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  <front>
    <journal-meta id="journal-meta-e35534ab0dfb4c5cafb61773b4a4c71c">
      <journal-id journal-id-type="nlm-ta">Sciresol</journal-id>
      <journal-id journal-id-type="publisher-id">Sciresol</journal-id>
      <journal-id journal-id-type="journal_submission_guidelines">http://ugit.net/publication_fsjoaj3qdho/geoeye_cm_ts9ypx7s/</journal-id>
      <journal-title-group>
        <journal-title>Geo-Eye</journal-title>
      </journal-title-group>
      <issn publication-format="electronic">XXXX-XXXX</issn>
      <issn publication-format="print"/>
    </journal-meta>
    <article-meta id="article-meta-5f9f4bc858494048b5b7fb80a14c9efb">
      <article-id pub-id-type="doi">10.53989/bu.ge.v10i2.7a</article-id>
      <article-categories>
        <subj-group>
          <subject>Research article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title id="article-title-12a2961ecbfc414ea4acd2c16dca65aa">
          <bold id="strong-ddb46b8763ff41848aef2fdabf9237c5">Spatio-temporal </bold>
          <bold id="strong-6f8040e665c24b71bb5c177a63d6cb51">A</bold>
          <bold id="strong-a19b8a99b8b24c56b4441ecca788523e">nalysis of Land Use Land Cover </bold>
          <bold id="strong-29129ac818a449d197735813e448b69c">C</bold>
          <bold id="strong-3c8d6e9c5f194bed99785754fc88fd1b">hanges in Sind </bold>
          <bold id="strong-0f14fd661d4b4711af8823262742ab59">C</bold>
          <bold id="strong-440840f0657544138e68a8f605f8f11e">atchment of the Kashmir </bold>
          <bold id="strong-1053c1a1f2174f93aecd666f5b6d3ca0">Valley, India</bold>
        </article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes">
          <name id="name-2eefc2c5d8d5471893f939b57e0dd6da">
            <surname>Saleem</surname>
            <given-names>Shahid</given-names>
          </name>
          <email>shahidsulmani630@gmail.com</email>
          <xref id="xref-15a302b477c04c568c69bc5aaec28ed9" rid="aff-838937e4bba84112877dc75fb0a8f5d2" ref-type="aff">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name id="name-56ac63ee8842465692dc6abeece6d6cc">
            <surname>Ahmed Hajam</surname>
            <given-names>Firoz</given-names>
          </name>
          <xref id="xref-265d2c5696914ce3983c07e51bd87512" rid="aff-838937e4bba84112877dc75fb0a8f5d2" ref-type="aff">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name id="name-c311b692f41c4635a7113ca15a3f87ea">
            <surname>Rather</surname>
            <given-names>Javeed Ahmed</given-names>
          </name>
          <xref id="xref-5c1a48e648594d279f255386fe3e61b4" rid="aff-462482e4673a4051b447b61e2060cb98" ref-type="aff">2</xref>
        </contrib>
        <aff id="aff-838937e4bba84112877dc75fb0a8f5d2">
          <institution>Research Scholar, Department of Geography &amp; Disaster Management, University of Kashmir</institution>
        </aff>
        <aff id="aff-462482e4673a4051b447b61e2060cb98">
          <institution>Senior Assistant Professor, Department of Geography &amp; Disaster Management, University of Kashmir</institution>
        </aff>
      </contrib-group>
      <volume>10</volume>
      <issue>2</issue>
      <fpage>35</fpage>
      <permissions>
        <copyright-year>2021</copyright-year>
      </permissions>
      <abstract id="abstract-abstract-title-f1b606d5e9df47f0906c970d7ff968fc">
        <title id="abstract-title-f1b606d5e9df47f0906c970d7ff968fc">Abstract</title>
        <p id="paragraph-bc2af47d66f946f3a2374e6a1a72c739">Analysis of land use and land cover dynamics in the Sind watershed of Kashmir provides valuable insights into the changes that have occurred in the region over the past three decades. The use of satellite images from Landsat 5 TM in 1995, 2005, and Landsat 8 TM in 2015 and 2019 with a spatial resolution of 30m allows for a comprehensive and detailed analysis of the changes in the study area. The images were processed using ArcGIS 10.2.2 and ERDAS Imagine 14, and land use classes identified. The results of the study showed that there has been a significant increase in farmland and settlement areas in the past 25 years. This increase can be attributed to the growing population and the increasing demand for agricultural production and urbanization. These changes have important implications for the ecosystem and the environment, as well as for the livelihoods of the local people. The conversion of natural lands, such as forests and grasslands, into agricultural or urban areas can lead to habitat loss and degradation, soil erosion, and increased runoff and pollution. The retrospective analysis of land use and land cover dynamics in the Sind watershed of Kashmir provides important information for decision-makers and highlights the need for sustainable land use practices to ensure the protection of the ecosystem and the livelihoods of the local people. The findings of this study can inform policies aimed at promoting sustainable land use and environmental management in the region.</p>
        <p id="p-74d6763b6bc8"/>
      </abstract>
      <kwd-group id="kwd-group-edaf1372f7f2400f84aac358ffca6be9">
        <title>Keywords</title>
        <kwd>Spatio-temporal analysis</kwd>
        <kwd>Sind catchment</kwd>
        <kwd>Kashmir valley</kwd>
        <kwd>Land use land cover</kwd>
        <kwd>Watershed</kwd>
      </kwd-group>
      <funding-group>
        <funding-statement>None</funding-statement>
      </funding-group>
    </article-meta>
  </front>
  <body>
    <sec>
      <title id="title-83630d69eaf14cfaa42598acdf4df918">Introduction</title>
