تحلیل ژئوآنتروپوژنیک پوشش گیاهی ارتفاعات تالش، جلگه‌ها و دشت‌های پیرامون

نوع مقاله : مقاله پژوهشی

نویسنده

استادیار، گروه جغرافیا، ژئومورفولوژی، دانشکده ادبیات و علوم انسانی، دانشگاه گیلان، رشت، ایران.

10.22034/gmpj.2023.364420.1381

چکیده

پوشش گیاهی و نحوه توزیع، انتشار و پراکنش آن در عرصه‌های جغرافیایی و نسبتی که با فعالیت‌های آنتروپوژنیک دارند . پوشش گیاهی و الگوهای فضایی و زمانی آن و روابط و نسبتی که با مؤلفه‌های فضای جغرافیایی و فعالیت‌های آنتروپوژنیک وجود دارد در ارتفاعات تالش و جلگه ها و اراضی پیرامون مورد بررسی قرار گرفت. بدین منظور از محصول مربوط به شاخص پوشش گیاهی NDVI ماهواره‌های ترا و آکوا به نام MOD13Q1 و MYD13Q1 در بازه زمانی 2003 تا 2020 با قدرت تفکیک زمانی 16 روزه استفاده شد . تصاویر ماهواره‌های ترا و آکوا با اپراتور میانگین ترکیب شد و به فرمت.asc تبدیل گردید. پروسه آماده سازی و تنظیم تصاویر با زبان برنامه نویسی پایتون انجام شد.. هسته تحلیل در پژوهش حاضر مربوط به تحلیل توزیع جغرافیایی، حریم و تغییرات زمانی است. در بخش توزیع جغرافیایی، الگوهای توزیع ژئوبوتانیک دنبال شد در بخش آنالیز حریم، الگوهای حریم 30، 7 و 2 کیلومتری عوارض و مراکز شهری، روستایی و زهکش های هیدرولوژیک اصلی دنبال گردید و در بخش تحلیل تغییرات زمانی، مدل مجموع قدر مطلق تغییرات یا انحرافات پوشش گیاهی به نام SAD پیکربندی و پیشنهاد شد و نتایج آن مورد تحلیل و بررسی قرار گرفت. در بخش تحلیل های ژئوبوتانیک، مؤلفه‌های ارتفاع، شیب، جهت شیب، تحدب سطح زمین فاصله از زهکش های اصلی مورد بررسی قرار گرفت. در نهایت دو مفهوم افت آنتروپوژنیک و ژئوبوتانیک پوشش گیاهی منطقه موردمطالعه تبیین و تحلیل شد و مقادیر آن به ترتیب معادل 2/0 و 4/0 برآورد گردید.

کلیدواژه‌ها

عنوان مقاله [English]

Geoanthropogenic Analysis of the Vegetation cover in the Talesh Mountain and surrounding plains

نویسنده [English]

  • somayeh sadat shahzeidi
University of Guilan
چکیده [English]

Introduction

The importance of vegetation, especially forests in mountainous areas, can’t be ignored. Forests and other plants provide a suitable environment for many other animal and plant species and increase the production capacity of ecosystems. Vegetation affects regional micro-climatic components regionally and locally and also controls soil erosion. The economy of local communities and the millions of people living on the edge of mountainous areas depend on the forests and plants that have grown and developed in those areas. It is worth mentioning that vegetation effectively protects the people living in these areas against environmental hazards such as rockslides, landslides, landslides, floods, etc. Therefore, the distribution and growth pattern of vegetation, taking into account the effective factors in these areas, is of particular importance. Elevation, direction and slope of the ground are three important and influential topographic factors in the distribution and pattern of establishment and expansion of vegetation in mountainous areas, which directly affects the vegetation. Among these three factors, height plays a very important role. Because as a factor controlling rainfall and temperature, it directly and indirectly affects the vegetation of mountainous areas and adjacent lands. Elevation, along with the slope direction and slope of the topographic surfaces of the earth, determines and controls the microclimate, and microclimate fluctuations will affect the spatial distribution and growth patterns of vegetation. One of the effective tools in studying the spatial distribution of vegetation is technology and remote sensing tools. This science is commonly used in large-scale and global assessments of vegetation and its fluctuations and geographical distribution. In this research, the researcher in the first step with a geo-botanical approach, has studied the horizontal and vertical patterns of distribution, distribution and distribution of vegetation in the Talesh mountainous unit and near lands. In the second step, the effect and relationship of anthropogenic activities with the results of the first part are compared and analyzed.



