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Gas Pipeline Project (Phase 1) Environmental and Social Impact Assessment Project Number: 52167-001 December 2020 Regional: TAPI Gas Pipeline Project (Phase 1) Pakistan: Main (Part 6.1) Prepared by the TAPI Pipeline Company Limited for the Asian Development Bank. This environmental and social impact assessment is a document of the borrower. The views expressed herein do not necessarily represent those of ADB's Board of Directors, Management, or staff, and may be preliminary in nature. Your attention is directed to the “terms of use” section on ADB’s website. In preparing any country program or strategy, financing any project, or by making any designation of or reference to a particular territory or geographic area in this document, the Asian Development Bank does not intend to make any judgments as to the legal or other status of any territory or area. 6 ENVIRONMENTAL AND SOCIAL BASELINE 6.1 Project Setting and Current Land Use 6.1.1 Overview Information in this section has been compiled using remote sensing analysis undertaken along the pipeline route. This information has been crucial in supporting input to the assessment in other chapters. The TAPI pipeline corridor enters Pakistan northeast of Chaman, to run north of Quetta City and cross the Sulaiman Mountains before reaching the Indus Plain area. Within the Indus Plain, the route bypasses the Dera Ghazi Kahn City (north) and Multan City (south). The geographical diversity of Pakistan falls into three main categories: the northern highlands, the Indus River plain, and the Balochistan Plateau (Penspen, 2015). The pipeline route runs through the Balochistan Plateau in the west of Pakistan, before descending through the east of the Balochistan region and the agricultural lowlands in the Punjab region. It crosses the range of Khwaja Amran Mountains near the Afghanistan - Pakistan border (Balochistan Province) and ascends to a maximum elevation of approximately 2,500 masl. From the highest point, the pipeline corridor descends into the Punjab region within the Sulaiman Mountains, before remaining at low-lying levels throughout the rest of Pakistan, that is the Indus Plain area. Within the Indus Plain, the pipeline corridor crosses the Indus River, Chenab River, and the Sutlej River just before reaching the Pakistan - India border (ILF, 2017a). The Punjab part of the pipeline corridor is extensively covered by agricultural lands and is the most intensively used by the local population. Most of the Balochistan part is dry, barren, and sparsely vegetated, with some spots with natural vegetation and limited lands used for agriculture, and many scattered settlements (Penspen, 2015). Within the two provinces, the pipeline corridor passes through a total of 16 district areas and 26 tehsil areas, as presented in Table 6.1-1. ESIA_Pakistan_Chapter_6.1_Project_Setting_and_Current_Land_Use Page 6.1-1 Table 6.1-1: List of Districts and Tehsils Affected by the Project Province District Tehsil Punjab Province Killam Abdullah District Chaman Killa Abdullah Pishin District Karezat Pishin Saranan Ziarat District Sinjawi Sub Ziarat Sub Division Lorelai District Loralai Mekhtar Zhob (small portion) Zhob Musakehlo District Drug Sub Musa Khel Barkhan District (small portion) Barkhan Vehari District Burewala Vehari Balochistan Province Dera Ghazi Khan District De-Excluded Area Dera Ghazi Khan Dera Ghazi Khan Khanewal District Mian Channu Multan District Multan City Multan Saddar Muzaffargarh District Muzaffargarh Okara District Depalpur Pakpattan District Arifwala Pak Pattan Sahiwal District Chichawatni Bahawalnagar District Minchinabad Source: ILF, 2017b As with most linear projects, the land use changes along the TAPI pipeline corridor. Therefore, the Project setting and current land use are described in this section in accordance with the five construction spreads within Pakistan. The five spreads, as shown in Figure 6.1-1, include: · Spread 1: KP 815.8 – KP 977.0, covering approximately 161.2 km; · Spread 2: KP 977.0 – KP 1170.0, covering approximately 193.0 km; · Spread 3: KP 1170.0 – KP 1299.0, covering approximately 129.0 km; · Spread 4: KP1299.0 – KP 1438.0 (including Indus and Chenab Rivers), covering approximately 139.0 km; and · Spread 5: KP 1438.0 – KP 1634.6 (including Sutlej River), covering approximately 196.6 km. ESIA_Pakistan_Chapter_6.1_Project_Setting_and_Current_Land_Use Page 6.1-2 ENVIRONMENTAL & SOCIAL IMPACT ASSESSMENT STUDY C-PAK-TAPI-ESIA-REP-0001-07 CHAPTER 6. ENVIRONMENTAL AND SOCIAL BASELINE 09/12/2020 Figure 6.1-1: Overview of Spreads 1 to 5 Source: Jacobs, 2020 ESIA_Pakistan_Chapter_6.1_Project_Setting_and_Current_Land_Use Page 6.1-3 ENVIRONMENTAL & SOCIAL IMPACT ASSESSMENT STUDY C-PAK-TAPI-ESIA-REP-0001-07 CHAPTER 6. ENVIRONMENTAL AND SOCIAL BASELINE 09/12/2020 6.1.2 