      <p id="paragraph-a25586e458e34374a68f690de60bf567">Land is one of the most important resources, which not only supplies food for biotic populations but also eases human subsistence by offering a variety of economic advantages. Since the beginning of time, humans have altered the natural land cover of their surrounds to fulfil the ever-increasing demands of their needs for many reasons, including economic, social, and political, recreation, etc. According to Riebsame et al.<xref id="xref-76eaebeb70bd476d9adbb4af0bb8f892" rid="R181837727593064" ref-type="bibr">1</xref> the human population is the only force that can affect the earth's surface, making it the main driver of environmental change in addition to natural influences. Due to the world's tremendous population expansion over the past few decades, ﻿land usage has substantially changed worldwide <xref rid="R181837727593066" ref-type="bibr">2</xref>, <xref rid="R181837727593474" ref-type="bibr">3</xref>. The exponential increase in human population and related activities is changing land resources  <xref id="xref-55acd1638c1941b7830c956b023d35a2" rid="R181837727593082" ref-type="bibr">4</xref> and altering housing, industrial, and agricultural infrastructure  <xref id="xref-09950beca862467bb55fbea26e807a13" rid="R181837727593061" ref-type="bibr">5</xref>. To fulfil the rising demands on land resources, the rapidly expanding populace causes people to relocate near vulnerable ecosystems like mountains, marshes, forests, etc. This immediately interferes with and changes the natural land cover of a region, resulting in serious environmental issues. The expansion of settlements and farmland at the expense of natural land cover, particularly aquatic bodies like wetlands and lakes, is the result of the human population's disproportionately high growth rate. These changes have a substantial influence on physical surroundings as well as on the social and economic circumstances of its inhabitants  <xref id="xref-4649dcd946d84883a6ece0a1f17facf3" rid="R181837727593050" ref-type="bibr">6</xref>. Changes in land use and land cover are among the most significant elements affecting the world's ecological systems  <xref id="xref-9b3a0d15f266449786462ecb4a45118c" rid="R181837727593075" ref-type="bibr">7</xref>. Besides the consequence of population increase; industrialisation, and urbanisation, have equally contributed towards the radical LULC change. Land use and land cover is one of the primary drivers of global change, even while changes in land cover brought on by land use may not always indicate deterioration of the land. Along with other features of the geoenvironment and natural ecosystems, this has an effect on biodiversity, hydrology, and the radiation budget. Changes in land cover condition and composition have an impact on the climate, biogeochemical cycles, energy flows, and people's livelihoods  <xref rid="R181837727593065" ref-type="bibr">8</xref>, <xref rid="R181837727593072" ref-type="bibr">9</xref>. </p>
      <p id="paragraph-c8831161aaa34b8ebd6a19cf54d032ff">Land use and land cover (LULC) are two interchangeable concepts in land change research, although they have different implications. Land use is defined as "human actions on and in connection to the land that are typically not readily visible from imagery" <xref id="x-bb94075656a5" rid="R181837727593756" ref-type="bibr">10</xref>, whereas land cover is defined as "the vegetation and manmade constructions covering the land surface"  <xref id="xref-2645bafeecec46ce8fb36c625ae32cc8" rid="R181837727593083" ref-type="bibr">11</xref>. Humans' ability to regulate the rate and pattern of LULC change is mostly determined by their social, economic, and political traits. Around the world, many land use categories are increasingly replacing land cover  <xref id="xref-d9ecd6244c284fda97c2ecf91600357f" rid="R181837727593063" ref-type="bibr">12</xref>. The terms "land use" and "land cover" are commonly used interchangeably when discussing changes in land use on the surface of the globe. Planning and management of natural resources require knowledge of LULC and its effects <xref rid="R181837727593084" ref-type="bibr">13</xref>, <xref rid="R181837727593074" ref-type="bibr">14</xref>, <xref rid="R181837727593831" ref-type="bibr">15</xref>, <xref rid="R181837727593834" ref-type="bibr">16</xref>. Ecological conditions, elevations, geological structure, slope, as well as technical, social, and institutional elements, all have an impact on land-use patterns <xref rid="R181837727593054" ref-type="bibr">17</xref>, <xref rid="R181837727593839" ref-type="bibr">18</xref>, <xref rid="R181837727593842" ref-type="bibr">19</xref>. Current systems for tracking environmental changes and managing natural resources now include LULC change as a key and fundamental element <xref rid="R181837727593081" ref-type="bibr">20</xref>, <xref rid="R181837727593847" ref-type="bibr">21</xref>. LULC and its resources have been used for the social, material, cultural, and spiritual needs of humans, while, in the process, humans have caused significant changes <xref id="xref-2c0fc75e42aa402583c7a15821f6f53a" rid="R181837727593049" ref-type="bibr">22</xref>. Researchers, policymakers, and planners utilize LULC information to determine changes in natural resources, including evaluating growth patterns  <xref id="xref-a72a2e55cd6647ca845d9c79947dff6a" rid="R181837727593070" ref-type="bibr">23</xref>. A better understanding of land dynamics requires the detection of LULC change <xref id="x-81dc24941dda" rid="R181837727593847" ref-type="bibr">21</xref>. Empirical studies by researchers from diverse