Modeling and mapping of climatic classification of Pakistan by using remote sensing climate compound index (2000 to 2018)
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The entire world is collectively facing the problem of climate change. The deterioration of the earth’s climate change is being noticed and felt most apparently in Southeast Asia and predominantly in Pakistan. Pakistan is a victim of climate change, due to which Pakistan faces several geographical, political, economic and even social problems. The harmful impacts of climate change in the form of smog and abnormal heat waves have claimed the life of many Pakistanis. Climate change has brought disastrous impact on the agrarian economy of Pakistan, which has plunged the country into awful straits. Climatic change is a slow and continuous process. It is needed that climatic changes in an area should be traced out in time to face upcoming climatic challenges. Present research work has traced out such changes and introduced a new climatic classification scheme for the climate of Pakistan, by using remote sensing (RS) and a new climatic compound index that is RSCCI gives a calculated value, which is used to describe the state and the changes in the climatic system of an area. RSCCI is the combination of different indices. On the basis of RSCCI, a climatic index, spatiotemporal investigation is conducted to measure aridity, humidity and semi-aridity all over Pakistan. In order to find out the extent of these climatic conditions, three MODIS dataset images of 250 m resolution were acquired. RS applications are used effectively to assess the changing climatic trends for the period of eighteen years in Pakistan from 2000 to 2018. On the basis of the above-mentioned results, a new climatic classification has been introduced with five major classes, i.e., drought, aridity, humidity, wetlands and cold drought. The area of five classes has been calculated by using RS tools and RSCCI for the years of 2000 and 2018. New climatic classification of Pakistan divides Pakistan into five regions which is based on RSCCI . There is an increase in arid region of 1.84% in Pakistan from RSCCI 2000 to RCSSI 2018. Similarly, there is also increase in an area of wetlands and humid regions of Pakistan, i.e., 1.9% and 9.72%, respectively, from RSCCI 2000 to RCSSI 2018. On the other hand, there is 0.78% reduction of area of cold drought region, 8.49% reduction in moderate drought and 4.19% reduction in an area of intense drought classes from RSCCI 2000 to RCSSI 2018, which is a positive change. The results show dramatic changes which advocate the need of a new climatic classification for Pakistan. This new climate classification of Pakistan is based on 18 years of data only. Dramatic climatic changes could be imagined and predicted within next 30 years in Pakistan.
KeywordsRSCCI Climatic index Climate change MODIS Remote sensing
Moderate resolution imaging spectroradiometer
Remote sensing climatic compound index
World meteorological organization
Geographic information sciences
Land surface temperature
Normalized difference snow index
Normalized difference vegetation index
Transformed normalized difference vegetation index
Moisture stress index
Soil-adjusted vegetation index
United states geological survey
Area of interest
Short wave infrared
Climate change performance index
Climate change has been one of the most talked about issue, particularly since the start of the twenty-first century. Climate is usually defined as an average weather in a narrow sense or more thoroughly, as the statistical description in terms of the mean and variability of relevant quantities over a time ranging from months to thousands or millions of years. The classical period for averaging these variables is 30 years, as defined by the World Meteorological Organization. The relevant quantities are most often surface variables such as temperature, precipitation and wind. Climate in a wider sense is the state that includes a statistical description of the climatic system (Odoh and Chilaka 2012).
