Wheat Girdawari 2015: Using Remote Sensing Technology
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Wheat Girdawari 2015: Using Remote Sensing Technology The Urban Unit 1 Wheat Girdawari 2015: Using Remote Sensing Technology The Urban Unit 2 Wheat Girdawari 2015: Using Remote Sensing Technology Acknowledgement The preparation of this Atlas of wheat grown areas of the Punjab province is the team effort of the Remote sensing sector. The team gratefully acknowledges the support of agriculture and food department. The team received valuable feedback through a rich consultation and peer review process. We also appreciate our CEO, his experiences and insight was instrumental in identifying the gaps and shortcomings and then shaping the content of this document. Disclaimer No part of this document may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording or information storage and retrieval system, without prior written permission of the USPMSU. Team Members Review Team Dr. Nasir Javed Dr. Farooq Ahmad Technical Team Muhammad Luqman Samiullah Khan Qurat-ul-ain Fatima Sahar Mirza Hira Jannat Butt Iqra Khalid Hamad Ali Design and Layout Razia Liaqat The Urban Unit 3 Wheat Girdawari 2015: Using Remote Sensing Technology Wheat Grown Area INDEX District Name Tehsil Name Page District Name Tehsil Name Page District Name Tehsil Name Page Attock Kasur Kallar Syedan 112 Fateh Jang Kasur Kot Radha Kishan Kotli Sattian 113 Hassan Abdal Pattoki Rawalpindi Muree 114 Attock Hazro Jahania 54 Rawalpindi Jand Kabirwala 55 Taxila Khanewal Pindi Gheb Khanewal 56 Chichawatni 115 Sahiwal Bahawalnagar 10 Mian Channu 57 Sahiwal 116 Chishtian 11 Khushab 58 Bhalwal Bahawalnagar Fort Abbas 12 Khushab Noorpur Thal 59 Kot Moman 118 Haroonabad 13 Quaidabad Sahiwal 119 Sargodha Minchinabad 14 Lahore Cantt 60 Sargodha 120 Ahmadpur East 15 Lahore City 61 Shahpur 121 Bahawalpur City 16 Lahore Model Town 62 Sillanwali Bahawalpur Saddar 17 Raiwind Ferozwala 122 Bahawalpur Hasilpur 18 Shalimar 63 Muridkey 123 Khairpur Tame Wali 19 Chaubara 64 Sheikhupura Safdarabad 124 Yazman 20 Layyah Karor Lalisan 65 Sharaqpur 125 Bhakkar 21 Layyah Sheikhupura 126 Darya Khan 22 Dunyapur 66 Daska 127 Bhakar Kallur Kot 23 Lodhran Kahror Pakka 67 Pasrur 128 Sialkot Mankera 24 Lodhran 68 Sambrial 130 Chakwal 25 Malikwal 69 Sialkot 131 Choa Saidanshah 26 Mandi Bahauldin Mandi Bahauddin 70 Gojra Chakwal Kallar Kahar 27 Phalia 71 Toba Tek Singh Kamalia Talagang 28 Isakhel 72 T.T.Singh Bhowana 29 Mianwali Mianwali 73 Burewala 133 Chiniot Chiniot 30 Piplan 74 Vehari Mailsi 134 Lalian 31 Jalal Pur Pirwala 75 Vehari 136 DG Khan 32 Multan City 76 Multan D G Khan Taunsa 33 Multan Saddar 77 Tribal Area 34 Shujabad 78 Chak Jhumra 35 Alipur 79 Faisalabad City 36 Jatoi 80 Muzaffargarh Faisalabad Saddar 37 Kot Addu 81 Faisalabad Jaranwala 38 Muzaffargarh 82 Samundri Nankana Sahib 83 Tandianwala 40 Nankana Sahib Sangla Hill 85 Gujranwala City Shahkot 86 Gujranwala Sadar Narowal 87 Gujranwala Kamoke 41 Narowal Shakargarh 89 Noshera Virkan Zafarwal 91 Wazirabad 42 Depalpur 93 Gujrat 43 Okara Okara 94 Gujrat Kharian Renala Khurd 95 Sarai Alam Gir Arifwala 96 Pakpattan Hafizabad 44 Pakpattan 97 Hafizabad Pindi Bhattian 45 Khanpur 98 Ahmadpur Sial 46 Liaqatpur 100 Rahim Yar Khan Athara Hazari 47 Rahimyar Khan 102 Jhang Jhang 48 Sadiqabad 104 Shorkot 49 Jampur 106 Dina 50 Rajanpur 107 Rajanpur Jhelum 51 Rojhan 108 Jhelum Pind Dadan Khan 52 Tribal Area 109 Sohawa 53 Gujar Khan 110 Rawalpindi Kasur Chunian Kahuta Volume : 1 Punjab Girdawari 2015 The Urban Unit 4 Wheat Girdawari 2015: Using Remote Sensing Technology METHODOLOGY AND RESEARCH DESIGN Spectral Reflectance of Earth Surface Features Remote sensing is a technique to observe the earth surface or the atmosphere from out of space using satellites (space borne) or from the air using aircrafts (airborne). Remote sensing uses a part or several parts of the electromagnetic spectrum. It records the electromagnetic energy reflected or emitted by the earth’s surface. The amount of radiation from an object (called radiance) is influenced by both the properties of the object and the radiation hitting the object (irradiance). Remote sensing techniques allow taking images of the earth surface in various wavelength region of the electromagnetic spectrum (EMS). One of the major characteristics of a remotely sensed image is the wavelength region it represents in the EMS. Some of the images represent reflected solar radiation in the visible and the near infrared regions of the electromagnetic spectrum, others are the measurements of the energy emitted by the earth surface itself i.e. in the thermal infrared wavelength region. The energy measured in the microwave region is the measure of relative return from the earth’s surface, where the energy is transmitted