Landsat archive for detection of change in Mediterranean ecosystems: The case of Northern Morocco
Keywords:
biodiversity monitoring; ecosystem structure; remote sensing; land cover; time series; Google Earth Engine
Abstract
The study of changes in land cover provides a better understanding of the interactions between humans and natural ecosystems. In this context, the present study focused on the dynamics of natural ecosystems in the Rif region of Northern Morocco. The methodology was based on the inspection and visual interpretation of Landsat and Google Earth image captures, the time series of five Landsat 4-8 image bands, and the Tasseled Cap indices for a random sample of 500 points from 1984 to 2022. The study found that changes affected practically the whole study region over the study period, with around a third of them being ignored due to their very tiny magnitudes or being false positives. The findings demonstrated a general declining trend in the measured changes, indicating a reduction in pressure on different ecosystems. Furthermore, this tendency may be due in part to the availability of Google Earth images during the 2000s, which has significantly reduced the number of false positives. In terms of the year of first change, only 5.7% of pixels experienced their first events after the year 2000, implying that these pixels underwent no change for at least the first 16 years of the study period. On the other hand, 2.5% of the pixels had their last events during the first ten years and have thus remained unmodified for at least 27 years. For the year 2020, the confidence rating of the visual land cover categorization is medium to high for 88.9% of pixels using high-resolution Google Earth photos, whereas the classification quality was inadequate for 64% of pixels in 1984. Despite the stresses on the ecosystems structured by shrubs/shrubs, forests, and herbaceous/shrubs caused by the different disturbances identified, the majority of these ecosystems have not been converted to new land cover classes. According to the study, agriculture is the primary driving force underlying the conversion of forests, herbaceous/shrublands, and even shrublands/shrublands. The area increases for the latter three ecosystems represent, on the one hand, their ability to regenerate themselves and, on the other, Morocco's restoration efforts.References
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Arevalo, P., Bullock, E., Woodcock, C., & Olofsson, P. (2020). A suite of tools for continuous land change monitoring in google earth engine. Frontiers in Climate, 2, 1–19.
Azedou, A., Amine, A., Kisekka, I., Lahssini, S., Bouziani, Y., & Moukrim, S. (2023). Enhancing land cover/land use (LCLU) classification through a com-parative analysis of hyperparameters optimization approaches for deep neural network (DNN). Ecological Informatics, 78, 102333.
Banskota, A., Kayastha, N., Falkowski, M. J., Wulder, M. A., Froese, R. E., & White, J. C. (2014). Forest monitoring using Landsat time series data: A review. Canadian Journal of Remote Sensing, 40(5), 362–384.
Benabid, A. (2000). Flore et écosystèmes du Maroc: Évaluation et préservation de la biodiversité [Flora and ecosystems of Morocco: Evaluation and preservation of biodiversity]. Ibis Press, Paris (in French).
Benabou, A., Moukrim, S., Lahssini, S., Aboudi, A. E., Menzou, K., Elmalki, M., Madihi, M. E., & Rhazi, L. (2022). Impact of climate change on potential distribution of Quercus suber in the conditions of North Africa. Biosystems Diversity, 30(3), 289–294.
Brooks, E. B., Wynne, R. H., Thomas, V. A., Blinn, C. E., & Coulston, J. W. (2014). On-the-fly massively multitemporal change detection using statistical quality control charts and Landsat data. IEEE Transactions on Geoscience and Remote Sensing, 52(6), 3316–3332.
Brown, J. F., Tollerud, H. J., Barber, C. P., Zhou, Q., Dwyer, J. L., Vogelmann, J. E., Loveland, T. R., Woodcock, C. E., Stehman, S. V., Zhu, Z., Pengra, B. W., Smith, K., Horton, J. A., Xian, G., Auch, R. F., Sohl, T. L., Sayler, K. L., Gallant, A. L., Zelenak, Reker, R. R., & Rover, J. (2020). Lessons learned implementing an operational continuous United States national land change monitoring capability: The land change monitoring, assessment, and projection (LCMAP) approach. Remote Sensing of Environment, 238, 111356.
