scholarly journals Soil moisture index estimation from landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia

2021 ◽  
Vol 11 (3) ◽  
pp. 2101
Author(s):  
Noraisyah Tajudin ◽  
Norsuzila Ya'acob ◽  
Darmawaty Mohd Ali ◽  
Nor Aizam Adnan
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Vegetation Index ◽  
Landsat 8 ◽  
Northeast Monsoon ◽  
Contributing Factors ◽  
Moisture Index ◽  
Rainfall Distribution ◽  
Rainfall Analysis

Soil moisture is one of the contributing factors that accelerates soil erosion and landslide events due to the increase in pore pressure which eventually reduces the soil strength. For landslide prediction and monitoring purposes, large-scale measurement involves estimating the soil moisture. However, estimation of soil moisture usually involves point-based measurements at a particular site and time, which is difficult to capture the spatial and temporal soil moisture dynamics. This paper presents the estimation of the SMI using Landsat 8 images for prediction and monitoring of landslide events in Ulu Kelang, Selangor. The selected SMI map for dry, moist, and wet seasons are obtained from climatology rainfall analysis over 20-year periods (1998-2017). SMI is assessed based on remote sensing data which are land surface temperature (LST) and normalized difference vegetation index (NDVI) using GIS software. Overall results indicated that rainfall distribution is high during inter-monsoon (IM), followed by northeast monsoon (NEM) and southwest monsoon (SWM) season. High rainfall distribution is a direct contributor towards SMI condition. Results from simulation show that April 2017 is known to have the highest SMI estimation season and selected to be the best SMI mapping parameter to be applied for prediction and monitoring of landslide events.

scholarly journals A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements: the simplified triangle

2020 ◽  
Author(s):  
Toby N. Carlson ◽  
George Petropoulos
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Heat Fluxes ◽  
Surface Model ◽  
Landsat 8 ◽  
Simple Method ◽  
Practical Applications ◽  
Infrared Measurements

Earth Observation (EO) provides a promising approach towards deriving accurate spatiotemporal estimates of key parameters characterizing land surface interactions, such as latent (LE) and sensible (H) heat fluxes as well as soil moisture content. This paper proposes a very simple method to implement, yet reliable to calculate evapotranspiration fraction (EF) and surface moisture availability (Mo) from remotely sensed imagery of Normalized Difference Vegetation Index (NDVI) and surface radiometric temperature (Tir). The method is unique in that it derives all of its information solely from these two images. As such, it does not depend on knowing ancillary surface or atmospheric parameters, nor does it require the use of a land surface model. The procedure for computing spatiotemporal estimates of these important land surface parameters is outlined herein stepwise for practical application by the user. Moreover, as the newly developedscheme is not tied to any particular sensor, it can also beimplemented with technologically advanced EO sensors launched recently or planned to be launched such as Landsat 8 and Sentinel 3. The latter offers a number of key advantages in terms of future implementation of the method and wider use for research and practical applications alike.

scholarly journals SURFACE TEMPERATURE DYNAMICS IN RESPONSE TO LAND COVER TRANSFORMATION

2020 ◽  
Vol 11 (2) ◽  
pp. 94-110 ◽  
Author(s):  
Syed Riad Morshed Riad Morshed ◽  
Md. Abdul Fattah ◽  
Asma Amin Rimi ◽  
Md. Nazmul Haque
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Moisture Index ◽  
Type Transformation ◽  
Spatial Environment ◽  
Supervised Image Classification

This research assessed the micro-level Land Surface Temperature (LST) dynamics in response to Land Cover Type Transformation (LCTT) at Khulna City Corporation Ward No 9, 14, 16 from 2001 to 2019, through raster-based analysis in geo-spatial environment. Satellite images (Landsat 5 TM and Landsat 8 OLI) were utilized to analyze the LCTT and its influences on LST change. Different indices like Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI) were adopted to show the relationship against the LST dynamics individually. Most likelihood supervised image classification and land cover change direction analysis shows that about 27.17%, 17.83% and 4.73% buildup area has increased at Ward No 9, 14, 16 correspondingly. On the other hand, the distribution of change in average LST shows that water, vacant land, and buildup area recorded the highest increase in temperature by 2.720C, 4.150C, 4.590C, respectively. The result shows the average LST increased from 25.800C to 27.150C in Ward No 9, 26.840C to 27.230C in Ward No 14 and 26.870C to 27.120C in Ward No 16. Here, the most responsible factor is the transformation of land cover in buildup areas.

