scholarly journals Quantifying the Spatiotemporal Variation of Evapotranspiration of Different Land Cover Types and the Contribution of Its Associated Factors in the Xiliao River Plain

2022 ◽  
Vol 14 (2) ◽  
pp. 252
Author(s):  
Nan Lin ◽  
Ranzhe Jiang ◽  
Qiang Liu ◽  
Hang Yang ◽  
Hanlin Liu ◽  
...  
Keyword(s):  
Land Cover ◽  
Regression Model ◽  
Influencing Factors ◽  
Ridge Regression ◽  
Influencing Factor ◽  
Hydrologic Cycle ◽  
Water Evaporation ◽  
Land Cover Types ◽  
Spatiotemporal Trends ◽  
River Plain

Evapotranspiration (ET) is a vital constituent of the hydrologic cycle. Researching changes in ET is necessary for understanding variability in the hydrologic cycle. Although some studies have clarified the changes and influencing factors of ET on a regional or global scale, these variables are still unclear for different land cover types due to the range of possible water evaporation mechanisms and conditions. In this study, we first investigated spatiotemporal trends of ET in different land cover types in the Xiliao River Plain from 2000 to 2019. The correlation between meteorological, NDVI, groundwater depth, and topographic factors and ET was compared through spatial superposition analysis. We then applied the ridge regression model to calculate the contribution rate of each influencing factor to ET for different land cover types. The results revealed that ET in the Xiliao River Plain has shown a continuously increasing trend, most significantly in cropland (CRO). The correlation between ET and influencing factors differed considerably for different land cover types, even showing an opposite result between regions with and without vegetation. Only precipitation (PRCP) and NDVI had a positive impact on ET in all land cover types. In addition, we found that vegetation can deepen the limited depth of land absorbing groundwater, and the influence of topographic conditions may be mainly reflected in the water condition difference caused by surface runoff. The ridge regression model eliminates multicollinearity among influencing factors; R2 in all land cover types was over 0.6, indicating that it could be used to effectively quantify the contribution of various influencing factors to ET. According to the results of our model calculations, NDVI had the greatest impact on ET in grass (GRA), cropland (CRO), paddy (PAD), forest (FOR), and swamp (SWA), while PRCP was the main influencing factor in bare land (BAR) and sand (SAN). These findings imply that we should apply targeted measures for water resources management in different land cover types. This study emphasizes the importance of comprehensively considering differences among various hydrologic cycles according to land cover type in order to assess the contributions of influencing factors to ET.

scholarly journals Extracting relationship between air pollution and precipitation using spatio-temporal analysis in Tehran metropolis

2019 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Shokouh Dareshiri ◽  
Mohammadreza Sahelgozin ◽  
Maryam Lotfian ◽  
Jens Ingensand
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Regression Model ◽  
Influencing Factors ◽  
Polynomial Regression ◽  
Human Life ◽  
Pearson Correlation ◽  
Influencing Factor ◽  
Polynomial Equation ◽  
Temporal Analysis

<p><strong>Abstract.</strong> Precipitation is one of the main stages of the water cycle, and it is required for the organisms to survive on the planet. In contrast, air pollution is a phenomenon that has greatly affected the human life nowadays. Population growth, development of factories and increasing number of fossil fuel vehicles are the most influencing factors on air pollution. In addition to understand nature of precipitation and air pollution, finding relationship between these two phenomena is necessary to make appropriate policies for reducing air pollution. Furthermore, studying trends of precipitation and air pollution in the past, is helpful to forecast the times and places with less precipitation and more air pollution for a better urban management. In this study, we tried to extract any probable relationship between these two parameters by investigating their monthly measured amounts in 22 municipal districts of Tehran in three epochs of time (2009, 2013 and 2017). Carbon Monoxide (CO) was considered as the indicator of air pollution. Results of the study show that the parameters have a significant relationship with each other. By using Pearson Correlation Coefficient and One-Way Variance (ANOVA) test, relationship between the data for each month and for each district of Tehran were studied separately. As the time has passed and the air pollution has increased, the correlation between the parameters in districts has decreased. In addition, during the cold months of the year, the correlations decrease since the fact that precipitation is not the only influencing factor on the air pollution due to the rise of air “Inversion”. Finally, the polynomial regression model of carbon monoxide based on precipitation was extracted for each of the three years. The model suggests a degree three polynomial equation. The obtained coefficients from the regression model show that the relationship between parameters was stronger in the years with more rainfalls. This can be due to the more significant impact of other influencing factors on air pollution, such as population density, wind direction, vehicles and factories in the areas or conditions with a less rainfall.</p>

scholarly journals Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province, China