Methodology

At first, the study background, the required spatial database was prepared and adjusted. Spatial database setup was performed in two axes including vector database and raster database. In the vector data section, urban and rural areas, layers and geographical features related to human activities were adjusted for use in the two sections of cartography and analysis. Raster data includes digital elevation model (DEM) and satellite imagery. The study used digital altitude data published by the Japan Space Agency in May and October 2015 with a horizontal resolution of about 23 meters. This data taken from ALOS satellite images.

Topographic position index, slope and slope direction were derived from digital elevation model. The product related to the NDVI vegetation index of Terra and Aqua satellites called MOD13Q1 and MYD1 3Q1 was used in the period 2003 to 2020 with a resolution of 16 days. The above data was downloaded separately from the NASA site for each of the Terra and Aqua satellites in (hdf) format. A total of 820 images from the study area, which belonged to the MODI images of the Terra and Aqua satellites, were downloaded and adjusted in (tiff) format. In the next step, the images of Terra and Aqua satellites were combined with the average operator and converted to (.asc) format. The entire image preparation and editing process was performed using the Python. After preparing vegetation data, topographic and hydrological data and human activity data, geobotanic and geoanthropologenic analysis of vegetation was followed in the context of analysis of geographical distribution, buffer analysis and temporal changes of vegetation.



Results and Discussion

Discuss Vegetation analysis was performed in two main axes of geobotany and geoanthropogenic at Talesh heights and plains and surrounding lands in different long-term, annual, seasonal and monthly time periods. The core of the present study analyzes is related to geographical distribution, buffer analysis and time changes. The time periods studied in this research are focused on long-term (2003 to 2020), annual, seasonal and monthly. In the geographical distribution section, geobotanical distribution patterns were followed. In the buffer analysis section, buffer pattern 30, 7 and 2 km of tolls and urban, rural and main hydrological drainage centers were followed. Vegetation called SAD was configured and proposed and the results were analyzed. In all time periods, a significant difference was observed between the eastern and western slopes of Talesh due to the moisture supply of the Caspian Sea and the hot and humid weather that moves towards the eastern side of Talesh. In the lowlands compared to the mountains and slopes, the predominant decline in vegetation index has been caused by anthropogenic activities and human interventions and deforestation and conversion of forest lands into farms and fields. The monthly pattern indicates the minimum vegetation index in February because the deciduous vegetation in this section lacks greenery and due to the cold weather and the cessation of the growing season, the greenery concentration is at a minimum. The highest degree of greenery has been observed in June and July, which coincides with the peak of the growing season as well as cultivation of the Caspian plain.



Conclusion

The distribution and distribution of vegetation in geographical areas and their relationship with anthropogenic activities is necessary and important in regulating the human relationship with the environment. In the geobotanical analysis section, the components of height, slope, aspect, convexity of the terrain surface and distance from the main drainage path were studied. Finally, the two concepts of anthropogenic and geobotanical vegetation decline in the study area were explained and analyzed and its values were estimated to be 0.2 and 0.4, respectively.

