Multi-temporal analysis of land cover changes using Landsat data through Google Earth Engine platform

Capolupo, Alessandra; Monterisi, Cristina; Saponaro, Mirko; Tarantino, Eufemia

doi:10.1117/12.2571228

Cited by 16 publications

(5 citation statements)

References 22 publications

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“…Most of the observed outliers in both areas overlapped (Figs. 14,15). In Palermo, overlapping outliers represented more than 89% of the totals for both images and the applied method; in Cagliari, these accounted for more than 76% and 43% of the totals for da Silva et al's and Liang's methods, respectively.…”

Section: Impact Of Tukey's Filter On Statistics Metricsmentioning

confidence: 93%

“…Figures 12 and in the Palermo and Cagliari regions that were obtained after using da Silva et al's and Liang's algorithms, respectively.Most of the observed outliers in both areas overlapped (Figs 14,15)…”

mentioning

confidence: 89%

“…Indeed, albedo information (which quantifies a surface's ability to reflect incident solar radiation) allows for an evapotranspiration (ET) estimate, which is particularly valuable for water resource management and tackling the issue of drought [1][2][3][4][5]. In urban areas, lower ET values correspond to impermeable surfaces, implying that the heat from the Sun is stored and later released into the atmosphere, resulting in an urban heat island (UHI) effect [5][6][7][8][9][10][11][12][13][14][15]. Monitoring surface albedo is, therefore, relevant for evaluating the impacts of climate changes, which are defined in the United Nations' (UN) 17 Sustainable Development Goals (SDGs) for safeguarding the Earth from global threats [3,13,[16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Extracting Land Surface Albedo from Landsat 9 Data in GEE Platform to Support Climate Change Analysis

Barletta,

Capolupo,

Tarantino

2023

GaEE

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Land surface albedo is a relevant variable in many climatic, environmental, and hydrological studies; its monitoring allows researchers to identify changes on the Earth’s surface. The open satellite data that is provided by the USGS/NASA Landsat mission is quite suitable for estimating this parameter through the remote sensing technique. The purpose of this paper is to evaluate the potentialities of the new Landsat 9 data for retrieving Earth’s albedo by applying da Silva et al.’s algorithm (developed in 2016 for the Landsat 8 data) using the Google Earth Engine cloud platform and R software. Two urban areas in Southern Italy with similar geomorphologic and climatic characteristics were chosen as study sites. After obtaining thematic maps of the albedos here, a statistical analysis and comparison among the Landsat 8 and Landsat 9 results was performed considering the entire study areas and each land use/land cover class that is provided by the Copernicus Urban Atlas 2018. This approach was also applied to the data after being filtered through Tukey’s test (used to detect and remove outliers). The analysis showed a very good correlation between the Landsat 8 and Landsat 9 estimations (ρ > 0.94 for both sites), with some exceptions that were related to some mis-corresponding values. Furthermore, the Landsat 8 and Landsat 9 outliers were generally overlapping. In conclusion, da Silva et al.’s approach appears to also be reasonably applicable to the Landsat 9 data despite some radiometric differences.

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Section: Impact Of Tukey's Filter On Statistics Metricsmentioning

confidence: 93%

mentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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Extracting Land Surface Albedo from Landsat 9 Data in GEE Platform to Support Climate Change Analysis

Barletta,

Capolupo,

Tarantino

2023

GaEE

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“…Additional details about these data are reported in Table I. [29,44]. All users may access them and select the data meeting their needs through the application programming interface (API).…”

Section: A Data-sources and Gee Database Construction (Phase 1)mentioning

confidence: 99%

Improving the Accuracy of Global DEM of Differences (DoD) in Google Earth Engine for 3-D Change Detection Analysis

Capolupo¹

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…It is composed of specific Artificial Intelligence (AI) algorithms dealing with building systems aimed at learning and/or improving behaviors based on the input data 23 . The ML algorithms family includes many procedures such as Artificial Neural Networks (ANN), k-Nearest Neighbors (kNN), Decision Trees (DT), Support Vector Machine (SVM) and Random Forest (RF) [24][25][26] .…”

Section: Introductionmentioning

confidence: 99%

Combining OBIA approach and machine learning algorithm to extract photovoltaic panels from Sentinel-2 images automatically

Ladisa

Capolupo²,

Ripa³

et al. 2022

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV

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The Energy Union Framework Strategy is pushing the entire world to move from fossil fuels to renewable energy to tackle climate changes and mitigate their effects. Among the clean energy alternatives, the sun is recognized as the most abundant and inexhaustible source and the energy production can be carried out through photovoltaic panels. Nevertheless, such a solar park requires the use of large land areas, stolen, in such a way, from food production, which demand has strongly increased in the last few years due to the growing world population. Thus, agrophotovoltaic systems, also known as agrivoltaic structures, are under way to meet the above-mentioned needs synergistically. This has led to the necessity of monitoring solar panels amount and allocation. Their detection is challenging since, albeit their spectral signature is totally different from that one emitted from other land covers, their occurrence received little attention in the field of remote sensing. Thus, in this study, a proper rule-based model for distinguishing photovoltaic panels developed on eCognition environment was proposed. Such a model is based on the combination of Object-Based Image Analysis and machine learning algorithm. Indeed, after optimizing segmentation parameters and analyzing morphological features of the panels, the Random Forest classification algorithm was implemented. Lastly, classification accuracy was evaluated. The experimentation was conducted on the study area of Viterbo (Lazio Region, Italy) by adopting open medium-resolution satellite data (Sentinel 2). This research showed promising results in classifying targets for almost all months of the time series, except for the months of October and November where there is a lowering of the accuracy value due to the variability of spectral signatures.

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Multi-temporal analysis of land cover changes using Landsat data through Google Earth Engine platform

Cited by 16 publications

References 22 publications

Extracting Land Surface Albedo from Landsat 9 Data in GEE Platform to Support Climate Change Analysis

Extracting Land Surface Albedo from Landsat 9 Data in GEE Platform to Support Climate Change Analysis

Improving the Accuracy of Global DEM of Differences (DoD) in Google Earth Engine for 3-D Change Detection Analysis

Combining OBIA approach and machine learning algorithm to extract photovoltaic panels from Sentinel-2 images automatically

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