scholarly journals Estimating the area burned by agricultural fires from Landsat 8 Data using the Vegetation Difference Index and Burn Scar Index

2018 ◽  
Vol 27 (4) ◽  
pp. 217 ◽  
Author(s):  
Shudong Wang ◽  
Muhammad Hasan Ali Baig ◽  
Suhong Liu ◽  
Huawei Wan ◽  
Taixia Wu ◽  
...  
Keyword(s):  
Accurate Estimate ◽  
Central China ◽  
Background Information ◽  
Haihe River Basin ◽  
Burned Area ◽  
Model Parameters ◽  
Landsat 8 ◽  
Haihe River ◽  
Burn Scar ◽  
The Haihe River Basin

Obtaining an accurate estimate of the area of burned crops through remote sensing provides extremely useful data for the assessment of fire-induced trace gas emissions and grain loss in agricultural areas. A new method, incorporating the Vegetation Difference Index (VDI) and Burn Scar Index (BSI) models, is proposed for the extraction of burned crops area. The VDI model can greatly reduce the confounding effect of background information pertaining to green vegetation (forests and grasslands), water bodies and buildings; subsequent use of the BSI model could improve the accuracy of burned area estimations because of the reduction in the influence of background information. The combination of VDI and BSI enables the VDI to reduce the effect of non-farmland information, which in turn improves the accuracy and speed of the BSI model. The model parameters were established, and an effects analysis was performed, using a normalized dispersion value simulation based on a comparison of different types of background information. The efficacy of the VDI and BSI models was tested for a winter wheat planting area in the Haihe River Basin in central China. In comparison with other models, it was found that this method could effectively extract burned area information.

An improved combined vegetation difference index and burn scar index approach for mapping cropland burned areas using combined data from Landsat 8 multispectral and thermal infrared bands

2020 ◽  
Vol 29 (6) ◽  
pp. 499
Author(s):  
Shufu Liu ◽  
Shudong Wang ◽  
Tianhe Chi ◽  
Congcong Wen ◽  
Taixia Wu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Land Surface ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Thermal Infrared ◽  
Haihe River Basin ◽  
Burned Area ◽  
Landsat 8 ◽  
Burn Scar ◽  
Burned Areas

The accurate extraction of agricultural burned area is essential for fire-induced air quality models and assessments of agricultural grain loss and wildfire disasters. The present study provides an improved approach for mapping uncontrolled cropland burned areas, which involves pre-classification using a difference vegetation index model for various agricultural land scenarios. Land surface temperature was analysed in burned and unburned areas and integrated into a previous burn scar index (BSI) model, and multispectral and thermal infrared information were combined to create a new temperature BSI (TBSI) to remove background noise. The TBSI model was applied to a winter wheat agricultural region in the Haihe River Basin in northern China. The extracted burned areas were validated using Gaofen-1 satellite data and compared with those produced by the previous BSI model. The producer and user accuracy of the new TBSI model were measured at 92.42 and 95.31% respectively, with an overall kappa value of 0.92, whereas those of the previous BSI model were 83.33, 87.30% and 0.86. The results indicate that the new method is more appropriate for mapping uncontrolled winter wheat burned area. Potential applications of this research include trace gas emission models, agricultural fire management and agricultural wildfire disaster assessment.

Long-term variation of water vapor content and precipitation in the Haihe river basin

2014 ◽  
Vol 6 (2) ◽  
pp. 341-351 ◽  
Author(s):  
Chun Chang ◽  
Ping Feng ◽  
Fawen Li ◽  
Yunming Gao
Keyword(s):  
Water Vapor ◽  
River Basin ◽  
Reanalysis Data ◽  
Water Vapor Content ◽  
Haihe River Basin ◽  
Haihe River ◽  
Annual Water ◽  
Environmental Prediction ◽  
High Consistency ◽  
The Haihe River Basin