کلیدواژه‌ها [English]

  • Geo anthropogenic Analysis
  • Anthropology
  • Geo botany
  • Talesh Mountains Vegetation Cover

مراجع

منتظری، مجید؛ کفایت مطلق، امیدرضا، (1397)، واکاوی میانگین بلندمدت پوشش گیاهی ایران به کمک نمایة ‏NDVI، مجله جغرافیا و برنامه ریزی محیطی، دوره 29، شماره 3 - شماره پیاپی 71 ، آبان 1397 ، صفحه 1-14.
کفایت مطلق، امیدرضا؛ مسعودیان، سید ابوالفضل، (1397)، واکاوی میانگین بلندمدت پوشش گیاهی ایران به کمک نمایة ‏NDVI، مجله جغرافیا و برنامه ریزی محیطی، دوره 29، شماره 3 - شماره پیاپی 71، آبان 1397، صفحه 1-14.
کفایت مطلق، امیدرضا؛ یوسفی، فیصل؛ محمدی، رستگار، (1394)، میانگین بلندمدت پوشش گیاهی ایران به کمک نمایه ی NDVI، دومین همایش ملی چالش ها و راهکارهای توسعه.
Allen, R.B., and Peet, R.K., 1990. Gradient analysis of forests of the Sangre de Cristo Range. Colorado. Canadian Journal of Botany, 68, pp. 193–201.
Atzberger C., Eilers PHC .,2011. A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America. International Journal of Digital Earth 4(5):365-386.
Bai, Y., Broersma, K., Thompson, D. and Ross, T.J., 2004.Landscape-level dynamics of grassland–forest transitions in British Columbia. Journal of Range Management, 55, pp. 66–75.
Beatriz ,M., Gilabert MA .,2009. Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sensing of Environment 113(9):1823-1842.
Ben‐Ze'ev E, Karnieli A, Agam N, et al., 2006. Assessing vegetation condition in the presence of biomass burning smoke by applying the Aerosol-free Vegetation Index (AFRI) on MODIS images. International Journal of Remote Sensing 27(15):3203-3221.
Bishal Roy, (2021), Optimum machine learning algorithm selection for forecasting vegetation indices: MODIS NDVI & EVI, Remote Sensing Applications: Society and Environment, Volume 23,2021,100582, ISSN 2352-9385.
Brang, P., Schonenberger, W., Ott, E. and Gardner, R.H., 2001, Forests as protection from natural hazards. In The Forests Handbook, J. Evans (Ed.), pp. 53–81 (Oxford: Blackwell Science).
Braselton, J.P.,2013. "What is Plant Biology?". Ohio University. Archived from the original on September 24, 2015. Retrieved June 3, 2013.
Busing, R.T., White, P.S. and Mckenzie, M.D., 1993. Gradient analysis of old spruce-fir forest of the Great Smokey Mountains circa 1935. Canadian Journal of Botany, 71, pp. 951–958.
Chamaille-Jammes S, Fritza H, Murindagomoc F.,2006. Spatial patterns of the NDVI–rainfall relationship at the seasonal and interannual time scales in an African savanna. International Journal of Remote Sensing 27 (23):5185-5200.
Chang CT, Lin TC, Wang SF, et al., 2011. Assessing growing season beginning and end dates and their relation to climate in Taiwan using satellite data. International Journal of Remote Sensing 32(18):5035-5058.
Chen, D. and Brutsaert, W., 1998. Satellite-sensed distribution and spatial patterns of vegetation parameters over a tall grass Prairie. Journal of the Atmospheric Sciences, 55, pp. 1225–1238.
Crimmins TM, Crimmins MA, David Bertelsen C.,2010. Complex responses to climate drivers in onset of spring flowering across a semiarid elevation gradient. Journal of Ecology 98(5):1042-1051.
Day, F.P. and Monk, C.D., 1974. Vegetation patterns on a Southern Appalachian watershed. Ecology, 55, pp. 1064–1074.
Deering, D.W., 1978. Rangeland reflectance characteristics measured by aircraft and spacecraft sensors. Ph.D. Dissertation, Texas A & M University, College Station, TX, 338 pp.
