LAnd surface remote sensing Products VAlidation System (LAPVAS) and its preliminary application

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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Remote Sensing Based Assessment of Agricultural Droughts in Sub-Watersheds of Upper Bhima Basin, India

10.21523/gcj1.18020204 ◽

2019 ◽

Vol 2 (2) ◽

pp. 105-111 ◽

Cited By ~ 1

Author(s):

Nayan Zagade ◽

Ajaykumar Kadam ◽

Bhavana Umrikar ◽

Bhagyashri Maggirwar

Keyword(s):

Remote Sensing ◽

Land Surface Temperature ◽

Land Surface ◽

Agricultural Sector ◽

Health Index ◽

Drought Assessment ◽

Drought Conditions ◽

Moderate Resolution ◽

Resolution Imaging ◽

Vegetation Health Index

Drought assessment for agricultural sector is vital in order to deal with the water scarcity in Ahmednagar and Pune districts, particularly in sub-watersheds of upper catchment of the River Bhima. Moderate Resolution Imaging Spectro-radiometer (MODIS) satellite data (2000, 2002, 2009, 2014, 2015 and 2017) for the years receiving less rainfall have been procured and various indices were computed to understand the intensity of agricultural droughts in the area. Vegetation health index (VHI) is computed on the basis of vegetation moisture, vegetation condition and land surface temperature condition. Most of the reviewed area shows moderate to extreme drought conditions.

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Remote Sensing Characterization of Upper Great Lakes Forest Disturbance and Recovery to Improve Modeled Impacts on Land Surface Energetic and Hydrologic Fluxes

10.13031/cc.20152143978 ◽

2015 ◽

Keyword(s):

Remote Sensing ◽

Great Lakes ◽

Land Surface ◽

Forest Disturbance ◽

Upper Great Lakes ◽

Disturbance And Recovery

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Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data

Remote Sensing ◽

10.3390/rs13010113 ◽

2020 ◽

Vol 13 (1) ◽

pp. 113

Author(s):

Antonio-Juan Collados-Lara ◽

Steven R. Fassnacht ◽

Eulogio Pardo-Igúzquiza ◽

David Pulido-Velazquez

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Air Temperature ◽

Land Surface ◽

National Park ◽

Rocky Mountain ◽

Rocky Mountain National Park ◽

Temperature Fields ◽

Lapse Rate ◽

Validation Experiment

There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 °C2 for the application and validation, respectively.

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A New Strategy for Comparison of Land Surface Temperature Retrieval Methods with Landsat Remote Sensing Images Considering Regional Consistency

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/687/1/012166 ◽

2021 ◽

Vol 687 (1) ◽

pp. 012166

Author(s):

Xu Qingyu ◽

Xu Xiong ◽

Xie Huan ◽

Zhang Xiaochun ◽

Huang Yuting

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Remote Sensing Images ◽

New Strategy ◽

Temperature Retrieval

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An Assessment of Drought Stress in Tea Estates Using Optical and Thermal Remote Sensing

Remote Sensing ◽

10.3390/rs13142730 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2730

Author(s):

Animesh Chandra Das ◽

Ryozo Noguchi ◽

Tofael Ahamed

Keyword(s):

