Pub Date : 2019-09-18DOI: 10.33411/ijist/2019010412
Muhammad Zubair Atiq, M. Arslan, Z. Baig, A. Ahmad, M. Tanveer, Azeem Akhtar, Kashif Naeem, S. A. Mahmood
Selection of suitable sites for construction of dam is the most important phase because a number of factors are required to consider that include topography, geology, tectonic settlements and the slope. We selected Diamer Basha dam site to analyze it feasibility considering real-time field data. Geologically the study site is a part of Chilas Mafic Igneous Complex which is not ophiolite. Matic complex is a block which is 40km in depth and 300km in length. These rocks are comparatively hard in nature and are considered good for construction of dam. Tectonically, we observed that the area under investigation was highly active tectonically. Surface deformation rates of the study site are highest throughout the world because this area is comprised of multiple fault lines that include Main Mantle Thurst (MMT), Main Karakoram Thrurst (MKT), Main Boundary Thurst (MBT) and many others. This area has become a hot cake for the geologist worldwide due to it’s very high surface deformation rates.Tectonically active regions are considered worst for construction sites, e.g., for dam sites. The dam site is actcually laying on the MKT which is not favorable for construction of Diamir Basha dam. A low-level earthquake may generate small cracks in concrete structure and any leakage of water may produce big holes with passage of time which are not remidable. A big level earthquake may vanish the dam site completely. Therefore, the current site is not favorable for construction of dam. 1, 2,3, 4, Muhammad Usman Tanveer1, Azeem Akhtar1, Azam Sohail1, Kashif Naeem1 and Syed Amer Mahmood1. 1 Department of Space Science, University of the Punjab Lahore, Punjab Pakistan. 2 University of the Central Punjab Lahore. 3 Punjab University, College of Information and Technology. 4 Punjab University, Centre for Geographic Information System.
{"title":"Dam Site Identification Using Remote Sensing and GIS (A case study Diamer Basha Dam Site)","authors":"Muhammad Zubair Atiq, M. Arslan, Z. Baig, A. Ahmad, M. Tanveer, Azeem Akhtar, Kashif Naeem, S. A. Mahmood","doi":"10.33411/ijist/2019010412","DOIUrl":"https://doi.org/10.33411/ijist/2019010412","url":null,"abstract":"Selection of suitable sites for construction of dam is the most important phase because a number of factors are required to consider that include topography, geology, tectonic settlements and the slope. We selected Diamer Basha dam site to analyze it feasibility considering real-time field data. Geologically the study site is a part of Chilas Mafic Igneous Complex which is not ophiolite. Matic complex is a block which is 40km in depth and 300km in length. These rocks are comparatively hard in nature and are considered good for construction of dam. Tectonically, we observed that the area under investigation was highly active tectonically. Surface deformation rates of the study site are highest throughout the world because this area is comprised of multiple fault lines that include Main Mantle Thurst (MMT), Main Karakoram Thrurst (MKT), Main Boundary Thurst (MBT) and many others. This area has become a hot cake for the geologist worldwide due to it’s very high surface deformation rates.Tectonically active regions are considered worst for construction sites, e.g., for dam sites. The dam site is actcually laying on the MKT which is not favorable for construction of Diamir Basha dam. A low-level earthquake may generate small cracks in concrete structure and any leakage of water may produce big holes with passage of time which are not remidable. A big level earthquake may vanish the dam site completely. Therefore, the current site is not favorable for construction of dam.\u0000\u00001, 2,3, 4, Muhammad Usman Tanveer1, Azeem Akhtar1, Azam Sohail1, Kashif Naeem1 and Syed Amer Mahmood1.\u00001 Department of Space Science, University of the Punjab Lahore, Punjab Pakistan. \u00002 University of the Central Punjab Lahore. \u00003 Punjab University, College of Information and Technology. \u00004 Punjab University, Centre for Geographic Information System.","PeriodicalId":337703,"journal":{"name":"International Journal of Agriculture & Sustainable Development","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130793532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-06DOI: 10.33411/ijasd/2019010401
