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.

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.

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 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.

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 Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series

2017 ◽  
Vol 9 (1) ◽  
pp. 95 ◽  
Author(s):  
Jordi Inglada ◽  
Arthur Vincent ◽  
Marcela Arias ◽  
Benjamin Tardy ◽  
David Morin ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Image ◽  
Land Cover Map

Generation and analysis of the 2005 land cover map for Mexico using 250m MODIS data

2012 ◽  
Vol 123 ◽  
pp. 541-552 ◽  
Author(s):  
René R. Colditz ◽  
Gerardo López Saldaña ◽  
Pedro Maeda ◽  
Jesús Argumedo Espinoza ◽  
Carmen Meneses Tovar ◽  
...  
Keyword(s):  
Land Cover ◽  
Modis Data ◽  
Land Cover Map

scholarly journals Long Time Series High-Quality and High-Consistency Land Cover Mapping Based on Machine Learning Method at Heihe River Basin

2021 ◽  
Vol 13 (8) ◽  
pp. 1596
Author(s):  
Bo Zhong ◽  
Aixia Yang ◽  
Kunsheng Jue ◽  
Junjun Wu
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Regional Scale ◽  
Heihe River ◽  
Land Cover Mapping ◽  
High Quality ◽  
Long Time Series ◽  
Training Samples ◽  
Long Time ◽  
Land Cover Map

Long time series of land cover changes (LCCs) are critical in the analysis of long-term climate, environmental, and ecological changes. Although several moderate to fine resolution global land cover datasets have been publicly released and they show strong consistency at the global scale, they have large deviations at the regional scale; furthermore, high-quality land cover datasets from before 2000 are not available and the classification consistency among different datasets is not very good. Thus, long time series of land cover datasets with high quality and consistency are in great demand but they are still unavailable, even at the regional scale. The Landsat series of satellite imagery composed of eight successive satellites can be traced back to 1972 and it is, therefore, possible to produce a long time series land cover dataset. In addition, the newly available satellite data have the capability to construct time series satellite images and a time series analysis method such as LCMM can be employed for making high-quality land cover datasets. Therefore, by taking the advantages of the two categories of satellite data, we proposed a new time series land cover mapping method based on machine learning and it, thereafter, is applied to Heihe River Basin (HRB) for verification purposes. Firstly, the high-quality land cover datasets at HRB from 2011–2015, which were retrieved using the LCMM method, are used for quickly and accurately making training samples. Secondly, a strategy for transferring the training samples after 2011 to earlier years is established. Thirdly, the random forest model is employed to train the selected yearly samples and a land cover map for every year is subsequently made. Finally, comprehensive analysis and validation are carried out for evaluation. In this study, a long time series land cover dataset including 1986, 1990, 1995, 2000, 2005, 2010, 2011, 2012, 2013, 2014, and 2015 is finally made and an average precision of about 90% is achieved. It is the longest time series land cover map with 30 m resolution at HRB and the dataset has good time continuity and stability.

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

2015 ◽  
Vol 12 (6) ◽  
pp. 5789-5840 ◽  
Author(s):  
C. Mathison ◽  
A. J. Wiltshire ◽  
P. Falloon ◽  
A. J. Challinor
Keyword(s):  
Water Resources ◽  
South Asia ◽  
Water Resource ◽  
River Flow ◽  
Regional Climate ◽  
Climate Projections ◽  
The South ◽  
River Flows ◽  
Flow Rates ◽  
The Future

Abstract. South Asia is a region with a large and rising population and a high dependance on industries sensitive to water resource such as agriculture. The climate is hugely variable with the region relying on both the Asian Summer Monsoon (ASM) and glaciers for its supply of fresh water. In recent years, changes in the ASM, fears over the rapid retreat of glaciers and the increasing demand for water resources for domestic and industrial use, have caused concern over the reliability of water resources both in the present day and future for this region. The climate of South Asia means it is one of the most irrigated agricultural regions in the world, therefore pressures on water resource affecting the availability of water for irrigation could adversely affect crop yields and therefore food production. In this paper we present the first 25 km resolution regional climate projections of river flow for the South Asia region. ERA-Interim, together with two global climate models (GCMs), which represent the present day processes, particularly the monsoon, reasonably well are downscaled using a regional climate model (RCM) for the periods; 1990–2006 for ERA-Interim and 1960–2100 for the two GCMs. The RCM river flow is routed using a river-routing model to allow analysis of present day and future river flows through comparison with river gauge observations, where available. In this analysis we compare the river flow rate for 12 gauges selected to represent the largest river basins for this region; Ganges, Indus and Brahmaputra basins and characterize the changing conditions from east to west across the Himalayan arc. Observations of precipitation and runoff in this region have large or unknown uncertainties, are short in length or are outside the simulation period, hindering model development and validation designed to improve understanding of the water cycle for this region. In the absence of robust observations for South Asia, a downscaled ERA-Interim RCM simulation provides a benchmark for comparison against the downscaled GCMs. On the basis that these simulations are among the highest resolution climate simulations available we examine how useful they are for understanding the changes in water resources for the South Asia region. In general the downscaled GCMs capture the seasonality of the river flows, with timing of maximum river flows broadly matching the available observations and the downscaled ERA-Interim simulation. Typically the RCM simulations over-estimate the maximum river flows compared to the observations probably due to a positive rainfall bias and a lack of abstraction in the model although comparison with the downscaled ERA-Interim simulation is more mixed with only a couple of the gauges showing a bias compared with the downscaled GCM runs. 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; this trend is generally masked by the large annual variability of river flows for this region. The future seasonality of river flows does not change with the future maximum river flow rates still occuring during the ASM period, with a magnitude in some cases, greater than the present day natural variability. Increases in river flow during peak flow periods means additional water resource for irrigation, the largest usage of water in this region, but also has implications in terms of inundation risk. Low flow rates also increase which is likely to be important at times of the year when water is historically more scarce. However these projected increases in resource from rivers could be more than countered by changes in demand due to reductions in the quantity and quality of water available from groundwater, increases in domestic use due to a rising population or expansion of other industries such as hydro-electric power generation.

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