2018 ◽  
Author(s):  
Ke Zhang ◽  
Sheng Wang ◽  
Hongjun Bao ◽  
Xiaomeng Zhao
Keyword(s):  
Land Cover ◽  
Debris Flow ◽  
Influencing Factors ◽  
Soil Type ◽  
Influencing Factor ◽  
Northwest China ◽  
Shaanxi Province ◽  
Occurrence Rate ◽  
Spatiotemporal Characteristics ◽  
Ground Survey

Abstract. Shaanxi Province, located in Northwest China and spanning multiple hydroclimatic and geological zones, has many areas largely suffering from rainfall-induced landslide and debris flow. The objectives of this study are to reveal the spatiotemporal characteristics of the two hazards and identify their major controlling factors in this region based on a region-wide, comprehensive ground survey-based hazard inventory dataset from 2009–2012. We investigated the spatiotemporal characteristics of the two hazards and quantified the relationships between the occurrence rates of the two hazards and their influencing factors, including antecedent rainfall amount, rainfall duration, rainfall intensity, terrain slope, land cover type and soil type. The results show that landslide has a higher occurrence rate and more extensive distribution than debris flow in this region, while the two hazards are both concentrated in the south with ample rainfall and steep terrains. Both of the hazards show clear seasonalities: July–September for landslide and July for debris flow. Rainfall characteristics (amount, duration and intensity) and slope are the dominant factors controlling slope stability across this region. Debris flow is more sensitive to these rainfall metrics on the high-value ranges than landslide in this region. Land cover is another influencing factor but soil type does not appear to impose consistent impacts on the occurrence of the two hazards. This study not only provides an important inventory data for studying the landslide and debris flow hazards but also add valuable information for modeling and predicting the two hazards to enhance resilience to these hazards in this region.

scholarly journals Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province, China

2019 ◽  
Vol 19 (1) ◽  
pp. 93-105 ◽  
Author(s):  
Ke Zhang ◽  
Sheng Wang ◽  
Hongjun Bao ◽  
Xiaomeng Zhao
Keyword(s):  
Land Cover ◽  
Debris Flow ◽  
Influencing Factors ◽  
Soil Type ◽  
Influencing Factor ◽  
Northwest China ◽  
Shaanxi Province ◽  
Occurrence Rate ◽  
Spatiotemporal Characteristics ◽  
Ground Survey

Abstract. Shaanxi Province, located in northwest China and spanning multiple hydroclimatic and geological zones, has many areas largely suffering from rainfall-induced landslide and debris flow. The objectives of this study are to reveal the spatiotemporal characteristics of the two hazards and identify their major controlling factors in this region based on a region-wide, comprehensive ground-survey-based hazard inventory dataset from 2009 to 2012. We investigated the spatiotemporal characteristics of the two hazards and quantified the relationships between the occurrence rates of the two hazards and their influencing factors, including antecedent rainfall amount, rainfall duration, rainfall intensity, terrain slope, land cover type and soil type. The results show that landslide has a higher occurrence rate and more extensive distribution than debris flow in this region, while the two hazards are both concentrated in the south with ample rainfall and steep terrains. Both of the hazards show clear seasonalities: July–September for landslide and July for debris flow. Rainfall characteristics (amount, duration and intensity) and slope are the dominant factors controlling slope stability across this region. Debris flow is more sensitive to these rainfall metrics on the high-value ranges than landslide in this region. Land cover is another influencing factor but soil type does not appear to impose consistent impacts on the occurrence of the two hazards. This study not only provides important inventory data for studying the landslide and debris flow hazards but also adds valuable information for modeling and predicting the two hazards to enhance resilience to these hazards in this region.

Soil moisture dynamics of different land-cover types in the blacksoil regions of China

2009 ◽  
Vol 17 (2) ◽  
pp. 256-260 ◽  
Author(s):  
Feng WANG ◽  
Shu-Qi WANG ◽  
Xiao-Zeng HAN ◽  
Feng-Xian WANG ◽  
Ke-Qiang ZHANG
Keyword(s):  
Soil Moisture ◽  
Land Cover ◽  
Soil Moisture Dynamics ◽  
Land Cover Types ◽  
Moisture Dynamics

scholarly journals Tree Species Diversity, Composition and Aboveground Biomass Across Dry Forest Land-Cover Types in Coastal Ecuador

2021 ◽  
Vol 14 ◽  
pp. 194008292199541
Author(s):  
Xavier Haro-Carrión ◽  
Bette Loiselle ◽  
Francis E. Putz
Keyword(s):  
Species Diversity ◽  
Land Cover ◽  
Community Composition ◽  
Aboveground Biomass ◽  
Tree Species ◽  
Dry Forest ◽  
Stem Density ◽  
Tree Species Diversity ◽  
Land Cover Types ◽  
Schizolobium Parahyba

