Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series

Kosczor, E.; Forkel, Matthias; Hernández, Jaime; Kinalczyk, Daniel; Pirotti, Francesco; Kutchartt, E.

doi:10.1016/j.jag.2022.102862

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…Studies on plant phenology in different climatic regions indicated that the key influencing factors of vegetation phenology vary between regions [3,32,37]. A large number of studies on the SOS of grassland vegetation also showed that the differences in the date of green returning of grassland vegetation in different regions are mainly reflected in factors such as vegetation species [61], vegetation types [2], regional differences [56] and topographic and landform characteristics [62,63].…”

Section: Analysis Of Influencing Factorsmentioning

confidence: 99%

LAI-Based Phenological Changes and Climate Sensitivity Analysis in the Three-River Headwaters Region

et al. 2022

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Global climate changes have a great impact on terrestrial ecosystems. Vegetation is an important component of ecosystems, and the impact of climate changes on ecosystems can be determined by studying vegetation phenology. Vegetation phenology refers to the phenomenon of periodic changes in plants, such as germination, flowering and defoliation, with the seasonal change of climate during the annual growth cycle, and it is considered to be one of the most efficient indicators to monitor climate changes. This study collected the global land surface satellite leaf area index (GLASS LAI) products, meteorological data sets and other auxiliary data in the Three-River headwaters region from 2001 to 2018; rebuilt the vegetation LAI annual growth curve by using the asymmetric Gaussian (A-G) fitting method and extracted the three vegetation phenological data (including Start of Growing Season (SOS), End of Growing Season (EOS) and Length of Growing Season (LOS)) by the maximum slope method. In addition, it also integrated Sen’s trend analysis method and the Mann-Kendall test method to explore the temporal and spatial variation trends of vegetation phenology and explored the relationship between vegetation phenology and meteorological factors through a partial correlation analysis and multiple linear regression models. The results of this study showed that: (1) the SOS of vegetation in the Three-River headwaters region is concentrated between the beginning and the end of May, with an interannual change rate of −0.14 d/a. The EOS of vegetation is concentrated between the beginning and the middle of October, with an interannual change rate of 0.02 d/a. The LOS of vegetation is concentrated between 4 and 5 months, with an interannual change rate of 0.21 d/a. (2) Through the comparison and verification with the vegetation phenological data observed at the stations, it was found that the precision of the vegetation phonology extracted by the A-G method and the maximum slope method based on GLASS LAI data is higher (MAE is 7.6 d, RMSE is 8.4 d) and slightly better than the vegetation phenological data (MAE is 9.9 d, RMSE is 10.9 d) extracted based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS NDVI) product. (3) The correlation between the SOS of vegetation and the average temperature in March–May is the strongest. The SOS of vegetation is advanced by 1.97 days for every 1 °C increase in the average temperature in March–May; the correlation between the EOS of vegetation and the cumulative sunshine duration in August–October is the strongest. The EOS of vegetation is advanced by 0.07 days for every 10-h increase in the cumulative sunshine duration in August–October.

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Section: Analysis Of Influencing Factorsmentioning

confidence: 99%

LAI-Based Phenological Changes and Climate Sensitivity Analysis in the Three-River Headwaters Region

et al. 2022

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“…Due to the different orbital periods of satellites, the remote sensing images used in this study in 2000, 2005, and 2010 were Landsat 5 TM [46], and the remote sensing images used in 2015 and 2020 were Landsat 8 OLI/TIRS [47]. The GEE platform contains atmospherically corrected surface reflectance and land surface temperature derived from the data produced by the Landsat 5 TM (USGS Landsat 5 Level 2, Collection 2, Tier 1) and Landsat 8 OLI/TIRS sensors (USGS Landsat 8 Level 2, Collection 2, Tier 1).…”