disciplines proved that changes in LULC are key to various applications such as hydrology, agriculture, forest, environment, geology, and ecology <xref id="xref-3e71e9608f13426bbbec677235741114" rid="R181837727593051" ref-type="bibr">24</xref>. Assessment of changes in land use and land cover is essential to understanding many dimensions of the interaction between the natural environment and people <xref rid="R181837727593076" ref-type="bibr">25</xref>, <xref rid="R181837727593059" ref-type="bibr">26</xref>, <xref rid="R181837727593068" ref-type="bibr">27</xref>, <xref rid="R181837727593078" ref-type="bibr">28</xref>, <xref rid="R181837727593058" ref-type="bibr">29</xref>, <xref rid="R181837727594094" ref-type="bibr">30</xref>. In order to address a number of environmental problems at the regional level, including uncontrolled growth, the erosion of farmland lands, the degradation of wetlands, and the devastation of animal habitat, it is essential to evaluate land use and land cover  <xref id="xref-0bc3f39dab804a7fafbc046232594a8a" rid="R181837727593048" ref-type="bibr">31</xref>. Additionally, the LULC alterations should be given more thought in terms of land management due to their frequently imminent detrimental effects on the condition and stability of ecosystem functioning  <xref id="xref-81f77b4d8cb24c18963319a884e849ee" rid="R181837727593060" ref-type="bibr">32</xref>. Studies on land use and land cover change make an effort to identify the locations of change, and the types of land cover that are changing, (3) the kinds of transformation taking place, (4) the speeds or volumes of land change, and (5) the primary drivers and indirect causes of change (Loveland and Acevedo 2006) . Large-scale changes in the Earth's land surface are being brought on by growing anthropogenic activity in the biosphere, which has an impact on how well global systems function  <xref id="xref-95adcf28ec89426d85bfe845a6ac475d" rid="R181837727593073" ref-type="bibr">33</xref>. Techniques for mapping land use/cover and spotting changes have been created and used all around the world during the past few decades (<xref rid="R181837727593085" ref-type="bibr">34</xref>, <xref rid="R181837727593056" ref-type="bibr">35</xref>, <xref rid="R181837727593053" ref-type="bibr">36</xref>, <xref rid="R181837727594232" ref-type="bibr">37</xref>, <xref rid="R181837727594237" ref-type="bibr">38</xref>. GIS and remote sensing (RS) have long been acknowledged as crucial and effective methods for detecting LULC changes at various geographical dimensions  <xref id="xref-17a665e0cb9742f8954f97dab423b15f" rid="R181837727593055" ref-type="bibr">39</xref>. Evidence has been derived from remotely sensed data using a variety of change detection methods and image analysis  <xref id="x-f84a1ad12874" rid="R181837727593756" ref-type="bibr">10</xref>. In contrast, GIS incorporates data from RS to create a clear knowledge of LULC modelling. For the identification of LULC patterns, RS and GIS have proven to be quite helpful <xref rid="R181837727593080" ref-type="bibr">40</xref>, <xref rid="R181837727593756" ref-type="bibr">10</xref>, <xref rid="R181837727594252" ref-type="bibr">41</xref>. </p>
      <p id="paragraph-1e6f8a819c694de1b62b512b33ffc3f3">The Kashmir valley saw significant changes in land use and land cover, mostly as a consequence of population growth, economic expansion, modifications in agricultural methods, and the implementation of various development projects, notably over the past three decades. The LULC dynamics in the area are not well understood since the LULC changes are not routinely observed and estimations of the size of the changes are seldom. Large amounts of forest cover are being transformed to different land uses as a result of the growing population and urbanization in India's Himalayan states, causing significant soil erosion<xref id="x-6758d71f4249" rid="R181837727594237" ref-type="bibr">38</xref>. However, recent data shows that the amount of forest is growing in several Himalayan places  <xref id="xref-04b60a7362384b1a8b943d8168c7763c" rid="R181837727593079" ref-type="bibr">42</xref>. </p>
    </sec>
    <sec>
      <title id="title-6cf5c62bfccf46bfa63e7c37d2263c1e">Study Area</title>
      <p id="paragraph-e2cf9d9b2e534158a54c3ebd3afe27da"/>
      <fig id="figure-b43952c230144be490659d708c8f4793" orientation="portrait" fig-type="graphic" position="anchor">
        <label>Figure 1 </label>
        <caption id="caption-c898bca81781496c90c09eaa88faf452">
          <title id="title-94e3e793c21c4779ae28664a1762be14">Study area Map of Sind watershed </title>
        </caption>
        <graphic id="graphic-be31f4980627418aa346cdd1108d40b4" xlink:href="https://s3-us-west-2.amazonaws.com/typeset-prod-media-server/b573fa17-a870-4c1a-b09a-edbb4be3e82cimage1.jpeg"/>
      </fig>
      <p id="paragraph-0402fe86580b4f8da201661a8aff4ffc">The Sindh stream is a major tributary of River Jhelum in Kashmir valley arising in the Zojila mountain, flows towards the West and augments itself with the streams coming down from mountains on left and right sides from Holy Amarnath, Kolahai and Panjtami snowfields. After flowing up to Ganderbal it spreads out near Harran and escapes into the Anchar lake, while the other merge with Jhelum at Shadipur. Sindh is a fast-flowing torrential river in its upper and middle reaches, while as in the lower reaches it becomes calm and flows slowly. The region has abundant water resources, including several large lakes, springs, and rivers, which are crucial for irrigation and other uses. The region also supports a wide variety of flora and fauna, and is home to several wildlife reserves and protected areas. The Sindh stream is a crucial part of the hydrological system in the Kashmir valley and plays a vital role in the region's water resources. Its fast-moving upper and middle portions, as well as its calm lower reaches, make it a unique and important feature of the region's geography. In recent years, Ganderbal has experienced rapid urbanization and development, with an increase in population and construction activity. This has led to increased pressure on the region's natural resources and its unique cultural heritage, and highlights the need for sustainable land use and development practices.</p>
