Calculation of Salinity and Soil Moisture indices in south of Iraq - Using Satellite Image Data

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Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES18, Technologies and Materials for Renewable Energy, and Sustainability, TMREES18, 19–21 September 2018,Environment Athens, Greece 19–21 September 2018, Athens, Greece

Calculation of Salinity and Soil Moisture indices in south of Iraq International on District Heating and Cooling of Iraq CalculationThe of15th Salinity andSymposium Soil Moisture indices in south Using Satellite Image Data Using Satellite Image Data Assessing the Mustafa feasibility of using the heat demand-outdoor A. Raheem, Amal J. Hatem* Mustafa Raheem, Amal J. Hatem* temperature function for A. a long-term district heat demand forecast College of Education for Pure Science, University of Baghdad, Baghdad, Iraq College of Education for Pure Science, University of Baghdad, Baghdad, Iraq

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc

Abstract a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal Abstract b & Innovation, 291 index Avenue(SI) Dreyfous Daniel, 78520Multi-Band Limay, France A band rationing method is Veolia appliedRecherche to calculate the salinity and Normalized Drought Index (NMDI) as c Département Systèmes Énergétiques et Environnement IMT Atlantique, 4 rue Alfred Kastler, Nantes, France pre-processing to take Agriculture in these is presented. the land from 44300 other features that (NMDI) exist in the A band rationing method is applieddecision to calculate the areas salinity index (SI) To andseparate Normalized Multi-Band Drought Index as scene, the classical classification (Maximum likelihood classification) is usedthe byland classified the study area that to multi pre-processing to take Agriculturemethod decision in these areas is presented. To separate from other features existclasses in the (Healthy (HV), Grasslands (GL), Water (W), Urban (U), Bare Soil (BS)). 8 satellite image areaclasses in the scene, thevegetation classical classification method (Maximum likelihood classification) is usedAbyLandsat classified the study areaof toan multi south of Iraq are used, where the land cover classified indicator rangesAfor each (SI) and (NMDI). (Healthy vegetation (HV), Grasslands (GL), isWater (W), according Urban (U),toBare Soil (BS)). Landsat 8 satellite image of an area in the Abstract south of Iraq are used, where the land cover is classified according to indicator ranges for each (SI) and (NMDI). © 2018 The Authors. Published by Elsevier Ltd. heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the ©District 2019 The Authors. by Ltd. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. Published by Elsevier Elsevier Ltd. greenhouse gasaccess emissions thethe building sector. These systems require high investments which are returned through the heat This is an open articlefrom under CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection under responsibility of the scientific of Technologies and Materials for Renewable Energy, This is an and openpeer-review access article under the CC BY-NC-ND license committee (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection andtopeer-review under responsibility of and the scientific Technologies and Materials Renewable Energy, sales. Due the changed climate conditions building committee renovation ofpolicies, heat demand in thefor future could decrease, Environment and Sustainability, TMREES18. Selection and peer-review under responsibility of the scientific committee of Technologies and Materials for Renewable Energy, Environment andinvestment Sustainability, TMREES18. prolonging the return period. Environment and Sustainability, TMREES18. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand Keywords: Soil Indices, Landsat 8, Iraq south soil, Supervised classification, Image segmentation. forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 Keywords: Soil Indices, Landsat 8, Iraq south soil, Supervised classification, Image segmentation. buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were 1. Introduction compared with results from a dynamic heat demand model, previously developed and validated by the authors. 1.The Introduction results showed that when only weather change is considered, the margin of error could be acceptable for some applications The different parts of Iraqwas soil are formed byfor various processes as a considered). result of large differences in vegetation cover (the error in annual demand lower than 20% all weather scenarios However, after introducing renovation The different parts of Iraq soil are formed by various processes as a result of large differences in vegetation cover and climate for Iraq's regions, this variation results in these processes are given facts that Iraq's soil suffers from scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). and climate for Iraq's regions, this variation results in these processes are given facts that Iraq's soil suffers wide fundamental differences between them. The differences for each of the relief, parent material of soil and The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds from toage the wide fundamental differences between them. The differences for each the relief, parent material of of soil andsoils, age are often associated with regional differences. Most soils central andofsouthern Iraq are arid and semi-arid decrease in the number of heating hours of 22-139h during theofheating season (depending on the combination weather and renovation scenarios Ondifferences. theunder other natural hand, function increased for decade on the are often associated with regional Most soils intercept of central and southern Iraq and (depending semi-arid soils, which have so low a considered). moisture content conditions for the growth of 7.8-12.7% crop [1].areperarid coupled scenarios). values content suggested couldnatural be used to modifyfor thethe function for the scenarios considered, and which have so low aThe moisture under conditions growthparameters of crop [1]. improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. * Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 . Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and E-mail address: [email protected] * Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 . Cooling. E-mail address: [email protected]

