Assessing Intensity of Desertification and Land Rehabilitation Using the Change Vector Analysis Method (Case study: Dehshir Plain, Yazd province, Iran)

Azam Abolhasani ¹ ( PhD. of Desert Control and Management, Department of Reclamation of Arid and Mountainous Regions Faculty of Natural Resource, University of Tehran, Tehran, Iran )
Hassan Khosravi ² ( Associate Professor, Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resource, University of Tehran, Tehran, Iran )
Esmail Heydari Alamdarloo ³ ( Postdoc Researcher, Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resource, University of Tehran, Tehran, Iran )
Saeed Barkhori ⁴ ( Assistant Professor, Faculty of Natural Resources, Jiroft University, Jiroft, Iran )

تاريخ الإرسال : 22 الثلاثاء , ذو القعدة, 1443 تاريخ التأكيد : 16 الثلاثاء , ربيع الثاني, 1445

الکلمات المفتاحية: Degradation, NDVI, BSI, Change Direction, Change Strength,

ملخص المقالة :

Desertification is one of the main ecological and environmental problems in Iran. It is remarkably urgent to monitor and assess this phenomenon and its impact on ecosystem components such as vegetation covers to prevent and combat desertification. This study aimed to evaluate the Change Vector Analysis (CVA) method’s efficiency in assessing and monitoring desertification in Dehshir Plain, Yazd province, central Iran. For this purpose, the imagery of Landsat 5 from 1994 to 1998 (T1) and Landsat 8 from 2014 to 2018 (T2) were selected in June and July period on the Google Earth Engine platform. Then, the Bare Soil Index (BSI) and Normalized Difference Vegetation Index (NDVI) were calculated for the two periods. The direction and the strength of changes in NDVI and BSI were determined based on the CVA method. The results showed that 27.27% and 38.65% of the region surfaces were in degraded and rehabilitated states, respectively. The severity of degradation was higher in the central and northern parts of the study area. Adversely, rehabilitation has increased in the south, west, and east parts of the region. The results of CVA and field reality showed that this method could well reveal changes in ecosystem components over time, which is due to the comparison of two different periods using land cover indicators such as plants and soil. Our finding suggested that the CVA was an appropriate method for monitoring and assessing the desertification phenomenon and determining the area under degradation or rehabilitation in arid regions.

نص كامل:



Assessing Intensity of Desertification and Land Rehabilitation Using the Change Vector Analysis Method (Case study: Dehshir Plain, Yazd province, Iran)

 

Azam AbolhasaniA, Hassan KhosraviB*, Esmail Heydari AlamdarlooC, Saeed BarkhoriD

 

A PhD.  of Desert Control and Management, Department of Reclamation of Arid and Mountainous Regions Faculty of Natural Resource, University of Tehran, Tehran, Iran.

B Associate Professor, Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resource, University of Tehran, Tehran, Iran. *Corresponding author: Email: [e-mail]

C Postdoc Researcher, Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resource, University of Tehran, Tehran, Iran.

D Assistant Professor, Faculty of Natural Resources, Jiroft University, Jiroft, Iran.

        

Abstract. Desertification is one of the main ecological and environmental problems in Iran. It is remarkably urgent to monitor and assess this phenomenon and its impact on ecosystem components such as vegetation covers to prevent and combat desertification. This study aimed to evaluate the Change Vector Analysis (CVA) method’s efficiency in assessing and monitoring desertification in Dehshir Plain, Yazd province, central Iran. For this purpose, the imagery of Landsat 5 from 1994 to 1998 (T1) and Landsat 8 from 2014 to 2018 (T2) were selected in June and July period on the Google Earth Engine platform. Then, the Bare Soil Index (BSI) and Normalized Difference Vegetation Index (NDVI) were calculated for the two periods. The direction and the strength of changes in NDVI and BSI were determined based on the CVA method. The results showed that 27.27% and 38.65% of the region surfaces were in degraded and rehabilitated states, respectively. The severity of degradation was higher in the central and northern parts of the study area. Adversely, rehabilitation has increased in the south, west, and east parts of the region. The results of CVA and field reality showed that this method could well reveal changes in ecosystem components over time, which is due to the comparison of two different periods using land cover indicators such as plants and soil. Our finding suggested that the CVA was an appropriate method for monitoring and assessing the desertification phenomenon and determining the area under degradation or rehabilitation in arid regions. 

