and anthocyanin content of tree leaves using spectral indices. Int J Eng Sci, 2016, 6: 5465-5474
24	Matsushita B, Yang W, Chen J, Onda Y, Qiu GY. Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects. Sensors, 2007, 7: 2636-2651, DOI
25	Melaas EK, Friedl MA, Zhu Z. Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM plus data. Remote Sens Environ, 2013, 132: 176-185, DOI
26	Moreno-Fernández D, Viana-Soto A, Camarero J, Zavala M, Tijerin-Triviño J, García M. Using spectral indices as early warning signals of forest dieback: the case of drought-prone pinus pinaster forests. Sci Total Environ, 2021, 793, DOI
27	Pei ZF, Fang SB, Yang WN, Wang L, Wu MY, Zhang QF, Han W, Khoi DN. The relationship between NDVI and climate factors at different monthly time scales: a case study of grasslands in inner Mongolia, China (1982–2015). Sustainability, 2019, 11: 7243, DOI
28	R Core Team (2021) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https:/www.r-project.org
29	Riva MJ, Daliakopoulos IN, Eckert S, Hodel E, Liniger H. Assessment of land degradation in mediterranean forests and grazing lands using a landscape unit approach and the normalized difference vegetation index. Appl Geogr, 2017, 86: 8-21, DOI
30	Rodríguez-Moreno VM, Bullock SH. Vegetation response to hydrologic and geomorphic factors in an arid region of the baja California Peninsula. Environ Monit Assess, 2014, 186: 1009-1021, DOI
31	Roula S (2005) Caractérisations physicochimiques et valorisation des boues résiduaires urbaines pour la confection de substrats de culture en pépinière hors-sol. Mémoire de magistère en Sciences Agronomiques, Université Colonel El hadj Lakhdar, Batna, 115
32	Rouse JW, Jr, Haas RH, Deering DW, Schell JA, Harlan JC (1974) Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation, NASA/GSFC Type III Final Report; Texas A&M University: College Station, TX, USA; 371
33	Schoene D, Killmann W, von Luepke H, Loyche Wilkie M (2007) Definitional issues related to reducing emissions from deforestation in developing countries. FAO Forests and Climate Change Working Paper 5. Rome. http://www.fao.org/docrep/009/j9345e/j9345e00.htm
34	Shen M, Tang Y, Desai AR, Gough C, Chen J. Can EVI-derived land-surface phenology be used as a surrogate for phenology of canopy photosynthesis?. Int J Remote Sens, 2014, 35: 1162-1174, DOI
35	Soubry I, Doan T, Chu T, Guo XL. A systematic review on the integration of remote sensing and GIS to forest and grassland ecosystem health attributes, indicators, and measures. Remote Sens, 2021, DOI
36	Sugihara NG, van Wagtendonk JW, Shaffer KE, Fites- Kaufman J, Thode AE. Fire in California’s ecosystems, 2006 Berkeley University of California Press, DOI
37	Sun TY, Deng LZ, Fei K, Fan XJ, Zhang LP, Ni L, Sun R. Runoff characteristics and soil loss mechanism in the weathered granite area under simulated rainfall. Water, 2021, 13(23): 3453, DOI
38	Tariq A, Riaz I, Ahmad Z, Yang BS, Amin M, Kausar R, Andleeb S, Farooqi MA, Rafiq M. Land surface temperature relation with normalized satellite indices for the estimation of spatio-temporal trends in temperature among various land use land cover classes of an arid Potohar region using Landsat data. Environ Earth Sci, 2020, DOI
39	Thavorntam W, Tantemsapya N. Vegetation greenness modeling in response to climate change for Northeast Thailand. J Geogr Sci, 2013, DOI
40	Walker J, de Beurs K, Wynne RH. Phenological response of an Arizona dryland forest to short-term climatic extremes. Remote Sens, 2015, 7: 10832-10855, DOI
41	Wang J, Rich PM, Price KP. Temporal responses of NDVI to precipitation and temperature in the central great plains, USA. Int J Remote Sens, 2003, 24: 2345-2364, DOI
42	Xu C, Li YT, Hu J, Yang XJ, Sheng S, Liu MS. Evaluating the difference between the normalized difference vegetation index and net primary productivity as the indicators of vegetation vigor assessment at landscape scale. Environ Monit Assess, 2012, 184: 1275-1286, DOI
43	Young NE, Anderson RS, Chignell SM, Vorster AG, Lawrence R, Evangelista PH. A survival guide to landsat preprocessing. Ecology, 2017, 98: 920-932, DOI
44	Zhang LF, Jiao WZ, Zhang HM, Huang CP, Tong QX. Studying drought phenomena in the continental United States in 2011 and 2012 using various drought indices. Remote Sens Environ, 2017, 190: 96-106, DOI
45	Zhou Z, Woodcock CE, Olofsson P. Continuous monitoring of forest disturbance using all available landsat imagery. Remote Sens Environ, 2012, 122: 75-91, DOI