Carpenter, S. R., DeFries, R., Dietz, T., Mooney, H. A., Polasky, S., Reid, W. V., & Scholes, R. J. (2006). Millennium ecosystem assessment: Research needs. Science, 314(5797), 257–258.
Chance, C. M., Hermosilla, T., Coops, N. C., Wulder, M. A., & White, J. C. (2016). Effect of topographic correction on forest change detection using spectral trend analysis of Landsat pixel-based composites. International Journal of Applied Earth Observation and Geoinformation, 44, 186–194.
Chebli, Y., Chentouf, M., Ozer, P., Hornick, J.-L., & Cabaraux, J.-F. (2018). Forest and silvopastoral cover changes and its drivers in Northern Morocco. Applied Geography, 101, 23–35.
Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E. (2004). Digital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing, 25(9), 1565–1596.
Crist, E. P. (1985). A TM Tasseled Cap equivalent transformation for reflectance factor data. Remote Sensing of Environment, 17(3), 301–306.
Díaz, S. M., Settele, J., Brondízio, E., Ngo, H., Guèze, M., Agard, J., Arneth, A., Balvanera, P., Brauman, K., & Butchart, S. (2019). The global assessment report on biodiversity and ecosystem services. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. United Nations Environment Programme, Nairobi.
Frazier, R. J., Coops, N. C., & Wulder, M. A. (2015). Boreal shield forest disturbance and recovery trends using Landsat time series. Remote Sensing of Environment, 170, 317–327.
Fu, P., & Weng, Q. (2016). A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sensing of Environment, 175, 205–214.
Gao, B.-C. (1996). NDWI – a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.
Gutman, G., Janetos, A. C., Justice, C. O., Moran, E. F., Mustard, J. F., Rindfuss, R. R., Skole, D., Turner II, B. L., & Cochrane, M. A. (2004). Land change science: Observing, monitoring and understanding trajectories of change on the earth’s surface. Springer Science & Business Media. Vol. 6.
Halabisky, M., Moskal, L. M., Gillespie, A., & Hannam, M. (2016). Reconstructing semi-arid wetland surface water dynamics through spectral mixture analysis of a time series of Landsat satellite images (1984–2011). Remote Sensing of Environment, 177, 171–183.
Hamunyela, E., Verbesselt, J., & Herold, M. (2016). Using spatial context to improve early detection of deforestation from Landsat time series. Remote Sensing of Environment, 172, 126–138.
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., & Townshend, J. R. G. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160), 850–853.
Houghton, R. A., & Nassikas, A. A. (2017). Global and regional fluxes of carbon from land use and land cover change 1850–2015. Global Biogeochemical Cycles, 31(3), 456–472.
Jin, S., & Sader, S. A. (2005). MODIS time-series imagery for forest disturbance detection and quantification of patch size effects. Remote Sensing of Environment, 99(4), 462–470.
Kennedy, R. E., Yang, Z., Cohen, W. B., Pfaff, E., Braaten, J., & Nelson, P. (2012). Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan. Remote Sensing of Environment, 122, 117–133.
Kennedy, R. E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W. B., & Healey, S. (2018). Implementation of the LandTrendr algorithm on Google earth engine. Remote Sensing, 10(5), 691.
Kibret, K. S., Marohn, C., & Cadisch, G. (2016). Assessment of land use and land cover change in South Central Ethiopia during four decades based on integrated analysis of multi-temporal images and geospatial vector data. Remote Sensing Applications: Society and Environment, 3, 1–19.
Masek, J. G., Vermote, E. F., Saleous, N. E., Wolfe, R., Hall, F. G., Huemmrich, K. F., Gao, F., Kutler, J., & Lim, T. K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters, 3(1), 68–72.
Moukrim, S., Lahssini, S., Naggar, M., Lahlaoi, H., Rifai, N., Arahou, M., Rhazi, L., Moukrim, S., Lahssini, S., Naggar, M., Lahlaoi, H., Rifai, N., Arahou, M., & Rhazi, L. (2019). Local community involvement in forest rangeland management: Case study of compensation on forest area closed to grazing in Morocco. The Rangeland Journal, 41(1), 43–53.