    </sec>
    <sec>
      <title id="title-6b01161c98da4797b4b028443d0f10a4">Materials and Methods</title>
      <sec>
        <title id="title-8e5f6cd25fe64d2c9a06f5e00d4158c9">Data Acquisition and Preparation</title>
        <p id="paragraph-4d098209742444a8a15f660d820f9ece">Numerous studies have supported the effectiveness of space-borne imaging in evaluating changes in land use and land cover (LULC). Geospatial application, including LULC classification, is among the most commonly used applications of remote sensing technology. The present study, we used Landsat data of four-time points for 1995 and 2005 the Landsat 5 (TM) data while for 2015 and 2019, the Landsat 8 (OLI/TIRS) data downloaded from United States Geological Survey (USGS) Earth Explorer has been used in this study for LULC mapping and change analysis with 30m resolution to analyze and highlight the status of Landuse/Landcover change in the study area. Satellite data was downloaded from the web portal www.earthexplorer.usgs.gov which is hosted by Global land Survey (GLS) data system, After the determination of data quality, The obtained satellite imagery have been atmospheric, radiometric corrected during preprocessing stage. The analysis of the acquired Landsat data was done in ERDAS and Arc GIS environment and subsequently, Landuse classes’ map of the study region was prepared by supervised classification using Maximum Likelihood Algorithmic in the Arc GIS platform. A classification scheme was developed and tested for change detection in the study area based on prior knowledge and was validated using ground truthing.</p>
        <table-wrap id="table-wrap-e155a3572cde46ddbe21b2b328048bd2" orientation="portrait">
          <label>Table 1</label>
          <caption id="caption-b3c0e7b4e80f461aa7ac541ce01b86f2">
            <title id="title-d16612cf38f44d52a7b55cbd86501ced">
              <bold id="s-7eb784596f7f">Showing data sources</bold>
            </title>
          </caption>
          <table id="table-a140120360cc4635beedb89243248a5b" rules="rows">
            <colgroup/>
            <thead id="table-section-c126790d10f14279a28042d48de9207f">
              <tr id="table-row-a96bb84791f84d3f8fdc7e0594490a98">
                <th id="table-cell-a3174a35f7204927bdd0c49986aa8c45" align="left">
                  <p id="paragraph-0fec96cfe9024aaca881cd3be97730c5"> <bold id="strong-26fed3646c0f407aaa16f8fd6ff4e1da">Sensor</bold></p>
                </th>
                <th id="table-cell-98b78e7116a042d79928bcca596c87ff" align="left">
                  <p id="paragraph-afdcc13f19094998b4e03b8fbf76e77a"> <bold id="strong-11e2cd2f5d7d4bb1b11c327d19c6a30e">Year</bold></p>
                </th>
                <th id="table-cell-893511bf98414dabb320a5dbc4d2fe0b" align="left">
                  <p id="paragraph-f0db77e5455e43ff9250b1af8a6fb290"> <bold id="strong-611d049147b54881980d1d2a541573a4">Path/Row</bold></p>
                </th>
                <th id="table-cell-41f8f9b80ba44837a0cb1b523a92c551" align="left">
                  <p id="paragraph-3033bc4509aa477399fba1c503ace0ae"> <bold id="strong-520672d1afe146a997790d5725b5cfd4">Source</bold></p>
                </th>
                <th id="table-cell-1348409e32f64e9b8175c2bdea0448a5" align="left">
                  <p id="paragraph-3d8cb2228d6e455a8886c0f45b7f5e47"> <bold id="strong-ffcbe717b9484b5cbb5393fdc2bc32bc">Acquisition Date</bold></p>
                </th>
                <th id="table-cell-c9ef59b178194c9d8d0bdf98b16a804d" align="left">
                  <p id="paragraph-8229c9cbf11d4d5e96b464bc5a77cf1d"> <bold id="strong-bc5ae4fbafff49ea93f9ae6c8655b18b">Spatial Resolution (m)</bold></p>
                </th>
              </tr>
            </thead>
            <tbody id="table-section-e894e496d37a4aa7bfb9c9aed0801d5a">
              <tr id="table-row-01d6bedfc9d147f3b3758e664d51766c">
                <td id="table-cell-2744530f14ce4f66999ddda1e4bfb706" align="left">
                  <p id="paragraph-7b88fa2ed160433b98afea76d96ce6ad"> Landsat 5 TM</p>
                </td>
                <td id="table-cell-c84352a38c784df4a49621128e1e9cf8" align="left">
                  <p id="paragraph-75f49d841575422aa34c160ac619a77f"> 2000</p>
                </td>
                <td id="table-cell-e3c732e8c86b4407a90356224a159bd4" align="left">
                  <p id="paragraph-081a03615ce64fd6adb54558dda73f29"> 149/36</p>
                </td>
                <td id="table-cell-34c99f45aa394f50a8cab9e7837d0aa6" align="left">
                  <p id="paragraph-b29084bd8e4548e6b7acb480e42fda27"> USGS</p>
                </td>
                <td id="table-cell-121a81d2f15a472d82be86fdaa26db6d" align="left">
                  <p id="paragraph-0ebb4a181c5142b584e3b8ecc6153e8e"> May-June 2000</p>
                </td>
                <td id="table-cell-fc82039de52e4c10b2813f3a43ebd001" align="left">