Remote Sensing Assessment No existing land use map was publicly available at the time of drafting this ESIA report; therefore, a remote sensing exercise was undertaken to better characterize the Project Study Area where the greatest direct potential impacts may occur. Using Landsat 8 imagery from 2017 (3-month period of June, July, and August), a remote sensing analysis was undertaken to identify land cover types. Landsat 8 imagery was initially processed to a 7-band mosaic dataset. One of the common methods of producing land cover maps from remote sensing is supervised classification. To train a classifier, a set of reference data is required. To collect reference data, the photo key presented in the Land Cover Atlas of Pakistan (FAO, 2014) was used. Polygon training samples were digitized within a 25 km corridor, representing eight classes as follows: · Planted/cultivated land; · Barren land (bare areas/bare areas with sparse vegetation, rocks); · Sand (sand dunes); · Herbaceous vegetation (crop irrigated/marginal/rain fed, natural vegetation); · Trees and tree crops (orchards, tree forest plantation, natural vegetation); · Shrubland (orchards crop, shrub, natural vegetation); · Developed area; and · Water resource. Different plant growth stages or crops temporarily without vegetation are reflected on images and can lead to confusion during classification. To determine training samples more accurately, polygons were collected using Landsat 8 imagery from three different months. Additionally, maps of Normalized Difference Vegetation Index were used to recognize areas with live green vegetation. Google Earth was used to obtain current imagery and a 3D view of terrain to develop a better understanding of spatial context. Classification was executed with an object-based classification algorithm1, using the collected training samples and Landsat imagery as input. In the first stage, the segmentation image was processed by grouping together neighboring pixels with similarity in color and shape. Spectral detail level was set as higher-than-spatial detail. The first and second parameter refer to the level of importance given to the spectral differences of features and their proximity, respectively. The higher spectral detail, the greater discrimination between features and the more detailed segments (more applicable for urban areas). Minimal segment size was set to 20 pixels. Preliminary classification result was generalized to remove single pixels smooth boundaries of objects and to produce final results with a 65 to 70% accuracy level. As 1 Support Vector Machine (SVM) classifier was used to classify segmented image. SVM is a supervised machine learning algorithm, less susceptible to noise and correlated bands than other widely used algorithms. In general, SVMs are based on the concept of decision planes that define decision boundaries. A decision plane separates between a set of objects having different class memberships. ESIA_Pakistan_Chapter_6.1_Project_Setting_and_Current_Land_Use Page 6.1-4 ENVIRONMENTAL & SOCIAL IMPACT ASSESSMENT STUDY C-PAK-TAPI-ESIA-REP-0001-07 CHAPTER 6. ENVIRONMENTAL AND SOCIAL BASELINE 09/12/2020 there is a generic problem with the classification of urban areas using Landsat imagery, confusion between barren land class and developed class was noted. Results of this land use classification process for each construction spread within Pakistan are discussed in the following subsections are indicated on Figures 6.1-2 to 6.1-6. 6.1.2.1 Spread 1: KP 815.8 – KP 977.0 Ground elevation varies between 1,450 and 2,500 masl within Spread 1, crossing pediments, mountain ranges, and valleys. Spread 1 contains crossings of the main Quetta-Chaman Road, N-50 Kuchlak-Zhob Highway, Chaman Bostan Railway Link, and of the Quetta Bostan Railway line. Spread 1 contains three river crossings and two seismic fault crossings, namely, the Chaman Fault and the Ghazaband Fault. Based on the Landsat 8 imagery land use classification, the current land use within Spread 1 is dominated by barren land (78%), followed by planted/cultivated land (15%), with the remaining attributed to trees and tree crops, developed areas, and shrubland (7%). 6.1.2.2 Spread 2: KP 977.0 – KP 1170.0 Ground elevation varies between 2,300 and 1,200 masl within Spread 2, crossing dissected mountainous terrain. Spread 2 contains four crossings of the NH-70 Highway and five rivers crossings. Based on the Landsat 8 imagery land use classification, the current land use within Spread 2 is dominated by barren land (85%), followed by planted/cultivated land (7%), with the remaining attributed to sand, developed areas, and water resources (8%). 6.1.2.3 Spread 3: KP 1170.0 – KP 1299.0 Ground elevation varies between 2,020 and 125 masl within Spread 3, crossing the Sulaiman
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