 

 

Keywords: Degradation, NDVI, BSI, Change Direction, Change Strength

 

 

 

 

 

 

 

 

 

 

 

Introduction

Desertification, which was considered as a major global issue at the United Nations Conference on Desertification in Nairobi, Kenya, in 1977, is a significant challenge in many countries especially developing countries (Silakhori, 2014). The desertification process has threatened more than 785 million people in arid regions (Sadeghiravesh et al., 2014). Desertification has affected 110 countries globally, with more than 1 billion populations (Marinică and Marinică, 2014). Many global efforts have been made to combat this phenomenon during the past decades. Desertification is defined as reducing land productivity resulting from natural factors or inappropriate human activities, including water and wind erosion, vegetation degradation, water resource degradation, salinization, etc. (Vali et al., 2015). Desertification leads to ecosystem degradation and a decrease in the performance of biological production (Davari et al., 2017). According to the UNCCD, desertification is defined as a process of land degradation in arid, semi-arid, and dry sub-humid regions due to various factors, including climatic variations and human activities (UNCCD, 2012). For combating this phenomenon, monitoring and prioritizing the areas with a high risk of desertification are crucial and essential stages (Amiraslani and Dragovic, 2011).  

 Desertification has gradually affected half provinces in Iran (Amiraslani et al., 2018). So, determining the new methods for assessing desertification and its causes has high priority in Iran, in which 85% of the country's surface is under arid, semi-arid, and hyper-arid climates (Bakhshandeh Mehr et al., 2014; Hakimzadeh et al., 2017). Due to the great extent and the dynamic nature of phenomena such as land degradation and desertification, human attention has been focused on using new technologies to assess and monitor these phenomena. On the other hand, combating and controlling desertification in an area has to be based on current and past situations of desertification (Sadeghiravesh et al., 2021). One of the most important technologies is remote sensing, which has an essential role in determining and monitoring desertification of land degradation on local, regional, and global scales (Rahimi et al., 2013).

Change detection is a process that evaluates spatial variations of different phenomena (e.g., plant and bare soil cover), prys, and posts a specific factor resulting from natural or anthropogenic factors using multi-temporal satellite imagery. This process includes various methods such as Gramm-Schmidt, Principle Component Analysis, Biophysical Parameter Method, and Change Vector Analysis (CVA). Among change detection techniques, CVA can provide spectral change information regarding the change strength and direction (Allen and Kupfer, 2000). Generally, the CVA method is an effective procedure for change detection and land cover change (Khiry et al., 2015). This method is a multivariate technique for pixel-by-pixel analysis of bands or spectral indices that are obtained by placing them on two axes of a Cartesian plane and can evaluate changes between two different times (Lorena et al., 2002; Dawelbait and Morari, 2012).

Many studies have been conducted using CVA for assessing land degradation, desertification, vegetation changes, etc. in different parts of the world. Khiry et al. (2015) used CVA to analyze and map desertification processes in an arid and semi-arid region, North Kordofan State, Sudan. Their results showed that the CVA and Landsat data appeared to be a reliable and low-cost technique for analyzing and mapping desertification processes in Sudan's arid region. Becerril-Pina et al. (2016) integrated several remote sensing techniques for monitoring desertification in a semi-arid region of the central Mexican plateau. Their results indicated that 48.3% of land use corresponded with agricultural areas; 2.7% of the district did not present any degree of desertification, while 49% gave the following degrees of desertification: 5.5% extreme, 10.9% severe, 18.9% medium, and 13.7% low. Lamchin et al. (2016) assessed land cover change and desertification using remote sensing technology in a local region of Mongolia. They concluded that the desertification of the area was increasing annually. Vorovencii (2017) assessed the desertification risk in Southwest of Romania from 1984 to 2011 using the CVA technique. Its results indicated that both anthropogenic and climatic factors had affected the desertification of the region. Shao et al. (2018) monitored the grassland change in the Qinghai-Tibetan Plateau in Aba County, China, using the CVA method and concluded that vegetation re-growing has occurred since 2003. 

