Spatial and temporal variations in soil respiration among different land cover types under wet and dry years in an urban park

2017 ◽  
Vol 167 ◽  
pp. 378-385 ◽  
Author(s):  
Jeehwan Bae ◽  
Youngryel Ryu
Keyword(s):  
Soil Respiration ◽  
Land Cover ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
Urban Park ◽  
Land Cover Types

scholarly journals Extracting the spatio-temporal linkages between land use/land cover and water quality parameters using spatio-temporal weighted regression

2018 ◽  
Vol 53 (4) ◽  
pp. 205-218
Author(s):  
Farid Karimipour ◽  
Arash Madadi ◽  
Mohammad Hosein Bashough
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Land Cover ◽  
Temporal Variations ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Spatial And Temporal Variations ◽  
Weighted Regression ◽  
Land Use Land Cover ◽  
Spatio Temporal

Abstract Studies in water quality management have indicated significant relationships between land use/land cover (LULC) variables and water quality parameters. Thus, understanding this linkage is essential in protecting and developing water resources. This article extends the conventional geographical weighted regression (GWR) to a temporal version in order to take both spatial and temporal variations of such linkages into account, which has been ignored by many of the previous efforts. The approach has been evaluated for total nitrates and nitrites' concentration as the case study. For this, observations of 45 water quality sampling stations were examined in a time interval of 20 years (1992–2011), and the linkages between LULC variables and NO2 + NO3 concentration were extracted through Pearson correlation coefficient as a global regression model, the conventional geographic weighted regression, and the proposed spatio-temporal weighted regression (STWR). Comparing the results based on two global criteria of goodness-of-fitness (R2) and residual sum of squares (RSS) verifies that the simultaneous consideration of spatial and temporal variations by STWR substantially improves the results.

Spatial and temporal variations of soil respiration in a Eucalyptus plantation in Congo

2004 ◽  
Vol 202 (1-3) ◽  
pp. 149-160 ◽  
Author(s):  
Daniel Epron ◽  
Yann Nouvellon ◽  
Olivier Roupsard ◽  
Welcome Mouvondy ◽  
André Mabiala ◽  
...  
Keyword(s):  
Soil Respiration ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
Eucalyptus Plantation

scholarly journals ESTIMATION OF BVOC EMISSIONS IN GUANGZHOU AND ITS SPATIAL-TEMPORAL VARIATIONS: PRELIMINARY RESULTS FROM GLOBEIS

2018 ◽  
Vol XLII-3/W5 ◽  
pp. 33-38
Author(s):  
L. Li ◽  
Y. Wang ◽  
Y. Zheng ◽  
T. Chen
Keyword(s):  
Land Cover ◽  
Meteorological Data ◽  
Seasonal Pattern ◽  
Temporal Variations ◽  
Emission Rates ◽  
Area Index ◽  
Land Cover Types ◽  
The North ◽  
Result Show ◽  
High Emission

<p><strong>Abstract.</strong> Biogenic VOC emissions greatly exceed anthropogenic emissions and are regarded as significant precursors to secondary organic aerosol (SOA) and ozone. Using the Global Biosphere Emission and Interactions System (GloBEIS) model, 1<span class="thinspace"></span>&amp;times;<span class="thinspace"></span>1<span class="thinspace"></span>km gridded and hourly BVOC emissions in Guangzhou were estimated for the year of 2012. This study used satellite-retrieved land cover data, cloud product and leaf area index (LAI), observed meteorological data and local emission rates for land cover types in South China. The result show that the total BVOC emission in Guangzhou, 2012 was 4.39<span class="thinspace"></span>kt and the average area emission was 5.93<span class="thinspace"></span>t/(km<sup>2</sup>&amp;sdot;a), of which isoprene contributed about 55.7% (2.44<span class="thinspace"></span>kt)), monoterpenes about 11.9% (0.52<span class="thinspace"></span>kt) and OVOC about 32.4% (1.42<span class="thinspace"></span>kt). Emission factors of land cover types and correction parameters including LAI, wind speed and relative humidity have great effects on the estimation results of the model. BVOC emissions in Guangzhou exhibit a marked monthly and seasonal pattern with the peak emission in July to August and the lowest emission in January and are mainly distributed in the east-western of Conghua, the north of Zengcheng and the border of Huadu and Conghua, mostly covered by evergreen broadleaf forest with high emission factor, while areas of BVOC emission below 50<span class="thinspace"></span>kg/(km<sup>2</sup>&amp;sdot;a) are distributed in highly urbanized areas like Tianhe, Yuexiu, Liwan and Haizhu district.</p>

scholarly journals Spatio-temporal Analysis of Sediment Yield Estimation Using Integrated RUSLE and GIS Technique: Case of Nashe Dam Reservoir, Abay Basin, Ethiopia