                  <p id="paragraph-cc3be8cf92ec4ad1b9b57073f299a7cd"> 30</p>
                </td>
              </tr>
              <tr id="table-row-ef28e94f7e9c42d6a8207d8b63f8cd77">
                <td id="table-cell-96eea693366948ce8ba6b94f6abc6285" align="left">
                  <p id="paragraph-b1e0888596334cd1880e62da31a98b14"> Landsat 5 ETM</p>
                </td>
                <td id="table-cell-85c6e2c97d5947438008a85b5f0e43d2" align="left">
                  <p id="paragraph-e9f31f3ab83c485582184a8ea7a43321"> 2010</p>
                </td>
                <td id="table-cell-6880a1e2664a4b03b9e0f77c066f65ba" align="left">
                  <p id="paragraph-928e653759584883a59912f67ab2096d"> 149/36</p>
                </td>
                <td id="table-cell-d2b17a8cfc7d47ecbe3b96002628291c" align="left">
                  <p id="paragraph-58ca53ef7b8443189cfd9efac5725f0d"> USGS</p>
                </td>
                <td id="table-cell-b6b1e104c40c452880e81019dd389c24" align="left">
                  <p id="paragraph-25586f48ce174449b623eedda039755c"> May-June 2010</p>
                </td>
                <td id="table-cell-d65645d3180540169e5251e2fbfd41f9" align="left">
                  <p id="paragraph-14f91414f8684c0db143bae3f609756c"> 30</p>
                </td>
              </tr>
              <tr id="table-row-e7b1342ae974442bab7b41f49fc27010">
                <td id="table-cell-f40725f521d14191a320bbee79d2ba38" align="left">
                  <p id="paragraph-3529cfed833647908d6729ac3832826c"> Landsat 8 OLI</p>
                </td>
                <td id="table-cell-7e35b55835684b0488f86fa26911e47d" align="left">
                  <p id="paragraph-3474a1c6ab3d44288233ac275a15a2f1"> 2015</p>
                </td>
                <td id="table-cell-1adc7021bb7c459289b6d45e57d4a29c" align="left">
                  <p id="paragraph-8514144a1c774ae89ea1a362b38b33bf"> 149/36</p>
                </td>
                <td id="table-cell-80a0bc5c73fb43cf9492aa99aa9afcae" align="left">
                  <p id="paragraph-47625b61198f4c7e99aa6d43b5164174"> USGS</p>
                </td>
                <td id="table-cell-631162eae1094e479570a1b32682c8b6" align="left">
                  <p id="paragraph-b387783f230946498447682fde8f4795"> May-June 2010</p>
                </td>
                <td id="table-cell-1953ad59bbec42bcb9b3168c6e6b0fea" align="left">
                  <p id="paragraph-57daac04135e423a824b5b71f6d32fa6"> 30</p>
                </td>
              </tr>
              <tr id="table-row-2feebfccb9924b4a898446250fde4ebf">
                <td id="table-cell-e7b6beddec0446af948fd93f33afb8c1" align="left">
                  <p id="paragraph-fe05c9a95c654abcaccfc101fe9c914b"> Landsat 8 OLI</p>
                </td>
                <td id="table-cell-057485e1e36b48268bce82ab5b924b0c" align="left">
                  <p id="paragraph-08927bda9fe443908fea42adad4c2676"> 2020</p>
                </td>
                <td id="table-cell-4cca76daf25b4d57981e1d45a635ee19" align="left">
                  <p id="paragraph-1f48d389d8ae495d98c1cdbf45245a4d"> 149/36</p>
                </td>
                <td id="table-cell-bedac059cc814549a2b02171a2094ed8" align="left">
                  <p id="paragraph-3da4cec901174c7695d60a1b5ef8f061"> USGS</p>
                </td>
                <td id="table-cell-28e691b095b84557a0f9a63a6c5d2d27" align="left">
                  <p id="paragraph-a880b04b109142389dc6f2e9ba71267c"> May-June 2020</p>
                </td>
                <td id="table-cell-146c5bc338754c6b962af86e76529c7c" align="left">
                  <p id="paragraph-c2974d8b96e945b89ae832a11ceb5a37"> 30</p>
                </td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
      </sec>
      <sec>
        <title id="title-e6dcf5f2cbe64c349ad91f5f21e5616c">Accuracy assessment</title>
        <p id="paragraph-51f79719a0de469087a8031dd6fd3f92">The term ‘accuracy’ is often used to represent the degree of ‘correctness’ of a map or classification in thematic mapping from remotely sensed data. If a thematic map accurately depicts the land cover of the region it depicts, it can be said to have been created using categorization. The degree to which the resultant image categorization agrees with reality or complies with the ‘truth’ is essentially what is meant by classification accuracy (C). Several factors may lead to an accuracy assessment. It could be done, for example, to give a general assessment of a map’s quality, to serve as the foundation for comparing various categorization systems, or to try to comprehend error.</p>
        <p id="paragraph-ee8feec962d34628938b476558034f23">The overall accuracy and kappa coefficient was determined using the <xref rid="disp-formula-group-7abef71cb0a849af9c77a7873ae99e68" ref-type="disp-formula"/>, <xref rid="disp-formula-group-f42421d933c14c0a9940b08bf4834a63" ref-type="disp-formula"/>.</p>
        <disp-formula-group id="disp-formula-group-7abef71cb0a849af9c77a7873ae99e68"> <disp-formula><label>1</label><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>Overall accuracy </mml:mtext><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mrow><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>r</mml:mi></mml:msubsup><mml:mo> </mml:mo></mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mi>x</mml:mi></mml:mfrac></mml:math></disp-formula></disp-formula-group>
