scholarly journals Identification of desertified and preserved areas in a conservation unit in the state of Paraíba – Brazil

2021 ◽  
Author(s):  
Leandro F. da Silva ◽  
Bartolomeu I. De Souza ◽  
Rafael Camara Artigas
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Surface ◽  
Conservation Unit ◽  
Moisture Index ◽  
Rainy Period ◽  
Colour Image ◽  
Semi Arid Region ◽  
Set Of Points

The objective of this study is to identify and analyse the main characteristics of areas potentially degraded by desertification and of preserved areas using the Soil Surface Moisture Index (SSMI), alongside the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI). The study is based on a set of points obtained in the field and from the RGB false colour image for the Environmental Protection Areas (EPA) of the Cariri, in the semi-arid region of Paraíba, using a space-time cross-section covering both rainy and dry periods. The results showed that at all points in Desertified Areas, the main characteristics were a low SSMI, high LST and low NDVI in both periods. The Preserved Areas, on the other hand, presented a high SSMI, moderate LST and high NDVI in the rainy period, with the same characteristics repeated in the dry period for SSMI and NDVI, but with a low LST. Timely identification of these characteristics, both in areas degraded by desertification and in better preserved areas, can provide useful information for future decisions relating to the physical and territorial management of the Conservation Unit.

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.

scholarly journals Using Synthetic Remote Sensing Indicators to Monitor the Land Degradation in a Salinized Area

2021 ◽  
Vol 13 (15) ◽  
pp. 2851
Author(s):  
Tao Yu ◽  
Guli Jiapaer ◽  
Anming Bao ◽  
Guoxiong Zheng ◽  
Liangliang Jiang ◽  
...  
Keyword(s):  
Land Degradation ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Salt Content ◽  
Principal Component ◽  
Water Withdrawal ◽  
Moisture Index ◽  
High Positive Correlation ◽  
Amu Darya

Land degradation poses a critical threat to the stability and security of ecosystems, especially in salinized areas. Monitoring the land degradation of salinized areas facilitates land management and ecological restoration. In this research, we integrated the salinization index (SI), albedo, normalized difference vegetation index (NDVI) and land surface soil moisture index (LSM) through the principal component analysis (PCA) method to establish a salinized land degradation index (SDI). Based on the SDI, the land degradation of a typical salinized area in the Central Asia Amu Darya delta (ADD) was analysed for the period 1990–2019. The results showed that the proposed SDI had a high positive correlation (R2 = 0.89, p < 0.001) with the soil salt content based on field sampling, indicating that the SDI can reveal the land degradation characteristics of the ADD. The SDI indicated that the extreme and strong land degradation areas increased from 1990 to 2019, mainly in the downstream and peripheral regions of the ADD. From 1990 to 2000, land degradation improvement over a larger area than developed, conversely, from 2000 to 2019, and especially, from 2000 to 2010, the proportion of land degradation developed was 32%, which was mainly concentrated in the downstream region of the ADD. The spatial autocorrelation analysis indicated that the SDI values of Moran’s I in 1990, 2000, 2010 and 2019 were 0.82, 0.78, 0.82 and 0.77, respectively, suggesting that the SDI was notably clustered in space rather than randomly distributed. The expansion of unused land due to land use change, water withdrawal from the Amu Darya River and the discharge of salt downstream all contributed to land degradation in the ADD. This study provides several valuable insights into the land degradation monitoring and management of this salinized delta and similar settings worldwide.

scholarly journals Environmental sensitivity to mosquito transmitted diseases in El-Fayoum using spatial analyses

2020 ◽  
Vol 167 ◽  
pp. 03002
Author(s):  
Asmaa M. El-Hefni ◽  
Ahmed M. El-Zeiny ◽  
Hala A. Effat
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Malaria Vectors ◽  
Moisture Index ◽  
Risk Area ◽  
Gis Modeling ◽  
Breeding Sites ◽  
Mosquito Breeding ◽  
Fayoum Governorate

El-Fayoum governorate has unique characteristics which induces mosquito proliferation and thus increased the risk arisen from diseases transmission. Present study explores the role of remote sensing and GIS modeling integrated with field survey for mapping mosquito breeding sites and the areas under risk of diseases transmission in El-Fayoum governorate. Entomological surveys were conducted for a total number of 40 accessible breeding sites during the period 12-16 November 2017. A calibrated Landsat OLI image, synchronized with the field trip, was processed to produce Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST). A cartographic GIS model was generated to predict breeding sites in the whole governorate and to assess the potential risk. The main filarial disease vector (Culex pipiens) was abundant at Atsa district, while Malaria vectors (Anopheles sergentii and Anopheles multicolor) were mainly distributed in El-Fayoum and Youssef El-Seddiq districts. Means levels of NDVI, NDMI and LST at breeding habitats were recorded; 0.18, 0.08 and 21.75° C, respectively. Results of the model showed that the highest predicted risk area was reported at Atsa district (94.4 km2) and Yousef El-Sediq (81.8 km2) while the lowest prediction was observed at Abshawai district (35.9 km2). It can be concluded that Atsa, Yousef El-Sedik and El-Fayoum districts are more vulnerable to Malaria and Filaria diseases outbreaks, thus precaution and pest control methods must be applied to mitigate the possible risks.

