ارزیابی بیابان زایی در محدوده دشت قزوین با استفاده از تصاویر سنتینل ۲، شاخص های طیفی و درجه بیابان زایی (DDI) | ||
| کاوش های جغرافیایی مناطق بیابانی | ||
| دوره 11، شماره 2، اسفند 1402، صفحه 41-56 اصل مقاله (1.5 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/grd.2023.20385.1586 | ||
| نویسنده | ||
| مهدی فیض اله پور* | ||
| عضو هیات علمی دانشگاه زنجان | ||
| چکیده | ||
| در این تحقیق بر ارزیابی کمی بیابان زایی در محدوده دشت قزوین از تصاویر سنتینل ۲ استفاده شده است. در ابتدا، شاخص های TGSI، albedo، NDVI و MSAVI استخراج شد. سپس برای تحلیل رگرسیون خطی، ترکیب مختلفی از شاخص ها در نظر گرفته شده و رابطه همبستگی پیرسون بین شاخص های NDVI و آلبدو، MSAVI و آلبدو، TGSI و آلبدو و TGSI و MSAVI برقرار گردید. سپس با بهره گیری از روابط رگرسیونی بین NDVI-albedo و MSAVI-albedo شاخص درجه بیابان زایی DDI برآورد گردید. در نهایت نقشه بیابان زایی برای کل محدوده مورد مطالعه در پنج طبقه خیلی شدید، شدید، متوسط، کم و خیلی کم ترسیم شد. نتایج به دست آمده نشان داد بین آلبدو و شاخص های MSAVI و NDVI همبستگی منفی و بین این شاخص با TGSI همبستگی مثبت برقرار است. ترکیب MSAVI-albedo بهترین ضرایب همبستگی را با مقدار 417/۰- دارا بوده و کمترین همبستگی نیز به میزان 33/0 بین آبدو و TGSI برآورد گردیده است. بر اساس شاخص DDI وضعیت بیابان زایی در منطقه در محدوده هشدار دهنده بوده است. در واقع بر اساس نست آلبدو با شاخص MSAVI، 6/55 درصد از منطقه در طبقه بیابان زایی خیلی شدید قرار گرفته اند. در حالیکه تنها 9/1 درصد در محدوده بیابان زایی خیلی کم بوده است. بر اساس نسبت albedo-NDVI 68 درصد منطقه در وضعیت بیابان زایی خیلی شدید قرار دارد. مقادیر به دست آمده برای دو مدل بسیار نزدیک به هم بوده و در تخمین مقادیر شدید و خیلی کم به تخمین های مشابهی دست یافته اند. | ||
| کلیدواژه‌ها | ||
| NDVI؛ albedo؛ TGSI؛ بیابان زایی؛ دشت قزوین | ||
| عنوان مقاله [English] | ||
| Evaluation of desertification in Qazvin Plain using Sentinel 2 images and spectral indices and degree of desertification (DDI) | ||
| چکیده [English] | ||
| 1. Introduction Different researches provide a wide range of remote sensing-based techniques for desertification monitoring. Among them, we can mention the techniques of spectral composition analysis and spectral indices. Approaches based on different indicators are widely used to investigate land degradation. For example, Normalized Difference Vegetation Index (NDVI) and Modified Soil Vegetation Index (MSAVI) are used to monitor vegetation conditions in desertification assessment. Indices such as albedo and surface soil particle size index (TGSI) can detect the spatial heterogeneity of soil texture. In fact, many researchers around the world have successfully used these indicators to evaluate desertification. In some researches, albedo-NDVI and albedo-MSAVI models have been proposed for desertification analysis. Other researchers also used albedo-TGSI based model. However, among these indices, the albedo-NDVI index has been widely considered. This index shows a strong negative correlation and reflects the state of desertification. This research is based on TGSI, NDVI, albedo and MSAVI spectral indices. The combination of these indicators is tested through Pearson correlation. The aim of this research is to map the different classes of desertification in the Qazvin Plain in 2023 using Sentinel 2 images, to integrate the indicators obtained from remote sensing data to identify areas affected by desertification and to establish correlation between MSAVI indicators., albedo, NDVI and TGSI and create a linear regression to determine the DDI index. 2. Methodology In this research, using the Sentinel 2 satellite image in 2023, the degree of desertification is evaluated. This image covers the entire region. The spatial resolution of the images provides the opportunity to observe the ground accurately. Sentinel 2's multispectral images consist of 13 spectral bands with a resolution of 10 meters (4 bands), 20 meters (6 bands) and 60 meters (3 bands). These 13 bands cover a wide range of wavelengths from 440 to 2200 nm. In this research, red, infrared, blue, green, SWIR1 and SWIR2 bands were used. The satellite image of July 11, 2023 for a day without cloud cover was used to perform the analysis. In addition, July images allow the separation of agricultural land from natural vegetation. The method adopted in this research is based on the extraction of spectral indices NDVI, TGSI, albedo and MSAVI, which provides the possibility to evaluate and monitor desertification. This research included acquiring satellite images, calculating spectral indices, analyzing the correlation between spectral indices, calculating the degree of desertification index (DDI) and regression analysis. The estimation of four indices NDVI, albedo, MSAVI and TGSI is done using Sentinel 2 image. The extraction of different degrees of desertification is based on the analysis of several combinations of spectral indices in order to select a combination that shows the best classes of desertification. To extract the degree