Satellite-based land use mapping: comparative analysis of Landsat-8, Advanced Land Imager, and big data Hyperion imagery

Linear Imaging Self Scanning Sensor (LISS-3) onboard Resoucesat-1 and 2 Satellites have been used extensively for various land cover-land use applications. In this study, we examined the potential of using Resourcesat-2 LISS-3 images in the absence of LANDSAT-8 Operational Land Imager (OLI) images. This paper compares the capabilities of LISS-3 sensor with OLI sensor. LISS-3 images were selected for comparison because of their close resemblance in electromagnetic spectrum range with LS-8 OLI images. Images of LS-8 OLI and RS-2 LISS-3 of the same area in Andhra Pradesh were used to evaluate the comparative performances based on the intra-inter band correlation, spectral vegetation indices and land cover classification. The results showed that in most cases the LS-8 OLI and the RS-2 LISS-3 images are comparable. This study also indicated that LISS-3 images could fill the data gaps in OLI images for land-cover studies, vice versa.

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Land Use Pattern in India and Karnataka: A Comparative Analysis

International Journal of Scientific Research ◽

10.15373/22778179/oct2013/35 ◽

2012 ◽

Vol 2 (10) ◽

pp. 1-3 ◽

Cited By ~ 1

Author(s):

Dr. Premakumara Dr. Premakumara ◽

◽

Seema Seema

Keyword(s):

Land Use ◽

Comparative Analysis ◽

Land Use Pattern ◽

Use Pattern

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Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽

10.3390/geosciences11080312 ◽

2021 ◽

Vol 11 (8) ◽

pp. 312

Author(s):

Barbara Wiatkowska ◽

Janusz Słodczyk ◽

Aleksandra Stokowska

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Satellite Imagery ◽

Urban Areas ◽

Agricultural Land ◽

Urban Expansion ◽

Landsat 8 ◽

Rate Of Increase ◽

Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

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Changes in Coastal Agricultural Land Use in Response to Climate Change: An Assessment Using Satellite Remote Sensing and Household Survey Data in Tien Hai District, Thai Binh Province, Vietnam

Land ◽

10.3390/land10060627 ◽

2021 ◽

Vol 10 (6) ◽

pp. 627

Author(s):

Duong H. Nong ◽

An T. Ngo ◽

Hoa P. T. Nguyen ◽

Thuy T. Nguyen ◽

Lan T. Nguyen ◽

...

Keyword(s):

Climate Change ◽

Land Use ◽

Agricultural Land ◽

Land Use Changes ◽

Management Strategies ◽

Household Survey ◽

Extreme Weather Events ◽

Agricultural Land Use ◽

Landsat 8 ◽

Land Uses

We analyzed the agricultural land-use changes in the coastal areas of Tien Hai district, Thai Binh province, in 2005, 2010, 2015, and 2020, using Landsat 5 and Landsat 8 data. We used the object-oriented classification method with the maximum likelihood algorithm to classify six types of land uses. The series of land-use maps we produced had an overall accuracy of more than 80%. We then conducted a spatial analysis of the 5-year land-use change using ArcGIS software. In addition, we surveyed 150 farm households using a structured questionnaire regarding the impacts of climate change on agricultural productivity and land uses, as well as farmers’ adaptation and responses. The results showed that from 2005 to 2020, cropland decreased, while aquaculture land and forest land increased. We observed that the most remarkable decreases were in the area of rice (485.58 ha), the area of perennial crops (109.7 ha), and the area of non-agricultural land (747.35 ha). The area of land used for aquaculture and forest increased by 566.88 ha and 772.60 ha, respectively. We found that the manifestations of climate change, such as extreme weather events, saltwater intrusion, drought, and floods, have had a profound impact on agricultural production and land uses in the district, especially for annual crops and aquaculture. The results provide useful information for state authorities to design land-management strategies and solutions that are economic and effective in adapting to climate change.