The synergistic use of Sentinel SAR and optical remote sensing for mapping high-resolution soil moisture

2020 ◽  
Author(s):  
Jianxiu Qiu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Large Scale ◽  
Vegetation Index ◽  
Estimation Accuracy ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Biological Variables ◽  
Research Activities

<p>The launch of series of Sentinel constellations has provided data continuity of ERS, Envisat, and SPOT-like observations, in order to meet various observational needs for spatially explicit physical, biogeophysical, and biological variables of the ocean, cryosphere, and land research activities. The synergistic use of this publicly-accessible SAR images and temporally collocated optical remote sensing datasets has provided great potential for estimating high-resolution soil moisture information. In this study, advanced integral equation model (AIEM) which simulates the backscattering coefficient of bare soil and the Water-Cloud Model (WCM) accounting for the scattering effect from vegetation, are coupled to map high-resolution soil moisture. Validation conducted in large-scale campaign of Heihe Watershed Allied Telemetry Experimental Research (HiWATER-MUSOEXE) in northwest of China showed RMSE of 0.04~0.071 m3m3. In addition, the accuracies in describing vegetation contribution from backscatter coefficient were intercompared between different models including WCM and ratio vegetation model. Sensitivity analysis of soil moisture estimation accuracy to vegetation index also extends to different optical remote sensing data sets including Sentinel-2, Landsat 8 and MODIS.</p>

scholarly journals Assessment of LST and NDMI indices using MODIS and Landsat images in Karun riparian forest

2019 ◽  
Vol 65 (No. 1) ◽  
pp. 27-32 ◽  
Author(s):  
Marjan Firoozy Nejad ◽  
Amin Zoratipour
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Riparian Forest ◽  
Support Vector ◽  
Thermal Method ◽  
Landsat 8 ◽  
Moisture Index ◽  
Landsat Images

Riparian forest plays a significant role in ecosystems. Also, research on land surface temperature and soil moisture is essential in earth science and forest studies. Because measuring methods are difficult to apply in large areas and especially in dense forests, in this study normalized difference moisture index (NDMI) and land surface temperature (LST) were estimated using the infrared thermal method by data of Landsat 8 and Moderate Resolution Imaging Spectroradiometer (MODIS) in the Karun riparian forest that is of ecological importance in the Khuzestan province of Iran. The results showed that the accuracy for estimated NDMI and LST was appropriate (root mean square error = 3.45). In addition, the used polynomial support vector machine algorithm for classification by four classes (forest, agriculture, river, and others) and the validity of classification in these areas were suitable (overall accuracy = 95%, kappa coefficient = 0.93). Also, the NDMI index was dependent on changes in LST and Pearson coefficients were 0.94 and 0.84 for Landsat 8 and MODIS data, respectively. The average temperature of the area was obtained as 43.22 and 42.77 for Landsat 8 and MODIS, respectively. Finally, more protection of this forest against LST enhancement and reduction in soil moisture is necessary.

scholarly journals Calibration of aerodynamic roughness over the Tibetan Plateau with Ensemble Kalman Filter analysed heat flux

2012 ◽  
Vol 16 (11) ◽  
pp. 4291-4302 ◽  
Author(s):  
J. H. Lee ◽  
J. Timmermans ◽  
Z. Su ◽  
M. Mancini
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Large Scale ◽  
Vegetation Index ◽  
Roughness Parameter ◽  
Heat Flux Estimation ◽  
Flux Estimation ◽  
Aerodynamic Roughness

