scholarly journals Intensity of land use by pasture as result of pedogeoclimatic characteristics and anthropogenic management

2021 ◽  
Author(s):  
Arthur Calegario ◽  
Demetrius da Silva ◽  
Elpídio Fernandes Filho ◽  
Roberto Filgueiras ◽  
Luis Flávio Pereira ◽  
...  
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Landsat 8 ◽  
Pasture Management ◽  
Cloud Coverage ◽  
Continuous Grazing ◽  
Rainfall Seasonality ◽  
Natural Land ◽  
Landsat 7 ◽  
Most Significant Change

In the world, the most significant change in the ecosystems structure is the conversion from natural land surface into cultivated systems. In 2018, 26.8% of the Brazilian territory was occupied by agricultural activities, from which 73% is pasture. Considering that the management adopted in Brazilian pastures is incipient and leads to degradation, there is a need to characterize the state of the pastures to diagnose the intensity of this use on the soil. However, the diagnosis of large areas using satellites with more detailed resolution is limited by cloud coverage and low temporal resolution. In this sense, the present work aims to diagnose the intensity of land use by pastures (ILUP) in large areas based on the mosaic of images from Landsat 8 (LS8), Landsat 7 (LS7), Sentinel-2 (S2), and MODIS. The methodology consists of harmonizing the NDVI from LS7 and S2 satellites with LS8. For MODIS, the harmonization was carried out based on ILUP obtained previously from NDVI LS8. The methodology was applied at the Doce river basin (DRB). The combination of different sensors allowed to overcome the cloud coverage limitation. DRB has 61.3% of its area occupied by pastures and 78.2% of them have some degree of degradation. ILUP was dependent on DRB’s pedological and climatic characteristics. This dependence is enhanced due to pasture management in the basin, mainly characterized by continuous grazing, which commonly leads to overgrazing scenarios. The areas with great rainfall seasonality and associated with Acrisols/Cambisols are the most susceptible to degradation.

scholarly journals ANALYSIS OF RELATIONSHIP BETWEEN URBAN HEAT ISLAND EFFECT AND LAND USE/COVER TYPE USING LANDSAT 7 ETM+ AND LANDSAT 8 OLI IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 821-828
Author(s):  
N. Aslan ◽  
D. Koc-San
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Vegetation Index ◽  
Irrigated Agriculture ◽  
Time Interval ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat Images ◽  
Study Results ◽  
Landsat 7

The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88&thinsp;% for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6&thinsp;&deg;C for 2001 and 6.8&thinsp;&deg;C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r<sup>2</sup>&thinsp;=&thinsp;0.7 and r<sup>2</sup>&thinsp;=&thinsp;0.9 for 2001 and 2014, respectively).

scholarly journals Cross-Comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) Derived Vegetation Indices in a Mediterranean Environment

2020 ◽  
Vol 12 (2) ◽  
pp. 291 ◽  
Author(s):  
Giuseppe Mancino ◽  
Agostino Ferrara ◽  
Antonietta Padula ◽  
Angelo Nolè
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Earth Observation ◽  
Landsat 8 ◽  
Mediterranean Environment ◽  
Water Index ◽  
Land Use Types ◽  
Landsat 7

Landsat 8 is the most recent generation of Landsat satellite missions that provides remote sensing imagery for earth observation. The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, together with Landsat-8 Operational Land Imager (OLI) and Thermal Infrared sensor (TIRS) represent fundamental tools for earth observation due to the optimal combination of the radiometric and geometric images resolution provided by these sensors. However, there are substantial differences between the information provided by Landsat 7 and Landsat 8. In order to perform a multi-temporal analysis, a cross-comparison between image from different Landsat satellites is required. The present study is based on the evaluation of specific intercalibration functions for the standardization of main vegetation indices calculated from the two Landsat generation images, with respect to main land use types. The NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), LSWI (Land Surface Water Index), NBR (Normalized Burn Ratio), VIgreen (Green Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and EVI (Enhanced Vegetation Index) have been derived from August 2017 ETM+ and OLI images (path: 188; row: 32) for the study area (Basilicata Region, located in the southern part of Italy) selected as a highly representative of Mediterranean environment. Main results show slight differences in the values of average reflectance for each band: OLI shows higher values in the near-infrared (NIR) wavelength for all the land use types, while in the short-wave infrared (SWIR) the ETM+ shows higher reflectance values. High correlation coefficients between different indices (in particular NDVI and NDWI) show that ETM+ and OLI can be used as complementary data. The best correlation in terms of cross-comparison was found for NDVI, NDWI, SAVI, and EVI indices; while according to land use classes, statistically significant differences were found for almost all the considered indices calculated with the two sensors.

