مدلسازی تولید اولیه سطح زمین با استفاده از شاخصهای ماهواره لندست-8 در مراتع سیاهپوش و گنجگاه استان اردبیل، ایران
محورهای موضوعی : کشاورزی، مرتع داری، آبخیزداری و جنگلداری
پشمینه محمدنژاد
1
,
مهدی معمری
2
*
,
اردوان قربانی
3
,
فرید دادجو
4
,
ودود محمدی
5
1 - گروه مرتع و آبخیزداری، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران
2 - گروه علوم گیاهی و گیاهان داروئی، دانشکده کشاورزی مشگین شهر، دانشگاه محقق اردبیلی، اردبیل، ایران
3 - گروه مرتع و آبخیزداری، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران
4 - گروه مرتع و آبخیزداری، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران
5 - گروه مرتع و آبخیزداری، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران
کلید واژه: لندست 8, مراتع اردبیل, شاخص گیاهی, فرم رویشی,
چکیده مقاله :
هدف از این مطالعه برآورد تولید (تولید اولیه سطح زمین) فرمهای رویشی و کل با استفاده از تصاویر لندست 8 در مراتع سیاهپوش و گنجگاه استان اردبیل بود. نمونهبرداری میدانی در خرداد ماه 1398 انجام شد و تصویر ماهوارهای همزمان با آن دریافت شد. تعداد هفت مکان نمونهبرداری انتخاب شد و در هر مکان سه ترانسکت 100 متری موازی و عمود بر جهت شیب مستقر شد و در امتداد هر ترانسکت از 10 پلات (یک متر مربعی)، تولید فرمهای رویشی به روش تصادفی-سیستماتیک برداشت شد (در مجموع 210 پلات). تعداد 22 شاخص گیاهی با توجه به مرور منابع انتخاب و برای منطقه محاسبه شد. سپس، همبستگی بین تولید فرمهای رویشی و کل با شاخصهای گیاهی محاسبه و شاخص گیاهی دارای بالاترین همبستگی برای مدلسازی انتخاب شد. برای مدلسازی از معادله خطی درجه یک استفاده شد و معادلات بهدست آمده در نرمافزار ArcGIS بهصورت نقشه برآورد شد. نتایج نشانداد از بین شاخصهای مورد بررسی، شاخص NDVI مناسبترین شاخص برای مدلسازی بود؛ با اینحال بیشترین همبستگی این شاخص با تولید کل (88/0) و تولید گندمیان (78/0) بود؛ درحالیکه همبستگی کمتری با تولید پهنبرگان علفی (41/0) و بوتهایها (31/0) داشت. محدوده تغییرات تولید مدلسازی شده برای گندمیان 0 تا 1857 کیلوگرم در هکتار، پهنبرگان علفی 9 تا 766 کیلوگرم در هکتار، بوتهایها 0 تا 458 کیلوگرم در هکتار و برای تولید کل 9 تا 3081 کیلوگرم در هکتار بود. ارزیابی صحت مدلها با معیارهای RMSE، MDE و MAE انجام شد و صحت در حد قابل قبول بود. همچنین مقدار اختلاف میانگین دادههای واقعی با مدلسازی شده تقریباً برابر صفر بود. از نتایج این مطالعه میتوان برای ایجاد تعادل بین عرضه و تقاضای تولید مرتع در راستای توسعه پایدار اکوسیستمهای مرتعی منطقه استفاده کرد.
