Off-peak NDVI correction to reconstruct Landsat time series for post-fire recovery in high-latitude forests

Wang, Cuizhen; Wang, Aiai; Guo, Dianfan; Li, Haibo; Zang, Shuying

doi:10.1016/j.jag.2022.102704

Cited by 9 publications

(6 citation statements)

References 41 publications

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“…The widespread availability of Landsat data on cloud-computing platforms like Google Earth Engine (Gorelick et al 2017) has exposed a broad user base to this issue, underscoring the need for better understanding of its mechanisms and potential consequences on environmental studies. Although the concept of this maximum NDVI sampling bias is acknowledged and methods for its correction have been proposed (Berner et al, 2020;Karlsen et al, 2018;Wang et al, 2022;Berner et al 2023), demonstrations to date have focused only on the relationship between the estimated maximum NDVI and the number of observations (Berner et al 2020;Berner et al 2023). Nevertheless, no studies have thoroughly evaluated or quanti ed how the maximum NDVI sampling bias impacts the magnitude of greening trends, the factors in uencing its magnitude, and the potential consequences on our understanding of plant dynamics.…”

Section: Introductionmentioning

confidence: 99%

Landsat greening trends in alpine ecosystems are inflated by multidecadal increases in summer observations

Bayle,

Gascoin,

Berner

et al. 2024

Preprint

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Remote sensing is an invaluable tool for tracking decadal-scale changes in vegetation greenness in response to climate and land use changes. While the Landsat archive has been widely used to explore these trends and their spatial and temporal complexity, its inconsistent sampling frequency over time and space raises concerns about its ability to provide reliable estimates of annual vegetation indices such as the annual maximum NDVI, commonly used as a proxy of plant productivity. Here we demonstrate for seasonally snow-covered ecosystems, that greening trends derived from annual maximum NDVI can be significantly overestimated because the number of available Landsat observations increases over time, and mostly that the magnitude of the overestimation varies along environmental gradients. Typically, areas with a short growing season and few available observations experience the largest bias in greening trend estimation. We show these conditions are met in late snowmelting habitats in the European Alps, which are known to be particularly sensitive to temperature increases and present conservation challenges. In this critical context, almost 50% of the magnitude of estimated greening can be explained by this bias. Our study calls for greater caution when comparing greening trends magnitudes between habitats with different snow conditions and observations. At a minimum we recommend reporting information on the temporal sampling of the observations, including the number of observations per year, when long term studies with Landsat observations are undertaken.

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Section: Introductionmentioning

confidence: 99%

Landsat greening trends in alpine ecosystems are inflated by multidecadal increases in summer observations

Bayle,

Gascoin,

Berner

et al. 2024

Preprint

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“…The normalized difference vegetation index (NDVI) is used to describe and conduct terrestrial vegetation condition assessments due to its ease of accessibility at different spatial and temporal resolutions and its advantage of eliminating noise caused by solar angle, topographic illumination, cloud cover and atmospheric conditions, and the NDVI is widely used in studies related to grasslands, forests and agricultural lands ( Estel et al., 2015 ; Kowalski et al., 2022 ; Wang et al., 2022 ; Xun et al., 2022 ). Previously, the response of vegetation to drought was expressed as a correlation between the NDVI and drought index, demonstrating that there is a good correlation between the NDVI, which represents vegetation, and a drought index, which represents moisture conditions, and that there is a significant effect of drought occurrence on vegetation.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic assessment of drought stress vulnerability in grasslands of Xinjiang, China

et al. 2023

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In the process of climate warming, drought has increased the vulnerability of ecosystems. Due to the extreme sensitivity of grasslands to drought, grassland drought stress vulnerability assessment has become a current issue to be addressed. First, correlation analysis was used to determine the characteristics of the normalized precipitation evapotranspiration index (SPEI) response of the grassland normalized difference vegetation index (NDVI) to multiscale drought stress (SPEI-1 ~ SPEI-24) in the study area. Then, the response of grassland vegetation to drought stress at different growth periods was modeled using conjugate function analysis. Conditional probabilities were used to explore the probability of NDVI decline to the lower percentile in grasslands under different levels of drought stress (moderate, severe and extreme drought) and to further analyze the differences in drought vulnerability across climate zones and grassland types. Finally, the main influencing factors of drought stress in grassland at different periods were identified. The results of the study showed that the spatial pattern of drought response time of grassland in Xinjiang had obvious seasonality, with an increasing trend from January to March and November to December in the nongrowing season and a decreasing trend from June to October in the growing season. August was the most vulnerable period for grassland drought stress, with the highest probability of grassland loss. When the grasslands experience a certain degree of loss, they develop strategies to mitigate the effects of drought stress, thereby decreasing the probability of falling into the lower percentile. Among them, the highest probability of drought vulnerability was found in semiarid grasslands, as well as in plains grasslands and alpine subalpine grasslands. In addition, the primary drivers of April and August were temperature, whereas for September, the most significant influencing factor was evapotranspiration. The results of the study will not only deepen our understanding of the dynamics of drought stress in grasslands under climate change but also provide a scientific basis for the management of grassland ecosystems in response to drought and the allocation of water in the future.

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“…Furthermore, ref. [23] demonstrated the use of Holt-Winters modelling to detect trend and seasonal vegetation patterns using Python's Scikit-Learn library, and [24] demonstrated the use of the partial least squared regression (PLSR) model in the SIMCA-P software to track the non-linear relationships between the NDVI and climatic variables. The recent development of machine learning tools through the use of the Python programming language enabled the use of a convolutional neural network (CNN) and deep neural networks (DNNs) for the environmental analysis and estimation of the VIs [25][26][27][28].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Multispectral Satellite Image Analysis for Computing Vegetation Indices by R in the Khartoum Region of Sudan, Northeast Africa

Lemenkova

Debeir

2023

J. Imaging

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Desertification is one of the most destructive climate-related issues in the Sudan–Sahel region of Africa. As the assessment of desertification is possible by satellite image analysis using vegetation indices (VIs), this study reports on the technical advantages and capabilities of scripting the ‘raster’ and ‘terra’ R-language packages for computing the VIs. The test area which was considered includes the region of the confluence between the Blue and White Niles in Khartoum, southern Sudan, northeast Africa and the Landsat 8–9 OLI/TIRS images taken for the years 2013, 2018 and 2022, which were chosen as test datasets. The VIs used here are robust indicators of plant greenness, and combined with vegetation coverage, are essential parameters for environmental analytics. Five VIs were calculated to compare both the status and dynamics of vegetation through the differences between the images collected within the nine-year span. Using scripts for computing and visualising the VIs over Sudan demonstrates previously unreported patterns of vegetation to reveal climate–vegetation relationships. The ability of the R packages ‘raster’ and ‘terra’ to process spatial data was enhanced through scripting to automate image analysis and mapping, and choosing Sudan for the case study enables us to present new perspectives for image processing.

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Off-peak NDVI correction to reconstruct Landsat time series for post-fire recovery in high-latitude forests

Cited by 9 publications

References 41 publications

Landsat greening trends in alpine ecosystems are inflated by multidecadal increases in summer observations

Landsat greening trends in alpine ecosystems are inflated by multidecadal increases in summer observations

Probabilistic assessment of drought stress vulnerability in grasslands of Xinjiang, China

Multispectral Satellite Image Analysis for Computing Vegetation Indices by R in the Khartoum Region of Sudan, Northeast Africa

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