Using multiple linear regression to analyze changes in forest area: the case study of Akdeniz Region

Year 2022, Volume: 7 Issue: 3, 247 - 263, 15.10.2022

Neşe BAŞARAN Dilek KÜÇÜK MATCI Uğur AVDAN

https://doi.org/10.26833/ijeg.976418

Cited By: 3

Abstract

Forest area losses are one of the most significant changes in land cover. These losses negatively affect ecosystems and cause severe economic and social life problems. It is necessary to monitor the process carefully and analyze the effects well to minimize all these negative effects in forest land losses and improve the development in urban areas positively. It is of great importance that these analyses are carried out quickly and accurately in terms of developing the natural environment. In this study, the effects that cause forest losses in the Mediterranean Region over the years are examined with the data obtained with the Google Earth Engine (GEE). Within the scope of the study, the changes in forest areas in the Mediterranean Region between 2004 and 2019 have been examined by considering many factors. In the study, Normalized Difference Vegetation Index (NDVI), precipitation, temperature, land surface temperature, aerosol optical depth, ozone, fire, urban areas, and population data were obtained with GEE. The data obtained were analyzed statistically, and the factors affecting the losses in forest areas the most were determined.

Keywords

Remote sensing, Google Earth Engine, Forest Areas Changes, Statistical Analyze

References

• Ahady A B & Kaplan G (2022). Classification comparison of Landsat-8 and Sentinel-2 data in Google Earth Engine, study case of the city of Kabul. International Journal of Engineering and Geosciences, 7(1), 24-31.
• Andrée B P J, Chamorro A, Spencer P, Koomen E & Dogo H (2019). Revisiting the relation between economic growth and the environment; a global assessment of deforestation, pollution and carbon emission. Renewable and Sustainable Energy Reviews, 114, 109221.
• Atmış E & Günşen H B (2016). Kentleşmenin Türkiye ormancılığının dönüşümüne etkisi (1990-2010 Dönemi). Journal of the Faculty of Forestry Istanbul University, 66(1), 16-29.
• Avdan U, Kucuk Matci D, Kaplan G, Yigit Avdan Z, Erdem F, Demirtas I & Mızık E T (2021). Evaluating the Atmospheric Correction Impact on Landsat 8 and Sentinel-2 Data for Soil Salinity Determination. Geodetski list, 75(3), 255-240.
• Betts M G, Wolf C, Ripple W J, Phalan B, Millers K A, Duarte A, . . . Levi T (2017). Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature, 547(7664), 441-444.
• Çömert R, Matci Küçük D & Avdan U (2019). Object Based Burned Area Mapping with Random Forest Algorithm. International Journal of Engineering and Geosciences, 4(2), 78-87.
• DeFries R S, Rudel T, Uriarte M & Hansen M (2010). Deforestation driven by urban population growth and agricultural trade in the twenty-first century. Nature Geoscience, 3(3), 178-181.
• del Castillo E M, García-Martin A, Aladrén L A L & de Luis M (2015). Evaluation of forest cover change using remote sensing techniques and landscape metrics in Moncayo Natural Park (Spain). Applied geography, 62, 247-255.
• Demir N (2020). NDVI Analysis of Australian Bushfires with Cloud Computing. Türk Uzaktan Algılama ve CBS Dergisi, 1(2), 78-84.
• Desbureaux S & Damania R (2018). Rain, forests and farmers: Evidence of drought induced deforestation in Madagascar and its consequences for biodiversity conservation. Biological Conservation, 221, 357-364.
• Doğaner S (2015). Akdeniz Bölgesi Coğrafyası. İstanbul: İ.Ü Edebiyat Fakültesi Coğrafya Bölümü.
• Dündar C, Oğuz K & Güllü G (2015). Aerosol Optik Derinliği Verilerinin Türkiye İçin Alansal ve Zamansal Değişimlerin İzlenmesi. Paper presented at the VII. Uluslararası Katılımlı Atmosfer Bilimleri Sempozyumu, İstanbul.
• Fearnside P M (1995). Potential impacts of climatic change on natural forests and forestry in Brazilian Amazonia. Forest Ecology and Management, 78(1-3), 51-70.
• Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, . . . Hoell A (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific data, 2(1), 1-21.
• Gasparri N I & Grau H R (2009). Deforestation and fragmentation of Chaco dry forest in NW Argentina (1972–2007). Forest Ecology and Management, 258(6), 913-921.
• 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, 850-853.
