scholarly journals Estimation of Vegetation Proportion Cover to Improve Land Surface Emissivity

2020 ◽  
Author(s):  
Elnaz Neinavaz ◽  
Andrew K. Skidmore ◽  
Roshanak Darvishzadeh
Keyword(s):  
Land Surface ◽  
Prediction Accuracy ◽  
Vegetation Index ◽  
Dynamic Range ◽  
Landsat 8 ◽  
Surface Emissivity ◽  
Threshold Method ◽  
Wide Dynamic Range ◽  
Land Surface Emissivity ◽  
The Impact

<p>Precise estimation of land surface emissivity (LSE) is essential to predict land surface energy budgets and land surface temperature, as LSE is an indicator of material composition. There exist several approaches to LSE estimation employing remote sensing data; however, the prediction of LSE remains a challenging task. Among the existing approaches for calculating LSE, the NDVI threshold method appears to hold well over vegetated areas. To apply the NDVI threshold method, it is necessary to know the proportion of vegetation cover (Pv). This research aims to investigate the impact of Pv's prediction accuracy on the estimation of LSE over the forest ecosystem. In this regard, a field campaign coinciding with a Landsat-8 overpass was undertaken for the mixed temperate forest of the Bavarian Forest National Park, in southeastern Germany. The Pv in situ measurements were made for 37 plots. Four vegetation indices, namely NDVI, variable atmospherically resistant index, wide dynamic range vegetation index, and three-band gradient difference vegetation index, were applied to predict Pv for further use in LSE computing. Unlike previous studies that suggested variable atmospherically resistant index can be estimated Pv with higher prediction accuracy compared to NDVI over the agricultural area, our results showed that the prediction accuracy of Pv is not different when using NDVI over the forest (R<sup>2</sup><sub>CV </sub>= 0.42, RMSE<sub>CV </sub>= 0.06). Pv was measured with the lowest accuracy using the wide dynamic range vegetation index (R<sup>2</sup><sub>CV </sub>= 0.014, RMSE<sub>CV </sub>= 0.197) and three-band gradient difference vegetation index (R<sup>2</sup><sub>CV </sub>= 0.032, RMSE<sub>CV </sub>= 0.018).  The results of this study also revealed that the variation in the prediction accuracy of the Pv has an impact on the results of LSE calculation.</p>

scholarly journals Application of Geo-informatics Technology to Access the Surface Temperature Using LANDSAT 8 OLI/TIRS Satellite Data: A Case Study in Ampara District in Sri Lanka

2020 ◽  
Author(s):  
Ibra Lebbe Mohamed Zahir
Keyword(s):  
Sri Lanka ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Surface Emissivity ◽  
Land Surface Emissivity

Land Surface Temperature is a one of the key variable of Global climate changes and model which estimate radiating budget in heat balance as control of climate model. It is a major influenced factor by the ability of the surface emissivity. In this study, were used Landsat 8 satellite image that have Operational Land Imager and Thermal Infrared Sensor to calculate Land Surface Temperature through geospatial technology over Ampara district, Sri Lanka. The Land Surface Temperature was estimated with respect to Land Surface Emissivity and Normalized Difference Vegetation Index values determined from the Red and Near Infrared channels. Land Surface Emissivity was processed directly by the thermal Infrared bands. Pixels based calculation were used to effort at LANDSAT 8 images that thermal Band 10 various dates in this study. The results were achievable to compute Normalized Difference Vegetation Index, Land Surface Emissivity, and Land Surface Temperature with applicable manner to compare with land use/ land cover data. It determines and predicts the changes of surface temperature to favorable to decision making process for the society. Study area faces seasonal drought in Sri Lanka, the prediction method that how land can be efficiently used with the present condition. Therefore, the Land Surface Temperature estimation can prove whether new irrigation systems for agricultural activities or can transformed source of energy into useful form that introducing solar hubs for energy production in future.

scholarly journals An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations

2006 ◽  
Vol 19 (12) ◽  
pp. 2867-2881 ◽  
Author(s):  
Menglin Jin ◽  
Shunlin Liang
Keyword(s):  
Land Surface ◽  
Regression Equations ◽  
Surface Emissivity ◽  
Land Surface Models ◽  
Community Land Model ◽  
Land Surface Emissivity ◽  
Variable Surface ◽  
Surface Models ◽  
Sensitivity Studies ◽  
The Impact

