قابليت شاخصهاي VCADI، TSDI و TVDI در برآورد خشکسالي اراضي زراعي روستاي حصار شهرستان ماهنشان
محورهای موضوعی : کاربرد کامپیوتر در مسائل آب و خاک
1 - استاديار گروه جغرافيا، دانشگاه زنجان، زنجان، ايران.
کلید واژه: شاخص خشکسالي, TVDI, TSDI, VCADI, حصار ماهنشان,
چکیده مقاله :
زمينه و هدف: امروزه شاخصهاي خشکسالي زيادي بر اساس روابط رگرسيوني شاخصهاي پوشش گياهي و دماي سطح زمين ارائه شده است. هدف از اين تحقيق، ارزيابي قابليت هر يک از شاخصهاي شاخص خشکسالي دماي پوشش گياهي (TVDI)، شاخص خشکسالي شرايط آلبدوي پوشش گياهي (VCADI) و شاخص خشکسالي اصلاح شده خاک پوشش گياهي (TSDI) در برآورد وضعيت خشکسالي در محدوده حصار ماهنشان در ساحل رودخانه قزل اوزن ميباشد.
روش پژوهش: در اين تحقيق تفاوتها و قابليتهاي 3 شاخص خشکسالي در ساحل رودخانه قزل اوزن در بخش حصار ماهنشان مورد بررسي قرار گرفت. به اين منظور از تصاوير لندست 5 و 8 در سال هاي 1990 و 2023 استفاده شد. اين شاخصها بر اساس روابط رگرسيوني بين پوشش گياهي، دماي سطح زمين و آلبدو بنا نهاده شده و بين شاخصهاي NDVI، LST، albedo و MSAVI رابطه رگرسيوني برقرار شده و شاخصهاي TVDI، TSDI و VCADI ايجاد شد. هر يک از اين شاخصها از باندهاي معيني استفاده کرده و براي برآورد دماي سطح زمين از باند 6 ماهواره لندست 5 و باند 10 ماهواره لندست 8 استفاده شد. براي ترسيم نمودار پراکنش نيز از نرم افزار origin 8 استفاده شده و معادله رگرسيوني مربوطه براورد شد. از مقادير a و b براي ترسيم نقشه شاخصها بهره گرفته شد. صحت هر يک از اين شاخصها با استفاده از ضريب کاپا مورد بررسي قرار گرفت.
يافتهها: به منظور بررسي شرايط خشکسالي منطقه مورد مطالعه به پنج طبقه با خشکي بسيار کم، کم، متوسط، زياد و بسيار زياد تقسيم ميشود و بر اساس يافتههاي به دست آمده مشاهده شد که منطقه با خشکسالي زياد در شاخص VCADI از 65/0 کيلومتر مربع در سال 1990 به 53/1 کيلومتر مربع در سال 2023 افزايش يافته و از 10 درصد مساحت منطقه به 6/23 درصد رسيده است. اين ميزان در پهنه خيلي زياد براي شاخصهاي TSDI و TVDI به ترتيب از 47/0 و 65/0 کيلومتر مربع به 7/18 و 64/23 درصد افزايش يافته است.
نتايج: نتايج نشان داد که بيشترين رابطه ضريب همبستگي پيرسون به ميزان 55/0- بين شاخص LST و NDVI برقرار بوده و در سال 2023 رخ داده است. بر اساس شاخصهاي خشکسالي مشاهده شد که در شاخص VCADI مناطق برخوردار از خشکسالي خيلي زياد از 65/0 کيلومتر مربع به 53/1 کيلومتر مربع افزايش يافته و از 10 درصد به 6/23 درصد رسيده است. در شاخصهاي TSDI و TVDI نيز نتايج مشابهي حاصل شده و به ترتيب از 10 و 26/7 درصد در سال 1990 به 64/23 و 7/18 درصد در سال 2023 رسيده است. بر اساس روابط همبستگي پيرسون و ضريب کاپا مشاهده شد که شاخص TVDI نسبت به ساير شاخصها از قابليت بهتري در بررسي خشکسالي برخوردار بوده است.
