scholarly journals Monitoring land use changing detection maps with MODIS and AVHRR data from 2001 to 2015 in South Asia regions

2021 ◽  
Author(s):  
shahzad ali ◽  
Huang An Qi ◽  
Malak Henchiri ◽  
Zhang Sha ◽  
Fahim Ullah Khan ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
South Asia ◽  
Earth Science ◽  
Regional Climate ◽  
The South ◽  
Time Dynamics ◽  
Land Cover Map ◽  
Gimms Ndvi3g

Abstract In South Asia, annual land cover and land use (LCLU) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vital essential to obtain correct information on the LCLU in the South Asia regions. LULC annual map covering the entire period is the primary dataset for climatological research. Although the LULC annual global map was produced from the MODIS dataset in 2001, this limited the perspective of the climatological analysis. This study used AVHRR GIMMS NDVI3g data from 2001 to 2015 to randomly forests classify and produced a time series of the annual LCLU map of the South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using of the annual map of LCLU time series, and the space-time dynamics of the LCLU map were shown in the last 15 years, from 2001 to 2015. The overall precision of our 15-year land cover map simplifies 16 classes, which is 1.23% and 86.70% significantly maximum as compared to the precision of the MODIS data map. Findings of the past 15 years shows the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrub-lands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions.

Studying of Land Cover And Use Maps of South Asia Regions From 2001 To 2015 With AVHRR And MODIS Data

2021 ◽  
Author(s):  
Shahzad Ali ◽  
Huang An Qi ◽  
Malak Henchiri ◽  
Zhang Sha ◽  
Fahim Ullah Khan ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
South Asia ◽  
Earth Science ◽  
Regional Climate ◽  
The South ◽  
Time Dynamics ◽  
Modis Data ◽  
Land Cover Map ◽  
Gimms Ndvi3g

Abstract In South Asia, annual land cover and land use (LCLU) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vital essential to obtain correct information on the LCLU in the South Asia regions. LULC annual map covering the entire period is the primary dataset for climatological research. Although the LULC annual global map was produced from the MODIS dataset in 2001, this limited the perspective of the climatological analysis. This study used AVHRR GIMMS NDVI3g data from 2001 to 2015 to randomly forests classify and produced a time series of the annual LCLU map of the South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using of the annual map of LCLU time series, and the space-time dynamics of the LCLU map were shown in the last 15 years, from 2001 to 2015. The overall precision of our 15-year land cover map simplifies 16 classes, which is 1.23% and 86.70% significantly maximum as compared to the precision of the MODIS data map. Findings of the past 15 years shows the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrub-lands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions.

A time series of land cover maps of South Asia from 2001 to 2015 generated using AVHRR GIMMS NDVI3g data

2020 ◽  
Vol 27 (16) ◽  
pp. 20309-20320
Author(s):  
Shahzad Ali ◽  
Malak Henchiri ◽  
Zhang Sha ◽  
Kalisa Wilson ◽  
Bai Yun ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
South Asia ◽  
Land Cover Maps ◽  
Gimms Ndvi3g

scholarly journals Characterizing Land Use/Land Cover Using Multi-Sensor Time Series from the Perspective of Land Surface Phenology

2019 ◽  
Vol 11 (14) ◽  
pp. 1677 ◽  
Author(s):  
Lan H. Nguyen ◽  
Geoffrey M. Henebry
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Land Surface ◽  
Quadratic Model ◽  
Observation Data ◽  
Land Use Land Cover ◽  
Land Surface Phenology ◽  
Land Cover Map ◽  
Phenological Metrics

Due to a rapid increase in accessible Earth observation data coupled with high computing and storage capabilities, multiple efforts over the past few years have aimed to map land use/land cover using image time series with promising outcomes. Here, we evaluate the comparative performance of alternative land cover classifications generated by using only (1) phenological metrics derived from either of two land surface phenology models, or (2) a suite of spectral band percentiles and normalized ratios (spectral variables), or (3) a combination of phenological metrics and spectral variables. First, several annual time series of remotely sensed data were assembled: Accumulated growing degree-days (AGDD) from the MODerate resolution Imaging Spectroradiometer (MODIS) 8-day land surface temperature products, 2-band Enhanced Vegetation Index (EVI2), and the spectral variables from the Harmonized Landsat Sentinel-2, as well as from the U.S. Landsat Analysis Ready Data surface reflectance products. Then, at each pixel, EVI2 time series were fitted using two different land surface phenology models: The Convex Quadratic model (CxQ), in which EVI2 = f(AGDD) and the Hybrid Piecewise Logistic Model (HPLM), in which EVI2 = f(day of year). Phenometrics and spectral variables were submitted separately and together to Random Forest Classifiers (RFC) to depict land use/land cover in Roberts County, South Dakota. HPLM RFC models showed slightly better accuracy than CxQ RFC models (about 1% relative higher in overall accuracy). Compared to phenometrically-based RFC models, spectrally-based RFC models yielded more accurate land cover maps, especially for non-crop cover types. However, the RFC models built from spectral variables could not accurately classify the wheat class, which contained mostly spring wheat with some fields in durum or winter varieties. The most accurate RFC models were obtained when using both phenometrics and spectral variables as inputs. The combined-variable RFC models overcame weaknesses of both phenometrically-based classification (low accuracy for non-vegetated covers) and spectrally-based classification (low accuracy for wheat). The analysis of important variables indicated that land cover classification for this study area was strongly driven by variables related to the initial green-up phase of seasonal growth and maximum fitted EVI2. For a deeper evaluation of RFC performance, RFC classifications were also executed with several alternative sampling scenarios, including different spatiotemporal filters to improve accuracy of sample pools and different sample sizes. Results indicated that a sample pool with less filtering yielded the most accurate predicted land cover map and a stratified random sample dataset covering approximately 0.25% or more of the study area were required to achieve an accurate land cover map. In case of data scarcity, a smaller dataset might be acceptable, but should not smaller than 0.05% of the study area.

