References

gesj

GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY

2071-93882542-1565

Russian Geographical Society

10.24057/2071-9388-2021-006

gesj-2327

Research Article

RESEARCH PAPER

Monitoring of Fragile Ecosystems with Spectral Indices Using Sentinel-2A MSI Data in Shahdagh National Park

Jabrayilov

Emil A.

115 H.Javid ave, Baku, AZ1143

jabrayilov@gmail.com

Institute of Geography Azerbaijan National Academy of SciencesAzerbaijan

2022

28032022

1517077

2022

Jabrayilov E.A.

Данная работа распространяется под лицензией Creative Commons Attribution 4.0.

This work is licensed under a Creative Commons Attribution 4.0 License.

https://ges.rgo.ru/jour/article/view/2327

Studying ecosystems using remote sensing technologies is very relevant since it checks the accuracy of the results of modern research. This study aims to monitor environmental changes in ecosystems of the Shahdagh National Park and its surrounding areas in Azerbaijan using Sentinel 2A MSI data. The study aimed to examine and monitor changes in vegetation, water resources, and drought conditions of the study area in recent years. For analyzing and observing these ecosystems Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI) were calculated using multi-band methods. Obtained indices were compared and changes were investigated analyzing satellite-derived methods. For proper monitoring and assessment of relevant ecosystems, there had been determined 3,825 fishnet points for the study area. This made it possible to compare and coordinate the results of the indices more accurately. After linking fishnet points to raster indices, classification had been made for measuring ecosystems indicators. Vegetation assessments revealed a partial expansion of sparse vegetation or bare rocks, river valleys, as well as nival, subnival, and partial subalpine meadows from 15.1% to 18.1%. Another growth indicator is a significant increase of dense forest ecosystems from 2.3% to 9.2%. According to the results decreases are observed in sparse forests, arable lands, pastures, and shrubs, which are more sensitive to anthropogenic factors. Monitoring of the indices shows that low-humidity areas increase as droughts intensify, especially in plain areas. Finally, the study revealed that the introduction of a specially protected regime within the national park makes ecosystems more sustainable.

environmental monitoringNDVINDWINDDIShahdagh National Parkecosystem

The author would like to acknowledge to European Union’s Copernicus Earth Observation Programme for accessing and using Sentinel 2 data and express his sincere gratitude to anonymous reviewers for the necessary recommendations and valuable suggestions

References1

Ali M.I., Dirawan G.D., Hasim A.H., & Abidin M.R. (2019). Detection of Changes in Surface Water Bodies Urban Area with NDWI and MNDWI Methods. International Journal on Advanced Science Engineering Informatioan Technology, 9(3), 946–951. DOI: 10.18517/ijaseit.9.3.8692.

Belgiu M., & Csillik O. (2018). Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote sensing of environment, 204, 509–523. DOI: 10.1016/j.rse.2017.10.005.

Brundtland G.H. (1987). Report of the world commission on environment and development: our common future (1987). World Commission on Environment and Development, UN, Oxford; New York.

Carlson T.N., Ripley D.A. (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment. 62(3), 241–252. DOI: 10.1016/S0034-4257(97)00104-1.

Chu T., & Guo X. (2014). Remote sensing techniques in monitoring post-fire effects and patterns of forest recovery in boreal forest regions: A review. Remote Sensing, 6(1), 470–520. DOI: 10.3390/rs6010470.

Dataset: © JAXA/METI ALOS PALSAR L1.0 (2007). Accessed through NASA’s Alaska Satellite Facility Distributed Active Archive Center (accessed June 25, 2021), DOI:10.5067/J4JVCFDDPEW1.

Dobri R.V., Sfîcă L., Amihăesei V.A., Apostol L., & Țîmpu S. (2021). Drought Extent and Severity on Arable Lands in Romania Derived from Normalized Difference Drought Index (2001–2020). Remote Sensing, 13(8), 1478. DOI:10.3390/rs13081478.

Filipponi F. (2018). BAIS2: Burned area index for Sentinel-2. Proceedings, 2(7), 364; DOI:10.3390/ecrs-2-05177.