Background and Aim: Today, many drought indices have been presented based on the regression relationships of vegetation indices and surface temperature. The purpose of this research is to evaluate the capability of each of the vegetation temperature aridity index (TVDI), vegetation albedo aridity index (VCADI) and modified vegetation soil aridity index (TSDI) in estimating the aridity condition in the Hesar of Mahneshan in the shore of the river Qezelozan.
Method: In this research, the differences and capabilities of 3 aridity indicators on the shore of Qezelozan River in Hesar Mahneshan section were investigated. For this purpose, Landsat 5 and 8 images were used in 1990 and 2023. These indices are based on the regression relations between vegetation, surface temperature and albedo, and a regression relation was established between NDVI, LST, albedo and MSAVI indices, and TVDI, TSDI and VCADI indices were created. Each of these indicators used certain bands and band 6 of Landsat 5 satellite and band 10 of Landsat 8 satellite were used to estimate the earth's surface temperature. Origin 8 software was used to draw the scatter diagram and the corresponding regression equation was obtained. The values of slope and intercept were used to draw the index map. The accuracy of each of these indicators was checked using the Kappa coefficient.
Results: In order to check the drought condition, the studied area is divided into five classess with very low, low, medium, high and very high dryness and it was observed that the area with high dryness in the VCADI index increased from 0.65 km2 in 1990 to 53. 1 square kilometer has increased in 2023 and has reached 23.6% from 10% of the area of the region. This amount has increased from 0.47 and 0.65 km2 to 18.7 and 23.64 percent in the very large area for TSDI and TVDI indicators, respectively.
Conclusion: The results showed that the highest Pearson correlation coefficient of -0.55 was established between LST index and NDVI and occurred in 2023. Based on the drought indices, it was observed that in the VCADI index, the areas with very dry areas increased from 0.65 square kilometers to 1.53 square kilometers and reached 23.6% from 10%. In TSDI and TVDI indices, similar results have been obtained and have reached 23.64 and 18.7 percent in 2023 from 10 and 7.26 percent in 1990, respectively. Based on Pearson's correlation and Kappa coefficient, it was observed that the TVDI index has a better ability to assess drought compared to other indices, and the TSDI index with a correlation coefficient of -0.54% is in the second place in 2023.
Amani, M., Salehi, B., Mahdavi, S., Masjedi, A., & Dehnavi, S. (2017). Temperature-vegetation-soil moisture dryness index (TVMDI). Remote Sens. Environ 197: 1–14.
Amazirh, A., Merlin, O., Er-Raki, S., Gao, Q.i., Rivalland, V., Malbeteau, Y., Khabba, S., & Escorihuela, M.J. (2018). Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between sentinel-1 Radar and Landsat thermal data: a study case over bare soil. Remote Sens. Environ 211: 321–337.
Bento, V.A., Gouveia, C.M., DaCamara, C.C., & Trigo, I.F. (2018). A climatological assessment of drought impact on vegetation health index. Agr. Forest Meteorol 259: 286–295.
Brown, J.F., Wardlow, B.D., Tadesse, T., Hayes, M.J., & Reed, B.C. (2008). The vegetation drought response index (VegDRI): a new integrated approach for monitoring drought stress in vegetation. GISci. Remote Sens 45 (1): 16–46.
Cammalleri, C., & Vogt, J.V. (2019). Non-stationarity in MODIS FAPAR time-series and its impact on operational drought detection. Int. J. Remote Sens 40 (4): 1428–1444.
Dang, C.Y., Liu, Y., Yue, H., Qian, J.X., & Zhu, R. (2020). Autumn crop yield prediction using data-driven approaches: support vector machines, random forest, and deep neural network methods. Can. J. Remote Sens., 1–20
Ghulam, A., Qin, Q., & Zhan, Z. (2007). Designing of the perpendicular drought index. Environ. Geol 52 (6): 1045–1052.
Hamzeh, S., Farahani, Z., Mahdavi, S., Chatrobgoun, O., & Gholamnia, M. (2017). Spatio-temporal monitoring of agricultural drought using remotely sensed data (Case study of Markazi province of Iran). Journal of Spatial Analysis Environmental Hazards 4 (3) :53-70 [In Persian]
Han, L., Wang, P., Yang, H., Liu, S., & Wang, J. (2006). Study on NDVI-Ts space by combining LAI and evapotranspiration. Sci. China Earth Sci 49 (7): 747–754.