The aim of this study was to estimate the aboveground net primary production (ANPP) of life forms and total ANPP using Landsat 8 images in Siahpoosh and Ganjgah rangelands of Ardabil province. Field sampling was conducted in June 2019 and a satellite image was received at the same time. Seven sampling sites was selected, and three 100-meter transects parallel and perpendicular to the slope direction was established in each sites, and along each transects from 10 plots (1m2), the ANPP of life forms with random-systematic method were collected (210 plots in total). Then, 22 plant indices were selected according to previous references and calculated for the region. Next, the correlation between the ANPP of life forms and total ANPP with plant indices was calculated and the plant index with the highest correlation was selected for modeling. The first-order linear equation was used for modeling and the equations were estimated in ArcMap software as a map. The results showed that among the studied indices, NDVI was the most appropriate index for modeling; however, the highest correlation of this index was with total ANPP (0.88), and grasses ANPP (0.78); while it had less correlation with the forbs ANPP (0.41), and shrubs ANPP (0.31). The range of changes were 0 to 1857 kg/ha for grasses, 9 to 766 kg/ha for forbs, 0 to 458 kg/ha for shrubs, and 9 to 3081 kg/ha for the total ANPP. The accuracy of the models was evaluated by RMSE, MDE and MAE criteria and the accuracy was acceptable. Also, there was essentially no difference between the mean of real and modeled data. The results of this study can be used to balance the supply and demand of rangeland production for sustainable development of rangeland ecosystems.
1. Abbasi Khalaki M, Ghorbani A, Dadjou F. 2019. Using a network analysis process in the restore of low yielding and abounded dry farming lands with range planting (Case study: Balekhli Chay watershed). Journal of RS and GIS for Natural Resources, 10(2): 102-120. (In Persian)
2. Abdolalizadeh Z, Ghorbani A, Mostafazadeh R, Moameri M. 2020. Rangeland canopy cover estimation using Landsat OLI data and vegetation indices in Sabalan rangelands, Iran. Arabian Journal of Geosciences, 13: 245. doi: 10.1007/s12517-020-5150-1.
3. Al-bukhari A, Hallett S, Brewer T. 2018. A review of potential methods for monitoring rangeland degradation in Libya. Research, Policy and Practice, 8: 13. doi: 10.1186/s13570-018-0118-4.
4. Batten G D. 1998. Plant analysis using near infrared reflectance spectroscopy: The potential and the limitations. Australian Journal of Experimental Agriculture, 38(7): 697-706.
5. Byrne K M, Lauenroth W K, Adler P B, Byrne C M. 2011. Estimating Aboveground Net Primary Production in Grasslands: A Comparison of Nondestructive Methods. Rangeland Ecology & Management, 64(5): 498-505. doi: 10.2111/REM-D-10-00145.1.
6. Carlos A, Eduardo J, Oscar A, Marco A, Jose R, Guillermo S, Reija H, Alejandro I, Liliana M. 2014. Mapping aboveground biomass by integrating geospa- tial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico. Journal of Arid Land, 6(1): 80-96. doi: 10.1007/s40333-013-0191-x.
7. Chang L, Peng-Sen S, Liu Sh R. 2016. A review of plant spectral reflectance response to water physiological changes. Chinese Journal of Plant Ecology, 40(1): 80-91. doi: 10.17521/cjpe.2015.0267.
8. Costanza R. 2012. Ecosystem health and ecological engineering. Ecological Engineering, 45: 24-9. doi: 10.1016/j.ecoleng.2012.03.023.
9. Dadjou F, Ghorbani A, Moameri M, Mostafazadeh R, Hazbavi Z. 2021. Modeling of production parameters and canopy cover to introduce the most effective environmental factor in the semi-steppe rangelands of Baghro, Ardabil province. Iranian Journal of Applied Ecology, 10(3): 1-14. (In Persian)
10. Dong T, Liu J, Qian B, He L, Liu J, Wang R, Jing Q, Champagne C, McNarin H, Powers J, Shi Y, Chen J.M, Shang J. 2020. Estimating crop biomass using leaf area index derived from Landsat 8 and Sentinel-2 data. Journal of Photogrammetry and Remote Sensing, 168: 236-50. doi: 10.1016/j.isprsjprs.2020.08.003.
11. Ghorbani A, Dadjou F, Moameri M, Biswas A. 2020. Estimating Aboveground Net Primary Production (ANPP) Using Landsat 8-Based Indices: A Case Study from Hir-Neur Rangelands, Iran. Rangeland Ecology & Management, 73: 649-57. doi: 10.1016/j.rama.2020.06.006.