of desertification intensity and determine the best correlation, four combinations of albedo-NDVI, albedo-TGSI, albedo-MSAVI and TGSI-MSAVI were considered. A linear regression relationship was established between these indices. To draw the intensity of desertification, regression relationships between the above indicators were used. 3. Results and Discussion In this research, desertification was analyzed by building two albedo-NDVI and albedo-MSAVI models. The results obtained from these models achieve better results than the traditional models based on vegetation. Among these two models, the albedo-MSAVI model can be the most suitable for the studied area, which is mainly composed of areas with low vegetation cover. The albedo-NDVI model obtains better results for areas with dense vegetation. However, these areas include only a small part of the studied territory. Considering the effectiveness of albedo-MSAVI and albedo-NDVI models in the study area, these models can be used as a reference for decision makers in natural resource management. This model can be used for other areas with similar characteristics. Based on the results, special operational plans should be implemented to overcome the desertification situation and reduce land degradation. At first, TGSI, albedo, NDVI and MSAVI spectral indices were extracted. Then, for linear regression analysis, different combinations of indices were considered and Pearson correlation relationship was established between NDVI and albedo, MSAVI and albedo, TGSI and albedo, TGSI and MSAVI indices. Then, by using the regression relationships between NDVI-albedo and MSAVI-albedo, DDI desertification degree index was estimated. Finally, the map of desertification was drawn for the entire studied area in five classes: very severe, severe, moderate, low and very low. 4. Conclusion The obtained results showed that there is a negative correlation between albedo and MSAVI and NDVI indices, and a positive correlation between this index and TGSI. MSAVI-albedo combination has the best correlation coefficients with a value of -0.417 and the lowest correlation is estimated at 0.33 between albedo and TGSI. According to the DDI index, the desertification situation in the region has been in the alarming range. In fact, according to Nest Albedo with MSAVI index, 55.6% of the region is classified as very severe desertification. While only 1.9% was very low in the area of desertification. According to albedo-NDVI ratio, 68% of the region is in a state of extreme desertification. The values obtained for the two models are very close to each other and similar estimates have been obtained in estimating extreme and very low values. | ||
| کلیدواژه‌ها [English] | ||
| NDVI, albedo, TGSI, desertification, Qazvin Plain | ||
| مراجع | ||
|
Afrasinei GM, Melis MT, Arras C et al (2018) Spatiotemporal and spectral analysis of sand encroachment dynamics in southern Tunisia. European Journal Remote Sensing 51:352–374 Anderson, J.R (1976) Report- USGS Numbered Series-964. In: A Land Use and Land Cover Classification System for Use with Remote Sensor Data. US Government Printing Office 24: 127- 296. Bahman Abadi B, Kaviani A, Daneshkar P, Nazari R (2018) Estimation of Actual evapotranspiration using satellite imageries and single source and two source surface energy balance Algorithms in Qazvin Plain, Journal of water research in Agriculture, 32: 227-246. Becerril-Piña R, Díaz-Delgado C, Mastachi-Loza CA, González-Sosa E (2016) Integration of remote sensing techniques for monitoring desertification in Mexico. Hum Ecol Risk Assess an Int J 22:1323–1340 Collado AD, Chuvieco E, Camarasa A (2002) Satellite remote sensing analysis to monitor desertification processes in the croprangeland boundary of Argentina. J Arid Environ 52:121–133 Darouiche FZ, Assaoud S, Ouarhache D (2015) La dynamique de la désertification dans le Nord-Est du Maroc au cours des deux dernières décennies: Etat des lieux et précision des zones d’intérêt. Mots du Com d’organisation, p 228 Ding J, Juan QU, Yongmeng S et al (2013) The retrieval model of soil salinization information in arid region based on MSAVI-WI feature space: a case study of the delta oasis in Weigan-Kuqa watershed. Geogr Res 32(2):223–232 Ding J, Yuan Y, Fei W (2014) Detecting soil salinization in arid regions using spectral feature space derived from remote sensing data. Acta Ecol Sin 34(16):4620–4631 Dregne HE (2002) Land degradation in the drylands. Arid L Res Manag 16:99–132 Feng J, Ding JL, Wei WY (2018) A Study of soil salinization in Weigan and Kuqa rivers oasis based on Albedo-MSAVI feature space. China Rural Water Hydropower 2:147–152 Geloogerdi, S., Vali, A., Sharifi, M., (2021). Investigation of Desertification Trend in the Center of Khuzestan province Using Remote Sensing Time Series Data, Iranian Journal of Soil and water research, 52(11), 2843- 2857. (In Persian) Guang Y, Dong C, Xinlin