Giuliani C., Veisz A.C., Piccinno M., & Recanatesi F. (2019). Estimating vulnerability of water body using Sentinel-2 images and environmental modelling: the study case of Bracciano Lake (Italy). European Journal of Remote Sensing, 52(s4), 64–73. DOI:10.1080/227972 54.2019.1689796.

Griffiths P., Kuemmerle T., Baumann M., Radeloff V.C., Abrudan I.V., Lieskovsky J., ... & Hostert P. (2014). Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites. Remote Sensing of Environment, 151, 72–88. DOI:10.1016/j.rse.2013.04.022.

Gu Y., Brown J.F., Verdin J.P., Wardlow B.D. (2006) A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysical Research Letters 34(L06407):6, DOI: 10.1029/2006GL029127.

Higginbottom T.P., & Symeonakis E. (2020). Identifying Ecosystem Function Shifts in Africa Using Breakpoint Analysis of Long-Term NDVI and RUE Data. Remote Sensing, 12(11), 1894. DOI:10.3390/rs12111894.

Hou, K., Li, X., Wang, J., et al., (2016). Evaluating ecological vulnerability using the GIS and Analytic Hierarchy Process (AHP) method in Yan’an, China. Polish J. Environ. Stud. 25 (2), 599–605.

Ismayilov M., Jabrayilov E. (2019) Protected Areas in Azerbaijan: Landscape-Ecological Diversity and Sustainability. Ankara Üniversitesi Çevrebilimleri Dergisi. 7(2): 31–42. [Google Scholar].

Jabrayilov E.A. (2021) Ecological Network Model in Shahdagh National Park. Proceedings of Voronezh State University. Series: Geography. Geoecology, 2, pp. 61–69, DOI: 10.17308/geo.2021.2/3449.

Jiang W., Ni Y., Pang Z., Li X., Ju H., He G., ... & Qin X. (2021). An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery. Water, 13(12), 1647. DOI:10.3390/w13121647.

Lange M., Dechant B., Rebmann C., Vohland M., Cuntz M., & Doktor D. (2017). Validating MODIS and sentinel-2 NDVI products at a temperate deciduous forest site using two independent ground-based sensors. Sensors, 17(8), 1855. DOI:10.3390/s17081855.

Li Y., Cao Z., Long H., Liu Y., & Li W. (2017). Dynamic analysis of ecological environment combined with land cover and NDVI changes and implications for sustainable urban–rural development: the case of Mu Us Sandy Land, China. Journal of Cleaner Production, 142, 697–715, DOI:10.1016/j.jclepro.2016.09.011.

Mammadov R.M., Hasanov M.S., Ismayilov M.C. (2020) Freshwater ecosystems of Azerbaijan: problems and expectations. Geography and Natural Resources, 2 (12), p. 16–22.

McFeeters S.K. (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens, 17, 1425–1432. DOI:10.1080/01431169608948714.

Nilsson C., Nilsson G., (1995) The Fragility of Ecosystems: A Review. Journal of Applied Ecology 32(4):677–692, DOI: 10.2307/2404808.

Othman A.A., Al-Saady Y.I., Al-Khafaji A.K., & Gloaguen R. (2014). Environmental change detection in the central part of Iraq using remote sensing data and GIS. Arabian Journal of Geosciences, 7(3), 1017–1028, DOI:10.1007/s12517-013-0870-0.

Pettorelli N., Vik J.O., Mysterud A., Gaillard J.M., Tucker C.J., Stenseth N.C., (2005) Using the satellite-derived NDVI to assess ecological responses to environmental change, Trends in Ecology & Evolution, 20 (9), pages 503–510, DOI: 10.1016/j.tree.2005.05.011.

Phinzi K., & Ngetar N.S. (2019). The assessment of water-borne erosion at catchment level using GIS-based RUSLE and remote sensing: A review. International Soil and Water Conservation Research, 7(1), 27–46, DOI:10.1016/j.iswcr.2018.12.002.

Qiao C., Luo J., Sheng Y., Shen Z., Zhu Z., & Ming D. (2012). An adaptive water extraction method from remote sensing image based on NDWI. Journal of the Indian Society of Remote Sensing, 40(3), 421–433, DOI:10.1007/s12524-011-0162-7.

