scholarly journals Gap-Free LST Generation for MODIS/Terra LST Product Using a Random Forest-Based Reconstruction Method

2021 ◽  
Vol 13 (14) ◽  
pp. 2828
Author(s):  
Yao Xiao ◽  
Wei Zhao ◽  
Mingguo Ma ◽  
Kunlong He
Keyword(s):  
Random Forest ◽  
Land Surface ◽  
Mean Squared Error ◽  
Input Parameter ◽  
Correlation Coefficients ◽  
Visual Assessment ◽  
Energy Budgets ◽  
Reconstruction Method ◽  
Thermal Infrared Remote Sensing ◽  
The Impact

Land surface temperature (LST) is a crucial input parameter in the study of land surface water and energy budgets at local and global scales. Because of cloud obstruction, there are many gaps in thermal infrared remote sensing LST products. To fill these gaps, an improved LST reconstruction method for cloud-covered pixels was proposed by building a linking model for the moderate resolution imaging spectroradiometer (MODIS) LST with other surface variables with a random forest regression method. The accumulated solar radiation from sunrise to satellite overpass collected from the surface solar irradiance product of the Feng Yun-4A geostationary satellite was used to represent the impact of cloud cover on LST. With the proposed method, time-series gap-free LST products were generated for Chongqing City as an example. The visual assessment indicated that the reconstructed gap-free LST images can sufficiently capture the LST spatial pattern associated with surface topography and land cover conditions. Additionally, the validation with in situ observations revealed that the reconstructed cloud-covered LSTs have similar performance as the LSTs on clear-sky days, with the correlation coefficients of 0.92 and 0.89, respectively. The unbiased root mean squared error was 2.63 K. In general, the validation work confirmed the good performance of this approach and its good potential for regional application.

scholarly journals Combined measurement and modeling of the hydrological impact of hydraulic redistribution using CLM4.5 at eight AmeriFlux sites

2016 ◽  
Vol 20 (5) ◽  
pp. 2001-2018 ◽  
Author(s):  
Congsheng Fu ◽  
Guiling Wang ◽  
Michael L. Goulden ◽  
Russell L. Scott ◽  
Kenneth Bible ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Bowen Ratio ◽  
Hydraulic Redistribution ◽  
Energy Budgets ◽  
Vegetation Types ◽  
Ecological Processes ◽  
Community Land Model ◽  
Hydrological Impact ◽  
The Impact

Abstract. Effects of hydraulic redistribution (HR) on hydrological, biogeochemical, and ecological processes have been demonstrated in the field, but the current generation of standard earth system models does not include a representation of HR. Though recent studies have examined the effect of incorporating HR into land surface models, few (if any) have done cross-site comparisons for contrasting climate regimes and multiple vegetation types via the integration of measurement and modeling. Here, we incorporated the HR scheme of Ryel et al. (2002) into the NCAR Community Land Model Version 4.5 (CLM4.5), and examined the ability of the resulting hybrid model to capture the magnitude of HR flux and/or soil moisture dynamics from which HR can be directly inferred, to assess the impact of HR on land surface water and energy budgets, and to explore how the impact may depend on climate regimes and vegetation conditions. Eight AmeriFlux sites with contrasting climate regimes and multiple vegetation types were studied, including the Wind River Crane site in Washington State, the Santa Rita Mesquite savanna site in southern Arizona, and six sites along the Southern California Climate Gradient. HR flux, evapotranspiration (ET), and soil moisture were properly simulated in the present study, even in the face of various uncertainties. Our cross-ecosystem comparison showed that the timing, magnitude, and direction (upward or downward) of HR vary across ecosystems, and incorporation of HR into CLM4.5 improved the model-measurement matches of evapotranspiration, Bowen ratio, and soil moisture particularly during dry seasons. Our results also reveal that HR has important hydrological impact in ecosystems that have a pronounced dry season but are not overall so dry that sparse vegetation and very low soil moisture limit HR.

scholarly journals Combined measurement and modeling of the hydrological impact of hydraulic redistribution using CLM4.5 at eight AmeriFlux sites

