Improving the MODIS LAI compositing using prior time-series information

Pu, Jiabin; Yan, Kai; Gao, Si; Zhang, Yiman; Park, Taejin; Sun, Xuping; Weiss, Marie; Knyazikhin, Yuri; Myneni, Ranga B.

doi:10.1016/j.rse.2023.113493

Cited by 11 publications

(4 citation statements)

References 85 publications

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“…In particular, we used zones with increasing distance to the urban centre to map the spatial development of the parameter. The results provided a characteristic spatial pattern, gradients and landscape metrics, which support understanding of Bucharest spatial growth and future modeling of urban development in Romania 20,21 . Figure 3 presents temporal pattern of MODIS Terra 13Q1/250m_16_days_NDVI/EVI composites with a 250 m spatial resolution for two test areas centered latitude 44.4355381 o N and longitude 26.100049 o E. Bucharest central city and Bucharest metropolitan area for an areal extent of approximately 40.5 km Wide x 40.5 km High.…”

Section: Resultssupporting

confidence: 57%

Exploring with time-series satellite data of multiple stressors effects on urban/periurban vegetation

Zoran,

Savastru,

Savastru

et al. 2023

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023)

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Urban vegetation and its carbon storage capacity are critical factors for terrestrial carbon cycling and global sustainable development goals (SDGs). Scientific research for a better managing of urban green land cover is an essential issue of the sustainable urban development initiative promoted by the EU governments. Owing to a crucial role that urban vegetation perform in an urban ecosystem, being effective in mitigating the air pollution and urban heat island effect by canopy shade and evapotranspiration, studying the vegetation dynamics in terms of its derived satellite biogeophysical variables becomes inevitable in the context of climate-smart city planning and design. However, in order to explain the urban-rural gradient in vegetation greenness trends and its responses to climate and antropogenic drivers of land-cover changes this study used time-series of derived satellite data (normalized difference vegetation index-NDVI, enhanced vegetation index-EVI, land surface temperature-LST, leaf area index LAI, and fraction of photosynthetically active radiation absorbed by vegetation-FPAR). Satellite datasets of MODIS Terra, and Landsat TM/ETM + have been used for urban vegetation analysis over 2002-2022 period of Bucharest metropolitan area in Romania. Was found that the average decrease in vegetation land cover was 0.1 to 0.3. The correlation analyses revealed that, at the pixel-scale, LST possessed a strong positive correlation with NDVI, EVI, FPAR and LAI during the entire investigated period. Only during summer period NDNI/EVI are inversely correlated with LST (r= -0.67; p<0.01) The spatio-temporal pattern of urban/periurban vegetation dynamics trends and their association with other atmospheric, biological, and soil indicators need to be studied with different satellite sensors and resolutions over the long-term periods of time.

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Section: Resultssupporting

confidence: 57%

Exploring with time-series satellite data of multiple stressors effects on urban/periurban vegetation

Zoran,

Savastru,

Savastru

et al. 2023

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023)

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“…LAI is commonly defined as the total singlesided green leaf area per unit horizontal ground area (Baret et al, 2007). Quantifying the drivers of temporal and spatial changes in vegetation is crucial due to its significant role in regulating climate change (Piao et al, 2020;Mota et al, 2021;Shabanov et al, 2021;Yan et al, 2021;Dai et al, 2022;Kobayashi et al, 2023;Pu et al, 2023), land surface modeling (Running et al, 1999;Albergel et al, 2018;Peng et al, 2021;Zhu et al, 2023), and vegetation dynamic monitoring (Iwahashi et al, 2021;Zhao et al, 2021;Abubakar et al, 2022;Amin et al, 2022;Zhang et al, 2022;Bajocco et al, 2022;Caballero et al, 2022). The variation in vegetation structure and function not only affects biodiversity and energy supply but also provides valuable insights into ecological feedback to climatic changes.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal analysis of LAI using multisource remote sensing data for source region of Yellow River Basin

Zhang,

Hou,

Han

et al. 2024

Front. Environ. Sci.