This paper evaluates the CVA method efficiency and presents a new approach for assessing and monitoring desertification in Dehshir Plain, Yazd province, Iran which faced excessive droughts in recent years and has been exposed to desertification. Although several studies related to vegetation cover have been done using the CVA method in different parts of the world and Iran, there is no comprehensive study about the efficiency of this technique for assessing desertification in the Dehshir Plain, Yazd province, Iran. Therefore, the results of this study can be useful for decision-makers in combating desertification and land degradation in the study area.

Materials and Methods

Study area

The study area, Dehshir Plain, is located between the southern slopes of Shirkooh Mountain and Abarkooh Plain in Yazd province, Iran. The total area of the region is 3233 km2. Based on the isohyetal map, the annual precipitation is less than 50 mm in the southwest and more than 300 mm in the north and northeast (Iran Meteorological Organization). Most of the rainfall occurs in the winter. Also, the mean annual temperature of the region is between 8-16°C. Two main occupations are agriculture and animal husbandry, but many agricultural lands have been abandoned in recent years due to drought occurrence. The location of the study area is presented in Fig. 1.

 

 

Fig 1. The location of the study area

 

Methodology

In this research, the CVA method was used to determine the desertification changes. As every vector has two dimensions including direction and strength, the results of this method provide the direction and strength of changes (Fig. 2). According to the investigations and consultation with experts in the study area, April to July period was selected to evaluate the changes. First, 32 Landsat 5 images during the years 1994-1998 (T1) and 32 Landsat 8 images during the years 2014-2018 (T2) were selected from April to July. Finally, images with cloud cover below 1% were considered for assessing changes. Then the bare soil index (BSI) and normalized vegetation difference index (NDVI) were calculated for each selected image, and the average of both indices was computed in both periods.

 

 

Figure 2. The components of the Change Vector Analysis (CVA) method (Eastman, 2015)

 

In arid regions, soil and vegetation degradation due to climatic factors or human activities is an index for desertification (Karnieli et al., 2014). Regarding this issue, we used two indices of NDVI (Normalized Difference Vegetation Index) and BSI (Bare Soil Index) to assess desertification based on the CVA method.

 NDVI; representing vegetation parameters such as green biomass (Zaady et al., 2007); has been used in many studies in arid and semi-arid areas (Abolhasani et al., 2022; Heydari Alamdarloo et al., 2018; Khosravi et al.,2017). The NDVI is based on the fact that the chlorophyll in the plant structure can absorb red light, and the mesophyll layers of the leaf reflect near-infrared light (Rafiei Sardooi et al., 2021). The BSI index is also used to assess the desertification processes related to the soil criteria (Rikimaru et al., 2002; Xiao et al., 2006).

The two indices of BSI (Fitrianto et al., 2018) and NDVI were obtained based on the following equations.

 

                          (Equation 1)

               (Equation 2)

 

Where:

NIR= Near-infrared,

R= Red

SWIR= are, and short-wave infrared bands.

NDVI varies between 1 and -1; the NDVI ratio closer to 1 shows there is more vegetation.

BSI varies between 0-200; the BSI ratio relative to 200 indicates more bare soil (Fitrianto et al., 2018).

Using NDVI and BSI indices, the strength of change (Equation 3) and the direction of change (Equation 4) were determined based on the CVA method in GIS software (Becerril-Pina et al., 2016).

 

       (Equation 3) 

            

                       (Equation 4)

Where:

 and show the NDVI in the first and the second periods, respectively.

 and , bare soil indices, are related to the first and second periods.

M is the strength of changes, and

  is the gradient of changes.  shows the direction of changes so that it's the amount between 90 to 180 indicates the increase of vegetation (re-grow), and between 270 to 360 shows the growth of bare soil (degradation). Also, 0-90 and 180-270 show the situation's stability or no change in vegetation or bare soil condition (Lorena et al. 2002). On the other hand, the areas where  had positive values been considered unchanged, where  and ∆NDVI had negative values found under degradation, and where  had negative values. ∆NDVI had positive values that were considered under rehabilitation. Then, each area was classified into three classes using the natural break method in ArcGIS. This method minimizes the difference between data in each category and maximizes the difference among different classes. In this research, ArcGIS var. 10.6 was used for CVA calculation and classification. The stages of this study are shown in a flow chart (Fig. 3).