Abstract. Aerodynamic roughness height (Zom) is a key parameter required in several land surface hydrological models, since errors in heat flux estimation are largely dependent on optimization of this input. Despite its significance, it remains an uncertain parameter which is not readily determined. This is mostly because of non-linear relationship in Monin-Obukhov similarity (MOS) equations and uncertainty of vertical characteristic of vegetation in a large scale. Previous studies often determined aerodynamic roughness using a minimization of cost function over MOS relationship or linear regression over it, traditional wind profile method, or remotely sensed vegetation index. However, these are complicated procedures that require a high accuracy for several other related parameters embedded in serveral equations including MOS. In order to simplify this procedure and reduce the number of parameters in need, this study suggests a new approach to extract aerodynamic roughness parameter from single or two heat flux measurements analyzed via Ensemble Kalman Filter (EnKF) that affords non-linearity. So far, to our knowledge, no previous study has applied EnKF to aerodynamic roughness estimation, while the majority of data assimilation study have paid attention to updates of other land surface state variables such as soil moisture or land surface temperature. The approach of this study was applied to grassland in semi-arid Tibetan Plateau and maize on moderately wet condition in Italy. It was demonstrated that aerodynamic roughness parameter can be inversely tracked from heat flux EnKF final analysis. The aerodynamic roughness height estimated in this approach was consistent with eddy covariance method and literature value. Through a calibration of this parameter, this adjusted the sensible heat previously overestimated and latent heat flux previously underestimated by the original Surface Energy Balance System (SEBS) model. It was considered that this improved heat flux estimation especially during the summer Monsoon period, based upon a comparison with precipitation and soil moisture field measurement. For an advantage of this approach over other previous methodologies, this approach is useful even when eddy covariance data are absent at a large scale and is time-variant over vegetation growth, as well as is not directly affected by saturation problem of remotely sensed vegetation index.

scholarly journals Impact of COVID-19 Induced Lockdown on Environmental Quality in Four Indian Megacities Using Landsat 8 OLI and TIRS-Derived Data and Mamdani Fuzzy Logic Modelling Approach

2020 ◽  
Vol 12 (13) ◽  
pp. 5464 ◽  
Author(s):  
Sasanka Ghosh ◽  
Arijit Das ◽  
Tusar Kanti Hembram ◽  
Sunil Saha ◽  
Biswajeet Pradhan ◽  
...  
Keyword(s):  
Environmental Quality ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fuzzy Ahp ◽  
Landsat 8 ◽  
Moisture Index ◽  
Biophysical Parameters ◽  
The World

The deadly COVID-19 virus has caused a global pandemic health emergency. This COVID-19 has spread its arms to 200 countries globally and the megacities of the world were particularly affected with a large number of infections and deaths, which is still increasing day by day. On the other hand, the outbreak of COVID-19 has greatly impacted the global environment to regain its health. This study takes four megacities (Mumbai, Delhi, Kolkata, and Chennai) of India for a comprehensive assessment of the dynamicity of environmental quality resulting from the COVID-19 induced lockdown situation. An environmental quality index was formulated using remotely sensed biophysical parameters like Particulate Matters PM10 concentration, Land Surface Temperature (LST), Normalized Different Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). Fuzzy-AHP, which is a Multi-Criteria Decision-Making process, has been utilized to derive the weight of the indicators and aggregation. The results showing that COVID-19 induced lockdown in the form of restrictions on human and vehicular movements and decreasing economic activities has improved the overall quality of the environment in the selected Indian cities for a short time span. Overall, the results indicate that lockdown is not only capable of controlling COVID-19 spread, but also helpful in minimizing environmental degradation. The findings of this study can be utilized for assessing and analyzing the impacts of COVID-19 induced lockdown situation on the overall environmental quality of other megacities of the world.

scholarly journals Mapping Soil Moisture as an Indicator of Wildfire Risk Using Landsat 8 Images in Sri Lanna National Park, Northern Thailand

2016 ◽  
Vol 8 (10) ◽  
pp. 107 ◽  
Author(s):  
Kansuma Burapapol ◽  
Ryota Nagasawa
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Linear Regression Analysis ◽  
Landsat 8 ◽  
Fuel Moisture ◽  
Wildfire Risk ◽  
Dry Conditions ◽  
Scatter Plots

<p>Severely dry climate plays an important role in the occurrence of wildfires in Thailand. Soil water deficits increase dry conditions, resulting in more intense and longer burning wildfires. The temperature vegetation dryness index (TVDI) and the normalized difference drought index (NDDI) were used to estimate soil moisture during the dry season to explore its use for wildfire risk assessment. The results reveal that the normalized difference wet index (NDWI) and land surface temperature (LST) can be used for TVDI calculation. Scatter plots of both NDWI/LST and the normalized difference vegetation index (NDVI)/LST exhibit the triangular shape typical for the theoretical TVDI. However, the NDWI is more significantly correlated to LST than the NDVI. Linear regression analysis, carried out to extract the maximum and minimum LSTs (LST<sub>max</sub>, LST<sub>min</sub>), indicate that LST<sub>max </sub>andLST<sub>min</sub> delineated by the NDWI better fulfill the collinearity requirement than those defined by the NDVI. Accordingly, the NDWI-LST relationship is better suited to calculate the TVDI. This modified index, called TVDI<sub>NDWI-LST</sub>, was applied together with the NDDI to establish a regression model for soil moisture estimates. The soil moisture model fulfills statistical requirements by achieving 76.65% consistency with the actual soil moisture and estimated soil moisture generated by our model. The relationship between soil moisture estimated from our model and leaf fuel moisture indicates that soil moisture can be used as a complementary dataset to assess wildfire risk, because soil moisture and fuel moisture content (FMC) show the same or similar behavior under dry conditions. <strong></strong></p>