H et al (2017) Land use change characteristics affected by water saving practices in Manas River Basin, China using Landsat satellite images. International Journal Agriculture Biology Engineer 10:123– 33 Guo B, Zang W, Han B et al (2020) Dynamic monitoring of desertification in Naiman Banner based on feature space models with typical surface parameters derived from LANDSAT images. L Degradation Development 31(12):1573–1592 Hu Y, Han Y, Zhang Y (2020) Land desertification and its influencing factors in Kazakhstan. Journal Arid Environment 180:104203 Jiang M, Lin Y (2018) Desertification in the south Junggar Basin, 2000–2009: Part I. Spatial analysis and indicator retrieval. Advance Sp Resource 62:1–15 Khodaei Geshlag, F., roostaei, S., mokhtari, D., (2020). Monitoring the Desertification Trend in the Areas Surrounding Lake Urmia (2000-2018), Geography and Environmental planning, 31(3), 21-40. (In Persian) Kosmas C, Kairis O, Karavitis C et al (2014) Evaluation and selection of indicators for land degradation and desertification monitoring: methodological approach. Environ Manage 54:951–970 Lamaamri M, Lghabi N, Ghazi A, El Harchaoui N (2023) Evaluation of Desertification in the Middle Moulouya Basin (North- East Morocco) using Sentinel-2 image and spectral index. Earth systems and environment 7: 473-492 Lamchin M, Lee J-Y, Lee W-K et al (2016) Assessment of land cover change and desertification using remote sensing technology in a local region of Mongolia. Advance Sp Resource 57:64–77 Liang S, Shuey CJ, Russ AL et al (2003) Narrowband to broadband conversions of land surface albedo: II Validation. Remote Sens Environ 84:25–41 Liu Q, Zhao Y, Zhang X et al (2018b) Spatiotemporal patterns of desertification dynamics and desertification effects on ecosystem Services in the Mu Us Desert in China. Sustainability 10:589 Louis J, Debaecker V, Pflug B, et al (2016) Sentinel-2 Sen2Cor: L2A processor for users. In: Proceedings Living Planet Symposium 2016. Spacebooks Online, pp 1–8 Mijani, K., Mahdavi, R., Gholami, H., rezaei, M. (2022). Monitoring and Modeling of Desertification Intensity Using Landsat Satellite Images (Case Study of Yazdanabad-Zarand watershed). Journal of Irrigation and Water Engineering, 13(2), 446- 462. (In Farsi) Pan J, Li T (2013) Extracting desertification from Landsat TM imagery based on spectral mixture analysis and Albedo-Vegetation feature space. Nat Hazards 68:915–927 Qi J, Chehbouni A, Huete AR et al (1994) A modified soil adjusted vegetation index. Remote Sens Environ 48:119–126 Rashvand S, Mosafaee J, Darvish M, Rafee emam A (2013). Investigation on potential of desertification in terms of decay of vegetation case study: Rude Shoor, Qazvin, Iranian Journal of Range and Desert research, 20(1): 38- 49 Robert KG, Running SW (1993) Community type differentiation using NOAA/AVHRR data within a sagebrush-steppe ecosystem. Remote Sens Environ 46(3):311–318 Sun G, Chen X, Ren J et al (2017) Stratified spectral mixture analysis of medium resolution imagery for impervious surface mapping. Int J Appl Earth Obs Geoinf 60:38–48 Tabib Mahmoudi F, Ghasemi N (2020) Investigating the potential of soil moisture indices for desertification monitoring, Journal of Natural Environment, 73: 529- 542. UNCCD UNC to CD (2015) Climate change and land degradation: Bridging Knowledge and Stakeholders Verstraete MM, Pinty B (1996) Designing optimal spectral indexes for remote sensing applications. IEEE Trans Geosci Remote Sens 34:1254–1265 Wang T, Yan CZ, Song X, Xie JL (2012) Monitoring recent trends in the area of aeolian desertified land using Landsat images in China’s Xinjiang region. ISPRS J Photogramm Remote Sensing 68:184–190 Wei H, Wang J, Cheng K et al (2018) Desertification information extraction based on feature space combinations on the Mongolian plateau. Remote Sensing 10:1614 William H, Schlesinger JF, Reynolds GL et al (1990) Biological feedbacks in global desertification. Science 247(4946):1043–1048 Wu Z, Lei S, Bian Z et al (2019) Study of the desertification index based on the albedo-MSAVI feature space for semi-arid steppe region. Environment Earth Science 78:232 Yu P, Han D, Liu S et al (2018) Soil quality assessment under different land uses in an alpine grassland. CATENA 171:280–287 Zandifar S, Ebrahimikhusfi Z, khosroshahi M, Naeimi M. (2020) Analysis of the Effect of Climatic Parameters and Meteorological Droughts on the Variation of Internal Dust Events (A Case Study: Qazvin City). Journal of water and soil science 24 (3) :239-256 Zhao H, Liu R, Zhou R et al (2013) Properties and mechanisms of change of soil macro-fauna communities in the desertification process of Horqin sandy grassland. Acta Prataculturae Sin 22:70 Zeng Y, Feng Z, Xiang N (2006) Albedo-NDVI space and remote sensing synthesis index models for desertification monitoring. Sci Geogr Sin 26:75
| ||
|
آمار تعداد مشاهده مقاله: 400 تعداد دریافت فایل اصل مقاله: 227 |
||