Faizan Mahmood, M. Saifullah, M. Zafar, Shazma Saman, Sumira Yasmeen, Awais Karamat, Syed Usman Tanveer
Rice is one of major corps grown in Pakistan. It is considered as the backbone of Pakistan’s economy. Almost all the rural population of Pakistan is involved in rice preparation from its germination to final harvest. Although its contribution is less in Gross Domestic Product (GDP) of Pakistan, however efforts are on the way to enhance its productivity. The land of Punjab Pakistan produces rice of export quality which is famous throughout the world. Rice crop growth promoting parameters are considered essential to achieve high yield e.g., temperature, humidity, pressure, soil pH, soil type, drainage and electric conductivity. We used Multi-Criteria decision analysis to map temperature base and soil-based rice friendly zones. The results show that the total area under investigation was 3151km2, out of which 2075 km2 was observed highly suitable, 772 km2 was moderately suitable and 303 km2 was not suitable for rice cultivation. On ground validation, it was observed that the areas which were not suitable for rice cultivation, were actually urban area. The urban areas had high temperatures due to anthropogenic activities and fossil fuel emissions. Remote sensing and GIS techniques proved efficient in mapping the suitability zones for rice crop.
{"title":"Identification of Potential Sites for Rice Plant Growth using Multi Criteria Decision (MCE) Technique Through Remote Sensing and GIS","authors":"Faizan Mahmood, M. Saifullah, M. Zafar, Shazma Saman, Sumira Yasmeen, Awais Karamat, Syed Usman Tanveer","doi":"10.33411/ijasd/2019010401","DOIUrl":"https://doi.org/10.33411/ijasd/2019010401","url":null,"abstract":"Rice is one of major corps grown in Pakistan. It is considered as the backbone of Pakistan’s economy. Almost all the rural population of Pakistan is involved in rice preparation from its germination to final harvest. Although its contribution is less in Gross Domestic Product (GDP) of Pakistan, however efforts are on the way to enhance its productivity. The land of Punjab Pakistan produces rice of export quality which is famous throughout the world. Rice crop growth promoting parameters are considered essential to achieve high yield e.g., temperature, humidity, pressure, soil pH, soil type, drainage and electric conductivity. We used Multi-Criteria decision analysis to map temperature base and soil-based rice friendly zones. The results show that the total area under investigation was 3151km2, out of which 2075 km2 was observed highly suitable, 772 km2 was moderately suitable and 303 km2 was not suitable for rice cultivation. On ground validation, it was observed that the areas which were not suitable for rice cultivation, were actually urban area. The urban areas had high temperatures due to anthropogenic activities and fossil fuel emissions. Remote sensing and GIS techniques proved efficient in mapping the suitability zones for rice crop.","PeriodicalId":337703,"journal":{"name":"International Journal of Agriculture & Sustainable Development","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121250326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-15DOI: 10.33411/IJASD/2019010307
H. Raza, Muhammad Shehzad, Arshad Baloach, Rana Muhmmad Ikram
This research was conducted to highlight the benefits of fruits and their juices in terms of their role in preventing harmful substances which cause different types of diseases within human body. The key properties that are investigated in this research are antioxidant and antimicrobial properties of fruit juices that are important in healthcare and food science. This study explores the effect of fresh juices and determine how it prevents the human body cells to get damage. It also investigates the capacity of fruit juices to kill microorganisms in human body. Three fruit juices (apple, grapes and pomegranate) were selected to analyze their anti-microbial activity. The results proved that the fruits with high acidity are considered more antimicrobial and antioxidant in nature, hence, more helpful to react against diseases and to make strengthen the immunity of human’s body. Apple has high anti-microbial activity as compared to grapes and pomegranates which is very nice supplement for human body to react against bacteria and other harmful antibodies. Most of diseases will be cured with fruits in future instead of intaking high potency antibiotics.