1876-6102 © 2018 The Authors. Published by Elsevier Ltd. Keywords: Heat demand; Forecast; Climate change license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access under the CC BY-NC-ND 1876-6102 © 2018 Thearticle Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of the scientific of Technologies and Materials for Renewable Energy, Environment This is an open access article under the CC BY-NC-ND licensecommittee (https://creativecommons.org/licenses/by-nc-nd/4.0/) and Sustainability, TMREES18. Selection and peer-review under responsibility of the scientific committee of Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES18. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES18. 10.1016/j.egypro.2018.11.185

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Soil quality indicators may be distributed to 4 common sets: physical, biological, chemical and optical[2]. Soil salinity was known as the accumulation of soluble salt in the soil [3]. Soil salinity negatively effects on plant growth, crop production and soil quality, and finally is the product of soil erosion and saline degradation [4]. Soil moisture plays a significant role in water and energy budgets for climatic studies, which is one of the hydrological variables that observed directly. There are many agricultural applications where soil moisture information is important of it, such as improved crop yield, vegetation stress and irrigation scheduling [5]. In this study the satellite image was classified based on Maximum Likelihood Supervised classification method, where this method is depended on the probability which the unity of the image of any earth phenomena types is equal [6]. The Maximum Likelihood Supervised classification method is ideal in that it approximates the minimum amount of variation for large data and this is why it is more commonly used to estimate unknown parameters.[7] In this paper, the minimum and maximum values for each salinity soil index and moisture soil index are calculated to use of determine the validity of land to growth of economic crops by apply Landsat_8 OLI portrait. 2. Available Data and Employed Image The study area was sit in south Iraq with area 854km2 and this region situated between (30° 55' 53.11" - 30° 39' 28.99") latitude and (46° 33' 56.92" - 46° 53' 30.02") longitude. Fig (1) a. The Landsat 8 satellite image data that picked by operational Land Imager (OLI) and thermal infrared sensors (TIRS) which picked in (3/11/ 2017) and located within space coordinates (path=167) and (row=38) that covering the study area with 11 spectral bands from 1 to 11 are used in this study, Fig (1) b. In this study the program that used was ENVI 5.3 where the selected bands for the region of interest (RIO) of the image were SWIR2, NIR and GREEN. 3. Methodology 3.1. Converting Landsat_8 DNs to Topmost of atosphere Reflectance Digital rate (DN) are mutate to Topmost of atmosphere (TOA) planetary reversal (physical units) in the Landsat 8 OLI sensor using reflectance rescaling degrees provided in the output information file of an image that utilized. The equation which is used to mutate DN values to reflectance for OLI data [8]. ��� ����� ��

ῤλ =

������

(1)

Mρ = reversal multiplicative scaling factor for the band. Aρ = Reflectance additive scaling factor for the band. Qcal L1 pixel value in DN. where ῤλ = TOA planetary reversal θ = sun altitude Angle The proposed indices which are utilized the salinity index (SI) and Normalized Multi-Band Drought Index (NMDI), giving in the following eq (2 and 3) SI =

��������� �������

�2� 

This spectral index has an effective role in detecting the degree of soil salinity that containing the dual salts. These salts need the band NIR to detect the presence of salts in the soil, so this indicator was used [9].  NMDI =