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 Assessing the Relationship among the Land Surface Features: A Geographic Information System (GIS) and Remote Sensing (RS) Based Approach for City Area

2021 ◽  
Vol 8 (2) ◽  
pp. 935-952
Author(s):  
Sharmin Siddika ◽  
Md. Nazmul Haque ◽  
Mizbah Ahmed Sresto
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Moisture Index ◽  
Gis And Remote Sensing ◽  
Positive Correlation

Due to climate change and urbanization, it is important to monitor and evaluate the components of the environment. For this reason, ward-22 and ward-27 of the Khulna City Corporation (KCC) area have been selected for the study. This research seeks to identify the existing land use profile and assess the land surface components such as topography, Normalized Difference Buildup Index (NDBI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Salinity Index (NDSI) and Land Surface Temperature (LST) to measure the relationships among the land surface components. The land use land cover map shows that about 59% of ward-22 and 71.5% area of ward-27 are built-up areas. Both of the wards contain little amount of water body, vegetation and open space. Both of the wards have residential land use types with commercial purposes on the periphery. Accordingly, 63.32% and 65% of structures of ward-22 and 27 are pucca. The land surface components reveal that both areas contain lower slopes, less vegetation, less moisture, severe salinity, highly built-up areas, and high land surface temperature. The relationships among the land surface components show that NDVI has a negative relation with LST and NDBI whereas NDVI represents a positive correlation with NDMI. On the other hand, NDBI shows a positive correlation with LST whereas NDMI negatively correlates with LST. NDSI and topography reflect no meaningful relationship between NDBI, NDVI, LST, and NDMI. However, the research findings may be essential to city planners and decision-makers for incorporating better urban management at the micro level concerning climate change.

scholarly journals Remote Sensing Technology for Land Farm Mapping Based on NDMI, NDVI, and LST Feature

2019 ◽  
Vol 3 (3) ◽  
pp. 75
Author(s):  
Ahmad Fauzi Mabrur ◽  
Noor Akhmad Setiawan ◽  
Igi Ardiyanto
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Measured Temperature ◽  
Residential Areas ◽  
Moisture Index ◽  
Sensing Technology ◽  
Agricultural Potential ◽  
Efficient Data

Remote Sensing is a reliable and efficient data acquisition techniques. This technique is widely used for land image processing. This technique has many advantages, especially in terms of cost and time. In this study, the classification between dry and irrigated land from irrigation canals is presented. Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST) values obtained from satellite imagery data are used in this process. It is expected that through this method, the distribution and control of irrigation water can optimize existing agricultural potential. Ground Check (GC) is used for validation process. The results showed that the error rate based on the moon was not so large, i.e., 18%. The highest errors occur in February and March. This happens because those months are the rainy season, so the measured temperature is mostly the temperature above the cloud layer. On the other hand, the lowest error occurs in November. Also, it can be seen that this method can function optimally when detecting residential areas or highways.

scholarly journals A Fused Variable by Night Light Images and MODIS Products for Improving Urban Built-Up Area Extraction

Technologies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 40
Author(s):  
Guang Yang ◽  
Yuntao Ma ◽  
Jiaqi Hu
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Extraction Methods ◽  
Average Error ◽  
Geographic Coordinate System ◽  
Optimal Thresholds ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Spatial Locations

The boundary of urban built-up areas is the baseline data of a city. Rapid and accurate monitoring of urban built-up areas is the prerequisite for the boundary control and the layout of urban spaces. In recent years, the night light satellite sensors have been employed in urban built-up area extraction. However, the existing extraction methods have not fully considered the properties that directly reflect the urban built-up areas, like the land surface temperature. This research first converted multi-source data into a uniform projection, geographic coordinate system and resampling size. Then, a fused variable that integrated the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night light images, the Moderate-resolution Imaging Spectroradiometer (MODIS) surface temperature product and the normalized difference vegetation index (NDVI) product was designed to extract the built-up areas. The fusion results showed that the values of the proposed index presented a sharper gradient within a smaller spatial range, compared with the only night light images. The extraction results were tested in both the area sizes and the spatial locations. The proposed index performed better in both accuracies (average error rate 1.10%) and visual perspective. We further discussed the regularity of the optimal thresholds in the final boundary determination. The optimal thresholds of the proposed index were more stable in different cases on the premise of higher accuracies.

scholarly journals Modeling the Radial Stem Growth of the Pine (Pinus sylvestris L.) Forests Using the Satellite-Derived NDVI and LST (MODIS/AQUA) Data

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Yulia Ivanova ◽  
Anton Kovalev ◽  
Vlad Soukhovolsky
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Principal Component ◽  
Stem Growth ◽  
Forest Growth ◽  
Parabolic Approximation ◽  
New Approach ◽  
The Relationship ◽  
Tree Increment

The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year.

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

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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