Abstract Because land surface emissivity (ɛ) has not been reliably measured, global climate model (GCM) land surface schemes conventionally set this parameter as simply constant, for example, 1 as in the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) model, and 0.96 for bare soil as in the National Center for Atmospheric Research (NCAR) Community Land Model version 2 (CLM2). This is the so-called constant-emissivity assumption. Accurate broadband emissivity data are needed as model inputs to better simulate the land surface climate. It is demonstrated in this paper that the assumption of the constant emissivity induces errors in modeling the surface energy budget, especially over large arid and semiarid areas where ɛ is far smaller than unity. One feasible solution to this problem is to apply the satellite-based broadband emissivity into land surface models. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument has routinely measured spectral emissivities (ɛλ) in six thermal infrared bands. The empirical regression equations have been developed in this study to convert these spectral emissivities to broadband emissivity (ɛ) required by land surface models. The observed emissivity data show strong seasonality and land-cover dependence. Specifically, emissivity depends on surface-cover type, soil moisture content, soil organic composition, vegetation density, and structure. For example, broadband ɛ is usually around 0.96–0.98 for densely vegetated areas [(leaf area index) LAI > 2], but it can be lower than 0.90 for bare soils (e.g., desert). To examine the impact of variable surface broadband emissivity, sensitivity studies were conducted using offline CLM2 and coupled NCAR Community Atmosphere Models, CAM2–CLM2. These sensitivity studies illustrate that large impacts of surface ɛ occur over deserts, with changes up to 1°–2°C in ground temperature, surface skin temperature, and 2-m surface air temperature, as well as evident changes in sensible and latent heat fluxes.

scholarly journals Assessment of the consistency among global microwave land surface emissivity products

2015 ◽  
Vol 8 (3) ◽  
pp. 1197-1205 ◽  
Author(s):  
H. Norouzi ◽  
M. Temimi ◽  
C. Prigent ◽  
J. Turk ◽  
R. Khanbilvardi ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Land Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Emissivity ◽  
Similar Frequency ◽  
Land Surface Emissivity ◽  
Microwave Imager

Abstract. The goal of this work is to intercompare four global land surface emissivity products over various land-cover conditions to assess their consistency. The intercompared land emissivity products were generated over a 5-year period (2003–2007) using observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E), the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and WindSat. First, all products were reprocessed in the same projection and spatial resolution as they were generated from sensors with various configurations. Then, the mean value and standard deviations of monthly emissivity values were calculated for each product to assess the spatial distribution of the consistencies/inconsistencies among the products across the globe. The emissivity products were also compared to soil moisture estimates and a satellite-based vegetation index to assess their sensitivities to changes in land surface conditions. Results show the existence of systematic differences among the products. Also, it was noticed that emissivity values in each product have similar frequency dependency over different land-cover types. Monthly means of emissivity values from AMSR-E in the vertical and horizontal polarizations seem to be systematically lower than the rest of the products across various land-cover conditions which may be attributed to the 01:30/13:30 LT overpass time of the sensor and possibly a residual skin temperature effect in the product. The standard deviation of the analyzed products was lowest (less than 0.01) in rain forest regions for all products and highest at northern latitudes, above 0.04 for AMSR-E and SSM/I and around 0.03 for WindSat. Despite differences in absolute emissivity estimates, all products were similarly sensitive to changes in soil moisture and vegetation. The correlation between the emissivity polarization differences and normalized difference vegetation index (NDVI) values showed similar spatial distribution across the products, with values close to the unit except over densely vegetated and desert areas.

scholarly journals Integration of Landsat-8 Thermal and Visible-Short Wave Infrared Data for Improving Prediction Accuracy of Forest Leaf Area Index

2019 ◽  
Vol 11 (4) ◽  
pp. 390 ◽  
Author(s):  
Elnaz Neinavaz ◽  
Roshanak Darvishzadeh ◽  
Andrew Skidmore ◽  
Haidi Abdullah
Keyword(s):  
Land Surface ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Surface Emissivity ◽  
Land Surface Emissivity ◽  
Shortwave Infrared ◽  
Reflectance Data ◽  
Infrared Data

