Simulating and forecasting spatio-temporal characteristic of land-use/cover change with numerical model and remote sensing: a case study in Fuxian Lake Basin, China

Yang, Chao; Wu, Guofeng; Chen, Junyi; Li, Qingquan; Ding, Kai; Wang, Guihua; Zhang, Chenchen

doi:10.1080/22797254.2019.1611387

Cited by 21 publications

(16 citation statements)

References 37 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Details of the scenarios are depicted in Table 1. [20,56]. The approach of CA-Markov in IDRISI involves two techniques: Markov chain analysis and CA.…”

Section: Impact Assessment Frameworkmentioning

confidence: 99%

Watershed Hydrological Response to Combined Land Use/Land Cover and Climate Change in Highland Ethiopia: Finchaa Catchment

Dibaba

Demissie

Miegel

2020

Water

124

View full text Add to dashboard Cite

Land use/land cover (LULC) and climate change affect the availability of water resources by altering the magnitude of surface runoff, aquifer recharge, and river flows. The evaluation helps to identify the level of water resources exposure to the changes that could help to plan for potential adaptive capacity. In this research, Cellular Automata (CA)-Markov in IDRISI software was used to predict the future LULC scenarios and the ensemble mean of four regional climate models (RCMs) in the coordinated regional climate downscaling experiment (CORDEX)-Africa was used for the future climate scenarios. Distribution mapping was used to bias correct the RCMs outputs, with respect to the observed precipitation and temperature. Then, the Soil and Water Assessment Tool (SWAT) model was used to evaluate the watershed hydrological responses of the catchment under separate, and combined, LULC and climate change. The result shows the ensemble mean of the four RCMs reported precipitation decline and increase in future temperature under both representative concentration pathways (RCP4.5 and RCP8.5). The increases in both maximum and minimum temperatures are higher for higher emission scenarios showing that RCP8.5 projection is warmer than RCP4.5. The changes in LULC brings an increase in surface runoff and water yield and a decline in groundwater, while the projected climate change shows a decrease in surface runoff, groundwater and water yield. The combined study of LULC and climate change shows that the effect of the combined scenario is similar to that of climate change only scenario. The overall decline of annual flow is due to the decline in the seasonal flows under combined scenarios. This could bring the reduced availability of water for crop production, which will be a chronic issue of subsistence agriculture. The possibility of surface water and groundwater reduction could also affect the availability of water resources in the catchment and further aggravate water stress in the downstream. The highly rising demands of water, owing to socio-economic progress, population growth and high demand for irrigation water downstream, in addition to the variability temperature and evaporation demands, amplify prolonged water scarcity. Consequently, strong land-use planning and climate-resilient water management policies will be indispensable to manage the risks.

show abstract

“…Details of the scenarios are depicted in Table 1. [20,56]. The approach of CA-Markov in IDRISI involves two techniques: Markov chain analysis and CA.…”

Section: Impact Assessment Frameworkmentioning

confidence: 99%

Watershed Hydrological Response to Combined Land Use/Land Cover and Climate Change in Highland Ethiopia: Finchaa Catchment

Dibaba

Demissie

Miegel

2020

Water

124

View full text Add to dashboard Cite

show abstract

“…Expansions are expected in the croplands, built-up and bare land categories, whereas the tree savanna, water body and shrubland categories are projected to decrease by 2035. This will have implications on the catchment's ecological integrity and water resources [69]. Expansions in built-up areas in the catchment is an indication of population growth, largely driven by rural-urban migration to the city of Gaborone, as settlements surrounding cities and towns tend to absorb the city's overspill, as also noted in other developing countries (e.g., Gidey et al [61], Maviza and Ahmed [24] and Munthali et al [20]).…”