According to World Meteorological Organization (WMO), over a long period of time, more than several years, climate is the statistical description in terms of the mean and variability of relevant quantities, and it is duration over years and decades, usually over 30 years. The dissimilar pattern of climate distinguishes from similar ones by using such classification of climate (Walterscheid 2011). Forming a classification system of climate is a very tough and a hard job. Many climatologists gave different climatic classification on the basis of their own experiences and criteria. “Climatic classification is merely a method of arranging various climatic parameters either singly or grouped into ranks or sets, so, to as both simplify the mass of data and to identify analogies” (Griffiths 1978). Qualitative, as well as quantitative, approaches are used not only in Pakistan but also all over the word to determine the climate classification. The climatic classification given on the basis of different approaches is different from each other. Permanide was the first Greek philosopher who divided the climate of the world based on solar concentration. Then the other well-known scientists (Koppen 1846–1940; Thornthwaite 1931, 1948; Blair 1942; Griffiths 1978) gave their own climatic classification of the world. On the other hand, excellent work on the climatic classification of Pakistan has been done by Kazi (1951), Shamshad (1986) and Khan et al. (2010)
Koppen classification was temperature based and developed by climate indices, despite zone variation among themselves. Globally, therefore, there was some lapse in it (Kopeen 1936). In 1948, Thornthwaite had introduced a climate classification based on rainfall and potential evapotranspiration (PET). This approach was adopted by various regions of the world for classification. The climate of Pakistan has been characterized by adopting Reddy-modified Thornthwaite approach using reference crop evapotranspiration (ETo) instead of potential evapotranspiration (PET) (Reddy et al. 1973). Thermal efficiency index (TEI) was also developed by Thornthwaite for climate classification (Thornthwaite 1948). There is indication of thermal periphery and the requirement of water in different climate types, because it is a growth index. This approach is also widely used around the world by different climatologists and scientists (Villmow 1962). The agro-climatic classification for Asia and Africa had been done by the United Nations Environment Program (White 1998). Pakistan has arid to semiarid climate with great variability in temperature (Chaudhary and Rasul 2004).
Remote sensing has been in used for spatial evaluation of an area, in the world of geographic information system. All the climatological and geographical aspects can be interpreted in a manageable way by using RS and GIS. The temporal estimation of land surface temperature (LST), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI) and moisture stress index (MSI) is done from 2000 to 2017 which clearly shows that stress on soil moisture increased in Kalat region of Pakistan that ultimately leads to aridity (Batool and Javid 2018). Pakistan is basically a semiarid region, and it has been classified into five regions (hot, warm, mild, cool and cold), based on temperature. The southern half experiences high temperatures and decreases while heading toward northern half of Pakistan. The eastern part of Pakistan receives heavy rainfall in summers due to monsoon, while western parts receive heavy rain in winters due to western disturbances. The most suitable area for crop production lies between 33°N and 35°N due to rain fed conditions. The agricultural production above and below these latitudes is only possible if there are supplementary conditions available for irrigation (Chaudhary and Rasul 2004). This is an era of information and technology which makes accessibility of spatial data for scientists and researchers on a broad scale. Access to aerial and satellite imagery is very helpful for them to study the climatic condition of an area. They can even predict changing climatic trends of an area by using RS applications. So, the main objective of this research is the formation of new climatic classification of Pakistan by using the remote sensing techniques. A new climatic classification of Pakistan is proposed by introducing an innovative climatic compound index that is RCCI. Comparative approach is used to modulate the revised classification of climate for Pakistan. The entire process has been done by creating a mosaic which is also applied to mix all the components of MODIS data of selected study area. Arc GIS10.5 and ERDAS Imagine 2015 are used to conduct analysis and generate maps.
Materials and methods
RS applications are used effectively to assess the changing climatic trends of Pakistan from 2000 to 2018. A spatiotemporal investigation is conducted to measure aridity, humidity and semiaridity all over Pakistan. In order to find out the extent of these climatic conditions all over Pakistan, three MODIS dataset images of 250 m resolution were acquired for the years 2000 and 2018. These images were obtained from USGS, an earth observatory website. In this present research, a new climatic classification of Pakistan has been introduced by using RS climatic compound index (RSCCI).
The entire process has been done by creating a mosaic which is also applied to mix all the components of MODIS data of the selected study area. Sticking with the operation of layer stacking, sub-setting of the mosaic image was performed through the clipping process by using digitized boundaries of the area of interest (AOI).