from the vehicle itself. This is known as active remote sensing, since the energy source is provided by the remote sensing platform. Whereas the systems where the remote sensing measurements depend upon the external energy source, such as sun are referred to as passive remote sensing systems. Detection and discrimination of objects or surface features means detecting and recording of radiant energy reflected or emitted by objects or surface material. Different objects return different amount of energy in different bands of the electromagnetic spectrum, incident upon it. This depends on the property of material (structural, chemical, and physical), surface roughness, angle of incidence, intensity, and wavelength of radiant energy. The Remote Sensing is basically a multi-disciplinary science which includes a combination of various disciplines such as optics, spectroscopy, photography, computer, electronics and telecommunication, satellite launching etc. All these technologies are integrated to act as one complete system in itself, known as Remote Sensing System. There are a number of stages in a Remote Sensing process, and each of them is important for successful operation. Stages in Remote Sensing Emission of electromagnetic radiation, or EMR (sun/self- emission) Transmission of energy from the source to the surface of the earth, as well as absorption and scattering Interaction of EMR with the earth’s surface: reflection and emission Transmission of energy from the surface to the remote sensor Sensor data output. The Urban Unit 5 Wheat Girdawari 2015: Using Remote Sensing Technology Remote Sensing Principles For Recognizing the Vegetation Vegetation behavior depends on the nature of the vegetation itself. Its interactions with solar radiation and other climate factors. The availability of chemical nutrients and water within the host medium (usually soil, or water in marine environments). Absorption centered at about 0.65 µm (visible red) by chlorophyll pigment in green-leaf chloroplasts. To a similar extent in the blue, removes these colours from white light, leaving the predominant but diminished reflectance for visible wavelengths concentrated in the green. Thus, most vegetation has a green-leafy colour. A strong reflectance between 0.7 and 1.0 µm (near IR) in the spongy mesophyll cells located in the interior or back of a leaf. These properties of vegetation account for their tonal signatures on multispectral images. Darker tones in the blue and, especially red, bands, somewhat lighter in the green band, and notably light in the near-IR bands. Light reflects mainly at cell wall/air space interfaces, much of which emerges as strong reflection rays. The intensity of this reflectance is commonly greater (higher percentage) than from most inorganic materials, so vegetation appears bright in the near-IR. Identifying vegetation in remote-sensing images depends on several plant characteristics e.g., in general, deciduous leaves tend to be more reflective than evergreen needles (coniferous). Thus, in infrared colour composites, the red colours associated with those bands in the 0.7 - 1.1 µm interval are normally richer in hue and brighter from tree leaves than from pine needles. These spectral variations facilitate fairly precise detection, identification and monitoring of vegetation on land surfaces. Thus, we can continually assess changes in forests, grasslands and range, shrub lands, crops and orchards, and marine plankton, often at quantitative levels. The Urban Unit 6 Wheat Girdawari 2015: Using Remote Sensing Technology Crop Identification using Remote Sensing Techniques Selection of Satellite Data and Acquisition time Geometric correction of Data Ground Truth Survey for the collection of Signature Integration of imagery with ground data Image Classification Assessment of Classification and Compilation of the final results. Image Classification The classification process is to categorize all pixels in a digital image into one of several land cover classes, or"themes". This categorized data may then be used to produce thematic maps of the land cover present in an image. With Supervised Classification/Spectral Based Classification, we identify examples of the information classes (i.e., land cover type) of interest in the image. These are called "training sites". The image processing software system is then used to develop a statistical characterization of the reflectance for each information class. This stage is often called "signature analysis" and may involve developing a characterization as simple as the mean or the rage of reflectance on each bands, or as complex as detailed analyses of the mean, variances and covariance over all bands. Once a statistical characterization has been achieved for each information class, the image