���������������������

���������������������

�3� 

Mustafa A. Raheem et al. / Energy Procedia 157 (2019) 228–233 Author name / Energy Procedia 00 (2018) 000–000

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3

Moisture index is Normalized Multi-Band Drought Index (NMDI) is suggested of remote sensing of soil water content from space by using three channels NIR, SWIR1 and SWIR2. This index is considered from the indices that depending on the Slope variation and this characteristic (Slope variation) be useful for extracted data concerning water [9].  3.2. Accuracy assessment In order to verify from the accuracy, image classified is contrast with data about the reality data. in order that measure the rating precision, the user, the product and the total precision were implemented. The measurement by which an individual category of pixels is categorized in the same group is the user's accuracy. The accuracy of the product is where the accuracy of the individual category may be obtained of divisive the total of the right sorted pixels [10]. Based on the confusion matrix is obtained from the precision of the user and the precision of the product, the total accuracy was calculated. The characterization of total precision is written in the equation (4) [11]. Overall accuracy =

����� �� ����� ����������� ����� ������ �� ������

𝑋𝑋 100 (4)

Another measure of measuring practice pixels with ground fact data is the Kappa coefficient, which ranges from 1.0 ~+1.0 The description of Kappa coefficient is shown below[11] �� ��� � � ��� � ���� � ����

𝐾𝐾 =

�� � � ���� � ����

(5)

Where 𝑛𝑛 = overall digit of practice pixel 𝑝𝑝 = digit of portion ∑𝑥𝑥𝑥𝑥𝑥𝑥 = overall crumb of confusion matrix. ∑𝑥𝑥𝑥𝑥+ = totality of line i ∑𝑥𝑥+𝑖𝑖 = totality of pole 𝑖𝑖. The outcomes of precision estimation of a ranking based upon the confusion template were obtained and shown in the next section. 3.3. Practical Work The proposed method in this research is indicated in the following steps : Step1: The process of image classification and segmentation as well as detection of the change in the image need to convert images from DN to Reflectance for a physical concept (Physical unit) where the Landsat 8 satellite image is 16 bit image means within the range 0 to 65535. Step 2: The casting for region of interest (ROI) and the training samples are Selected based on the band combination of SWIR2,NIR and Green. Which are Healthy vegetation(HV), Grasslands(GL), Water(W), Urban(U), Bare Soil(BS). Step 3: Classification image by Maximum likelihood method, Fig (1) c. Step 4: Segmentation the Bare soil class from the classes in the image. Fig (1) d. Step 5: This step involves introducing soil indicators that used in this research which are, Salinity Index (SI) and Normalized Multi _Drogue Index (NMDI) on the segment image in step 4. Fig (1) e and Fig (1) f, shows the resulting images for each (SI) and (NMDI) respectively. Then found the values of salinity index and moisture index associated with values of Gray scale for each of these images. Step 6: Make plot for each of the extracted values of the salinity and Moisture index with the corresponding values for GL in the indicator images to find the maximum and minimum for each. Fig (1) g and Fig (1) h, show the images for both (SI,NMDI) respectively.

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Step 7: By using the instruction (Color Mapping) in program ENVI, Soil Salinity and Soil moisture were obtained. Fig (1) (i and j).

a

b

c

d

e

f

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g

h

i

j

5

Fig. 1. (a) Iraq location of study area, (b) the Landsat 8 satellite image of the study area, (c) the classified image, d Segmented image of the bare soil class, (e) & (f) SI and NMDI resulting images for each respectively, (g) & (h) plot images for both (SI & NMDI) respectively and i, j The images of the Soil Salinity and Soil moisture in the study area depending on the range of each respectively

4. Results and Discussion In step 3, confusion matrix of the classified image is show that the result of overall accuracy was 97.6939 % while the result of Kappa coefficient was 0.9712. and this best result of the classification process compared with others results that were extracted because that overall accuracy value is very close to the hundred as well as the value of kappa coefficient approaching from 1 which is confined between 1 and -1 as a constant value. where the statistical results for the classified image are showing in table (1) from fig 1(c). Table 1. Class Distribution Summary of classified image by maximum likelihood method. Class 