{"title":"Antioxidant and Antimicrobial Activity of Fruit Juices","authors":"H. Raza, Muhammad Shehzad, Arshad Baloach, Rana Muhmmad Ikram","doi":"10.33411/IJASD/2019010307","DOIUrl":"https://doi.org/10.33411/IJASD/2019010307","url":null,"abstract":"This research was conducted to highlight the benefits of fruits and their juices in terms of their role in preventing harmful substances which cause different types of diseases within human body. The key properties that are investigated in this research are antioxidant and antimicrobial properties of fruit juices that are important in healthcare and food science. This study explores the effect of fresh juices and determine how it prevents the human body cells to get damage. It also investigates the capacity of fruit juices to kill microorganisms in human body. Three fruit juices (apple, grapes and pomegranate) were selected to analyze their anti-microbial activity. The results proved that the fruits with high acidity are considered more antimicrobial and antioxidant in nature, hence, more helpful to react against diseases and to make strengthen the immunity of human’s body. Apple has high anti-microbial activity as compared to grapes and pomegranates which is very nice supplement for human body to react against bacteria and other harmful antibodies. Most of diseases will be cured with fruits in future instead of intaking high potency antibiotics.","PeriodicalId":337703,"journal":{"name":"International Journal of Agriculture & Sustainable Development","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131207174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-11DOI: 10.33411/IJASD/2019010306
Ayesha Behzad, Muneeb Aamir, S. A. Raza, Ansab Qaiser, Syeda Yuman Fatima, Awais Karamat, S. A. Mahmood
Wheat is the basic staple food, largely grown, widely used and highly demanded. It is used in multiple food products which are served as fundamental constituent to human body. Various regional economies are partially or fully dependent upon wheat production. Estimation of wheat area is essential to predict its contribution in regional economy. This study presents a comparative analysis of optical and active imagery for estimation of area under wheat cultivation. Sentinel-1 data was downloaded in Ground Range Detection (GRD) format and applied the Random Forest Classification using Sentinel Application Platform (SNAP) tools. We obtained a Sentinel-2 image for the month of March and applied supervised classification in Erdas Imagine 14. The random forest classification results of Sentinel-1 show that the total area under investigation was 1089km2 which was further subdivided in three classes including wheat (551km2), built-up (450 km2) and the water body (89 km2). Supervised classification results of Sentinel-2 data show that the area under wheat crop was 510 km2, however the built-up and waterbody were 477 km2, 102 km2 respectively. The integrated map of Sentinel-1 and Sentinel-2 show that the area under wheat was 531 km2 and the other features including water body and the built-up area were 95 km2 and 463 km2 respectively. We applied a Kappa coefficient to Sentinel-2, Sentinel-1 and Integrated Maps and found an accuracy of 71%, 78% and 85% respectively. We found that remotely sensed algorithms of classifications are reliable for future predictions.