scholarly journals ANALYSIS OF RELATIONSHIP BETWEEN URBAN HEAT ISLAND EFFECT AND LAND USE/COVER TYPE USING LANDSAT 7 ETM+ AND LANDSAT 8 OLI IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 821-828 ◽  
Author(s):  
N. Aslan ◽  
D. Koc-San
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Vegetation Index ◽  
Irrigated Agriculture ◽  
Time Interval ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat Images ◽  
Study Results ◽  
Landsat 7

The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88&thinsp;% for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6&thinsp;&deg;C for 2001 and 6.8&thinsp;&deg;C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r&lt;sup&gt;2&lt;/sup&gt;&thinsp;=&thinsp;0.7 and r&lt;sup&gt;2&lt;/sup&gt;&thinsp;=&thinsp;0.9 for 2001 and 2014, respectively).

scholarly journals Land Use/Land Cover Change With Impact On Land Surface Temperature: A Case Study Of Mkda Planning Area, West Bengal, India

2020 ◽  
Vol 13 (4) ◽  
pp. 43-53
Author(s):  
Samrin Fatema ◽  
Dr. Abhisek Chakrabarty
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Landsat 8 ◽  
Hard Material ◽  
Local Climate ◽  
Landsat 7 ◽  
Multi Temporal

The type of surface influences the temperature of a surface. If it is made of concrete or another hard material, the temperature will be higher. Hence it is essential to study the land surface temperature (LST) of urban areas. The LST is an important parameter in the estimation of radiation budgets and heat balance and is a controlling factor of dynamic climate changes. In this work, we made an effort to identify the LST of the Midnapore Kharagpur Development Authority planning region. Multi-temporal images acquired by Landsat 7 ETM+, Landsat 5 TM and Landsat 8 using OLI sensors on 3 May 2001, 7 May 2011 and 29 May 2019, respectively, were corrected for radiometric and geometric errors and processed to extract LULC classes and LST. Thermal remote sensing can be used to monitor the temperature and local climate of urban areas. This study has shown that the temperature varies across the surface according to land use. It was found that the urbanized area increased from 6.79% (40.39 sq. km) to 11.6% (69.2 sq. km) between 2001 and 2011 and from 11.6% (69.2 sq. km) to 17.22 % (102.79 sq. km) between 2011 and 2019. The LST study has shown that there has been a tremendous change in the spatial pattern of the temperature between 2001 and 2019. Whereas in 2001 the highest temperature did not exceed 34°C, by 2019 it had increased by nearly 8°C, reaching 41.29°C. So, the findings of this study are significant.

Pastoralists-farmers’ conflicts in Nigeria’s mid-Benue Trough: Socio-ecological drivers and pathways to addressing the conflicts.

2021 ◽  
Author(s):  
Chukwudi Njoku ◽  
Francis Okpiliya ◽  
Joel Efiong ◽  
Chinwe Ifejika Speranza
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Surface ◽  
Land Development ◽  
Policy Formulation ◽  
Landsat 8 ◽  
Land Resource ◽  
Hotspot Analysis ◽  
Benue Trough ◽  
Year 2000

&lt;p&gt;Violent conflicts related to pastoralists-farmers&amp;#8217; interactions in Nigeria have assumed an unprecedented dimension, causing loss of lives and livelihoods. The mid-Benue trough (Benue and Taraba States) has suffered most from the conflicts. This study aims to provide knowledge on the socio-ecological drivers of pastoralists-farmers&amp;#8217; conflicts in the mid-Benue trough from the year 2000 to 2020 and to identify pathways to solving them. First, data from the Armed Conflict Location and Event Data Project were used to map the conflicts. Second, to understand the nexus of climate change, land use and the conflicts, the study analyzed satellite data of Land Surface Temperature (LST) as a proxy for climate change, using data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite and Land Use Land Cover (LULC), using LandSat 7 ETM and LandSat 8 ETM+ data, then linked them to the mapped conflicts. Third, to understand causes and impacts of the conflict on pastoralists and farmers&amp;#8217; livelihoods, 100 interviews were conducted, 50 for each group and analyzed using content analysis and descriptive statistics. Results showed that there were 2532 fatalities from 309 conflict events between pastoralists and farmers. The incidents exhibited statistically significant clustering and were minimal between the year 2000 and 2012, increasing gradually until the year 2013 when it began to rise geometrically. The Getis-Ord Gi hotspot analysis revealed the conflict hotspots to include Agatu, Oturkpo, Gwer East and Gashaka Local Government Areas. The results from the LST analysis showed that the area coverage of high LST increased from 30 percent in 2000 to 38 percent in 2020, while extremely high LST area also increased from 14 to 16 percent. A significantly high percentage of the conflicts (87 percent) occurred in areas with high LST (&gt;30&amp;#8304;C). In addition, the LULC analyses showed that built-up land area increased by 35 km&lt;sup&gt;2 &lt;/sup&gt;(0.1 percent) and dense forests reduced by 798 km&lt;sup&gt;2&lt;/sup&gt; (0.1 percent). Notably, shrublands and grasslands, which are the resource domains of the pastoralists reduced by 11,716 km&lt;sup&gt;2&amp;#160; &lt;/sup&gt;(13.1 percent) and croplands of farmers increased by 12,316 km&lt;sup&gt;2 &lt;/sup&gt;(13.8 percent)&lt;strong&gt;. &lt;/strong&gt;This presents an apparent transition of LULC from shrublands and grasslands to croplands in the area. Further analyses showed that 63 percent of the conflicts occurred in croplands and 16 percent in shrublands and grasslands. Hence, the reduction of land resource available to pastoralists and their subsequent cropland encroachment were identified as major causes of the conflict. It was therefore concluded that land development for other purposes is a major driver of pastoralists-farmers&amp;#8217; conflicts in the study area. There is thus a need to integrate conflict maps, LST and LULC dynamics to support dialogue, land use planning and policy formulation for sustainable land management to guide pastoral and farming activities.&lt;/p&gt;