 

 

Fig 3. Flow chart of the study

Results

The changes in BSI and NDVI during the two periods are shown in Fig. 4. The amount of the BSI index was high in the northern part of the region, but from the north to the southern parts of the area, its amount decreased during the two periods. The amount of NDVI was high in the north, northeast, east, and southwest parts of the region (Fig. 4).

 

 

 

 

 

 

 

Fig 4. The changes in BSI and NDVI during the two periods

 

After determining the changes in BSI and NVDI indices, the direction and the strength of changes were assessed based on the CVA analysis using ArcGIS 10.6. Based on the results, the strength of changes was higher in the north, northeast, south, and southwest parts of the region (Fig. 5), which may be due to the area's degradation or rehabilitation. The direction of changes is illustrated in Fig. 6. There are the regions under the degradation and rehabilitation process and also the regions with no changes. The degradation intensity was higher in the northern and central parts. Also, some areas in the east and the southeast had been degraded (Fig. 6).

 

Fig 5. The strength of changes	Fig 6. The direction of changes

             

The area percentage of the different conditions is shown in Fig. 7. Based on the results, 38.65% of the region was categorized in the rehabilitation condition, 34.08% had no changes during the studied period, and 27.27% of the area was under degradation.

 

 

Fig 7. The area percentage of different conditions

 

Figure (8) demonstrates the combination of the intensity of rehabilitation and degradation and the direction of changes (rehabilitation or degradation) in the region. The strength of changes was higher in the north and northeast than in other parts of the area. In most of these parts, the severe and medium degradation classes were consistent with drainage networks, the margins of rivers, valleys, and villages. Toward the south and southeast of the region, the intensity of degradation has reduced. Some parts of the central and southeast of the area have also degraded. The intensity of rehabilitation was medium in the north, northeast, northwest, and east of the region, and it has increased significantly in the west.

 

 

Fig 8. The different intensities of rehabilitation and degradation in the region

 

Based on the percentage of different classes' areas obtained from the Natural Break method. In the regions under rehabilitation, the medium-intensity rehabilitation class had the most surface (22.44%). The low-intensity rehabilitation class included 15.48% of these regions, and the high-intensity rehabilitation class included 0.73% of the regions. Regarding degradation, the low-intensity, medium-intensity, and high-intensity classes have included 26.31%, 0.71%, and 0.25% of the regions, respectively (Fig. 9).

 

 

 

 

Fig 9. The area percentage of different classes

Discussion

Desertification is a significant ecological problem worldwide which causes the loss of the productivity of the land due to human activities or environmental factors. Detecting spatial variations of desertification can help managers and decision-makers in combating this hazard. As mentioned in the literature, CVA can provide spectral change information regarding the change strength and direction of different phenomena. Therefore, in this study, the CVA method was employed to assess desertification in Dehshir Plain, Yazd province, Iran.

According to Fig. 5, the strength of changes was higher in the north, northeast, south, and southwest parts of the plain, which happened because of degradation and rehabilitation in the study area. This condition is consistent with changes in BSI and NDVI during two periods which is shown in Fig. 4. According to Fig. 4, the value of NDVI has increased in the southwest of the plain in the second period compared to the first period while the value of BSI has declined in this part in the second period compared to the first period. This result illustrated the relationship between these two indices and described that with an increase in vegetation cover (rehabilitation), the bare soil index showed a decline (desertification). This finding is consistent with Alqasemi et al. (2021).

According to Fig. 6, in the north, northeast, and southeast parts of the region, the direction of changes is degradation. Adversely, in the western and southwest parts of the plain, the direction of changes is rehabilitation. The increase in NDVI value toward these directions during two periods also confirmed this result. This finding is in line with Behrang Manesh et al. (2020), who showed an increase in NDVI has caused a decrease in degradation.

The severe and medium intensity classes of degradation were related to the northern and northeast parts of the region. These parts are mainly around the drainage networks, the margin of rivers, and big villages with agricultural land use that have been influenced by excessive droughts in recent years and faced the destruction of vegetation cover and increase of desertification. In other words, climate fluctuation, drop in rainfall, and successive droughts in recent years have led to the destruction of vegetation cover, the drying up of rivers, and the reduction of local communities’ desire to farm.