{"title":"Estimation of Wheat Area using Sentinel-1 and Sentinel-2 Datasets (A Comparative Analysis)","authors":"Ayesha Behzad, Muneeb Aamir, S. A. Raza, Ansab Qaiser, Syeda Yuman Fatima, Awais Karamat, S. A. Mahmood","doi":"10.33411/IJASD/2019010306","DOIUrl":"https://doi.org/10.33411/IJASD/2019010306","url":null,"abstract":"Wheat is the basic staple food, largely grown, widely used and highly demanded. It is used in multiple food products which are served as fundamental constituent to human body. Various regional economies are partially or fully dependent upon wheat production. Estimation of wheat area is essential to predict its contribution in regional economy. This study presents a comparative analysis of optical and active imagery for estimation of area under wheat cultivation. Sentinel-1 data was downloaded in Ground Range Detection (GRD) format and applied the Random Forest Classification using Sentinel Application Platform (SNAP) tools. We obtained a Sentinel-2 image for the month of March and applied supervised classification in Erdas Imagine 14. The random forest classification results of Sentinel-1 show that the total area under investigation was 1089km2 which was further subdivided in three classes including wheat (551km2), built-up (450 km2) and the water body (89 km2). Supervised classification results of Sentinel-2 data show that the area under wheat crop was 510 km2, however the built-up and waterbody were 477 km2, 102 km2 respectively. The integrated map of Sentinel-1 and Sentinel-2 show that the area under wheat was 531 km2 and the other features including water body and the built-up area were 95 km2 and 463 km2 respectively. We applied a Kappa coefficient to Sentinel-2, Sentinel-1 and Integrated Maps and found an accuracy of 71%, 78% and 85% respectively. We found that remotely sensed algorithms of classifications are reliable for future predictions.","PeriodicalId":337703,"journal":{"name":"International Journal of Agriculture & Sustainable Development","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114968510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-28DOI: 10.33411/IJASD/2019010204
Awais Karamat, M. Nawaz
Rice has become an essential part of four pillars of food security, especially in Asia, where it is produced over large spatial extents and also consumed widely. About 89 % of the global rice production is targeted and achieved from Asian countries. We downloaded Sentinel-1 datasets from official website of European Space Agency (ESA) for identification of rice patterns in the study site. The data was selected in Ground Range Detection (GRD) format and applied the toolbox in Sentinel Application Platform (SNAP) for further processing. We applied the orbit file for geometric and radiometric corrections, LEE filter for removal of spackles, resampling to convert 20*20m2 to 10*10m2 pixel size and finally the Random Forest Classification (RFC) to classify the satellite image. The classification results of Sentinel image for the year 2018, show that the total area of the study site was 360021 ha, including 144991 ha as rice area, 130598 as other vegetation, 19339 ha as water body and the built-up area was estimated as 5693 ha. Kappa statistics resulted the overall accuracy of 85% which is in strong agreement to ground reality. We observed that the rice area was increased from 140403 ha in 2017 to 144991 ha in 2018. The main reason of this increase in rice area was observed as the preference of local farmers to grow rice in comparison to other crops because the local government was offering high subsidy to rice farmers. Moreover, district Nankana-Sahib produces rice of expert quality which is famous throughout the world therefore, it is considered as cash crop.
{"title":"Step-by-Step Processing of Sentinel-1 data for Estimation of Rice Area.","authors":"Awais Karamat, M. Nawaz","doi":"10.33411/IJASD/2019010204","DOIUrl":"https://doi.org/10.33411/IJASD/2019010204","url":null,"abstract":"Rice has become an essential part of four pillars of food security, especially in Asia, where it is produced over large spatial extents and also consumed widely. About 89 % of the global rice production is targeted and achieved from Asian countries. We downloaded Sentinel-1 datasets from official website of European Space Agency (ESA) for identification of rice patterns in the study site. The data was selected in Ground Range Detection (GRD) format and applied the toolbox in Sentinel Application Platform (SNAP) for further processing. We applied the orbit file for geometric and radiometric corrections, LEE filter for removal of spackles, resampling to convert 20*20m2 to 10*10m2 pixel size and finally the Random Forest Classification (RFC) to classify the satellite image. The classification results of Sentinel image for the year 2018, show that the total area of the study site was 360021 ha, including 144991 ha as rice area, 130598 as other vegetation, 19339 ha as water body and the built-up area was estimated as 5693 ha. Kappa statistics resulted the overall accuracy of 85% which is in strong agreement to ground reality. We observed that the rice area was increased from 140403 ha in 2017 to 144991 ha in 2018. The main reason of this increase in rice area was observed as the preference of local farmers to grow rice in comparison to other crops because the local government was offering high subsidy to rice farmers. Moreover, district Nankana-Sahib produces rice of expert quality which is famous throughout the world therefore, it is considered as cash crop.","PeriodicalId":337703,"journal":{"name":"International Journal of Agriculture & Sustainable Development","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124200281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-22DOI: 10.33411/ijasd/2019010201