Rawat J.S., & Kumar M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77–84, DOI:10.1016/j.ejrs.2015.02.002.

Renza D., Martinez E., Arquero A., & Sanchez J. (2010). Drought estimation maps by means of multidate Landsat fused images. Proceedings of the 30th EARSeL Symposium. 775–782. [Google Scholar].

Sakowska K., Juszczak R., & Gianelle D. (2016). Remote sensing of grassland biophysical parameters in the context of the Sentinel-2 satellite mission. Journal of Sensors. DOI:10.1155/2016/4612809.

Semeraro T., Luvisi A., Lillo, A.O., Aretano R., Buccolieri R., & Marwan N. (2020). Recurrence Analysis of Vegetation Indices for Highlighting the Ecosystem Response to Drought Events: An Application to the Amazon Forest. Remote Sensing, 12(6), 907. DOI: 10.3390/rs12060907.

Shahabi H., Shirzadi A., Ghaderi K., Omidvar E., Al-Ansari N., Clague J.J., ... & Ahmad A. (2020). Flood detection and susceptibility mapping using Sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier. Remote Sensing, 12(2), 266, DOI:10.3390/rs12020266.

Sonobe R., Yamaya Y., Tani H., Wang X., Kobayashi N., & Mochizuki K. I. (2018). Crop classification from Sentinel-2-derived vegetation indices using ensemble learning. Journal of Applied Remote Sensing, 12(2), 026019, DOI: 10.1117/1.JRS.12.026019.

The Rio Declaration on Environment and Development (1992) United Nations Conference on the Environment and Development – UNCED, Rio de Janeiro.

Toming K., Kutser T., Laas A., Sepp M., Paavel B., & Nõges T. (2016). First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery. Remote Sensing, 8(8), 640, DOI: 10.3390/rs8080640.

Vihervaara P., Auvinen A. P., Mononen L., Törmä M., Ahlroth P., Anttila S., ... & Virkkala R. (2017). How essential biodiversity variables and remote sensing can help national biodiversity monitoring. Global Ecology and Conservation, 10, 43–59, DOI: 10.1016/j.gecco.2017.01.007.

Wang J., Ding J., Yu D., Ma X., Zhang Z., Ge X., ... & Guo Y. (2019). Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China. Geoderma, 353, 172–187, DOI: 10.1016/j.geoderma.2019.06.040.

Xu H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International journal of remote sensing, 27(14), 3025–3033, DOI: 10.1080/01431160600589179.

Yang G., Chen Z., (2015) RS-based fuzzy multiattribute assessment of eco-environmental vulnerability in the source area of the Lishui River of northwest Hunan Province, China. Natl. Hazards 78, 1145–1161, DOI: 10.1007/s11069-015-1762-2.

Yang X., Zhao S., Qin X., Zhao N., & Liang L. (2017). Mapping of urban surface water bodies from Sentinel-2 MSI imagery at 10 m resolution via NDWI-based image sharpening. Remote Sensing, 9(6), 596, DOI: 10.3390/rs9060596.

Yuan L., Chen X., Wang X., Xiong Z., & Song C. (2019). Spatial associations between NDVI and environmental factors in the Heihe River Basin. Journal of Geographical Sciences, 29(9), 1548–1564, DOI: 10.1007/s11442-019-1676-0.

Zhang W.J., Lu Q.F., Gao Z.Q., Peng J. (2008) Response of remotely sensed Normalized Difference Water Deviation Index to the 2006 Drought of eastern Sichuan Basin. Science in China Series D-Earth Sciences, 51(5) 748–758, DOI: 10.1007/s11430-008-0037-0.

Zhao C., & Lu Z. (2018). Remote sensing of landslides—A review. Remote Sensing, 10(2), 279, DOI: 10.3390/rs10020279.

Zhao J., Ji, G., Tian Y., Chen Y., & Wang Z. (2018). Environmental vulnerability assessment for mainland China based on entropy method. Ecological Indicators, 91, 410–422., DOI:10.1016/j.ecolind.2018.04.016

The authors declare that there are no conflicts of interest present.