The intensity of rehabilitation was medium in the north, northeast, northwest, and east of the region, and it has increased significantly in the west. The site with a high class of rehabilitation includes Pistachio planting lands which have been increasingly common in recent years. In this context, the change vector analysis method showed the changes well. The results of Becerril-Piña et al. (2016) also confirm the good efficiency of the method. Also, the low and medium classes of rehabilitation are related to the rangelands. This area was under the pressure of large numbers of livestock. Amani et al. (2018) have also attributed the degradation of the Bijar protected area's rangelands to animals' presence.

Conclusion

The desertification phenomenon is one of the most prominent aspects of natural resource degradation globally, and many global efforts have been made to combat it for a few decades. Providing management solutions for combating desertification requires monitoring and assessing the severity of this phenomenon. In this research, the Dehshir Plain's desertification in Yazd province was evaluated and monitored using the change vector analysis method. According to the results, the change vector analysis method was able to show the intensity of degradation in the study area. However, it's suggested that the change vector analysis method be used in different regions using different soil and vegetation indicators to better judge this method's efficiency.

Acknowledgment

This chapter forms part of Esmaeil Heydari Alamdarloo’s postdoctoral project (Number: 99017799) with the title of Feasibility Study of Proposing an Early Warning System for Desertification of IRAN using Remote Sensing Data. The Iran National Science Foundation (INS) is thanked for its financial support.

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ارزیابی شدت بیابان‌زایی و احیای اراضی با استفاده از روش تحلیل بردار تغییر (منطقه مطالعاتی: دشت دهشیر، استان یزد، ایران)

 

 

اعظم ابوالحسنیالف*، حسن خسرویب، اسماعیل حیدری علمدارلوج، سعید برخورید 

الف دکتری مدیریت و کنترل بیابان، گروه احیای مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران

ب دانشیار، گروه احیای مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران،

*(نگارنده مسئول)، پست الکترونیک: [e-mail] 

ج پژوهشگر پسا دکتری، گروه احیای مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران

د استادیار، دانشکده منابع طبیعی، دانشگاه جیرفت

 

چکیده. بیابان‌زایی یکی از معضلات اصلی اکولوژیکی و زیست محیطی در ایران است. نظارت و ارزیابی این پدیده و تأثیر آن بر اجزای اکوسیستم مانند پوشش گیاهی، برای جلوگیری و مقابله با بیابان‌زایی بسیار ضروری است. در این پژوهش کارایی روش تحلیل بردار تغییر (CVA) در ارزیابی و پایش بیابان‌زایی در دشت دهشیر، استان یزد واقع در مرکز ایران ارزیابی شد. بدین منظور از تصاویر ماهواره‌ای لندست 5 طی سال‌های 1994-1998 (دوره اول) و تصاویر ماهواره‌ای لندست 8 طی سال‌های 2014-2018 (دوره دوم) برای ماه‌های خرداد و تیر میانگین‌گیری شد. سپس شاخص‌های BSI و NDVI برای هر دو دوره محاسبه شد. بزرگی و جهت تغییرات با استفاده از این دو شاخص و روش تحلیل بردار تغییر در نرم‌افزار ArcGIS تعیین شد. نتایج نشان داد که 27/27% و 65/38% از منطقه مطالعاتی به‌ترتیب در شرایط تخریب و احیا قرار دارد. شدت تخریب در قسمت‌های شمالی و مرکزی دشت بیشتر از سایر نواحی بود، در حالی که به سمت جنوب، غرب و شرق منطقه شدت احیا افزایش یافته بود. نتایج تحلیل بردار تغییر و واقعیت زمینی نشان داد که این روش به دلیل مقایسه دو دوره زمانی با استفاده از شاخص‌های پوشش زمین از جمله پوشش خاک و پوشش گیاهی، به‌ خوبی توانست تغییرات اکوسیستم‌ها را در طول زمان نشان دهد. این مطالعه، روش تحلیل بردار تغییر، به عنوان روشی مناسب برای پایش و ارزیابی پدیده بیابان‌زایی و تعیین منطقه تحت تخریب یا احیا در مناطق خشک پیشنهاد شد.

کلمات کلیدی: تخریب، NDVI، BSI، جهت تغییر، بزرگی تغییر

 

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