A. Rehman, Muhammad Ayyaz, Farzeen Riaz, Sajid Ali, M. Tanveer, Iqra Manzoor, Hafiz Adnan Ashraf., S. Mahmood
Smallholder farmers are threatened by various vulnerable risks which include hostile weather conditions, rainfall at odd times, disease outbreaks and the market shocks. Crop insurance is the only solution to mitigate these risks. Crop yield records are of great importance to predict the crop yield/area into a region but the developing countries like Pakistan, have limited availability of crop yield records. Crop Reporting Service (CRS) in Punjab province of Pakistan has taken this initiative to save crop related data. We obtained the CRS based datasets of rice crop from (2008-2018) to predict the rice yield/area for the fiscal year 2019. The CRS based datasets were incorporated in collaboration with remotely sensed dataset to obtain more accurate results. The spectral responses of rice crop were taken as input to compute NDVI/RVI values of each year. We applied linear regression to NDVI/RVI and the CRS based yield to generate regression equations for prediction of rice yield for the year 2019 which was computed as 2.09 (ton/ha). The area under rice cultivation was estimated using supervised classification that was 139616 hectors. The net rice production was estimated as 219797 tons. Spectral responses of rice crop canopy proved efficient to determine the net productions.
{"title":"Estimation of Net Rice Production for the Fiscal year 2019 using Multisource Datasets.","authors":"A. Rehman, Muhammad Ayyaz, Farzeen Riaz, Sajid Ali, M. Tanveer, Iqra Manzoor, Hafiz Adnan Ashraf., S. Mahmood","doi":"10.33411/ijasd/2019010201","DOIUrl":"https://doi.org/10.33411/ijasd/2019010201","url":null,"abstract":"Smallholder farmers are threatened by various vulnerable risks which include hostile weather conditions, rainfall at odd times, disease outbreaks and the market shocks. Crop insurance is the only solution to mitigate these risks. Crop yield records are of great importance to predict the crop yield/area into a region but the developing countries like Pakistan, have limited availability of crop yield records. Crop Reporting Service (CRS) in Punjab province of Pakistan has taken this initiative to save crop related data. We obtained the CRS based datasets of rice crop from (2008-2018) to predict the rice yield/area for the fiscal year 2019. The CRS based datasets were incorporated in collaboration with remotely sensed dataset to obtain more accurate results. The spectral responses of rice crop were taken as input to compute NDVI/RVI values of each year. We applied linear regression to NDVI/RVI and the CRS based yield to generate regression equations for prediction of rice yield for the year 2019 which was computed as 2.09 (ton/ha). The area under rice cultivation was estimated using supervised classification that was 139616 hectors. The net rice production was estimated as 219797 tons. Spectral responses of rice crop canopy proved efficient to determine the net productions.","PeriodicalId":337703,"journal":{"name":"International Journal of Agriculture & Sustainable Development","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131124622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-02-18DOI: 10.33411/IJASD/2019010103
Ayesha Behzad, Usman Rafique
Estimation of Net Primary Production (NPP) is crucial for the supply of food/wood. Precise estimates of NPP are important for sustainable development. We used Light Use Efficiency (LUE) model to appraise various growth-related processes e.g., photosynthesis, respiration and transpiration, in the rice plant. The study site received 1213 actual sunshine hours in comparison to total possible sunshine hours which were 1595 during the complete Rice Growth Period (RGP). Water stress was estimated throughout the RGP which resulted in surplus of water in early growth stages (W=53) and deficiency in the final ripening stage with W=0.14. Careful results indicate that about 5128 kg/ha of wet biomass was generated during the complete RGP. We applied a harvest index of 0.50 to estimate the dry biomass that was 2564 kg/ha which is about (2.82 ton/ha). These estimates seem to be in exaggeration according to real time field estimates collected by Crop Reporting Service (CRS) department which were (1.83 ton/ha). To manage this exaggeration, we applied the Soil Suitability Constant (ħα) which resulted as 1.99 ton/ha in suitable zone, 1.21 ton/ha in less suitable, 1.76 ton/ha in moderately suitable and 0.73 ton/ha in not suitable zones. The average yield was estimated as 1.62 ton/ha. According to CRS department, the rice area in the study site was reported as 107000 ha and the net rice production was estimated as 1,73,340 tons in the study site. The LUE model is reliable to estimate NPP of rice crop which is useful for decision makers to determine the contribution of rice in Gross Domestic Product (GDP) at regional scales in term of surplus or shortfall.