scholarly journals PEMANFAATAN CITRA SATELIT LANDSAT 8 UNTUK MEMETAKAN SEBARAN TITIK PANASSEBAGAI INDIKASI PENINGKATAN SUHU PERMUKAAN TANAH DI KABUPATEN KUTAI TIMUR

2021 ◽  
Vol 46 (3) ◽  
pp. 383
Author(s):  
Donny Dhonanto ◽  
Nurul Puspita Palupi ◽  
Ghaisani Salsabila
Keyword(s):  
Land Use ◽  
Information System ◽  
Data Analysis ◽  
Geographic Information System ◽  
Surface Temperature ◽  
Land Surface ◽  
Maximum Temperature ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Area Distribution

 Transformation of land-use cause forest area decrease that will affect microclimate (weather tends heat), thus hotspot may possible to scattered in that area and raise the transformation of surface temperature. The objective of this research is to determine the indication of surface temperature in the East Kutai District. The advantage of this research is to give information about hotspot area distribution based on land use and relate between hotspots with surface temperature increase so it is supposed to be one of the consider to transform land use in East Kutai District. This research was held from April until May 2019 at the Laboratory of Carthography and Geographic Information System, Faculty of Agriculture, Mulawarman University. This research using calculation of Land Surface Temperature (LST) value to determine the transformation of surface temperature in East Kutai District by data analysis from Landsat-8 OLI/TIRS satellite. Hotspot area distribution adapted to map of land-use so we found the source of the hotspot. The result of the research shows there are about 6 hotspots in land-use of plantation in 2017 and the increase of the surface temperature is not static cause by depending of rainfall in East Kutai District. Increasing of surface temperature in East Kutai District in October 2013 become 22.35 oC (for minimum temperature), whereas in May 2017 become 37.24 oC (for maximum temperature). 

scholarly journals The Effects of Land Use Zoning and Densification on Changes in Land Surface Temperature in Seoul

2019 ◽  
Vol 11 (24) ◽  
pp. 7056 ◽  
Author(s):  
Jae-Ik Kim ◽  
Myung-Jin Jun ◽  
Chang-Hwan Yeo ◽  
Ki-Hyun Kwon ◽  
Jun Yong Hyun
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Descriptive Analysis ◽  
Landsat 8 ◽  
Land Use Type ◽  
Coverage Ratio

This study investigated how changes in land surface temperature (LST) during 2004 and 2014 were attributable to zoning-based land use type in Seoul in association with the building coverage ratio (BCR), floor area ratio (FAR), and a normalized difference vegetation index (NDVI). We retrieved LSTs and NDVI data from satellite images, Landsat TM 5 for 2004 and Landsat 8 TIRS for 2014 and combined them with parcel-based land use information, which contained data on BCR, FAR, and zoning-based land use type. The descriptive analysis results showed a rise in LST for the low- and medium-density residential land, whereas significant LST decreases were found in high-density residential, semi-residential, and commercial areas over the time period. Statistical results further supported these findings, yielding statistically significant negative coefficient values for all interaction variables between higher-density land use types and a year-based dummy variable. The findings appear to be related to residential densification involving the provision of more high-rise apartment complexes and government efforts to secure more parks and green spaces through urban redevelopment and renewal projects.

scholarly journals Remote Sensing Application for Exploring Land Use and Land Cover Dynamics in and Around Chatra Wetland, English Bazar, West Bengal