Remote sensing data analysis
Reflection is very important for the satellite sensors to capture the images and features of the earth. Solar energy can be absorbed, transmitted or spread out with interaction processes. Land covers do not absorb and reflect the radiation equally. All land covers behave differently due to their characteristics. For example, vegetation reflects highly in the infrared zone and near-infrared zone of the electromagnetic spectrum. Furthermore, spectral signatures are used to differentiate the earth’s surface substances. The chlorophyll content in leaves plays a very important role in absorption, transmission and reflection. Vegetation cover, arable land, soil, water bodies and physical structure of the earth should reflect differently from each other, vary from place to place and connected with the angle of the sun, an angle of the sensor and time of capturing the land surface by the satellite sensor. Water has less than 10% reflectance, and it is shown only in the visible range (0.4–0.7 μm). On the other hand, water absorbs all energy in the long range than 0.75 μm. Vegetation highly absorbs the radiation and reflects the energy in the infrared and near-infrared range, and at 0.65 μm vegetation highly reflects due to the presence of chlorophyll. Furthermore, 1.45–1.55 μm and 1.90–1.95 μm are high absorption ranges, due to the presence of water content in leaves. The soil has very less reflection curve with high reflection values as compared to vegetation and water because soil absorbs and reflects the high flux of energy. It goes to more high levels when bands increase. Its curve is formed due to the presence of water in the soil. During the integration of plant types and their leaves in infrared region, photosynthesis works properly and absorbs the radiation from 70 to 90% (Siddiqui and Javid 2018; Campbell 1996).
Characteristics of remotely sensed data used for desertification analysis
Spatial resolution (m)
Remote sensing climatic compound index (RSCCI)
Results and discussion
Area of climatic classification of Pakistan calculated from RSCCI for the year 2000 and 2018
Sum Area sq.km
Sum Area sq.km
Cold drought region
Intensely drought region
Moderate drought region
It is concluded from the results of this research that distinguished climatic change has been observed within the boundaries of Pakistan in a time span of eighteen years from 2000 to 2018. There is a huge change observed in these eighteen years; this dramatic climatic change is needed to be addressed on sound footing. Although the reduction in an area of drought region seems very positive for Pakistan but on the other hand increasing areas of humidity and wetlands have created an alarming and drastic situation for the future climatic challenges of Pakistan. Pakistan is a developing country and is not ready to face abrupt climate changes. According to Germanwatch, publisher of the Climate Change Performance Index (CCPI), Pakistan is the seventh most vulnerable country in terms of climate change. As a developing country with miserable poverty and severely limited resources, the climate change has the potential to become the biggest and most destructive problem for Pakistan in the future. Therefore, precautionary measures should be designed and taken against the upcoming climatic challenges like extensive flooding, droughts, storms, heat waves and cyclones. Presented climatic classification of Pakistan can be utilized as a comprehensive mitigated tool for the said situations.
Research was conducted by Ms Kanwal Javid, and manuscript was prepared and analyzed by Ms Kanwal Javid, Mrs Rumana Siddiqui and Ms Maria Mumtaz Ranjah, while Mr M Ameer Nawaz Akram reviewed the article.
This research study is not funded.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflicts of interest.
- Adam J, Sepulveda MT, Tercek RAC, Andrew MR, David PT, Blake RH, Gregory TP, Ann WR, Tom O, Juan AA (2015) The shifting climate portfolio of the greater yellowstone area. PLOS ONE 10:12Google Scholar
- Batool R, Javid K (2018) Spatio-temporal mapping to determine LST, MSI, Ndvi and Savi over Kalat, Pakistan, pp 2024–2454Google Scholar
- Blair TA (1942) Climatology, general and regional. Prentice-Hall, INC., New York, pp 484Google Scholar