12. Ghorbani A, Pournemati A, Panahandeh M. 2017. Estimating and mapping Sabalan rangelands aboveground phytomass using Landsat-8 images. Iranian Journal of Range and Desert Research, 24(1): 165-80. doi: 10.22092/ijrdr.2017.109858. (In Persian)
13. Haghighi Khomami M, Tajaddod M J, Ravanbakhsh M, Jamalzad Fallah F. 2021. Vegetation classification based on wetland index using object based classification of satellite images (Case study: Anzali wetland). Journal of RS and GIS for Natural Resources, 12(3): 1-17. (In Persian)
14. He M, Kimball J S, Maneta M P, Maxwell B D, Moreno A, Begueria S, Wu X. 2018. Regional crop gross primary productivity and yield estimation using fused Landsat-MODIS data. Remote Sensing, 10(3): 372. doi: 10.3390/rs10030372.
15. Homer C, Dewitz J, Yang L, Jin S, Danielson P, Xian G, Coulston J, Herold N, Wickham J, Megown K. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States--representing a decade of land cover change information. Photogrammetric Engineering & Remote Sensing, 81(5): 345-54. doi: 10.14358/PERS.81.5.345.
16. Imani J, Ebrahimi A, Gholinejad B, Tahmasebi P. 2021. Application of remote sensing information to estimate production and plant cover percentage (Study area: Rangelands around Choghakhor Wetland in Chaharmahal and Bakhtiari Province). Iranian Journal of Range and Desert Research, 28(3): 450-71. doi: 10.22092/ijrdr.2021.125012. (In Persian)
17. Jin Y, Yang X, Qiu J, Li J, Gao T, Wu Q, Zhao F, Ma H, Yu H, Xu B. 2014. Remote sensing-based biomass estimation and its spatio-temporal variations in temperate grassland, northern China. Remote Sensing, 6: 1496-513. doi: 10.3390/rs6021496.
18. Jones M O, Robinson N, Naugle D, Maestas J, Reeves M C, Lankston R, Allred B. 2021. Annual and 16-Day Rangeland Production Estimates for the Western United States. Rangeland Ecology & Management, 77: 112-7. doi: 10.1016/j.rama.2021.04.003.
19. Liu H, Dahlgren R A, Larsen R E, Devine S M, Roche L M, Geen A T, Wong A J, Covello S, Jin Y. 2019. Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite. Remote Sensing, 11(5): 595. doi: 10.3390/rs11050595.
20. Liu Z, Hu M, Hu Y, Wang G. 2018. Estimation of net primary productivity of forests by modified CASA models and remotely sensed data. International Journal of Remote Sensing, 39(4): 1092-116. doi: 10.1080/01431161.2017.1381352.
21. Lu Q, Gao Z, Ning J, Bi X, Wang Q. 2015. Impact of progressive urbanization and changing cropping systems on soil erosion and net primary production. Ecological Engineering, 75: 187-94. doi: 10.1016/j.ecoleng.2014.11.048.
22. Maguigan M, Rodgers J, Dash P, Meng Q. 2016. Assessing net primary production in montane wetlands from proximal, airborne, and satellite remote sensing. Advanced Remote Sensing, 5: 118-30. doi: 10.4236/ars.2016.52010.
23. Mahyou H, Tychon B, Lang M, Balaghi R. 2018. Phytomass estimation using eMODIS NDVI and ground data in arid rangelands of Morocco. African Journal of Range & Forage Science, 35(1): 1-12. doi: 10.2989/10220119.2018.1436088.
24. Mngadi M, Odindi J, Mutanga O, Sibanda M. 2022. Estimating aboveground net primary productivity of reforested trees in an urban landscape using biophysical variables and remotely sensed data. Science of the Total Environment, 802(3): 149958. doi: 10.1016/j.scitotenv.2021.149958.
