Accuracy comparison and driving factor analysis of LULC changes using multi-source time-series remote sensing data in a coastal area

Zheng, Qihui; Chen, Wei; Li, Siliang; Yu, Le; Zhang, Xiao; Liu, Lanfa; Singh, Ramesh P.; Liu, Cong‐Qiang

doi:10.1016/j.ecoinf.2021.101457

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…Today, there is a need to describe what is on the earth's surface at a given location (Zheng et al, 2021), especially with the world's rapidly changing land surface, which has made land use and land cover (LU/LC) an essential tool for land management and planning (Mallupattu & Sreenivasula Reddy, 2013). Furthermore, with global warming and climate change, land use and land cover is becoming a critical factor influencing many hazards (Shankar Prasad & Wasini Pandey, n.d.) such as floods (Zope et al, 2017) , flash floods (Sellami et al, 2022) , landslides (Shu et al, 2019) , debris-flow (Abuzied & Pradhan, 2020) .…”

Section: Introducationmentioning

confidence: 99%

A New Approach for Mapping Land Use / Land Cover Using Google Earth Engine: A Comparison of Composition Images

Sellami

Rhinane

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In view of the increase in human activities, climate change and related hazards, land use and land cover (LULC) mapping is becoming a fundamental part of the process of any development or hazard prevention project. From this perspective, we propose a new approach for mapping LULC using Machine learning algorithms by comparing the result of five composition methods based on Google Earth Engine in the city of Tetouan - Morocco. To achieve this goal, considering the Sentinel S2 L2 imageries as a source data , five datasets were derived to make the classification generating by aggregating functions (median , mean , max , min and mode). Then based on the very high resolution (VHR) satellite images provided by Google Earth comes the next step that involves selecting samples that are divided into five classes (barren land, water surface, vegetation, forest, and urban areas), which will be further split into two parts: 70% as a training data -used to feed the machine learning algorithms (support vector machine (SVM), random forest (RF) and classification and regression trees (CART))- and 30% as a testing data for evaluating the models using accuracy assessments. The results for all datasets indicate that the SVM algorithm has the highest accuracy and its performance is better than the other algorithms (RF and CART). The average overall accuracy of SVM , RF , and CART was 87.99% , 87.81% and 84.72% , respectively. Furthermore, for each algorithm, the comparison between the results of the different composites indicates that the use of the mean composite is the most suitable for LULC mapping. Finally, GEE has proven to be an effective and rapid method for LULC mapping, especially with the use of compositional imagery that can assist decision makers in future planning or risk prevention.

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Section: Introducationmentioning

confidence: 99%

A New Approach for Mapping Land Use / Land Cover Using Google Earth Engine: A Comparison of Composition Images

Sellami

Rhinane

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Perubahan terjadi akibat adanya faktor yang mendorong perubahan tersebut. Faktor pendorong perubahan penting untuk dianalisis karena dengan mengetahui faktor pendorong dapat mengungkap mekanisme perubahan yang terjadi (Liu et al 2020;Zheng et al 2021). Diduga ada tujuh variabel yang mempengaruhi perubahan lanskap pesisir di Kecamatan Kuta Selatan, yaitu Jarak Jalan (X1), Jarak Sungai (X2), Elevasi (X3), Kemiringan Lereng (X4), Kepadatan Penduduk (X5), Jarak dari Perguruan Tinggi Negeri (X6), dan Jarak dari Daya Tarik Wisata (X7) (Gambar 5). )…”