Class Color 

Percentage% of total area

Healthy Vegetation 

Bright green 

17.01% 

Grasslands 

light green 

20.45% 

Water 

Blue 

21.45% 

Urban 

Magenta 

6.36% 

Bare Soil 

Yellow 

28.64% 

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By combining the areas of the training classes, notice that the total area classified (area of items) from the study area is 93.91% meaning that 6.09% of the studied area is unclassified The segment image (Bare Soil) in Fig (1) d, is Gray scale image where the black color means that number of pixels is 0 whereas the white color means that number of pixels is 255 and shades of gray level between these two values. From the indicate plot in Fig(1) g for (SI), note that the soil salinity range in the area is between 0 as minimum value and 37 as maximum value. While for (NMDI) it is plot shown in Fig1 (h), It turns out that the minimum value is 0.085 of the moisture content in the area and the maximum value is 1.378. Table 2. shows soil salinity and soil moisture levels Ranges of SI 

Ranges of NMDI 

low 

0.000‐12.333 

low 

0.085‐0.516 

mid 

12.333‐24.666 

mid 

0.516‐0.947 

high 

24.666‐37.000 

high 

0.947‐1.378 

In Fig(1) i note that, The range of low value of salinity soil in the region is represented by red, while the green represent the range of mid value and the blue represent the high value of the salinity soil in the area. Where the low range covering little area from the image and so on to the others of the ranges (for mid the area is medium and for high the area is larg) depending on the level range. and note that the studied area has high salt content because the blue color is predominant in the picture (the high range). In Fig(1) j note that, the range of low soil moisture values in the studied area is red , the medium values are green and the high values are represented in blue. where the studied area has low moisture content because the dominant color is red (the low range). 5. conclusions The best choice for the installation of the packages used was the installation of SWIR2, NIR and green which gives the results clear and appropriate for the study area. The study concludes that the soil of the studied area is characterized by high saline and low moisture level, so that crops suitable for these conditions can be cultivated in this area. The maximum likelihood Method is an ideal method in this study and can be used in other applications to classify land affected by temperature. The obtained results of both salinity and moisture soil can used to determined soil quality for agriculture special type of the agriculture crops. 6. References [1] [2]

P. Buringh, “Soils and soil conditions in Iraq”, Ministry of agriculture, (1960). H. Kheyrodin, "Important of soil quality and soil agriculture indicators." Academia Journal of Agricultural Research 2(11) (2014): 231-238. [3] Dehni, and M. Lounis, “Remote sensing techniques for salt affected soil mapping: application to the Oran region of Algeria," Procedia Engineering 33 (2012): 188-198. [4] Allbed, and L. Kumar, "Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review," Advances in remote sensing, 2(4) (2013): 373. [5] S. Sharma, "Soil moisture estimation using active and passive microwave remote sensing techniques," Andhra University, Indian Institute of Remote Sensing, Dehradun, India (2006). [6] P. Gong, “Remote sensing and image analysis. University of California”, ESPM, (1997). [7] A. M. Hamad, "Estimation of the parameter of an exponential distribution when applying maximum likelihood and probability plot methods using simulation," Ibn Al-Haitham Journal for Pure and Applied Science 25(1) (2017). [8] K. Zanter, "Landsat 8 (L8) data users handbook," Survey, Department of the Interior US Geological (2015). [9] L. Wang and J. J. Qu, "NMDI: A normalized multi‐band drought index for monitoring soil and vegetation moisture with satellite remote sensing," Geophysical Research Letters 34(20) (2007). [10] S. N. Sivanandam, S. Sumathi, and S. N. Deepa, Introduction to Fuzzy Logic using MATLAB. Vol. 1. Berlin: Springer, 2007. [11] Taufik and S. S. S. Ahmad, "Land cover classification of Landsat 8 satellite data based on Fuzzy Logic approach," In IOP Conference Series: Earth and Environmental Science, vol. 37, no. 1, p. 012062. IOP Publishing, (2016).