scholarly journals PHENOLOGY ANALYSIS OF FOREST VEGETATION TO ENVIRONMENTAL VARIABLES DURING PRE- AND POST-MONSOON SEASONS IN WESTERN HIMALAYAN REGION OF INDIA

2016 ◽  
Vol XLI-B2 ◽  
pp. 15-19
Author(s):  
S. Khare ◽  
H. Latifi ◽  
K. Ghosh
Keyword(s):  
Land Surface ◽  
Environmental Variables ◽  
Large Scale ◽  
Vegetation Index ◽  
Linear Regression Analysis ◽  
Himalayan Region ◽  
Landsat 8 ◽  
Post Monsoon ◽  
Western Himalayan Region ◽  
Landsat 7

To assess the phenological changes in Moist Deciduous Forest (MDF) of western Himalayan region of India, we carried out NDVI time series analysis from 2013 to 2015 using Landsat 8 OLI data. We used the vegetation index differencing method to calculate the change in NDVI (NDVI<sub>change</sub>) during pre and post monsoon seasons and these changes were used to assess the phenological behaviour of MDF by taking the effect of a set of environmental variables into account. To understand the effect of environmental variables on change in phenology, we designed a linear regression analysis with sample-based NDVI<sub>change</sub> values as the response variable and elevation aspect, and Land Surface Temperature (LST) as explanatory variables. The Landsat-8 derived phenology transition stages were validated by calculating the phenology variation from Nov 2008 to April 2009 using Landsat-7 which has the same spatial resolution as Landsat-8. The Landsat-7 derived NDVI trajectories were plotted in accordance with MODIS derived phenology stages (from Nov 2008 to April 2009) of MDF. Results indicate that the Landsat -8 derived NDVI trajectories describing the phenology variation of MDF during spring, monsoon autumn and winter seasons agreed closely with Landsat-7 and MODIS derived phenology transition from Nov 2008 to April 2009. Furthermore, statistical analysis showed statistically significant correlations (p < 0.05) amongst the environmental variables and the NDVI<sub>change</sub> between full greenness and maximum frequency stage of Onset of Greenness (OG) activity.. The major change in NDVI was observed in medium (600 to 650 m) and maximum (650 to 750 m) elevation areas. The change in LST showed also to be highly influential. The results of this study can be used for large scale monitoring of difficult-to-reach mountainous forests, with additional implications in biodiversity assessment. By means of a sufficient amount of available cloud-free imagery, detailed phenological trends across mountainous forests could be explained.

scholarly journals A New Method for Estimating of Evapotranspiration and Surface Soil Moisture from Optical and Thermal Infrared Measurements: The Simplified Triangle

2019 ◽  
Author(s):  
Toby N. Carlson ◽  
George Petropoulos
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Heat Fluxes ◽  
Surface Model ◽  
Landsat 8 ◽  
Simple Method ◽  
Practical Applications ◽  
Infrared Measurements

Earth Observation (EO) provides a promising approach towards deriving accurate spatiotemporal estimates of key parameters characterizing land surface interactions, such as latent (LE) and sensible (H) heat fluxes as well as soil moisture content. This paper proposes a very simple method to implement, yet reliable to calculate evapotranspiration fraction (EF) and surface moisture availability (Mo) from remotely sensed imagery of Normalized Difference Vegetation Index (NDVI) and surface radiometric temperature (Tir). The method is unique in that it derives all of its information solely from these two images. As such, it does not depend on knowing ancillary surface or atmospheric parameters, nor does it require the use of a land surface model. The procedure for computing spatiotemporal estimates of these important land surface parameters is outlined herein stepwise for practical application by the user. Moreover, as the newly developed scheme is not tied to any particular sensor, it can also be implemented with technologically advanced EO sensors launched recently or planned to be launched such as Landsat 8 and Sentinel 3. The latter offers a number of key advantages in terms of future implementation of the method and wider use for research and practical applications alike.

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