25. Mohammadi V. 2021. Study of Aboveground Primary Production in Siahpoosh and Ganjgah Rangelands, Ardabil Province. Master Thesis. (In Persian)
26. Nanzad L, Zhang J, Tuvdendorj B, Yang S, Rinzin S, Prodhan F A, Sharma T P P. 2021. Assessment of Drought Impact on Net Primary Productivity in the Terrestrial Ecosystems of Mongolia from 2003 to 2018. Remote Sensing, 13: 2522. doi: 10.3390/rs13132522.
27. Quan Z, Xianfeng Z, Miao J. 2011. Eco-environment variable estimation from remote sensed data and eco-environment assessment: models and system. Acta Botanica Sinica, 47: 1073-80.
28. Reeves M, Washington-Allen R A, Angerer J, Hunt E R, Kulawardhana R W, Kumar L, Loboda T, Loveland T, Metternicht G, Ramsey R D. 2015. Global view of remote sensing of rangelands: Evolution, applications, future pathways [chapter 10]. . In Land resources monitoring, modeling, and mapping with remote sensing: 237-76.
29. Reinermann S, Asam S, Kuenzer C. 2020. Remote Sensing of Grassland Production and Management—A Review. Remote Sensing, 12: 1949. doi: 10.3390/rs12121949.
30. Roujean J L, Breon F M. 1995. Estimating PAR absorbed by vegetation from bidirec- tional reflectance measurements. Remote Sensing Environment, 51(3): 375-84. doi: 10.1016/0034-4257(94)00114-3.
31. Salarian F, Tatian M, Ghanghermeh A, Tamartash R. 2022. Modeling land cover changes in Golestan province using land change modeler (LCM). Journal of RS and GIS for Natural Resources, 12(4): 47-70. (In Persian)
32. Seyedi Kaleybar S A, Dadjou F, Hasanzadeh A, Mollazadeh Asl H. 2019. Canopy cover and production estimation and susceptible areas locating of Sumac (Rhus coriaria) cultivation in Khakriz rangelands of Ardabil province. Journal of RS and GIS for Natural Resources, 10(1): 60-71. (In Persian)
33. Smith R C G, Adams J, Stephens D J, Hick P T. 1995. Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite. Australian Journal of Agricultural Research, 1(46): 113-25. doi: 10.1071/AR9950113.
34. Svoray T, Perevolotsky A, Atkinson P M. 2013. Ecological sustainability in rangelands: The contribution of remote sensing. International Journal of Remote Sensing, 34(17): 6216-42. doi: 10.1080/01431161.2013.793867.
35. Uden D R, Twidwell D, Allen C R, Jones M O, Naugle D E, Maestas J D, Allred, B. W. 2019. Spatial Imaging and Screening for Regime Shifts. Frontiers in Ecology and Evolution, 7: 407. doi: 10.3389/fevo.2019.00407.
36. USGS. 2013. Using the USGS Landsat 8 Product. Cited at: http://landsat7usgsgov/ Landsat8 _ Using _ Productphp.
37. Xiong Q, Xiao Y, Halmy M W A, Dakhil M A, Liang P, Liu C, Zhang L, Pandey B, Pan K, El Kafraway S B, Chen J. 2019. Monitoring the impact of climate change and human activities on grassland vegetation dynamics in the northeastern Qinghai-Tibet Plateau of China during 2000−2015. Journal of Arid Land, 11: 637-51. doi: 10.1007/s40333-019-0061-2.
38. Xue J Su B. 2017. Significant remote sensing vegetation indices: A review of de- velopments and applications. Journal of Sensors: 1-17. doi: 10.1155/2017/1353691.
39. Zarineh E, Naderi Khorasgani M, Asadi Borujeni E. 2012. Estimating the rangeland vegetation cover of Tange Sayyad Region (Chaharmahal-o-Bakhtiary Province) Using IRS LISS-III Data. Polish Journal of Environmental Studies, 38(1): 117-30. (In Persian)
40. Zhang M. 2021. Modeling net primary productivity of wetland with a satellite-based light use efficiency model. Geocarto International. doi: 10.1080/10106049.2021.1886343.