{"title":"Estimation of Net Primary Production of Rice Crop using CASA model in Nankana Sahib.","authors":"Ayesha Behzad, Usman Rafique","doi":"10.33411/IJASD/2019010103","DOIUrl":"https://doi.org/10.33411/IJASD/2019010103","url":null,"abstract":"Estimation of Net Primary Production (NPP) is crucial for the supply of food/wood. Precise estimates of NPP are important for sustainable development. We used Light Use Efficiency (LUE) model to appraise various growth-related processes e.g., photosynthesis, respiration and transpiration, in the rice plant. The study site received 1213 actual sunshine hours in comparison to total possible sunshine hours which were 1595 during the complete Rice Growth Period (RGP). Water stress was estimated throughout the RGP which resulted in surplus of water in early growth stages (W=53) and deficiency in the final ripening stage with W=0.14. Careful results indicate that about 5128 kg/ha of wet biomass was generated during the complete RGP. We applied a harvest index of 0.50 to estimate the dry biomass that was 2564 kg/ha which is about (2.82 ton/ha). These estimates seem to be in exaggeration according to real time field estimates collected by Crop Reporting Service (CRS) department which were (1.83 ton/ha). To manage this exaggeration, we applied the Soil Suitability Constant (ħα) which resulted as 1.99 ton/ha in suitable zone, 1.21 ton/ha in less suitable, 1.76 ton/ha in moderately suitable and 0.73 ton/ha in not suitable zones. The average yield was estimated as 1.62 ton/ha. According to CRS department, the rice area in the study site was reported as 107000 ha and the net rice production was estimated as 1,73,340 tons in the study site. The LUE model is reliable to estimate NPP of rice crop which is useful for decision makers to determine the contribution of rice in Gross Domestic Product (GDP) at regional scales in term of surplus or shortfall.","PeriodicalId":337703,"journal":{"name":"International Journal of Agriculture & Sustainable Development","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114545148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Forests are the main source of food/wood and are important for a healthy environment. Removal of trees from forested landcover is known as deforestation. The main objective of this study was to estimate temporal variations in forested landcover located in district Mansehra for the years from 2008 to 2018 with two comparative time periods 1) 2008 to 2013 and 2) 2013 to 2018. Results indicates about deforestation in the study area during 2008-2013 and afforestation in 2013-2018. Vegetative landcover was increased from 43.3% to 47.2%. Afforestation at tehsil level showed that the vegetative area in Balakot was increased from 26.6% to 29.8%. Similarly, vegetation index increased from 72.2% to 74.42% in Manshera and 82.7% to 83.5% in Oghi. Kappa coefficient performed well to access accuracy of classified imagery which was maximum for the classified map obtained using Sentinel-2 dataset, therefore, Sentinel-2 imagery was proved more reliable in comparison to Landsat imagery. The spectral responses of various land use classes were also mapped which are useful of other researches to recognize features through optical datasets. Results proved the sincere efforts of Khyber Pakhtunkhwa government in promotion of vegetated landcover. The coverage of KPK project must be enhanced for increasing vegetation for a green Pakistan.
