European Space agency (ESA) Landsat MSS/TM/ETM+/OLI archive: 42 years of our history

Saunier, Sébastien; Northrop, Amy; Lavender, Samantha; Galli, Luca; Ferrara, R.; Mica, Stefano; Biasutti, Roberto; Goryl, Philippe; Gascon, Ferran; Meloni, Marco; Desclée, Baudouin; Altena, Bas

doi:10.1109/multi-temp.2017.8035252

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…The continuous progress in the availability of satellite products has supported increasingly detailed environmental studies. Analyses of long time-series have become possible through the now fully available Landsat archives, which span back to 1972 (Saunier et al, 2017). Simultaneously, high-frequency, highresolution imagery, provided by the Sentinel constellation enables a wider community of researchers to gain insights into complex, long-term, large-scale states and processes.…”

Section: Introductionmentioning

confidence: 99%

Input imagery, classifiers, and cloud computing: Insights from multi-temporal LULC mapping in the Cambodian Mekong Delta

Orieschnig

Belaud

Venot

et al. 2021

European Journal of Remote Sensing

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The increased open-access availability of radar and optical satellite imagery has engendered numerous land use and land cover (LULC) analyses combining these data sources. In parallel, cloud computing platforms have enabled a wider community to perform LULC classifications over long periods and large areas. However, an assessment of how the performance of classifiers available on these cloud platforms can be optimized for the use of multi-imagery data has been lacking for multi-temporal LULC approaches. This study provides such an assessment for the supervised classifiers available on the open-access Google Earth Engine platform: Naïve Bayes (NB), Classification and Regression Trees (CART), Random Forest (RF), Gradient Tree Boosting (GTB), and Support Vector Machines (SVM). A multi-temporal LULC analysis using Sentinel-1 and 2 is implemented for a study area in the Mekong Delta. Classifier performance is compared for different combinations of input imagery, band sets, and training datasets. The results show that GTB and RF yield the highest overall accuracies, at 94% and 93%. Combining optical and radar imagery boosts classification accuracy for CART, RF, GTB, and SVM by 10-15 percentage points. Furthermore, it reduces the impact of limited training dataset quality for RF, GTB, and SVM.

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Section: Introductionmentioning

confidence: 99%

Input imagery, classifiers, and cloud computing: Insights from multi-temporal LULC mapping in the Cambodian Mekong Delta

Orieschnig

Belaud

Venot

et al. 2021

European Journal of Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Table 1 shows the list of Landsat images used in the study. [59,60] and have been used appropriately to map long-term LULC changes (e.g., [61][62][63][64]). Since the LULC is only classified into broad categories, the use of these images is considered appropriate for this study.…”

Section: Remote Sensing Datamentioning

confidence: 99%

The Influence of Rainfall and Land Use/Land Cover Changes on River Discharge Variability in the Mountainous Catchment of the Bagmati River

Tuladhar

Dewan

Kühn

et al. 2019

Water

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Changes in rainfall and land use/land cover (LULC) can influence river discharge from a catchment in many ways. Homogenized river discharge data from three stations and average rainfall records, interpolated from 13 stations, were examined for long-term trends and decadal variations in the headwater, upper and middle catchments of the Bagmati River. LULC changes over five decades were quantified using multitemporal Landsat images. Mann-Kendall tests on annual time series showed a significant decrease in river discharge (0.61% per year) from the entire Bagmati catchment, although the decrease in rainfall was statistically insignificant. However, declines in river discharge and rainfall were both significant in upper catchment. Decadal departures from long-term means support these trend results. Over tenfold growth in urban area and a decrease in agricultural land were observed in the upper catchment, while forest cover slightly increased in the entire catchment between 1975 and 2015. Correlation analysis showed a strong association between surface runoff, estimated using the curve number method, observed river discharge and rainfall in the upper catchment, while the relationship was weaker in the headwater catchment. These results were also supported by multiple regression analysis, suggesting that human activities together with climate change have contributed to river discharge changes in the Bagmati catchment.Bosch and Hewlett [15] suggested that clearing of forests increases river discharge while recovery of vegetation or afforestation decreases the discharge. Growth in urban areas can increase surface runoff while evaporation and ground water recharge may have varying response [16,17]. Increasing river discharge is also linked with expansion of agricultural land and decrease in shrubs and forests [18,19]. In the Ganges basin, natural and climatic variability in precipitation and river discharge are further affected by rapid population growth and subsequent LULC changes, including urbanization and deforestation [20]. Nevertheless, a few studies also reported no significant changes in rainfall, evapotranspiration and river discharge in relation to forest-cover changes [21,22].Within Nepal, significant LULC changes have occurred, especially with conversion from forest to agriculture. Forest cover in Nepal decreased from 45% in 1966 to 29% in 1994 [23,24]. However, recent forest assessments over 20 districts of Terai region suggest that, the rate of decrease has slowed down to 0.06% per year during 1990-2010 [24]. Forest conditions are estimated to be improving with community-based forest management and government regulations [23][24][25]. However, increasing frequency of weather-related extreme events in the region [26] such as excessive rainfall, long drought spells, landslides and floods have already had negative impacts on agriculture, forestry and biodiversity in Nepal [23].The Bagmati River is not only important in relation to the initial settlement of Kathmandu valley, its tributary systems are also crucia...

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Bulk processing of the Landsat MSS/TM/ETM+ archive of the European Space Agency: an insight into the level 1 MSS processing

Saunier¹,

Northrop²,

Lavender³

et al. 2017

Image and Signal Processing for Remote Sensing XXIII

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European Space agency (ESA) Landsat MSS/TM/ETM+/OLI archive: 42 years of our history

Cited by 6 publications

References 23 publications

Input imagery, classifiers, and cloud computing: Insights from multi-temporal LULC mapping in the Cambodian Mekong Delta

Input imagery, classifiers, and cloud computing: Insights from multi-temporal LULC mapping in the Cambodian Mekong Delta

The Influence of Rainfall and Land Use/Land Cover Changes on River Discharge Variability in the Mountainous Catchment of the Bagmati River

Bulk processing of the Landsat MSS/TM/ETM+ archive of the European Space Agency: an insight into the level 1 MSS processing

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