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Introduction: The Leaf area index (LAI) of source region of yellow river basin is an important indicator for environmental sustainability. Most studies focus on the trend of LAI in Yellow River Source Region (YRSR) in accordance with both climate change and human actives. However, quantifying the effect of human activities on LAI is difficult but urgently needed. Specifically, Particle Matter 2.5 (PM2.5) can be an indirect indicator of human activities.Methods: In this study, we explored the potential dependence of LAI on temperature, precipitation, and PM2.5 in different land cover types in YRSR with linear regression and correlation analysis.Results: Over the period of 2001–2020, the climate in the region has been warming and becoming more humid, leading to overall improvements in vegetation. The mean LAI values varied between seasons, with summer having the highest and winter having the lowest LAI. The analysis of the LAI trends revealed that the mean LAI has been steadily increasing, particularly in the eastern region. The correlation analysis showed a significant positive correlation between annual average LAI and both annual precipitation and temperature, indicating that temperature has a greater impact on vegetation growth. The analysis of land cover types showed that most types exhibited a unimodal trend in LAI throughout the year, except for construction land which had two distinct peaks. Human-induced land cover change had a small impact on the overall increase in LAI. Furthermore, the interannual variation of PM2.5 showed a downward trend, with a strong correlation with the trend of LAI. Additionally, multiple linear regression analysis and residual trend analysis showed that climate factors had the strongest impact on LAI.Conclusion: The study highlights the spatiotemporal variations of LAI in the YRSR and its correlation with climatic and human factors. The findings suggest that climate change plays a crucial role in the vegetation growth and LAI in the region.

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“…As an essential biophysical variable with which to characterize the vegetation canopy [9], the leaf area index (LAI) usually refers to the ratio of a plant's total green leaf area to the per-unit horizontal ground surface area [10,11]. This crucial factor influences the processes of photosynthesis, respiration, and transpiration and is closely correlated with the growth stage of the plant [12][13][14]. Thus, the precise and nondestructive monitoring of the LAI during the critical growth phase of S. alterniflora can provide information on a plant's canopy structure, aboveground biomass, and growth in response to the ambient environment; thus, its final yield can be evaluated [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Leaf Area Index Inversion of Spartina alterniflora Using UAV Hyperspectral Data Based on Multiple Optimized Machine Learning Algorithms

Fang,

Man,

Liu

et al. 2023

Remote Sensing

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The leaf area index (LAI) is an essential biophysical parameter for describing the vegetation canopy structure and predicting its growth and productivity. Using unmanned aerial vehicle (UAV) hyperspectral imagery to accurately estimate the LAI is of great significance for Spartina alterniflora (S. alterniflora) growth status monitoring. In this study, UAV hyperspectral imagery and the LAI of S. alterniflora during the flourishing growth period were acquired. The hyperspectral data were preprocessed with Savitzky–Golay (SG) smoothing, and the first derivative (FD) and the second derivative (SD) spectral transformations of the data were then carried out. Then, using the band combination index (BCI) method, the characteristic bands related to the LAI were extracted from the hyperspectral image data obtained with the UAV, and spectral indices (SIs) were constructed through the characteristic bands. Finally, three machine learning (ML) regression methods—optimized support vector regression (OSVR), optimized random forest regression (ORFR), and optimized extreme gradient boosting regression (OXGBoostR)—were used to establish LAI estimation models. The results showed the following: (1) the three ML methods accurately predicted the LAI, and the optimal model was provided by the ORFR method, with an R2 of 0.85, an RMSE of 0.19, and an RPD of 4.33; (2) the combination of FD SIs improved the model accuracy, with the R2 value improving by 41.7%; (3) the band combinations screened using the BCI method were mainly concentrated in the red and near-infrared bands; (4) the higher LAI was distributed on the seaward side of the study area, while the lower LAI was located at the junction between the S. alterniflora and the tidal flat. This study serves as both theoretical and technological support for research on the LAI of S. alterniflora and as a solid foundation for the use of UAV remote sensing technologies in the supervisory control of S. alterniflora.

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Improving the MODIS LAI compositing using prior time-series information

Cited by 11 publications

References 85 publications

Exploring with time-series satellite data of multiple stressors effects on urban/periurban vegetation

Exploring with time-series satellite data of multiple stressors effects on urban/periurban vegetation

Spatio-temporal analysis of LAI using multisource remote sensing data for source region of Yellow River Basin

Leaf Area Index Inversion of Spartina alterniflora Using UAV Hyperspectral Data Based on Multiple Optimized Machine Learning Algorithms

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