- Campell JB (1996) Introduction to remote sensing, 2nd edn. The Guilford Press, New YorkGoogle Scholar
- Chaudhary QZ, Rasul G (2004) Agro-climatic classification of Pakistan. Sci Vis 9(1–4):59–66Google Scholar
- Griffiths JF (1978) Applied climatology, 2nd edn. Oxford University Press, London, p 136Google Scholar
- ICID Pakistan, retrieved from ICID (2005) org: http://www.icid.org/i_d_Pakistan.pdf
- Kazi SA (1951) Climatic regions of West Pakistan. Pak Geogr Rev 6(1):1–22 Google Scholar
- Khan SU, Hassan M, Khan FK, Bari A (2010) Climate classification of Pakistan. Balwois, Ohrid, pp 1–47Google Scholar
- Koppen W (1936) Das geographisca system der klimate. In: Koppen W, Geiger GC (eds) Handbuch der klimatologie. Borntraeger, Gebr, pp 1–44Google Scholar
- Lyon JG, Yuan D, Lunetta RS, Elvidge CD (1998) A change detection experiment using vegetation indices. Photogramm Eng Remote Sens 64(2):143–150Google Scholar
- Mazhar N, Shirazi SA, Javid K (2018) Desertification vulnerability and risk analysis of Southern Punjab Region, Pakistan using geospatial techniques. J Biodivers Environ Sci 12(6):273–282Google Scholar
- Odoh SI, Chilaka FC (2012) Climate change and conflict in Nigeria: a theoretical and empirical examination of the worsening incidence of conflict between Fulani herdsmen and farmers in Northern Nigeria. Oman Chapter Arabian J Bus Manag Rev 34(970):1–15Google Scholar
- Peters AJ, Walter-Shea EA, Ji L, Vina A, Hayes M, Svoboda MD (2002) Drought monitoring with NDVI-based standardized vegetation index. Photogramm Eng Remote Sens 68(1):71–75Google Scholar
- Prince SD, Kerr YH, Goutorbe JP, Lebel T, Tinga A, Bessemoulin P, Brouwer J, Dolman AJ, Engman ET, Gash JHC, Hoepffner M, Kabat P, Monteny B, Said F, Sellers P, Wallace J (1995) Geographical, biological and remote sensing aspects of the hydrologic atmospheric pilot experiment in the Sahel (HAPEX-Sahel). Remote Sens Environ 51(1):215–234CrossRefGoogle Scholar
- Rahman MDR, Islam AHMH, Rahman MDA (2004) NDVI derived sugar cane area identification and crop condition assessment, Planplus, vol 2. Urban and Rural Planning Discipline, Khula University, BangladeshGoogle Scholar
- Reddy SJ, Reddy RS (1973) A new method of estimation of water balance. In: International symposium on tropical meteorology meeting American meteorological society, Nairobi, pp 277–280Google Scholar
- Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great Plains with ERTS. In: Third ERTS symposium, NASA SP-351, vol 1, pp 309–317Google Scholar
- Sandham LA, Zietsman HL (1997) Surface temperature measurement from space: a case study in the south western cape of South Africa. S Afr J Enol Vitic 18(2):25–30Google Scholar
- Shamshad KM (1986) The meteorology of Pakistan: climate and weather of Pakistan. Royal Book Company, Karachi pp 313Google Scholar
- Siddiqui S, Javid K (2018) Spatio-temporal analysis of aridity over Punjab Province, Pakistan using remote sensing techniques. Int J Econ Environ Geol 9(2):01–10Google Scholar
- Soni C, Vaishnav DD, Bairwa D, Mittal H, Vijayvargiya H, Vijavargiya A, Singh V (2017) Automatic irrigation system. Int J Tech Res SciGoogle Scholar
- Villmow JR (1962) Regional pattern of climates in Europe according to the Thornthwaite classification. Ohio J Sci 62(1):39–53Google Scholar
- Walterscheid SK (2011) Climate classification for the earth’s oceanic areas using the Köppen System. Diss. Kansas State University, ManhattanGoogle Scholar
- White DH (1998) A global analysis of the distribution and production of the livestock communities. Report No 30 UNEP, ASIT Consulting, Hawker, pp 3–17Google Scholar
- WMO Statement on the State of the Global Climate (2018) https://library.wmo.int/doc_num.php?explnum_id=5789
- Yang Z, Willis P, Mueller R (2008) Impact of band-ratio enhanced AW IFS image to crop classification accuracy, the future of land imaging, going operational, the 17th William T. Pecora Memorial Remote Sensing Symposium, DenverGoogle Scholar
- Zhang N, Hong Y, Qin Q, Zhu L (2013) Evaluation of the visible and shortwave infrared drought index in China. Int J Disaster Risk SciGoogle Scholar
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