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Land use planning in coastal areas based on remote sensing images and big data education resources extraction

Arabian Journal of Geosciences ◽

10.1007/s12517-021-07421-8 ◽

2021 ◽

Vol 14 (11) ◽

Author(s):

Xiaoyan Lei

Keyword(s):

Remote Sensing ◽

Land Use ◽

Big Data ◽

Land Use Planning ◽

Coastal Areas ◽

Remote Sensing Images ◽

Education Resources

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Monitoring the Spatial Variation of Aerosol Optical Depth and Its Correlation with Land Use/Land Cover in Wuhan, China: A Perspective of Urban Planning

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18031132 ◽

2021 ◽

Vol 18 (3) ◽

pp. 1132

Author(s):

Qijiao Xie ◽

Qi Sun

Keyword(s):

Land Use ◽

Air Quality ◽

Urban Planning ◽

Land Cover ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Water Bodies ◽

Landsat 8 ◽

Land Use Land Cover ◽

The Impact

Aerosols significantly affect environmental conditions, air quality, and public health locally, regionally, and globally. Examining the impact of land use/land cover (LULC) on aerosol optical depth (AOD) helps to understand how human activities influence air quality and develop suitable solutions. The Landsat 8 image and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products in summer in 2018 were used in LULC classification and AOD retrieval in this study. Spatial statistics and correlation analysis about the relationship between LULC and AOD were performed to examine the impact of LULC on AOD in summer in Wuhan, China. Results indicate that the AOD distribution expressed an obvious “basin effect” in urban development areas: higher AOD values concentrated in water bodies with lower terrain, which were surrounded by the high buildings or mountains with lower AOD values. The AOD values were negatively correlated with the vegetated areas while positively correlated to water bodies and construction lands. The impact of LULC on AOD varied with different contexts in all cases, showing a “context effect”. The regression correlations among the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), and AOD in given landscape contexts were much stronger than those throughout the whole study area. These findings provide sound evidence for urban planning, land use management and air quality improvement.

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A comparative analysis of the carbon intensity of biofuels caused by land use changes

GCB Bioenergy ◽

10.1111/j.1757-1707.2012.01176.x ◽

2012 ◽

Vol 4 (4) ◽

pp. 392-407 ◽

Cited By ~ 29

Author(s):

Sylvestre Njakou Djomo ◽

Reinhart Ceulemans

Keyword(s):

Land Use ◽

Comparative Analysis ◽

Land Use Changes ◽

Carbon Intensity

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

10.5194/egusphere-egu21-13643 ◽

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

Violent conflicts related to pastoralists-farmers&#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&#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&#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 (>30&#8304;C). In addition, the LULC analyses showed that built-up land area increased by 35 km2 (0.1 percent) and dense forests reduced by 798 km2 (0.1 percent). Notably, shrublands and grasslands, which are the resource domains of the pastoralists reduced by 11,716 km2&#160; (13.1 percent) and croplands of farmers increased by 12,316 km2 (13.8 percent). 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&#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.

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Making Of Classification Land Cover Through Result Of Visual Data Satellite Image Analysis Landsat 8 OLI : Case Study in Tapaktuan District, South Aceh District

Jurnal Inotera ◽

10.31572/inotera.vol6.iss1.2021.id139 ◽

2021 ◽

Vol 6 (1) ◽

pp. 59-65

Author(s):

Safridatul Audah ◽

Muharratul Mina Rizky ◽

Lindawati

Keyword(s):

Land Use ◽

Land Cover ◽

Image Classification ◽

Satellite Image ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Sensing Technology ◽

Open Land ◽

Cover Class ◽

Satellite Image Analysis

Tapaktuan is the capital and administrative center of South Aceh Regency, which is a sub-district level city area known as Naga City. Tapaktuan is designated as a sub-district to be used for the expansion of the capital's land. Consideration of land suitability is needed so that the development of settlements in Tapaktuan District is directed. The purpose of this study is to determine the level of land use change from 2014 to 2018 by using remote sensing technology in the form of Landsat-8 OLI satellite data through image classification methods by determining the training area of the image which then automatically categorizes all pixels in the image into land cover class. The results obtained are the results of the two image classification tests stating the accuracy of the interpretation of more than 80% and the results of the classification of land cover divided into seven forms of land use, namely plantations, forests, settlements, open land, and clouds. From these classes, the area of land cover change in Tapaktuan is increasing in size from year to year.

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Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

Sustainability ◽

10.3390/su10103421 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3421 ◽

Cited By ~ 43

Author(s):

Rahel Hamad ◽

Heiko Balzter ◽

Kamal Kolo

Keyword(s):

Land Use ◽

Markov Chain ◽

Land Cover ◽

Land Use Changes ◽

Landsat 8 ◽

Chain Model ◽

Land Use Land Cover ◽

Kurdistan Region ◽

The Past ◽

Past Trend

Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.

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