2016 ◽  
Author(s):  
C. Fu ◽  
G. Wang ◽  
M. L. Goulden ◽  
R. L. Scott ◽  
K. Bible ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Hydraulic Redistribution ◽  
Energy Budgets ◽  
Soil Moisture Dynamics ◽  
Community Land Model ◽  
The Us ◽  
Hydrological Impact ◽  
The Impact ◽  
Moisture Dynamics

Abstract. Effects of hydraulic redistribution (HR) on hydrological, biogeochemical, and ecological processes have been demonstrated in the field, but the current generation of standard earth system models does not include a representation of HR. Though recent studies have examined the effect of incorporating HR into land surface models, few (if any) has tackled the magnitude of the HR flux itself or the soil moisture dynamics from which HR magnitude can be directly inferred. Here we incorporated Ryel et al.'s (2002) empirical equation describing HR into the NCAR Community Land Model Version 4.5 (CLM4.5), and examined the ability of the resulting hybrid model to capture the magnitude of HR flux and/or soil moisture dynamics from which HR can be directly inferred, to assess the impact of HR on surface water and energy budgets, and to explore how it may depend on climate regimes and vegetation conditions. Eight AmeriFlux sites characterized by contrasting climate regimes and multiple vegetation types were studied, including the US-Wrc Wind River Crane site in Washington State, the US-SRM Santa Rita Mesquite Savanna site in southern Arizona, and six sites along the Southern California Climate Gradient (US-SCs, g, f, w, c, and d). HR flux, evapotranspiration, and soil moisture were properly simulated in the present study, even in the face of various uncertainties. Our cross-ecosystem comparison showed that the timing, magnitude, and direction (upward or downward) of HR vary across ecosystems, and incorporation of HR into CLM4.5 improved the model-measurement match particularly during dry seasons. Our results also reveal that HR has important hydrological impact (on evapotranspiration, Bowen ratio, and soil moisture) in ecosystems that have a pronounced dry season but are not overall so dry that sparse vegetation and very low soil moisture limit HR.

scholarly journals GEOSPATIAL ANALYSIS OF IMPERVIOUS SURFACES AND THEIR EFFECT ON LAND SURFACE TEMPERATURE IN ABUJA, NIGERIA

2020 ◽  
Vol XLIV-3/W1-2020 ◽  
pp. 5-11
Author(s):  
E. A. Akomolafe ◽  
O. A. Isioye ◽  
J. U. Awulu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Expansion ◽  
Correlation Coefficients ◽  
Impervious Surfaces ◽  
Great Insight ◽  
Urban Heat ◽  
Positive Correlations ◽  
The Impact

Abstract. The rapid urban expansion in Abuja, Nigeria, has resulted in the replacement of land surface previously occupied by natural vegetation with various impermeable materials. This study examines the impact of the spatial distribution of impervious surfaces (IS) on land surface temperature (LST) in the study area using both graphical and quantitative approach. A Normalized Difference Impervious Surface Index (NDISI) was adopted to estimate IS and LST from Landsat ETM+ and OLI/TIRS satellite images (path: 189, row: 54) of Abuja for 4 distinct epochs of 2004, 2008, 2014 and 2018. In order to analyze the effect of IS on LST, the relationship between the normalized difference indices and LST, for each epoch, were determined using regression and correlation analyses. Results show the spatial patterns of impervious surfaces as distributed over Abuja, Nigeria and its impact on LST dynamics. It was observed that mean surface temperature increased by at least 2 °C every 4 years. Furthermore, results of the correlation analysis between NDISI and LST reveal that there exist varying positive correlations between the two variables in with correlation coefficients; R = 0.511, 0.166, 0.505, 0.785 in 2004,2008, 2014 and 2018 respectively, suggesting that impervious surfaces areas accelerate LST rise and Urban Heat Island (UHI) formation. This study gives great insight on the concept of impervious surfaces and its spatial pattern in Abuja city, Nigeria. The study recommends the widespread use of highly reflective or natural surfaces for rooftops, pavements and roads and that afforestation should be encouraged to increase green areas.