Section: Analisis Faktor Pendorong Perubahan Lahanunclassified

Analisis Kerentanan Lanskap Pesisir di Kecamatan Kuta Selatan, Bali

Fitriana

Gunawan

Munandar

2023

jal

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Vulnerability Analysis of Land Changes in Coastal Landscapes in Kuta Selatan District, Bali. Kuta Selatan District is a district located in the southern coastal part of Bali Province. Its existence in a coastal area makes this area rich in natural resources but also has a high level of vulnerability to damage, change, or disappearance of an ecosystem. This study aim to analyze the vulnerability of the landscape based on land cover changes in Kuta Selatan District and analyze the driving factors of landscape change. Land cover is classified into 4 (water bodies, vegetation areas, open areas, and built areas. Land cover change analysis used Landsat 7 ETM+ image data with test years 2000, 2010, and 2020. The spatial analysis method was used to analyze the land cover changes that occurred. The results showed that the rate of land cover change that occurs in Kuta Selatan District from 2000 to 2020 is 3-4% per year with dominated by increasing of open areas and built-up areas respectively. Meanwhile, the area of vegetation and water bodies has always experienced a decrease in area. This study also analyzes the driving factors of land cover change using the binary logistic regression analysis method. 7 variables are thought to affect changes in land cover that occur, namely distance to the main road (X1), distance to the river (X2), altitude/elevation (X3), slope (X4), population density (X5), distance from state universities (X6) and distance to tourist attractions (X7). The analysis results show that only the distance variable to the main road (X1) influences changes that occur in the 2000-2020 range. Supervision and policies that favor the sustainability of the existence of vegetation areas are the main focus of the recommendations.

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“…When a small fire occurs (less than 500 m × 500 m), the entire pixel needs to be assigned as burned, which affected the selection of our research area [38]. The classification accuracy of MCD12Q1 land cover type data is lower than official standards [50]. At the same time, we chose the land cover from 2015 to represent the period from 2010 to 2020, which may have presented some shortcomings.…”

Section: Uncertainty In the Selection Of Research Areasmentioning

confidence: 99%

Methane Emissions in Boreal Forest Fire Regions: Assessment of Five Biomass-Burning Emission Inventories Based on Carbon Sensing Satellites

Zhao,

Wang,

Shi

et al. 2023

Remote Sensing

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Greenhouse gases such as CH4 generated by forest fires have a significant impact on atmospheric methane concentrations and terrestrial vegetation methane budgets. Verification in conjunction with “top-down” satellite remote sensing observation has become a vital way to verify biomass-burning emission inventories and accurately assess greenhouse gases while looking into the limitations in reliability and quantification of existing “bottom-up” biomass-burning emission inventories. Therefore, we considered boreal forest fire regions as an example while combining five biomass-burning emission inventories and CH4 indicators of atmospheric concentration satellite observation data. By introducing numerical comparison, correlation analysis and trend consistency analysis methods, we explained the lag effect between emissions and atmospheric concentration changes and evaluated a more reliable emission inventory using time series similarity measurement methods. The results indicated that total methane emissions from five biomass-burning emission inventories differed by a factor of 2.9 in our study area, ranging from 2.02 to 5.84 Tg for methane. The time trends of the five inventories showed good consistency, with the Quick Fire Emissions Dataset version 2.5 (QFED2.5) having a higher correlation coefficient (above 0.8) with the other four datasets. By comparing the consistency between the inventories and satellite data, a lagging effect was found to be present between the changes in atmospheric concentration and gas emissions caused by forest fires on a seasonal scale. After eliminating lagging effects and combining time series similarity measures, the QFED2.5 (Euclidean distance = 0.14) was found to have the highest similarity to satellite data. In contrast, Global Fire Emissions Database version 4.1 with small fires (GFED4.1s) and Global Fire Assimilation System version 1.2 (GFAS1.2) had larger Euclidean distances of 0.52 and 0.4, respectively, which meant that they had lower similarity to satellite data. Therefore, QFED2.5 was found to be more reliable while having higher application accuracy compared to the other four datasets in our study area. This study further provided a better understanding of the key role of forest fire emissions in atmospheric CH4 concentrations and offered reference for selecting appropriate biomass burning emission inventory datasets for bottom-up inventory estimation studies.

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Accuracy comparison and driving factor analysis of LULC changes using multi-source time-series remote sensing data in a coastal area

Cited by 14 publications

References 19 publications

A New Approach for Mapping Land Use / Land Cover Using Google Earth Engine: A Comparison of Composition Images

A New Approach for Mapping Land Use / Land Cover Using Google Earth Engine: A Comparison of Composition Images

Analisis Kerentanan Lanskap Pesisir di Kecamatan Kuta Selatan, Bali

Methane Emissions in Boreal Forest Fire Regions: Assessment of Five Biomass-Burning Emission Inventories Based on Carbon Sensing Satellites

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