Kafy, A.A. (2021). Impact of Vegetation Cover Loss on Surface Temperature and Carbon Emission in a Fastest-Growing City, Cumilla, Bangladesh, 207. Building and Environment.
Khellouk, R., Barakat, A., Jazouli, A.E., Boudhar, A., & Benabdelouahab, T. (2019). An integrated methodology for surface soil moisture estimating using remote sensing data approach. Geocarto Int 30: 1–14
Kogan, F.N. (1995). Application of vegetation index and brightness temperature for drought detection. Adv. Space Res 15 (11): 91–100.
Le Page, M., & Zribi, M. (2019). Analysis and predictability of drought in northwest Africa using optical and microwave satellite remote sensing products. Sci. Rep-uk. 9, 1466.
Liu, Y., Wu, L.X., & Ma, B.D. (2013). Remote sensing monitoring of soil Moisture on the basis of TM/ETM + spectral space. J. China Univ. Min. Technol 42: 296–301 (in Chinese).
Liu, Y., Wu, L., & Yue, H. (2015). Biparabolic NDVI-Ts space and soil moisture remote sensing in an arid and semi-arid area. Can. J. Remote Sens 41 (3: 159–169.
Liu, Y., & Yue, H. (2017). Dynamic monitoring of drought conditions in Henan Province based on LAI-Ts space. IEEE Geosci. Remote Sens. Symp 23: 5097–15010
Liu, Y., & Yue, H. (2018). The temperature vegetation dryness index (TVDI) based on bi-parabolic NDVI-Ts space and gradient-based structural similarity (GSSIM) for long-term drought assessment across Shaanxi province, China (2000–2016). Remote Sens-Basel. 10 (6), 959. https:// doi.org/10.3390/rs10060959.
Liu, Y., Dang, C., Yue, H., Lyu, C., & Dang, X. (2021). Enhanced drought detection and monitoring using sun-induced chlorophyll fluorescence over Hulun Buir Grassland, China. Sci. Total Environ. 770, 145271. https://doi.org/10.1016/j.scitotenv.2021.145271.
Lu, Y., Horton, R., Zhang, X., & Ren, T. (2018). Accounting for soil porosity improves a thermal inertia model for estimating surface soil water content. Remote Sens. Environ 212: 79–89.
Lu, Y., Tao, H., & Wu, H. (2007). Dynamic drought monitoring in Guangxi using revised temperature vegetation dryness index. Wuhan Univ. J. Natl. Sci 12 (4): 663–668.
Marzban, F., Sodoudi, S., & Preusker, R. (2018). The influence of land-cover type on the relationship between NDVI–LST and LST-Tair. Int. J. Remote Sens 39 (5): 1377–1398.
Pandey, V., & Srivastava, P. (2019). Integration of microwave and optical/ infrared derived datasets for a drought hazard inventory in a subtropical region of India. Remote Sens-Basel. 11 (4), 439. https://doi. org/10.3390/rs11040439.
Patel, N.R., Anapashsha, R., Kumar, S., Saha, S.K., & Dadhwal, V.K. (2009). Assessing potential of modis derived temperature/vegetation condition index (tvdi) to infer soil moisture status. Int. J. Remote Sens 2009 (30): 23–39.
Peng, J., Muller, J.-P., Blessing, S., Giering, R., Danne, O., Gobron, N., Kharbouche, S., Ludwig, R., Mu¨ ller, B., Leng, G., You, Q., Duan, Z., & Dadson, S. (2019). Can we use satellite-based FAPAR to detect drought? Sensors-Basel. 19 (17), 3662. https://doi.org/10.3390/s19173662.
Pickett-Heaps, C.A., Canadell, J.G., Briggs, P.R., Gobron, N., Haverd, V., Paget, M.J., Pinty, B., & Raupach, M.R. (2014). Evaluation of six satellite-derived fraction of absorbed photosynthetic active radiation (FAPAR) products across the Australian continent. Remote Sens. Environ 140: 241–256.