2021 ◽  
Vol 9 (1) ◽  
pp. 15-27
Author(s):  
Saleha Jamal ◽  
Md Ashif Ali
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Use Changes ◽  
United States Geological Survey ◽  
Landsat 8 ◽  
Gis And Remote Sensing ◽  
Erdas Imagine ◽  
Landsat 7 ◽  
Vegetation Water

Wetlands are often called as biological “supermarket” and “kidneys of the landscape” due to their multiple functions, including water purification, water storage, processing of carbon and other nutrients, stabilization of shorelines and support of aquatic lives. Unfortunately, although being dynamic and productive ecosystem, these wetlands have been affected by human induced land use changes. India is losing wetlands at the rate of 2 to 3 per cent each year due to over-population, direct deforestation, urban encroachment, over fishing, irrigation and agriculture etc (Prasher, 2018). The present study tries to investigate the nature and degree of land use/land cover transformation, their causes and resultant effects on Chatra Wetland. To fulfil the purpose of the study, GIS and remote sensing techniques have been employed. Satellite imageries have been used from United States Geological Survey (USGS) Landsat 7 Enhanced Thematic Mapper plus and Landsat 8 Operational Land Imager for the year 2003 and 2018. Cloud free imageries of 2003 and 2018 have been downloaded from USGS (https://glovis.usgs.gov/) for the month of March and April respectively. Image processing, supervised classificationhas been done in ArcGis 10.5 and ERDAS IMAGINE 14. The study reveals that the settlement hasincreased by about 90.43 per cent in the last 15 years around the Chatra wetland within the bufferzone of 2 Sq km. Similarly agriculture, vegetation, water body, swamp and wasteland witnessed asignificant decrease by 5.94 per cent, 57.69 per cent, 26.64 per cent 4.52 per cent and 55.27 per centrespectively from 2003 to 2018.

scholarly journals Land use and land cover changes in the Lower Neretva Region from 1990 to 2020

2021 ◽  
Vol 83 (2) ◽  
pp. 7-31
Author(s):  
Josip Šetka ◽  
◽  
Petra Radeljak Kaufmann ◽  
Luka Valožić ◽  
◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Confusion Matrix ◽  
Hybrid Approach ◽  
Land Cover Changes ◽  
Landsat 8 ◽  
Economic Changes ◽  
Sparse Vegetation ◽  
Landsat 7

Changes in land use and land cover are the result of complex interactions between humans and their environment. This study examines land use and land cover changes in the Lower Neretva Region between 1990 and 2020. Political and economic changes in the early 1990s resulted in changes in the landscape, both directly and indirectly. Multispectral image processing was used to create thematic maps of land use and land cover for 1990, 2005, and 2020. Satellite images from Landsat 5, Landsat 7 and Landsat 8 were the main source of data. Land use and land cover structure was assessed using a hybrid approach, combining unsupervised and manual (visual) classification methods. An assessment of classification accuracy was carried out using a confusion matrix and kappa coefficient. According to the results of the study, the percentage of built-up areas increased by almost 33%. Agricultural land and forests and grasslands also increased, while the proportion of swamps and sparse vegetation areas decreased.

scholarly journals MODELING THE DEVELOPMENTS IN URBANISATION AND RELATIONSHIP WITH VEGETATION COVER IN ALANYA

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 291-297
Author(s):  
B. İşler ◽  
Z. Aslan
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Landsat 8 ◽  
World Population ◽  
Land Use Land Cover ◽  
Temperature And Precipitation ◽  
Atmospheric Variables

Abstract. The increase in the world population and the migration of people from rural to urban areas causes an increase in artificial surfaces and causes many negative effects on the ecosystem, regional climate variations and global diversity. Nowadays, as the effects of climate change are felt more and more, it has gained importance in researches on this subject. Therefore, the estimation of the change in the vegetation density for the coming years and the determination of the land use / land cover (LULC) change in cities are very essential for urban planning. In this study, the effects of regional urbanization on vegetation are examined by using satellite data and atmospheric variables. In the vegetation analysis, multi-time index values obtained from TERRA-MODIS satellite, EVI (Enhanced Vegetation Index) and LST (Land Surface Temperature) were taken into account between the years of 2005 and 2018 in Alanya, Turkey. Temperature and precipitation were selected as the atmospheric variables and expected variations in EVI value until 2030 were estimated. In the study employed a wavelet-transformed artificial neural network (WANN) model to generate long-term (12-year) EVI forecasts using LST, temperature and precipitation. The relationship between land use / land cover and urbanization is investigated with NDBI (Normalized Difference Built-up Index) data obtained from the Landsat 8 OLI / TIRS satellite sensor. The simulation results show that The EVI value, which was 0.30 in 2018, will decrease to 0.25 in 2030.

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