Defries , R., Hansen, M. and Townsshend, J.R.G., 1995. Global discrimination of land cover types from metrics derived from AVHRR pathfinder data. Remote Sensing of Environment, 54, pp. 209–222.
Endress, B.A., and Chinea, J.D., 2001. Landscape patterns of tropical forest recovery in the Republic of Palau. Biotropica, 33, pp. 555–565.
Fei Xie, Hui Fan, deriving drought indices from MODIS vegetation indices (NDVI/EVI) and Land Surface Temperature (LST): Is data reconstruction necessary? International Journal of Applied Earth Observation and Geoinformation, Volume 101,2021,102352, ISSN 1569-8432.
Franklin, J,.1995. Predictive vegetative mapping: geographic modeling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography. 19(4):474–499
Friedl, M.A., Mciver, D.K., Hodges, J.C.F., Zhang, X.Y., Muchoney, D., Strahler, A.H., Woodcock, C.E., Gopal, S., Schneider, A., Cooper, A., Baccini , A., Gao, F. and Schaaf, C., 2002. Global land cover mapping from MODIS: Algorithms and early results. Remote Sensing of Environment, 83, pp. 287–302.
Gallant, J.C., Wilson, J.P., 2000. Primary topographic attributes. In: Wilson, J.P., Gallant, J.C. (Eds.), Terrain Analysis: Principles and Applications. Wiley, New York, pp. 51–85.
Gao Y, Huang J, Li S, et al. (2012) Spatial pattern of non-stationarity and scale-dependent relationships between NDVI and climatic factors- a case study in Qinghai-Tibet Plateau, China. Ecological Indicators 20:170-176.
Gelger, R., 1966. The Climate Near the Ground. (Cambridge, MA: Harvard University Press).
Guangzhen Wang, Jingpu Wang, Xueyong Zou, Guoqi Chai, Mengquan Wu, Zhoulong Wang, Estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil from MODIS data: Assessing the applicability of the NDVI-DFI model in the typical Xilingol grasslands, International Journal of Applied Earth Observation and Geoinformation, Volume 76,2019, Pages 154-166, ISSN 1569-8432.
Guisan, A., Weiss, S.B., Weiss, A.D., 1999. GLM versus CCA spatial modeling of plant species distribution. Plant Ecology 143, 107–122.
Guisan, A., zimmermann, N.E., 2000, Predictive habitat distribution models in ecology. Ecological Modelling, 135, pp. 147–186.
Hansen, A.J., Rotella, J.J., Kraska, M.P.V. and Brown, D., 2000, Spatial patterns of primary productivity in the Greater Yellowstone Ecosystem. Landscape Ecology, 15, pp. 505–522.
Henebry, G.M., 1993. Detecting change in grasslands using measures of spatial dependence with Landsat TM data. Remote Sensing of Environment, 46, pp. 223–234.
Huang, K.Y., 2002. Evaluation of the topographic sheltering effects on the spatial pattern of Taiwan fir using aerial photography and GIS. International Journal of Remote Sensing, 23, pp. 2051–2069.
Issam Touhami, Hassane Moutahir, Dorsaf Assoul, Kaouther Bergaoui, Hamdi Aouinti, Juan Bellot, José Miguel Andreu, Multi-year monitoring land surface phenology in relation to climatic variables using MODIS-NDVI time-series in Mediterranean forest, Northeast Tunisia, Acta Oecologica, Volume 114,2022,103804, ISSN 1146-609X.
Iversen M, Brathen KA, Yoccoz NG, et al. (2009) Predictors of plant phenology in a diverse high-latitude alpine landscape: growth forms and topography. Journal of Vegetation Science 20(5):903-915.
Jenness, J., 2006. Topographic Position Index (tpi_jen.avx) Extension for ArcView 3.x, v. 1.3a. Jenness Enterprises.
Johnson, E.A., 1981, Vegetation organization and dynamics of lichen woodland communities in the Northwest Territories, Canada. Ecology, 62, pp. 200–215.