The Multibudget Soil, Vegetation, and Snow (SVS) Scheme for Land Surface Parameterization: Offline Warm Season Evaluation

2016 ◽  
Vol 17 (8) ◽  
pp. 2293-2313 ◽  
Author(s):  
Syed Zahid Husain ◽  
Nasim Alavi ◽  
Stéphane Bélair ◽  
Marco Carrera ◽  
Shunli Zhang ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Land Surface ◽  
Warm Season ◽  
Stomatal Resistance ◽  
Energy Budgets ◽  
Ground Vegetation ◽  
Bare Ground ◽  
Near Surface ◽  
Surface Parameterization ◽  
The Impact

Abstract A new land surface parameterization scheme, named the Soil, Vegetation, and Snow (SVS) scheme, was recently developed at Environment and Climate Change Canada to replace the operationally used Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme. The new scheme is designed to address a number of weaknesses and limitations of ISBA that have been identified over the last decade. Unlike ISBA, which calculates a single energy budget for the different land surface components, SVS introduces a new tiling approach that includes separate energy budgets for bare ground, vegetation, and two different snowpacks (over bare ground and low vegetation and under high vegetation). The inclusion of a photosynthesis module as an option to determine the surface stomatal resistance is another significant addition in SVS. The representation of vertical water transport through soil has also been substantially improved in SVS with the introduction of multiple soil layers. Overall, offline simulations conducted in the present study demonstrated clear improvements in warm season meteorological predictions with SVS compared to the ISBA scheme. The results also revealed considerable reduction of standard error in the SVS-predicted L-band brightness temperature. This demonstrates the scheme’s ability for better hydrological prediction and its potential for providing more accurate soil moisture analysis. The impact of the photosynthesis module within the current implementation of SVS is, however, found to be negligible on near-surface meteorological prediction and slightly negative for brightness temperature.

scholarly journals Dynamic PET Imaging Using Dual Texture Features

2022 ◽  
Vol 15 ◽  
Author(s):  
Zhanglei Ouyang ◽  
Shujun Zhao ◽  
Zhaoping Cheng ◽  
Yanhua Duan ◽  
Zixiang Chen ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Texture Features ◽  
Reconstruction Method ◽  
Gray Level ◽  
Dynamic Pet ◽  
Clinical Patient ◽  
Composite Frames ◽  
Imaging Results ◽  
Pet Reconstruction ◽  
The Impact

Purpose: This study aims to explore the impact of adding texture features in dynamic positron emission tomography (PET) reconstruction of imaging results.Methods: We have improved a reconstruction method that combines radiological dual texture features. In this method, multiple short time frames are added to obtain composite frames, and the image reconstructed by composite frames is used as the prior image. We extract texture features from prior images by using the gray level-gradient cooccurrence matrix (GGCM) and gray-level run length matrix (GLRLM). The prior information contains the intensity of the prior image, the inverse difference moment of the GGCM and the long-run low gray-level emphasis of the GLRLM.Results: The computer simulation results show that, compared with the traditional maximum likelihood, the proposed method obtains a higher signal-to-noise ratio (SNR) in the image obtained by dynamic PET reconstruction. Compared with similar methods, the proposed algorithm has a better normalized mean squared error (NMSE) and contrast recovery coefficient (CRC) at the tumor in the reconstructed image. Simulation studies on clinical patient images show that this method is also more accurate for reconstructing high-uptake lesions.Conclusion: By adding texture features to dynamic PET reconstruction, the reconstructed images are more accurate at the tumor.

scholarly journals New lessons on the Sudd hydrology learned from remote sensing and climate modeling

2005 ◽  
Vol 2 (4) ◽  
pp. 1503-1535 ◽  
Author(s):  
Y. A. Mohamed ◽  
H. H. G. Savenije ◽  
W. G. M. Bastiaanssen ◽  
B. J. J. M. van den Hurk
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Climate Model ◽  
Meteorological Data ◽  
Regional Climate ◽  
Remote Sensing Data ◽  
Open Water ◽  
Water Evaporation ◽  
Thermal Infrared Remote Sensing ◽  
The Impact