Section: Datamentioning

confidence: 99%

Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China

et al. 2022

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The Ulan Mulun River Basin is an essential ecological protective screen of the Mu Us Desert and a necessary energy base in Ordos City. With the acceleration of industrialization and urbanization, human activities have caused enormous challenges to the local ecological environment. To achieve the region’s economic sustainability and make local development plans more objective, it is necessary to evaluate the basin’s ecological environment quality over a period of time. First, in the Landsat historical images, we selected 5 years of data to investigate the changes in this time-period (2000–2020). Second, based on the opened remote sensing database on Google Earth Engine, we calculated the remote-sensing ecological index (RSEI) distribution map. RSEI includes greenness, temperature, humidity, and dryness. Thirdly, we assessed the ecological-environmental distribution and change characteristics in the Ulan Mulun River Basin. Finally, we analyzed the RSEI spatial auto-correlation distribution characteristics in the study area. The mean values of RSEI in 2000, 2005, 2010, 2015, and 2020 were 0.418, 0.421, 0.443, 0.456, and 0.507, respectively, which indicated that the ecological environment quality had gradually improved. The ecological environment quality from 2000 to 2005 had the biggest change, as the area with drastically changed water levels accounted for 78.98% of the total basin. It showed a downward trend in the central and western regions. It showed an upward trend in the eastern region. For 20 years, the area of deterioration decreased by 24.37%, and the slight change area increased by 45.84%. The Global Moran’s I value ranged from 0.324 to 0.568. The results demonstrated that the Ulan Mulun River Basin ecological environment quality spatial distribution was positively correlated, and the clustering degree decreased gradually. Local spatial auto-correlation of RSEI showed that high-high(H-H) was mainly distributed in the basin’s eastern and southern regions, where the population density was low and the vegetation was in good condition. Low-low(L-L) was mainly distributed in the basin’s central regions and western regions, where the population density was high, and the industrial and mining enterprises were concentrated. This study provided a theoretical basis for the sustainable development of the Ulan Mulun River Basin, which is crucial for the local ecological environment and economic development.

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“…L AND cover is the distribution of different feature types (e.g., forest, grassland, cultivated land, water bodies) on the surface of the Earth within a given area [1], [2]. Land cover has important implications for ecosystem function, climate change, the water cycle, biodiversity, and human activities.…”

Section: Introductionmentioning

confidence: 99%

MLC30: A New 30 m Land Cover Dataset for Myanmar From 1990 to 2020 Using Training Sample Migration Framework

Xing,

Zhu,

Zhang

et al. 2024

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

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Myanmar has experienced rapid socio-economic developments in recent decades, which have a greater impact on land cover change. Accurate long time series land cover datasets for Myanmar can be of great help in environmental protection and natural resource management. However, there are relatively few existing studies on long time series land cover datasets in Myanmar, and the acquisition of training samples within different time series is a big challenge. Therefore, this study used Google Earth Engine and Landsat imagery to produce a land cover dataset for every two years from 1990 to 2020 using a training sample migration framework. First, the differences in index change, spectral value change, and spectral shape change were used to determine whether the sample points had changed between the base year and the previous year, and then a small number of samples were manually selected. Second, the spectral features, index information, and texture information of the remote sensing images and the objectoriented segmentation method were used to obtain object-oriented multidimensional features. Finally, the random forest method was employed to train the samples of the previous year to obtain the land cover data of the previous year. The results of the study show that the average overall precision of the land cover classification results for Myanmar for 1990-2020 is 0.83 and Kappa is 0.79. In addition, the land cover classification results for Myanmar of 1990-2020 are significantly better than those of Globeland30-2020, FROM-GLC, and Dynamic World land cover, and comparing with these products showed good agreement.

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Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series

Cited by 4 publications

References 41 publications

LAI-Based Phenological Changes and Climate Sensitivity Analysis in the Three-River Headwaters Region

LAI-Based Phenological Changes and Climate Sensitivity Analysis in the Three-River Headwaters Region

Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China

MLC30: A New 30 m Land Cover Dataset for Myanmar From 1990 to 2020 Using Training Sample Migration Framework

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