scholarly journals Analysis Of Carbon Emission Level on Merauke Regency Land Cover

2018 ◽  
Vol 73 ◽  
pp. 08013
Author(s):  
Yus Untarf ◽  
Witdarko ◽  
Sembiring Jefri
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Development Strategy ◽  
Land Use Planning ◽  
Carbon Emission ◽  
Unit Area ◽  
Emission Level ◽  
Low Emission ◽  
Land Cover Map

This research is aimed to know level of carbon emission from land cover change in Merauke Regency. The data are historical baseline which is land cover map time series of 1990, 2000, 2005, 2010 and 2014, also zoning plan of Merauke Regency in 2010-2030. Furthermore, the data are processed with software LUMENS (land use planning for low emission development strategy). The result is presented descriptively. From the result, it can be concluded that based on analysis of carbon emission level in 1990 - 2000, it is found 1948773.523 Ton CO2/year with emission level per unit area in amount of 0.421 Ton CO2eq/ha.year; observation in 2000 - 2005 found emission level in amount of 6151442.314 Ton CO2/year with emission per unit area 1.336 Ton CO2eq/ha.year; observation in 2005 - 2010 found emission level per year in amount of 41386219.77 Ton CO2/year and emission level per unit area in amount of 9.4 Ton CO2eq/ha.year; meanwhile, Merauke Regency's emission level in 2010 - 2014 is 61816894.268 Ton CO2/year with emission level per unit area in amount of 13.928 Ton CO2eq/ha.year.

scholarly journals South Asia river-flow projections and their implications for water resources

2015 ◽  
Vol 19 (12) ◽  
pp. 4783-4810 ◽  
Author(s):  
C. Mathison ◽  
A. J. Wiltshire ◽  
P. Falloon ◽  
A. J. Challinor
Keyword(s):  
Water Resources ◽  
South Asia ◽  
River Flow ◽  
Climate Model ◽  
Asian Summer Monsoon ◽  
Regional Climate ◽  
Global Climate Models ◽  
Climate Projections ◽  
The South ◽  
River Flows

Abstract. South Asia is a region with a large and rising population, a high dependence on water intense industries, such as agriculture and a highly variable climate. In recent years, fears over the changing Asian summer monsoon (ASM) and rapidly retreating glaciers together with increasing demands for water resources have caused concern over the reliability of water resources and the potential impact on intensely irrigated crops in this region. Despite these concerns, there is a lack of climate simulations with a high enough resolution to capture the complex orography, and water resource analysis is limited by a lack of observations of the water cycle for the region. In this paper we present the first 25 km resolution regional climate projections of river flow for the South Asia region. Two global climate models (GCMs), which represent the ASM reasonably well are downscaled (1960–2100) using a regional climate model (RCM). In the absence of robust observations, ERA-Interim reanalysis is also downscaled providing a constrained estimate of the water balance for the region for comparison against the GCMs (1990–2006). The RCM river flow is routed using a river-routing model to allow analysis of present-day and future river flows through comparison with available river gauge observations. We examine how useful these simulations are for understanding potential changes in water resources for the South Asia region. In general the downscaled GCMs capture the seasonality of the river flows but overestimate the maximum river flows compared to the observations probably due to a positive rainfall bias and a lack of abstraction in the model. The simulations suggest an increasing trend in annual mean river flows for some of the river gauges in this analysis, in some cases almost doubling by the end of the century. The future maximum river-flow rates still occur during the ASM period, with a magnitude in some cases, greater than the present-day natural variability. Increases in river flow could mean additional water resources for irrigation, the largest usage of water in this region, but has implications in terms of inundation risk. These projected increases could be more than countered by changes in demand due to depleted groundwater, increases in domestic use or expansion of water intense industries. Including missing hydrological processes in the model would make these projections more robust but could also change the sign of the projections.

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