        <disp-formula-group id="disp-formula-group-f42421d933c14c0a9940b08bf4834a63"> <disp-formula><label>2</label><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>Kappa coefficient </mml:mtext><mml:mo>(</mml:mo><mml:mover><mml:mi>K</mml:mi><mml:mo>^</mml:mo></mml:mover><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>n</mml:mi><mml:mrow><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>r</mml:mi></mml:msubsup><mml:mo> </mml:mo></mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:mrow><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>r</mml:mi></mml:msubsup><mml:mo> </mml:mo></mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>+</mml:mo></mml:mrow></mml:msub><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mo>+</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msup><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>-</mml:mo><mml:mrow><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>r</mml:mi></mml:msubsup><mml:mo> </mml:mo></mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>+</mml:mo></mml:mrow></mml:msub><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mo>+</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:math></disp-formula></disp-formula-group>
      </sec>
      <sec>
        <title id="title-f29b0b547b0642cab397d74e50a7ed10">ToA correction</title>
        <p id="paragraph-c35baaac89034d63881e05b0c4db2da5">The reflectance estimated by a space-based sensor fixed in a device flying above the Upper orbit is referred to as top of atmosphere. Observations capture the effects of clouds, aerosols, and gases in the atmosphere.</p>
        <p id="paragraph-bfec9df879d14e12bb2a6c132201d518"> TOA reflectance can be derived using <xref id="x-c9ec8c02be4f" rid="disp-formula-group-7abef71cb0a849af9c77a7873ae99e68" ref-type="disp-formula">Equation 1</xref>:</p>
        <disp-formula-group id="disp-formula-group-13215d3f81654bb2b669c4d868d67627"> <disp-formula><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ρ</mml:mi><mml:mi>λ</mml:mi><mml:mo>=</mml:mo><mml:mi>π</mml:mi><mml:mo>*</mml:mo><mml:mi>L</mml:mi></mml:math></disp-formula></disp-formula-group>
        <p id="paragraph-0a6a3a2516804d25adb94e49c0b0ac92">were</p>
        <p id="paragraph-a4b81eaf0bd443fb948adfa53c60d2df"><inline-formula id="inline-formula-20690a8d20ae48ca92503a0b768de93b"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ρ</mml:mi></mml:math></inline-formula> = Unit less planetary reflectance</p>
        <p id="paragraph-f43ec5221eda4d19952465b24a19f233">d = Distance between Earth and Sun in astronomical units</p>
        <p id="paragraph-0e83440ec2ba42bea7b0c9e8c9ff4933">L = Spectral radiance</p>
        <p id="paragraph-090798c3caa442159c100fef3c25a5b8">s = Solar zenith angle</p>
        <p id="paragraph-46f1618f05f54d8abf29d169cad20521">ESUN = Average solar exo-atmospheric irradiances</p>
      </sec>
      <sec>
        <title id="title-3136cfccb7d44082ad7bde1fc218db10">Lulc generation</title>
        <p id="paragraph-c08b0268f7444434a1d5e6ce60fd40e5">Land use/land cover (LULC) classification is a commonly used method for extracting information from satellite imagery. In this study, False Color Composite (FCC) imagery was utilized to generate LULC data for the Sind watershed. Among various LULC classification techniques, a supervised classification method using the maximum likelihood classifier algorithm was employed to create LULC maps. The study identified seven LULC classes, including built-up, cropland, forest land, grassland, shrub/scrub, snow, and waterbodies. To address initial classification errors, post-classification techniques such as ground truths of classified scenes were employed.</p>
      </sec>
    </sec>
    <sec>
      <title id="title-8beae3cd54104ea686f9dc57114d5d09">Results and discussion</title>
      <p id="paragraph-fcb8cf5cd1364feeb635ffcbe65f853a">Land use Land cover (LULC) change is considered to be among the most massive and visible change of the earth’s surface. Land use land cover change has been evaluated worldwide. The rising trend of LULC change in the watersheds demonstrates the worldwide importance of economic considerations in driving principals for human land alteration. The land use land cover changes of various catchments of the Kashmir valley have been evaluated already. Most of the studies have shown LULC changes in almost all the catchments of the Kashmir valley. Transitions in LULU in the catchments of Kashmir are quite common from unirrigated system (Forests, Barren land) to irrigated system (agricultural and horticultural activities) due to enhanced crop production and economic gain<xref rid="R181837727593069" ref-type="bibr">43</xref>, <xref rid="R181837727594330" ref-type="bibr">44</xref>, <xref rid="R181837727593052" ref-type="bibr">45</xref>. Sindh Catchment has also registered a considerable land use land cover LULC change in almost all the category classes during the last 25 years across its length and breadth; however, the changes have been widespread within the selected areas of the study region because of newly created settlements. Although the area displays insignificant changes during 1995 to 2005 but the significant changes have occurred during the decade of 2005 to 2015. The observed land use trend is consistent with other catchments of Kashmir valley like Wular, Lolab and Sheshnag where habitation and cultivation were found to be dominant  <xref id="xref-5d2c2cef0018446eb88808484b99fdbc" rid="R181837727593061" ref-type="bibr">5</xref>. The detailed land use and land cover changes within the study area during the reference time period (1995–2005–2015-2020) are illustrated in Figure and Table 1 respectively. The table and figure reveal that most the classes under consideration shows slight changes in the period from 1995 to 2005. However, significant changes in Land use and Land cover have been recorded in the decade of 2005-2015. The remaining period from 2015 to 2020 also registered slow changes in LULC in the study region. The maximum net change (675.98) was recorded in Shrub/Scrub classes followed by forest (-368.19), snow (-216.82), cropland (-123.05), grassland (41.52), and buildup (22.36) respectively (<xref id="x-9257d4d516cd" rid="table-wrap-e155a3572cde46ddbe21b2b328048bd2" ref-type="table">Table 1</xref>, <xref id="x-c5a7ecb9e0e4" rid="figure-b43952c230144be490659d708c8f4793" ref-type="fig">Figure 1</xref>). The detailed analysis of all the land use categories are given below. </p>