Leaf area index (LAI) has been investigated in multiple studies, either by means of visible/near-infrared and shortwave-infrared or thermal infrared remotely sensed data, with various degrees of accuracy. However, it is not yet known how the integration of visible/near and shortwave-infrared and thermal infrared data affect estimates of LAI. In this study, we examined the utility of Landsat-8 thermal infrared data together with its spectral data from the visible/near and shortwave-infrared region to quantify the LAI of a mixed temperate forest in Germany. A field campaign was carried out in August 2015, in the Bavarian Forest National Park, concurrent with the time of the Landsat-8 overpass, and a number of forest structural parameters, including LAI and proportion of vegetation cover, were measured for 37 plots. A normalised difference vegetation index threshold method was applied to calculate land surface emissivity and land surface temperature and their relations to LAI were investigated. Next, the relation between LAI and eight commonly used vegetation indices were examined using the visible/near-infrared and shortwave-infrared remote sensing data. Finally, the artificial neural network was used to predict the LAI using: (i) reflectance data from the Landsat-8 operational land imager (OLI) sensor; (ii) reflectance data from the OLI sensor and the land surface emissivity; and (iii) reflectance data from the OLI sensor and land surface temperature. A stronger relationship was observed between LAI and land surface emissivity compared to that between LAI and land surface temperature. In general, LAI was predicted with relatively low accuracy by means of the vegetation indices. Among the studied vegetation indices, the modified vegetation index had the highest accuracy for LAI prediction (R2CV = 0.33, RMSECV = 1.21 m2m−2). Nevertheless, using the visible/near-infrared and shortwave-infrared spectral data in the artificial neural network, the prediction accuracy of LAI increased (R2CV = 0.58, RMSECV = 0.83 m2m−2). The integration of reflectance and land surface emissivity significantly improved the prediction accuracy of the LAI (R2CV = 0.81, RMSECV = 0.63 m2m−2). For the first time, our results demonstrate that the combination of Landsat-8 reflectance spectral data from the visible/near-infrared and shortwave-infrared domain and thermal infrared data can boost the estimation accuracy of the LAI in a forest ecosystem. This finding has implication for the prediction of other vegetation biophysical, or possibly biochemical variables using thermal infrared satellite remote sensing data, as well as regional mapping of LAI when coupled with a canopy radiative transfer model.

scholarly journals Impacts of Building Features on the Cooling Effect of Vegetation in Community-Based MicroClimate: Recognition, Measurement and Simulation from a Case Study of Beijing

2020 ◽  
Vol 17 (23) ◽  
pp. 8915
Author(s):  
Wei Chen ◽  
Jianjun Zhang ◽  
Xuelian Shi ◽  
Shidong Liu
Keyword(s):  
Wind Speed ◽  
Human Health ◽  
Land Surface ◽  
Vegetation Index ◽  
Thermal Environment ◽  
Cooling Effect ◽  
Landsat 8 ◽  
Community Based ◽  
Building Density ◽  
The Impact

Due to the accumulation of heat, the urban environment and human health are threatened. Land surface cover has effects on the thermal environment; nevertheless, the effects of land surface features and spatial patterns remain poorly known in a community-based microclimate. This study quantified and verified the impacts of normalized difference vegetation index (NDVI) on land surface temperature (LST) (K, the slope of the trend line of a linear regression between NDVI and LST) in different building density by using building outline and Landsat 8 satellite imagery. Comparing the cooling effect and distribution of vegetation showed that the vegetative cover had a cooling effect on LST, characterized by synchronous change, and building density had a significant impact on the cooling effect of vegetation. Through identification and simulation, it was found that the key factor is the wind speed between the buildings because, in different building densities, the wind speed was different, and studies had shown that when the building density was between 0.35 and 0.50, the wind speed between buildings was higher, resulting in a better cooling effect of vegetation. This conclusion has important reference significance for urban planning and mitigating the impact of the thermal environment on human health.

Sign in / Sign up

Export Citation Format

Share Document