Moukrim, S., Lahssini, S., Rhazi, M., Menzou, K., El Madihi, M., Rifai, N., Bouziani, Y., Azedou, A., Boukhris, I., & Rhazi, L. (2022). Climate change impact on potential distribution of an endemic species Trabut. Ekológia (Bratislava), 41(4), 329–339.
Pereira, H. M., Ferrier, S., Walters, M., Geller, G. N., Jongman, R. H. G., Scholes, R. J., Bruford, M. W., Brummitt, N., Butchart, S. H. M., Cardoso, A. C., Coops, N. C., Dulloo, E., Faith, D. P., Freyhof, J., Gregory, R. D., Heip, C., Höft, R., Hurtt, G., Jetz, W., Karp, D. S., McGeoch, M. A., Obura, D., Onoda, Y., Pettorelli, N., Reyers, B., Sayre, R., Scharlemann, J. P. W., Stuart, S. N., Turak, E., Walpole, M., & Wegmann, M. (2013). Essential biodiversity variables. Science, 339(6117), 277–278.
Reed, B. C., Brown, J. F., VanderZee, D., Loveland, T. R., Merchant, J. W., & Ohlen, D. O. (1994). Measuring phenological variability from satellite imagery. Journal of Vegetation Science, 5(5), 703–714.
Scholes, R. J., Walters, M., Turak, E., Saarenmaa, H., Heip, C. H., Tuama, É. Ó., Faith, D. P., Mooney, H. A., Ferrier, S., Jongman, R. H., Harrison, I. J., Yahara, T., Pereira, H. M., Larigauderie, A., & Geller, G. (2012). Building a global observing system for biodiversity. Current Opinion in Environmental Sustainability, 4(1), 139–146.
Secades, C., O’Connor, B., Brown, C., & Walpole, M. (2014). Earth observation for biodiversity monitoring: A review of current approaches and future opportunities for tracking progress towards the Aichi Biodiversity Targets. CBD Technical Series, 72. Secretariat of the Convention on Biological Diversity, Montreal.
Singh, A. (1989). Review article digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003.
Skidmore, A. K., Coops, N. C., Neinavaz, E., Ali, A., Schaepman, M. E., Paganini, M., Kissling, W. D., Vihervaara, P., Darvishzadeh, R., Feilhauer, H., Fernandez, M., Fernández, N., Gorelick, N., Geijzendorffer, I., Heiden, U., Heurich, M., Hobern, D., Holzwarth, S., Muller-Karger, F. E., Kerchove, R. V. D., Lausch, A., Leitão, P. J., Lock, M. C., Mücher, C. A., O’Connor, B., Rocchini, D., Roeoesli, C., Turner, W., Vis, J. K., Wang, T., Wegmann, M., & Wingate, V. (2021). Priority list of biodiversity metrics to observe from space. Nature Ecology and Evolution, 5(7), 896–906.
Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google earth engine for geo-big data applications: A meta-analysis and systematic review. International Society for Photogrammetry and Remote Sensing Journal of Photogrammetry and Remote Sensing, 164, 152–170.
Turner, B. L., Lambin, E. F., & Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences, 104(52), 20666–20671.
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.
Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185, 46–56.
Wulder, M. A., Masek, J. G., Cohen, W. B., Loveland, T. R., & Woodcock, C. E. (2012). Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sensing of Environment, 122, 2–10.
Wulder, M. A., White, J. C., Goward, S. N., Masek, J. G., Irons, J. R., Herold, M., Cohen, W. B., Loveland, T. R., & Woodcock, C. E. (2008). Landsat continuity: Issues and opportunities for land cover monitoring. Remote Sensing of Environment, 112(3), 955–969.
Zhu, Z. (2017). Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications. International Society for Photogrammetry and Remote Sensing Journal of Photogrammetry and Remote Sensing, 130, 370–384.
Zhu, Z., & Woodcock, C. E. (2014a). Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change. Remote Sensing of Environment, 152, 217–234.
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2023-10-14
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