Based on the Haihe river basin National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data from 1948 to 2010 and the precipitation data of 53 hydrological stations during 1957–2010, this study analyzed the variation of water vapor content and precipitation, and investigated the correlation between them using several statistical methods. The results showed that the annual water vapor content decreased drastically from 1948 to 2010. It was comparatively high from the late 1940s to the late 1960s and depreciated from the early 1970s. From the southeast to the northwest of the Haihe river basin, there was a decrease in water vapor content. For vertical distribution, water vapor content from the ground to 700 hPa pressure level accounted for 72.9% of the whole atmospheric layer, which indicated that the water vapor of the Haihe river basin was mainly in the air close to the ground. The precipitation in the Haihe river basin during 1957–2010 decreased very slightly. According to the correlation analysis, the precipitation and water vapor content changes showed statistically positive correlation, in addition, their break points were both in the 1970s. Furthermore, the high consistency between the precipitation efficiency and precipitation demonstrates that water vapor content is one of the important factors in the formation of precipitation.

scholarly journals Spatial and Temporal Variation Characteristics of Snowfall in the Haihe River Basin from 1960 to 2016

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1798
Author(s):  
Xu Wu ◽  
Su Li ◽  
Bin Liu ◽  
Dan Xu
Keyword(s):  
Temporal Variation ◽  
River Basin ◽  
Spatial And Temporal Variation ◽  
Haihe River Basin ◽  
Temperature Threshold ◽  
Threshold Method ◽  
Exponential Equation ◽  
Haihe River ◽  
The Haihe River Basin ◽  
Different Levels

The spatio-temporal variation of precipitation under global warming had been a research hotspot. Snowfall is an important part of precipitation, and its variabilities and trends in different regions have received great attention. In this paper, the Haihe River Basin is used as a case, and we employ the K-means clustering method to divide the basin into four sub-regions. The double temperature threshold method in the form of the exponential equation is used in this study to identify precipitation phase states, based on daily temperature, snowfall, and precipitation data from 43 meteorological stations in and around the Haihe River Basin from 1960 to 1979. Then, daily snowfall data from 1960 to 2016 are established, and the spatial and temporal variation of snowfall in the Haihe River Basin are analyzed according to the snowfall levels as determined by the national meteorological department. The results evalueted in four different zones show that (1) the snowfall at each meteorological station can be effectively estimated at an annual scale through the exponential equation, for which the correlation coefficient of each division is above 0.95, and the relative error is within 5%. (2) Except for the average snowfall and light snowfall, the snowfall and snowfall days of moderate snow, heavy snow, and snowstorm in each division are in the order of Zones III > IV > I > II. (3) The snowfall and the number of snowfall days at different levels both show a decreasing trend, except for the increasing trend of snowfall in Zone I. (4) The interannual variation trend in the snowfall at the different levels are not obvious, except for Zone III, which shows a significant decreasing trend.

Subdaily to Seasonal Change of Surface Energy and Water Flux of the Haihe River Basin in China: Noah and Noah-MP Assessment

2018 ◽  
Vol 36 (1) ◽  
pp. 79-92
Author(s):  
Fuqiang Yang ◽  
Li Dan ◽  
Jing Peng ◽  
Xiujing Yang ◽  
Yueyue Li ◽  
...  
Keyword(s):  
Surface Energy ◽  
River Basin ◽  
Seasonal Change ◽  
Water Flux ◽  
Haihe River Basin ◽  
Haihe River ◽  
The Haihe River Basin

scholarly journals Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Junfei Chen ◽  
Ming Li ◽  
Weiguang Wang
Keyword(s):  
Random Forest ◽  
Arima Model ◽  
Standardized Precipitation Index ◽  
Forecast Model ◽  
Haihe River Basin ◽  
Drought Forecasting ◽  
Haihe River ◽  
Proposed Model ◽  
The Haihe River Basin ◽  
Natural Climate

Drought is part of natural climate variability and ranks the first natural disaster in the world. Drought forecasting plays an important role in mitigating impacts on agriculture and water resources. In this study, a drought forecast model based on the random forest method is proposed to predict the time series of monthly standardized precipitation index (SPI). We demonstrate model application by four stations in the Haihe river basin, China. The random-forest- (RF-) based forecast model has consistently shown better predictive skills than the ARIMA model for both long and short drought forecasting. The confidence intervals derived from the proposed model generally have good coverage, but still tend to be conservative to predict some extreme drought events.

Sign in / Sign up

Export Citation Format

Share Document