Section: Simulation Of Lulc Changes For 2035mentioning

confidence: 95%

Analysis of the Future Land Use Land Cover Changes in the Gaborone Dam Catchment Using CA-Markov Model: Implications on Water Resources

et al. 2021

View full text Add to dashboard Cite

Land use/land cover (LULC) changes have been observed in the Gaborone dam catchment since the 1980s. A comprehensive analysis of future LULC changes is therefore necessary for the purposes of future land use and water resource planning and management. Recent advances in geospatial modelling techniques and the availability of remotely sensed data have become central to the monitoring and assessment of both past and future environmental changes. This study employed the cellular automata and Markov chain (CA-Markov) model combinations to simulate future LULC changes in the Gaborone dam catchment. Classified Landsat images from 1984, 1995, 2005 and 2015 were used to simulate the likely LULCs in 2015 and 2035. Model validation compared the simulated and observed LULCs of 2015 and showed a high level of agreement with Kappa variation estimates of Kno (0.82), Kloc (0.82) and Kstandard (0.76). Simulation results indicated a projected increase of 26.09%, 65.65% and 55.78% in cropland, built-up and bare land categories between 2015 and 2035, respectively. Reductions of 16.03%, 28.76% and 21.89% in areal coverage are expected for shrubland, tree savanna and water body categories, respectively. An increase in built-up and cropland areas is anticipated in order to meet the population’s demand for residential, industry and food production, which should be taken into consideration in future plans for the sustainability of the catchment. In addition, this may lead to water quality and quantity (both surface and groundwater) deterioration in the catchment. Moreover, water body reductions may contribute to water shortages and exacerbate droughts in an already water-stressed catchment. The loss of vegetal cover and an increase in built-up areas may result in increased runoff incidents, leading to flash floods. The output of the study provides useful information for land use planners and water resource managers to make better decisions in improving future land use policies and formulating catchment management strategies within the framework of sustainable land use planning and water resource management.

show abstract

“…In LUCC tasks, Kappa coefficient and FoM are mostly adopted to verify model accuracy 43,44 . Kappa coefficient is a statistical measure of the reliability or consistency of internal raters, and defined as follows: Here, K and N represent the number of classes and the total number of samples, respectively.…”

Section: Methodsmentioning

confidence: 99%

Voxel-level forecast system for lesion development in patients with COVID-19

Cheng

Duan

Cao

et al. 2020

Preprint

View full text Add to dashboard Cite

The global spread of COVID-19 seriously endangers human health and even lives. By predicting patients’ individualized disease development and further performing intervention in time, we may rationalize scarce medical resources and reduce mortality. Based on 1337 multi-stage (≥3) high-resolution chest computed tomography (CT) images of 417 infected patients from three centers in the epidemic area, we proposed a random forest + cellular automata (RF+CA) model to forecast voxel-level lesion development of patients with COVID-19. The model showed a promising prediction performance (Dice similarity coefficient [DSC] = 71.1%, Kappa coefficient = 0.612, Figure of Merit [FoM] = 0.257, positional accuracy [PA] = 3.63) on the multicenter dataset. Using this model, multiple driving factors for the development of lesions were determined, such as distance to various interstitials in the lung, distance to the pleura, etc. The driving processes of these driving factors were further dissected and explained in depth from the perspective of pathophysiology, to explore the mechanism of individualized development of COVID-19 disease. The complete codes of the forecast system are available at https://github.com/keyunj/VVForecast_covid19.

show abstract

Simulating and forecasting spatio-temporal characteristic of land-use/cover change with numerical model and remote sensing: a case study in Fuxian Lake Basin, China

Abstract: Zhang (2019) Simulating and forecasting spatio-temporal characteristic of land-use/ cover change with numerical model and remote sensing: a case study in Fuxian Lake Basin, China,

Cited by 21 publications

References 37 publications

Watershed Hydrological Response to Combined Land Use/Land Cover and Climate Change in Highland Ethiopia: Finchaa Catchment

Watershed Hydrological Response to Combined Land Use/Land Cover and Climate Change in Highland Ethiopia: Finchaa Catchment

Analysis of the Future Land Use Land Cover Changes in the Gaborone Dam Catchment Using CA-Markov Model: Implications on Water Resources

Voxel-level forecast system for lesion development in patients with COVID-19

Contact Info

Product

Resources

About