Tropical dry forests (TDF) are highly threatened ecosystems that are often fragmented due to land-cover change. Using plot inventories, we analyzed tree species diversity, community composition and aboveground biomass patterns across mature (MF) and secondary forests of about 25 years since cattle ranching ceased (SF), 10–20-year-old plantations (PL), and pastures in a TDF landscape in Ecuador. Tree diversity was highest in MF followed by SF, pastures and PL, but many endemic and endangered species occurred in both MF and SF, which demonstrates the importance of SF for species conservation. Stem density was higher in PL, followed by SF, MF and pastures. Community composition differed between MF and SF due to the presence of different specialist species. Some SF specialists also occurred in pastures, and all species found in pastures were also recorded in SF indicating a resemblance between these two land-cover types even after 25 years of succession. Aboveground biomass was highest in MF, but SF and Tectona grandis PL exhibited similar numbers followed by Schizolobium parahyba PL, Ochroma pyramidale PL and pastures. These findings indicate that although species-poor, some PL equal or surpass SF in aboveground biomass, which highlights the critical importance of incorporating biodiversity, among other ecosystem services, to carbon sequestration initiatives. This research contributes to understanding biodiversity conservation across a mosaic of land-cover types in a TDF landscape.

scholarly journals Cooling Effect of Different Land Cover Types: A Case Study in Xi’an and Xianyang, China

2021 ◽  
Vol 13 (3) ◽  
pp. 1099
Author(s):  
Yuhe Ma ◽  
Mudan Zhao ◽  
Jianbo Li ◽  
Jian Wang ◽  
Lifa Hu
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Cooling Effect ◽  
Heat Island ◽  
Land Cover Types ◽  
Island Effect ◽  
Urban Heat

One of the climate problems caused by rapid urbanization is the urban heat island effect, which directly threatens the human survival environment. In general, some land cover types, such as vegetation and water, are generally considered to alleviate the urban heat island effect, because these landscapes can significantly reduce the temperature of the surrounding environment, known as the cold island effect. However, this phenomenon varies over different geographical locations, climates, and other environmental factors. Therefore, how to reasonably configure these land cover types with the cooling effect from the perspective of urban planning is a great challenge, and it is necessary to find the regularity of this effect by designing experiments in more cities. In this study, land cover (LC) classification and land surface temperature (LST) of Xi’an, Xianyang and its surrounding areas were obtained by Landsat-8 images. The land types with cooling effect were identified and their ideal configuration was discussed through grid analysis, distance analysis, landscape index analysis and correlation analysis. The results showed that an obvious cooling effect occurred in both woodland and water at different spatial scales. The cooling distance of woodland is 330 m, much more than that of water (180 m), but the land surface temperature around water decreased more than that around the woodland within the cooling distance. In the specific urban planning cases, woodland can be designed with a complex shape, high tree planting density and large planting areas while water bodies with large patch areas to cool the densely built-up areas. The results of this study have utility for researchers, urban planners and urban designers seeking how to efficiently and reasonably rearrange landscapes with cooling effect and in urban land design, which is of great significance to improve urban heat island problem.

SateLoc: A Virtual Fingerprinting Approach to Outdoor LoRa Localization Using Satellite Images

2021 ◽  
Vol 17 (4) ◽  
pp. 1-28
Author(s):  
Yuxiang Lin ◽  
Wei Dong ◽  
Yi Gao ◽  
Tao Gu
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Satellite Images ◽  
Low Cost ◽  
Tracking Error ◽  
Location Based Services ◽  
Distance Constraint ◽  
Combination Strategy ◽  
Land Cover Types ◽  
High Resolution Satellite Images

With the increasing relevance of the Internet of Things and large-scale location-based services, LoRa localization has been attractive due to its low-cost, low-power, and long-range properties. However, existing localization approaches based on received signal strength indicators are either easily affected by signal fading of different land-cover types or labor intensive. In this work, we propose SateLoc, a LoRa localization system that utilizes satellite images to generate virtual fingerprints. Specifically, SateLoc first uses high-resolution satellite images to identify land-cover types. With the path loss parameters of each land-cover type, SateLoc can automatically generate a virtual fingerprinting map for each gateway. We then propose a novel multi-gateway combination strategy, which is weighted by the environmental interference of each gateway, to produce a joint likelihood distribution for localization and tracking. We implement SateLoc with commercial LoRa devices without any hardware modification, and evaluate its performance in a 227,500-m urban area. Experimental results show that SateLoc achieves a median localization error of 43.5 m, improving more than 50% compared to state-of-the-art model-based approaches. Moreover, SateLoc can achieve a median tracking error of 37.9 m with the distance constraint of adjacent estimated locations. More importantly, compared to fingerprinting-based approaches, SateLoc does not require the labor-intensive fingerprint acquisition process.

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