Leak, W.B. and Graber, R.E., 1974, Forest vegetation related to elevation in the White Mountains of New Hampshire. USDA Forest Service, Research paper NE-299, Northeastern Forest Experiment Station, Upper Darby, PA.
Li A, Deng W, Liang S, et al. (2010) Investigation on the patterns of global vegetation change using a Satellite-Sensed Vegetation Index. Remote Sensing 2(6):1530-1548.
Lookingbill, T.R. and Urban, D.L., 2005, Gradient analysis, the next generation: towards more plant-relevant explanatory variables. Canadian Journal of Forest Research, 35, pp. 1744–1753.
Loveland, T.R., Reed, B.C., Brown, J.F., Ohlen, D.O., Zhu, Z., Yang, L. and Merchant, J.W., 2000, Development of a global land cover characteristics database and IGBP DISCover from 1-km AVHRR data. International Journal of Remote Sensing, 21, pp. 1303–1330.
Loveland, T.R., Zhu, Z.L., Ohlen, D.O., Brown, J.F., Reed, B.C. and Yang, L.M., 1999. An analysis of the IGBP global land-cover characterization process. Photogrammetric Engineering and Remote Sensing, 65, pp. 1021–1032.
Marks, P.L. and Harcombe , P.A., 1981, Forest vegetation of the Big Thicket, southeast Texas. Ecological Monographs, 51, pp. 287–305.
Miller, J.R., Turner, M.G., Smithwick, E.A.H., Dent, C.L, Stanley, E.H., 2004, Spatial extrapolation: the science of predicting ecological patterns and processes. Bioscience, 54, pp. 310–320.
Min Lin, Lizhu Hou, Zhiming Qi, Li Wan, Impacts of climate change and human activities on vegetation NDVI in China’s Mu Us Sandy Land during 2000–2019, Ecological Indicators, Volume 142,2022,109164, ISSN 1470-160X.
Moulin S, Kergoat L, Viovy N, et al. ,1997. Global-Scale Assessment of Vegetation Phenology Using NOAA/AVHRR Satellite Measurements. Journal of Climate 10 (6):1154-1170.
Nelson A, Oberthür T, Cook, S., 2007. Multi-scale correlations between topography and vegetation in a hillside catchment of Honduras. International Journal of Geographical Information Science 21 (2):145-174.
Oliver, M.A. and Webster, R., 1986. Semi-variograms for modeling the spatial pattern of landform and soil properties. Earth Surface Processes and Landforms, 11, pp. 491–504.
Piao SL, Cui M, Chen A, et al., 2012. Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau. Agricultural and Forest Meteorology 151(12):1599-1608.
Pineda Jaimes NB, Sendra JB, et al., 2010. Exploring the driving forces behind deforestation in the state of Mexico (Mexico) using geographically weighted regression. Applied Geography 30(4):576-591.
Propastin , P ., 2012. Modifying geographically weighted regression for estimating aboveground biomass in tropical rainforests by multispectral remote sensing data. International Journal of Applied Earth Observation and Geoinformation 18:82-90.
Propastin ,P., 2011. Multiscale analysis of the relationship between topography and aboveground biomass in the tropical rainforests of Sulawesi, Indonesia. International Journal of Geographical Information Science 25 (3):455-472.
Pulak Das, (2021), 20 years MODIS-NDVI monitoring suggests that vegetation has increased significantly around Tehri Dam reservoir, Uttarakhand, India, Remote Sensing Applications: Society and Environment, Volume 24,2021,100610, ISSN 2352-9385.
Reu, JD,. Bourgeois, J,. Bats M,. Zwertvaegher, A,. Gelorini,V ,. Smedt P.D,. Wei,C., and et at. 2013, Application of the topographic position index to heterogeneous landscapes , Geomorphology,Volume 186, 15 March 2013, Pages 39-49