Pourkhosravani, M., Mehrabi, A., & Mousavi, S. (2018). Drought spatial analysis of Sirjan Basin using Remote sensing, Desert Ecosystem engineering Journal 7(20): 13-22. [In Persian]
Price, J.C. (1985). On the analysis of thermal infrared imagery: the limited utility of apparent thermal inertia. Remote Sens. Environ 18 (1): 59– 73.
Qin, H., Wang, C., Pan, F., Lin, Y., Xi, X., & Luo, S. (2017). Estimation of FPAR and FPAR profile for maize canopies using airborne Lidar. Ecol. Indic 83: 53–61.
Sandholt, I., Andersen, J., & Rasmussen, K. (2002). A simple interpretation of the surface temperature/vegetation index space for assessment of soil moisture status. Remote Sens. Environ 79: 213–224.
Sui, X.X., Qin, Q.M., Dong, H., Wang, J.L., Meng, Q.Y., & Liu, M.C. (2013). Monitoring of farmland drought based on LST-LAI spectral feature space. Spectrosc. Spect. Anal 33: 201–205
Thakur, S., Mondal, I., Bar, S., Nandi, S., Das, P., Ghosh, P.B., & De, T.K. (2020). Shoreline changes and its impact on the mangrove ecosystems of some Islands of Indian Sundarbans, North- East coast of India, J Clean Prod, 284, 124764. Elsevier.
Wang, P.X., Li, X.W., Gong, J.Y., & Song, C.H. (2001). Vegetation temperature condition index and its application for drought monitoring. In: Proceedings of the International Geoscience and Remote Sensing Symposium, Sydney, Australia, 9–13 July 2001.
Wang, H., He, N., Zhao, R., & Ma, X. (2020). Soil water content monitoring using joint application of PDI and TVDI drought indices. Remote Sens. Lett 11 (5): 455–464.
Wang, W., Huang, D., Wang, X.-G., Liu, Y.-R., & Zhou, F. (2011). Estimation of soil moisture using trapezoidal relationship between remotely sensed land surface temperature and vegetation index. Hydrol. Earth Syst. Sc. 15 (5): 1699–1712.
Wigmore, O., Mark, B., Mckenzie, J., Baraer, M., & Lautz, L. (2019). Submeter mapping of surface soil moisture in proglacial valleys of the tropical Andes using a multispectral unmanned aerial vehicle. Remote Sens. Environ. 222: 104–118.
Wu, Z., Lei, S., Bian, Z., Huang, J., & Zhang, Y. (2019). Study of the desertification index based on the albedo-MSAVI feature space for semi-arid steppe region. Environ. Earth. Sci. 78, 232.
Yagci, A.L., & Santanello, J.A. (2018). Estimating evapotranspiration from satellite using easily obtainable variables: a case study over the southern Great Plains, USA. IEEE J-Stars. 11 (1): 12–23.
Yildirima, T., & Asika, S. (2018). Index-based assessment of agricultural drought using remote sensing in the semi-arid region of western Turkey. J. Agr. Sci.-Tarim. Bili. 24: 510–516.
Yue, H., Liu, Y., & Qian, J. (2020). Soil moisture assessment through the SSMMI and GSSIM algorithm based on SPOT, WorldView-2, and Sentinel-2 images in the Daliuta Coal Mining Area, China. Environ. Monit. Assess. 192, 237.
Zhao, H., Li, Y.i., Chen, X., Wang, H., Yao, N., & Liu, F. (2021). Monitoring monthly soil moisture conditions in China with temperature vegetation dryness indexes based on an enhanced vegetation index and normalized difference vegetation index. Theor. Appl. Climatol. 143 (1-2): 159–176.
Zhao, L.H., Du, P.J., Pang, Y.F., & Zhang, H.P. (2010). Monitoring drought using temperature/vegetation drought index based on remote sensing images. Bull. Soil Water Conserv. 30, 110–115
Zormand, S., Jafari, R., & Koupaei, S.S. (2017). Assessment of PDI, MPDI and TVDI drought indices derived from MODIS Aqua/Terra Level 1B data in natural lands. Nat. Hazards. 86 (2): 757–777.