Abstract. Despite its local and regional importance, hydro-meteorological data on the Sudd (one of Africa's largest wetlands) is very scanty. This is due to the physical and political situation of this area of Sudan. The areal size of the wetland, the evaporation rate, and the influence on the micro and meso climate are still unresolved questions of the Sudd hydrology. The evaporation flux from the Sudd wetland has been estimated using thermal infrared remote sensing data and a parameterization of the surface energy balance (SEBAL model). It is concluded that the actual spatially averaged evaporation from the Sudd wetland over 3 years of different hydrometeorological characteristics varies between 1460 and 1935 mm/yr. This is substantially less than open water evaporation. The wetland area appears to be 70% larger than previously assumed when the Sudd was considered as an open water body. The groundwater table characterizes a distinct seasonality, confirming that substantial parts of the Sudd are seasonal swamps. The new set of spatially distributed evaporation parameters from remote sensing form an important dataset for calibrating a regional climate model enclosing the Nile Basin. The Regional Atmospheric Climate Model (RACMO) provides an insight not only into the temporal evolution of the hydro-climatological parameters, but also into the land surface climate interactions and embedded feedbacks. The impact of the flooding of the Sudd on the Nile hydroclimatology has been analysed by simulating two land surface scenarios (with and without the Sudd wetland). The paper presents some of the model results addressing the Sudd's influence on rainfall, evaporation and runoff of the river Nile, as well as the influence on the microclimate.

Effects of Lateral Flow on the Convective Environment in a Coupled Hydrometeorological Modeling System in a Semiarid Environment

2020 ◽  
Vol 21 (4) ◽  
pp. 615-642
Author(s):  
Timothy M. Lahmers ◽  
Christopher L. Castro ◽  
Pieter Hazenberg
Keyword(s):  
Land Surface ◽  
Field Experiments ◽  
Overland Flow ◽  
Regional Scale ◽  
Subsurface Flow ◽  
Hydrologic Model ◽  
Lateral Flow ◽  
Energy Budgets ◽  
Surface Atmosphere ◽  
The Impact

AbstractEvidence for surface and atmosphere coupling is corroborated in both modeling and observation-based field experiments. Recent advances in high-performance computing and development of convection-permitting regional-scale atmospheric models combined with high-resolution hydrologic models have made modeling of surface–atmosphere interactions feasible for the scientific community. These hydrological models can account for the impacts of the overland flow and subsurface flow components of the hydrologic cycle and account for the impact of lateral flow on moisture redistribution at the land surface. One such model is the Weather Research and Forecasting (WRF) regional atmospheric model that can be coupled to the WRF-Hydro hydrologic model. In the present study, both the uncoupled WRF (WRF-ARW) and otherwise identical WRF-Hydro model are executed for the 2017 and 2018 summertime North American monsoon (NAM) seasons in semiarid central Arizona. In this environment, diurnal convection is impacted by precipitation recycling from the land surface. The goal of this work is to evaluate the impacts that surface runoff and shallow subsurface flow, as depicted in WRF-Hydro, have on surface–atmosphere interactions and convection in a coupled atmospheric simulation. The current work assesses the impact of surface hydrologic processes on 1) local surface energy budgets during the NAM throughout Arizona and 2) the spectral behavior of diurnally driven NAM convection. Model results suggest that adding surface and subsurface flow from WRF-Hydro increases soil moisture and latent heat near the surface. This increases the amount of instability and moisture available for deep convection in the model simulations and enhances the organization of convection at the peak of the diurnal cycle.

