检索结果-南通市图书馆

Exploring the potential of Sentinel-2A satellite data for aboveground biomass estimation in fragmented Himalayan subtropical pine forest

引用

Journal of Mountain Science 2020年第12期17卷 2880-2896页

作者： mobushir riaz khani Iftikhar Ahmad KHAN Muhammad Hasan Ali BAIG LIU Zheng-jia Muhammad Irfan ASHRAF School of Environmental Science Charles Sturt UniversityAlburyNSW 2640Australia Institute of Geo-information&Earth Observation Arid Agriculture University RawalpindiPakistan&(ii)Department of ForestAzad Jammu&Kashmir Muzaffarabad-i3100Pakistan Institute of Geo-information&Earth Observation(IGEO) PMAS Arid Agriculture University Rawalpindi 46300Pakistan Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Forestry&Range Management PMAS Arid Agriculture University Rawalpindi 46300Pakistan

The Sentinel-2 A satellite having embedded advantage of red edge spectral bands offers multispectral imageries with improved spatial,spectral and temporal resolutions as compared to the other contemporary satellites providing medium resolution *** study was aimed at exploring the potential of Sentinel-2 A imagery to estimate Above Ground Biomass(AGB) of Subtropical Pine Forest in Pakistan administered *** developed an AGB predictive model using field inventory and Sentinel 2 A based spectral and textural parameters along with topographic features derived from ALOS Digital Elevation Model(DEM).Field inventory data was collected from 108 randomly distributed plots(0.1 ha each) across the study *** stepwise linear regression method was employed to investigate the potential relationship between field data and corresponding satellite *** and carbon mapping of the study area was carried out through established AGB estimation model with R(o.86),R2(0.74),adjusted R2(0.72) and RMSE value of 33 t/*** results showed that first order textures(mean,standard deviation and variance) significantly contributed in AGB predictive modeling while only one spectral band ratio made contribution from spectral *** study leads to the conclusion that Sentinel-2 A optical data is a potential source for AGB estimation in subtropical pine forest of the area of interest with added benefit of its free of cost availability,higher quality data and long-term continuity that can be utilized for biomass carbon distribution mapping in the resource constraint study area for sustainable forest management.

关键词： Field inventory Forest Biomass Sentinel 2A AGB Modelling Spectral features Textures

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Mapping of cropland,cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest

引用

Geo-Spatial Information Science 2023年第3期26卷 302-320页

作者： Aqil Tariq Jianguo Yan Alexandre S.Gagnon mobushir riaz Khan Faisal Mumtaz State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of Biological and Environmental Sciences Liverpool John Moores UniversityLiverpoolUK School of Environmental Science Charles Sturt UniversityAlburyAustralia State Key Laboratory of Remote Sensing Sciences Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences(UCAS) BeijingChina

Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote sensing is routinely ***,identifying specific crop types,cropland,and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral *** study applied a methodology to identify cropland and specific crop types,including tobacco,wheat,barley,and gram,as well as the following cropping patterns:wheat-tobacco,wheat-gram,wheat-barley,and wheat-maize,which are common in Gujranwala District,Pakistan,the study *** methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning(ML)methods,namely a Decision Tree Classifier(DTC)and a Random Forest(RF)*** best time-periods for differentiating cropland from other land cover types were identified,and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML *** methodology was subsequently evaluated using Landsat images,crop statistical data for 2020 and 2021,and field data on cropping *** results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images,together with ML techniques,for mapping not only the distribution of cropland,but also crop types and cropping patterns when validated at the county *** results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan,adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.

关键词： Sentinel-2 Random Forest cropland crop types cropping patterns Decision Tree Classifier

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Incidence of Violence Risk Mapping Using GIS: A Case Study of Pakistan

引用

Journal of Geographic Information System 2017年第6期9卷 623-636页

作者： Rizwan Ali mobushir riaz Khan Hannan Mehmood Institute of Geo-Information and Earth Observations PMAS Arid Agriculture University Rawalpindi Pakistan

Violence comes first by which human lives are being destroyed. Violence poses serious challenges to law enforcement agencies and policy makers as well as it threatens the writ of the Government. Violence condition of a country can be visualized through an accurate violence risk map. This paper presents the methodology to develop an incidence of violence (IOV) risk map using GIS. IOV data for year 2010 was collected from Pakistan Institute for Peace Studies (PIPS) that have 3104 records and cover 16 categories of attack types. IOV data was further geocoded by using online Google geocoding service. IOV risk maps were developed through two available methods: kernel density and getis-ord-gi* by giving single parameter “frequency”, and one new indexing method by giving two additional parameters as well “severity” and “probability”. For each district, IOV frequency and IOV severity values were calculated from PIPS data whereas IOV probability value was derived from the Benazir Income Support Program—Poverty Scorecard Survey (BISP—PSS). A value ranging from 1 to 5 was assigned to each of three parameters against each district and then all three parameters were multiplied with each other to generate IOV risk index that had values ranging from 1 to 125. IOV risk index was further classified through natural breaks into three categories: low risk (1 - 40), moderate risk (41 - 70) and high risk (71 - 125). For validation purpose, spatial overlay analysis was conducted between year 2011 IOV data (classified through natural breaks) and year 2010 IOV risk maps (developed through kernel density, getis-ord-gi* and indexing method). The indexing method has proved as a reliable method to develop an IOV risk map with an accuracy of 93 percent then getis-ord-gi* and kernel density having accuracy of 58 percent and 89 percent respectively. Furthermore, indexing method predicted IOV risk areas more efficiently in terms of spatial distribution. Indexing method highlighted Khuzdar, Zhob, Up

关键词： GIS Incidence of Violence Risk Map Getis-Ord-Gi* Kernel Density Indexing PIPS Pakistan

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Spatiotemporal Monitoring for Deforestation and Forest Degradation Activities in Selected Areas of Khyber Pakhtunkhwa (KPK)

引用

International Journal of Geosciences 2016年第10期7卷 1191-1207页

作者： Saif ur Rehman Khalid mobushir riaz Khan Muhammad Usman Muhammad Waqar Yasin Muhammad Shahid Iqbal Department of Geo-Informatics Pir Mehr Ali Shah Arid Agriculture University Rawalpindi Pakistan

The global significance of forest ecosystems requires precise determination of the amount of carbon stored in different forest ecosystems. Regular monitoring of forests can aid in designing efficient climate change control strategies at national and global scale specially in reducing emissions from deforestation and degradation strategies. This research is designed to focus on determining deforestation of study area from 2001 to 2011 using Remote Sensing (RS) and Geographic Information System (GIS) techniques. This research provided rate and amount of degradation of forests in the study area and was quite helpful in formulating a strategy to earn carbon credits consistently and, therefore, will help in the uplifting of the standards of local population.

关键词： Deforestation Degradation Carbon Credits Climate Change

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：