• Hersbach H, Bell B, Berrisford P, Biavati G, Horányi A, Muñoz Sabater J, . . . Rozum I (2018). ERA5 hourly data on single levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS).
• Kaplan G, & Avdan Z Y (2020). Space-borne air pollution observation from sentinel-5p tropomi: Relationship between pollutants, geographical and demographic data. International Journal of Engineering and Geosciences, 5(3), 130-137.
• Khorrami B, Gunduz O, Patel N, Ghouzlane S & Najjar M (2019). Land surface temperature anomalies in response to changes in forest cover. International Journal of Engineering and Geosciences, 4(3), 149-156.
• Koskinen J, Leinonen U, Vollrath A, Ortmann A, Lindquist E, d'Annunzio R, . . . Käyhkö N (2019). Participatory mapping of forest plantations with Open Foris and Google Earth Engine. Isprs Journal of Photogrammetry and Remote Sensing, 148, 63-74.
• Loreau M, Naeem S, Inchausti P, Bengtsson J, Grime J, Hector A, . . . Schmid B (2001). Biodiversity and ecosystem functioning: current knowledge and future challenges. Science, 294(5543), 804-808.
• Matcı D K & Avdan U (2020). Comparative analysis of unsupervised classification methods for mapping burned forest areas. Arabian Journal of Geosciences, 13(15), 1-13.
• Ocer N E, Kaplan G, Erdem F, Kucuk Matci D & Avdan U (2020). Tree extraction from multi-scale UAV images using Mask R-CNN with FPN. Remote sensing letters, 11(9), 847-856.
• OGM (2020). Orman Yangınları. Retrieved from https://www.ogm.gov.tr/Sayfalar/OrmanYanginlari.aspx
• Orhan O, Dadaser-Celik F & Ekercin S (2019). Investigating land surface temperature changes using Landsat-5 data and real-time infrared thermometer measurements at Konya closed basin in Turkey. International Journal of Engineering and Geosciences, 4(1), 16-27.
• Pellikka P K, Lötjönen M, Siljander M & Lens L (2009). Airborne remote sensing of spatiotemporal change (1955–2004) in indigenous and exotic forest cover in the Taita Hills, Kenya. International Journal of Applied Earth Observation and Geoinformation, 11(4), 221-232.
• Platnick S, King M & Hubanks P (2017). MODIS Atmosphere L3 Monthly Product. NASA MODIS Adaptive Processing System, Goddard 730 Space Flight Center. last access: 3 December 2018.
• Praticò S, Solano F, Di Fazio S & Modica G (2021). Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation. Remote Sensing, 13(4), 586.
• Roces-Díaz J V, Vayreda J, Banqué-Casanovas M, Cusó M, Anton M, Bonet J A, . . . de Aragón J M (2018). Assessing the distribution of forest ecosystem services in a highly populated Mediterranean region. Ecological indicators, 93, 986-997.
• Sari F (2021). Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Turkey: A comparative analysis of VIKOR and TOPSIS. Forest Ecology and Management, 480, 118644.
• Schepaschenko D, Shvidenko A, Lesiv M Y, Ontikov P, Shchepashchenko M & Kraxner F (2015). Estimation of forest area and its dynamics in Russia based on synthesis of remote sensing products. Contemporary Problems of Ecology, 8(7), 811-817.
• Şen G, Güngör E & Şevik H (2018). Defining the effects of urban expansion on land use/cover change: a case study in Kastamonu, Turkey. Environmental monitoring and assessment, 190(8), 1-13.
• TOB (2019). Çölleşmeyle Mücadele. Retrieved from http://cmusep.cem.gov.tr/Uploads/Documents/CMUSEP_baski_versiyonu-pdf(eylemplani).pdf
• Tolunay D (2013). Ormanlar ve İklim Değişikliği. İstanbul: İÜ Orman Fakültesi Toprak İlmi ve Ekoloji Anabilim Dalı.
• Tolunay D (2015). Türkiye'de Ormansızlaşma İle Kaybedilen Karbon Miktarları. Paper presented at the 6. Ulusal Hava Kirliliği ve Kontrolü Sempozyumu-2015 7-9 Ekim 2015, İzmir.
• TUIK (2021). TUIK Veri Portalı. Retrieved from https://data.tuik.gov.tr/Kategori/GetKategori?p=Nufus-ve-Demografi-109
• Wan Z (1999). MODIS Land-Surface Temperature Algorithm Theoretical Basis Document. NASA Earth Data. Retrieved from https://lpdaac.usgs.gov/products/mod11a2v006/
• Xiong J, Thenkabail P S, Tilton J C, Gumma M K, Teluguntla P, Oliphant A, . . . Gorelick N (2017). Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine. Remote Sensing, 9(10), 1065.
• Yu Z, Yao Y, Yang G, Wang X & Vejre H (2019). Strong contribution of rapid urbanization and urban agglomeration development to regional thermal environment dynamics and evolution. Forest Ecology and Management, 446, 214-225.