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

The Random Forest Method in Research of Impact of Macroeconomic Indicators of Regional Development on Informal Employment Rate

2020 ◽  
Vol 27 (6) ◽  
pp. 37-55
Author(s):  
E. V. Zarova ◽  
E. I. Dubravskaya
Keyword(s):  
Random Forest ◽  
Regional Development ◽  
Russian Federation ◽  
Regional Level ◽  
Informal Employment ◽  
Macroeconomic Factors ◽  
Random Forest Method ◽  
Macroeconomic Indicators ◽  
The Russian Federation ◽  
The Impact

The topic of quantitative research on informal employment has a consistently high relevance both in the Russian Federation and in other countries due to its high dependence on cyclicality and crisis stages in economic dynamics of countries with any level of economic development. Developing effective government policy measures to overcome the negative impact of informal employment requires special attention in theoretical and applied research to assessing the factors and conditions of informal employment in the Russian Federation including at the regional level. Such effects of informal employment as a shortfall in taxes, potential losses in production efficiency, and negative social consequences are a concern for the authorities of the federal and regional levels. Development of quantitative indicators to determine the level of informal employment in the regions, taking into account their specifics in the general spatial and economic system of Russia are necessary to overcome these negative effects. The article proposes and tests methods for solving the problem of assessing the impact of hierarchical relationships on macroeconomic factors at the regional level of informal employment in constituent entities of the Russian Federation. Majority of the works on the study of informal employment are based on basic statistical methods of spatial-dynamic analysis, as well as on the now «traditional» methods of cluster and correlation-regression analysis. Without diminishing the merits of these methods, it should be noted that they are somewhat limited in identifying hidden structural connections and interdependencies in such a complex multidimensional phenomenon as informal employment. In order to substantiate the possibility of overcoming these limitations, the article proposes indicators of regional statistics that directly and indirectly characterize informal employment and also presents the possibilities of using the «random forest» method to identify groups of constituent entities of the Russian Federation that have similar macroeconomic factors of informal employment. The novelty of this method in terms of research objectives is that it allows one to assess the impact of macroeconomic indicators of regional development on the level of informal employment, taking into account the implicit, not predetermined by the initial hypotheses, hierarchical relationships of factor indicators. Based on the generalization of the studies presented in the literature, as well as the authors’ statistical calculations using Rosstat data, the authors came to the conclusion about the high importance of macroeconomic parameters of regional development and systemic relationships of macroeconomic indicators in substantiating the differentiation of the informal level across the constituent entities of the Russian Federation.

Impact of LULC Changes on LST in Rajshahi District of Bangladesh: A Remote Sensing Approach

2020 ◽  
Vol 3 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Abdulla Al Kafy ◽  
Abdullah Al-Faisal ◽  
Mohammad Mahmudul Hasan ◽  
Md. Soumik Sikdar ◽  
Mohammad Hasib Hasan Khan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Agricultural Land ◽  
Global Climate ◽  
Urban Expansion ◽  
Water Bodies ◽  
Landsat 8 ◽  
Lulc Changes ◽  
The Impact

Urbanization has been contributing more in global climate warming, with more than 50% of the population living in cities. Rapid population growth and change in land use / land cover (LULC) are closely linked. The transformation of LULC due to rapid urban expansion significantly affects the functions of biodiversity and ecosystems, as well as local and regional climates. Improper planning and uncontrolled management of LULC changes profoundly contribute to the rise of urban land surface temperature (LST). This study evaluates the impact of LULC changes on LST for 1997, 2007 and 2017 in the Rajshahi district (Bangladesh) using multi-temporal and multi-spectral Landsat 8 OLI and Landsat 5 TM satellite data sets. The analysis of LULC changes exposed a remarkable increase in the built-up areas and a significant decrease in the vegetation and agricultural land. The built-up area was increased almost double in last 20 years in the study area. The distribution of changes in LST shows that built-up areas recorded the highest temperature followed by bare land, vegetation and agricultural land and water bodies. The LULC-LST profiles also revealed the highest temperature in built-up areas and the lowest temperature in water bodies. In the last 20 years, LST was increased about 13ºC. The study demonstrates decrease in vegetation cover and increase in non-evaporating surfaces with significantly increases the surface temperature in the study area. Remote-sensing techniques were found one of the suitable techniques for rapid analysis of urban expansions and to identify the impact of urbanization on LST.

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