{"title":"Appraisal of Deforestation in District Mansehra through Sentinel-2 and Landsat Imagery.","authors":"G. Nabi, I. S. Kaukab","doi":"10.33411/IJASD/20190102","DOIUrl":"https://doi.org/10.33411/IJASD/20190102","url":null,"abstract":"Forests are the main source of food/wood and are important for a healthy environment. Removal of trees from forested landcover is known as deforestation. The main objective of this study was to estimate temporal variations in forested landcover located in district Mansehra for the years from 2008 to 2018 with two comparative time periods 1) 2008 to 2013 and 2) 2013 to 2018. Results indicates about deforestation in the study area during 2008-2013 and afforestation in 2013-2018. Vegetative landcover was increased from 43.3% to 47.2%. Afforestation at tehsil level showed that the vegetative area in Balakot was increased from 26.6% to 29.8%. Similarly, vegetation index increased from 72.2% to 74.42% in Manshera and 82.7% to 83.5% in Oghi. Kappa coefficient performed well to access accuracy of classified imagery which was maximum for the classified map obtained using Sentinel-2 dataset, therefore, Sentinel-2 imagery was proved more reliable in comparison to Landsat imagery. The spectral responses of various land use classes were also mapped which are useful of other researches to recognize features through optical datasets. Results proved the sincere efforts of Khyber Pakhtunkhwa government in promotion of vegetated landcover. The coverage of KPK project must be enhanced for increasing vegetation for a green Pakistan.","PeriodicalId":337703,"journal":{"name":"International Journal of Agriculture & Sustainable Development","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114511427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
About half of world’s population intake rice as a staple food. As being water baby, rice need surplus of water to get targeted yield. Water scarcity has become a global issue therefore it has become a need to enhance the rice yield with reduced amount of water. In this research we used ecological parameters e.g., temperature, pressure, actual vapor pressure, sunshine hours and the extraterrestrial radiation to compute net radiations, ground and sensible heat fluxes on daily basis. Net shortwave radiations were observed as 23087 w/m2 in comparison to net longwave radiations which were 4387 w/m2 for the complete Rice Growth Period (RGP). The soil heat flux Go was observed as 3104 w/m2. Go was observed dependent upon the Leaf Area Index (LAI) with inverse relationship between them. Sensible heat flux (H) was measured as 1771 w/m2 throughout the RGP. H was observed dependent upon net radiations with a direct relationship between them. Rn, Go and H were used as input parameters to compute water stress which determines the excess of water in early growth stages of rice crop and water scarcity in the ripening stage. The flow of methodology is easily applicable at domestic level to determine water stress in rice fields.
{"title":"Estimation of Water Stress on Rice Crop Using Ecological Parameters.","authors":"M. Saifullah, Bilal Islam","doi":"10.33411/IJASD/20190103","DOIUrl":"https://doi.org/10.33411/IJASD/20190103","url":null,"abstract":"About half of world’s population intake rice as a staple food. As being water baby, rice need surplus of water to get targeted yield. Water scarcity has become a global issue therefore it has become a need to enhance the rice yield with reduced amount of water. In this research we used ecological parameters e.g., temperature, pressure, actual vapor pressure, sunshine hours and the extraterrestrial radiation to compute net radiations, ground and sensible heat fluxes on daily basis. Net shortwave radiations were observed as 23087 w/m2 in comparison to net longwave radiations which were 4387 w/m2 for the complete Rice Growth Period (RGP). The soil heat flux Go was observed as 3104 w/m2. Go was observed dependent upon the Leaf Area Index (LAI) with inverse relationship between them. Sensible heat flux (H) was measured as 1771 w/m2 throughout the RGP. H was observed dependent upon net radiations with a direct relationship between them. Rn, Go and H were used as input parameters to compute water stress which determines the excess of water in early growth stages of rice crop and water scarcity in the ripening stage. The flow of methodology is easily applicable at domestic level to determine water stress in rice fields.","PeriodicalId":337703,"journal":{"name":"International Journal of Agriculture & Sustainable Development","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124015688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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