      <fig id="figure-1c3b58d780d548518e3c4902285624cc" orientation="portrait" fig-type="graphic" position="anchor">
        <label>Figure 2 </label>
        <caption id="caption-46d709f9cf6a4a0280b10779529ad580">
          <title id="title-a6044e4f04a8438e9d1300049eeea7f7">Illustrating LULC variations for various time periods (a) 1995 (b) 2005 (c) 2015 (d) 2019</title>
        </caption>
        <graphic id="graphic-486b3170879041e5bf54a1c4df1b4a27" xlink:href="https://s3-us-west-2.amazonaws.com/typeset-prod-media-server/b573fa17-a870-4c1a-b09a-edbb4be3e82cimage2.png"/>
      </fig>
      <p id="p-3c443f1d058f"/>
      <p id="p-698df729ee52"/>
      <fig id="f-a081eafb00dc" orientation="portrait" fig-type="graphic" position="anchor">
        <label>Figure 3 </label>
        <caption id="c-42283b921e6b">
          <title id="t-784b8abd3e92">Showing NDVI time series for Sind catchment for two time periods (a) 2000 (b) 2019</title>
        </caption>
        <graphic id="g-c3a9cc6736dc" xlink:href="https://typeset-prod-media-server.s3.amazonaws.com/article_uploads/a1fd03d2-014c-44b8-b295-487468764142/image/156ea874-d1b5-421c-8d9e-20c05cddf946-uimage.png"/>
      </fig>
      <p id="p-16b7e47da280"/>
      <p id="p-ce36e4512da5"/>
      <sec>
        <title id="t-d0d4e4d4a4fa">
          <bold id="strong-65998a0bd42b46cd8fee1737e347b17c">Built-up</bold>
        </title>
        <p id="paragraph-4bb1debc60b64839ae51014050347511">The net change in the build-up class shows an increase of 22.36 sq. kms from 1995 to 2020. Although the change was slightly negative (0.04) from 1995-2005 but the class registered 21.50 sq. kms change during the period of next 10 years i.e., from 2005-2015 and is still increasing. The positive change in build-up class can be attributed to the increasing population pressure which thereby forces the people for constructions of houses and other commercial establishments. The built-up area has primarily encroached on agricultural land and wetlands The rising built-up area, particularly along roadways and in peripheral zones, has a negative impact on the region's environmental quality. This tendency is also congruent with India's national situation, where 0.7 million hectares of agricultural land were lost to urban growth between 2001 and 2020).</p>
      </sec>
      <sec>
        <title id="t-eb62c1d41d18">
          <bold id="strong-52a6d6e50ad94043be1483d62d7aab7e">Cropland </bold>
        </title>
        <p id="paragraph-df424e5e73054f438d7661991e37340c">Cropland forms one of the most important land use class in the study region as most of the population depends on agriculture for their source of livelihood. The crop land revealed a very high change of 123.05 sq. kms from 1995 to 2005 which means a decline of 54% of the agricultural land in the study region. Again, most of the change was observed during the period of 2005 to 2015. The drastic change in this class can be related to the significant shift in land use practices in the study area from rice to apple production between 1992 and 2015. Furthermore, at this time, a significant percentage of agricultural land was lost to built-up growth. The figures are consistent with earlier research undertaken in various areas of the Kashmir valley <xref id="x-0c43b87268e1" rid="R181837727594252" ref-type="bibr">41</xref>.</p>
      </sec>
      <sec>
        <title id="t-9adac92dd5b8">
          <bold id="strong-c284896d2c364a27ba6eb37387868d52">Forests</bold>
        </title>
        <p id="paragraph-28119467539041ac960162a78b4fd60b">The area under forests revealed a significant drop in the Sindh watershed of the Kashmir valley. From 1995 to 2005, a loss of 109.19 sq. kms was recorded in the class and the decline almost increased to one and a half times during 2005-15. The table and figure reveals that decline in the forest cover continued till 2020 and the overall decline 368.19 sq. kms was recorded which is more than half of the total forest cover of 1995. Gull recorded a loss of 12.56 square kilometers from 2003 to 2013 in the Sind catchment. The decline in the forest cover can be attributed to illegal logging and loss of forests owing to political instability in the Kashmir valley. Large forest were completely wiped out particularly in the higher ranges during these two decades. However, with the efforts of government and non-government agencies, the destruction of forest cover was curbed and therefore witnessed a slight decrease from 2015-2020.</p>
      </sec>
      <sec>
        <title id="t-573afea3761d"> <bold id="strong-fad92fbf84fc46118dd8197b71a262d2">Grassland </bold></title>
        <p id="paragraph-f3b1bee45879445ab16b6e624e77cdb8">Grasslands were observed to be revealing two change patterns during the selected time period i.e., the area under grassland was 179.54 sq. kms in 1995 which increased to 303.03 sq. kms during 1995 to 2005 and then again decreased from 303.03 sq. kms to 222.88 sq. kms. The overall increase in the grass land was found to be about 41.52 sq. kms. </p>