2021 ◽  
Author(s):  
Habtamu Tamiru ◽  
Meseret Wagari
Keyword(s):  
Land Cover ◽  
Sediment Yield ◽  
Temporal Variations ◽  
Driving Factors ◽  
Spatial And Temporal Variations ◽  
Dam Reservoir ◽  
Rusle Model ◽  
Gis Technique ◽  
Dam Reservoirs ◽  
Annual Sediment

Sediment accumulation in a dam reservoir is a common happening environmental problem throughout the world. Topographic conditions, land use land cover change, the intensity of rainfall, and the soil characteristics are the major driving factors for sedimentation to occur. The effect of sedimentation in a dam reservoir is very visible in the watershed as a result of hilly topographic conditions, high rainfall intensity, thin land cover, and less soil infiltration capacity. In this paper, an integrated RUSLE and GIS technique was implemented to estimate a mean annual sediment yield based on spatial and temporal variations in Nashe dam reservoir situated in Fincha catchment, Abaya River basin, Ethiopia. Spatial and temporal estimation of mean annual sediment yield was estimated using the Revised Universal Soil Loss Equation (RUSLE) model and GIS. Historical 6-year (2014-2019) rainfall for the temporal variations and other physical factors such as soil erodibility, slope and length steepness, management and land used land cover, and support practice for spatial variations were used as sediment driving factors. The mean annual sediment yield ranges from 0 to 2712.65 t ha-1 year-1 was seen. Spatially, Very high, high, moderate, low, and very low sediment yield severity with total area coverage with 25%, 10%, 30%, 15%, and 20% in 2017, 2015, 2019, 2014, and 2018 respectively. The information about the spatial and temporal variations of the severity of sediment yield in RUSLE model has a paramount role to control the entry of sediment into the dam reservoir in this watershed. The results of the RUSLE model can also be further considered along with the watershed for planning strategies for dam reservoirs in the catchment.

scholarly journals Comparison of Accuracy of Surface Temperature Images from Unmanned Aerial Vehicle and Satellite for Precise Thermal Environment Monitoring of Urban Parks Using In Situ Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1977
Author(s):  
Dongwoo Kim ◽  
Jaejin Yu ◽  
Jeongho Yoon ◽  
Seongwoo Jeon ◽  
Seungwoo Son
Keyword(s):  
Land Cover ◽  
Unmanned Aerial Vehicle ◽  
Thermal Environment ◽  
Green Spaces ◽  
Urban Park ◽  
Land Cover Type ◽  
Land Cover Types ◽  
Cover Type ◽  
Aerial Vehicle

Rapid urbanization has led to several severe environmental problems, including so-called heat island effects, which can be mitigated by creating more urban green spaces. However, the temperature of various surfaces differs and precise measurement and analyses are required to determine the “coolest” of these. Therefore, we evaluated the accuracy of surface temperature data based on thermal infrared (TIR) cameras mounted on unmanned aerial vehicles (UAVs), which have recently been utilized for the spatial analysis of surface temperatures. Accordingly, we investigated land surface temperatures (LSTs) in green spaces, specifically those of different land cover types in an urban park in Korea. We compared and analyzed LST data generated by a thermal infrared (TIR) camera mounted on an unmanned aerial vehicle (UAV) and LST data from the Landsat 8 satellite for seven specific periods. For comparison and evaluation, we measured in situ LSTs using contact thermometers. The UAV TIR LST showed higher accuracy (R2 0.912, root mean square error (RMSE) 3.502 °C) than Landsat TIR LST accuracy (R2 value lower than 0.3 and RMSE of 7.246 °C) in all periods. The Landsat TIR LST did not show distinct LST characteristics by period and land cover type; however, grassland, the largest land cover type in the study area, showed the highest accuracy. With regard to the accuracy of the UAV TIR LST by season, the accuracy was higher in summer and spring (R2 0.868–0.915, RMSE 2.523–3.499 °C) than in autumn and winter (R2 0.766–0.79, RMSE 3.834–5.398 °C). Some land cover types (concrete bike path, wooden deck) were overestimated, showing relatively high total RMSEs of 4.439 °C and 3.897 °C, respectively, whereas grassland, which has lower LST, was underestimated—showing a total RMSE of 3.316 °C. Our results showed that the UAV TIR LST could be measured with sufficient reliability for each season and land cover type in an urban park with complex land cover types. Accordingly, our results could contribute to decision-making for urban spaces and environmental planning in consideration of the thermal environment.

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