Remote Sensing ◽

Drought Stress ◽

Land Surface ◽

Climatic Factors ◽

Standardized Precipitation Index ◽

Landsat 8 ◽

Drought Frequency ◽

Moisture Index ◽

Area Index ◽

Drought Period

Drought is one of the detrimental climatic factors that affects the productivity and quality of tea by limiting the growth and development of the plants. The aim of this research was to determine drought stress in tea estates using a remote sensing technique with the standardized precipitation index (SPI). Landsat 8 OLI/TIRS images were processed to measure the land surface temperature (LST) and soil moisture index (SMI). Maps for the normalized difference moisture index (NDMI), normalized difference vegetation index (NDVI), and leaf area index (LAI), as well as yield maps, were developed from Sentinel-2 satellite images. The drought frequency was calculated from the classification of droughts utilizing the SPI. The results of this study show that the drought frequency for the Sylhet station was 38.46% for near-normal, 35.90% for normal, and 25.64% for moderately dry months. In contrast, the Sreemangal station demonstrated frequencies of 28.21%, 41.02%, and 30.77% for near-normal, normal, and moderately dry months, respectively. The correlation coefficients between the SMI and NDMI were 0.84, 0.77, and 0.79 for the drought periods of 2018–2019, 2019–2020 and 2020–2021, respectively, indicating a strong relationship between soil and plant canopy moisture. The results of yield prediction with respect to drought stress in tea estates demonstrate that 61%, 60%, and 60% of estates in the study area had lower yields than the actual yield during the drought period, which accounted for 7.72%, 11.92%, and 12.52% yield losses in 2018, 2019, and 2020, respectively. This research suggests that satellite remote sensing with the SPI could be a valuable tool for land use planners, policy makers, and scientists to measure drought stress in tea estates.

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High-Resolution Monitoring and Assessment of Evapotranspiration and Gross Primary Production Using Remote Sensing in a Typical Arid Region

Land ◽

10.3390/land10040396 ◽

2021 ◽

Vol 10 (4) ◽

pp. 396

Author(s):

Junxia Yan ◽

Yanfei Ma ◽

Dongyun Zhang ◽

Zechen Li ◽

Weike Zhang ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Land Surface ◽

Arid Region ◽

Management Practices ◽

Gross Primary Production ◽

Irrigation District ◽

Heihe River ◽

Freshwater Resource ◽

Oasis Area

Land surface evapotranspiration (ET) and gross primary productivity (GPP) are critical components in terrestrial ecosystems with water and carbon cycles. Large-scale, high-resolution, and accurately quantified ET and GPP values are important fundamental data for freshwater resource management and help in understanding terrestrial carbon and water cycles in an arid region. In this study, the revised surface energy balance system (SEBS) model and MOD17 GPP algorithm were used to estimate daily ET and GPP at 100 m resolution based on multi-source satellite remote sensing data to obtain surface biophysical parameters and meteorological forcing data as input variables for the model in the midstream oasis area of the Heihe River Basin (HRB) from 2010 to 2016. Then, we further calculated the ecosystem water-use efficiency (WUE). We validated the daily ET, GPP, and WUE from ground observations at a crop oasis station and conducted spatial intercomparisons of monthly and annual ET, GPP, and WUE at the irrigation district and cropland oasis scales. The site-level evaluation results show that ET and GPP had better performance than WUE at the daily time scale. Specifically, the deviations in the daily ET, GPP, and WUE data compared with ground observations were small, with a root mean square error (RMSE) and mean absolute percent error (MAPE) of 0.75 mm/day and 26.59%, 1.13 gC/m2 and 36.62%, and 0.50 gC/kgH2O and 39.83%, respectively. The regional annual ET, GPP, and WUE varied from 300 to 700 mm, 200 to 650 gC/m2, and 0.5 to 1.0 gC/kgH2O, respectively, over the entire irrigation oasis area. It was found that annual ET and GPP were greater than 550 mm and 500 gC/m2, and annual oasis cropland WUE had strong invariability and was maintained at approximately 0.85 gC/kgH2O. The spatial intercomparisons from 2010 to 2016 revealed that ET had similar spatial patterns to GPP due to tightly coupled carbon and water fluxes. However, the WUE spatiotemporal patterns were slightly different from both ET and GPP, particularly in the early and late growing seasons for the oasis area. Our results demonstrate that spatial full coverage and reasonably fine spatiotemporal variation and variability could significantly improve our understanding of water-saving irrigation strategies and oasis agricultural water management practices in the face of water shortage issues.

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Quantification of the Environmental Impacts of Highway Construction Using Remote Sensing Approach

Remote Sensing ◽

10.3390/rs13071340 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1340

Author(s):

Shuailong Feng ◽

Shuguang Liu ◽

Lei Jing ◽

Yu Zhu ◽

Wende Yan ◽

...

Keyword(s):

Remote Sensing ◽

Environmental Impacts ◽

Land Surface ◽

Vegetation Index ◽

Highway Construction ◽

Temporal Changes ◽

Moisture Index ◽

Buffer Zones ◽

Wide Range ◽

Spatial And Temporal Changes

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.

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