      </sec>
      <sec>
        <title id="t-7ca9791e5cda">
          <bold id="strong-32c7cd85b1104f1c932510ef22e69996">Shrub/Scrub</bold>
        </title>
        <p id="paragraph-48e5cb4c2fe34d9b8cb04cb95d4734e2">Shrubs are usually found in transition zones between forested and non-forested environments, taking up space mostly from forests and agricultural land. The table and figure reveals this LULC class has shown a significant change mostly in the time period of 2005-2015. The overall area under this class was 675.98 sq. kms in 2020 showing that the area has increased by 20 times since 1995.</p>
      </sec>
      <sec>
        <title id="t-151e86d7ace7">
          <bold id="strong-744e1eb26f9a47d4b28a54f67c94b4de">Snow</bold>
        </title>
        <p id="paragraph-a67d0423e2324b15a4c8131f6f3613d4">The snow cover has lost 216.82 sq. kms of area during the reference period which is second highest decrease after forests in the study area. During 2005-15, more than 200 sq. kms were lost under this category hence showing the significant impact of climatic change and anthropogenic activities. </p>
      </sec>
      <sec>
        <title id="t-1f73f7277951">
          <bold id="strong-46ff58463a2c4563a54912845c4d74bf">Water Bodies</bold>
        </title>
        <p id="paragraph-0bb4e0fbc0314013bc73ec6aec41be52">The area under water bodies was recorded as 7.28 sq. kms in 2020 hence showing a drastic decrease. More than 70% of the area under water bodies have lost during the reference period. Water bodies are the most stressed natural resource in the Kashmir valley due to changing climate and anthropogenic influences. The valley's lakes and rivers show considerable structural changes and loss of area as a result of extensive human involvement and oscillations in the water budget. Another key element contributing to the diminishing bathymetric features of water bodies in the study region has been the sediment load acquired from the feeder rivers. Furthermore, weak management techniques have led in pollution and deterioration of the health of these vital aquatic habitats. </p>
        <p id="p-19cc2a27c703"/>
        <fig id="f-f9caff566e34" orientation="portrait" fig-type="graphic" position="anchor">
          <label>Figure 4 </label>
          <caption id="c-9b218efedacd">
            <title id="t-d75800d6002e">Showing Land use land cover statistics for Sind watershed from (1995- 2109)</title>
          </caption>
          <graphic id="g-85d4be750054" xlink:href="https://typeset-prod-media-server.s3.amazonaws.com/article_uploads/a1fd03d2-014c-44b8-b295-487468764142/image/2a0a1e32-2d80-4aaf-9071-92960bbba1ad-uimage.png"/>
        </fig>
        <p id="p-856f4c5f2770"/>
        <fig id="f-2bed278a3b04" orientation="portrait" fig-type="graphic" position="anchor">
          <label>Figure 5 </label>
          <caption id="c-5348dd3fb036">
            <title id="t-95716451afc9">Showing Land use land cover change statistics in Sq. Kms for Sind watershed from (1995- 2109)</title>
          </caption>
          <graphic id="g-d0082e80e869" xlink:href="https://typeset-prod-media-server.s3.amazonaws.com/article_uploads/a1fd03d2-014c-44b8-b295-487468764142/image/1083093f-40aa-499f-aac3-3bf845d865b9-uimage.png"/>
        </fig>
        <p id="p-c444b19dce4b"/>
      </sec>
    </sec>
    <sec>
      <title id="title-1547f9ec41b743e1a523a09425f8c433">Conclusion</title>
      <p id="paragraph-d1f98f13422c4b14a5722868040f42b7">The assessment of LULC dynamics is of vital importance in assessing the global and regional ecosystem changes. LULC changes depict the direct relationship between the man and his environment. In the present study, geospatial techniques have been used for assessing the LULC changes in Sind Catchment from 1995 to 2020. Results of the study revealed that the study area has experienced various changes in LULC during the past 25 years. Significant positive increasing change is seen in horticulture and built-up area with a decrease in agriculture, forest area and water bodies. Agriculture which forms the backbone of the study area is being transformed into horticulture and settlement area due to the increased demand of the population. Forest land has been cleared for timber and expansion of seasonal agricultural activities besides rendering large areas of forests barren. The majority of these LULC changes are unintentional, primarily the product of human activity, and have a variety of detrimental effects on the environment. The study found that geospatial techniques can be effectively utilized for assessing and monitoring of natural resources and aiding the land use planners for arriving at sustainable land use policies. The study recommended that construction activities of settlements should not be encouraged at the cost of valuable agricultural land. We anticipate that the findings of this study will aid decision-makers in understanding the scenario of changing land use and land cover and in developing an efficient and environmentally friendly land use policy for the Kashmir valley as a whole and Sind catchment in particular.</p>
    </sec>
    <sec>
      <title id="t-37582793f26a">
        <bold id="strong-9d6f3bfb8a464d46a6122f20d541591b">Acknowledgements</bold>
      </title>
      <p id="p-28e47d551dcf">We would like to acknowledge the US Geological Survey (USGS) for dataset used to conduct this study. </p>
      <p id="paragraph-5bb6e44776be4710b3e4e60bdab90f2b"/>
    </sec>
  </body>
  <back>
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