Rowhani P, Linderman M, Lambin EF ., 2011. Global interannual variability in terrestrial ecosystems: sources and spatial distribution using MODIS-derived vegetation indices, social and biophysical factors. International Journal of Remote Sensing 32(19):5393-5411.
Rui Ba, Weiguo Song, Michele Lovallo, Hui Zhang, Luciano Telesca, (2022), Informational analysis of MODIS NDVI and EVI time series of sites affected and unaffected by wildfires, Physica A: Statistical Mechanics and its Applications, Volume 604,2022,127911, ISSN 0378-4371.
Sellers, P.J., 1985. Canopy reflectance, photosynthesis, and transpiration. International Journal of Remote Sensing, 6, pp. 1335–1371.
Souza AA, Galvao LS, Santos JR (2010) Relationships between Hyperion-derived vegetation indices, biophysical parameters, and elevation data in a Brazilian savannah environment. Remote Sensing Letters 1(1):55-64.
Stephenson ,.NL. 1990. Climatic Control of Vegetation Distribution: The Role of the Water Balance , The American Naturalist , Vol. 135, No. 5 (May, 1990), pp. 649-670 (22 pages) , Published By: The University of Chicago Press
Tasneem Ahmed, Dharmendra Singh, Probability density functions-based classification of MODIS NDVI time series data and monitoring of vegetation growth cycle, Advances in Space Research, Volume 66, Issue 4,2020, Pages 873-886, ISSN 0273-1177.
Tucker, C.J. and Sellers, P.J., 1986, Satellite remote sensing of primary vegetation. International Journal of Remote Sensing, 22, pp. 3827–3844.
Turner, C.L., Seastedt, T.R., Dyer, M.I., Kittel, T.G.F. and Schimel, D.S., 1992, Effects of management and topography on the radiometric response of a tallgrass prairie. Journal of Geophysical Research, 97(D17), 18855–18866.
Unwin, D. J. 1981. Introductory spatial analysis (Vol. 748). Taylor & Francis.
Weiser, R.L., Asrar, G., Miller, G.P. and Kanemasu, E.T., 1986. Assessing grassland biophysical characteristics from spectral measurements. Remote Sensing of Environment, 20, pp. 141–152.
Weiss, A.,2001. Topographic Position and landforms Analysis. Poster presentation, ESRI user Conference, San Diego, C.A.
Wolf, Eric.,1994. Perilous Ideas: Race, Culture, People. Current Anthropology 35: 1-7. p.227.
Xiaomei Jin, Li Wan, You-Kan Zhang, Guangcheng Hu,M.E. Schaepman§, J. G. P. W. Clevers§ And Z. Bob Su., 2009. Quantification of spatial distribution of vegetation in the Qilian Mountain area with MODIS NDVI, International Journal of Remote Sensing Vol. 30, No. 21, 10 November 2009, 5751–5766
Yue He, Shilong Piao, Xiangyi Li, Anping Chen, Dahe Qin, Global patterns of vegetation carbon use efficiency and their climate drivers deduced from MODIS satellite data and process-based models, Agricultural and Forest Meteorology, Volumes 256–257,2018, Pages 150-158, ISSN 0168-1923.
Zhao N, Yang Y, Zhou X., 2010. Application of geographically weighted regression in estimating the effect of climate and site conditions on vegetation distribution in Haihe Catchment, China. Plant Ecology 209(2):349-359.
Zhong L, Ma Y, Salama M, et al., 2010. Assessment of vegetation dynamics and their response to variations in precipitation and temperature in the Tibetan Plateau. Climatic Change 103(3):519-535.
پژوهشهای ژئومورفولوژی کمّی
دوره 12، شماره 1
تیر 1402
صفحه 152-180

فایل ها

هم رسانی

ارجاع به این مقاله

آمار