Comparison of single-year and multiyear ndvi time series principal components in cold temperate biomes

Hall-Beyer, Mryka

doi:10.1109/tgrs.2003.817274

Cited by 68 publications

(41 citation statements)

References 13 publications

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“…While the ability of principal components analysis to uncover significant intra-annual change events out of vegetation index time series has been widely proven (e.g., [25][26][27][28][29][30]), studies mainly focus on the low-order principal component (PC) images-generally the first three-which capture most of the variance. We decided to include the higher-order PC images in our analysis, since they are highly sensitive to subtle changes in the data and therefore capture change events which are localized both spatially and in time, such as the intra-seasonal variations and rapid changes induced by the agricultural practices.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, after performing a PCA on the corrected NDVI time series of 23 images, we retained the last 22 PC images (the high-order PC images) out of the 23 resulting PC images for further analysis, which contain 27% of the total variance. The first PC image was deliberately discarded after verification that the temporal variability of NDVI was not significantly captured, and that (as expected) the 73% of the total variance captured by the first PC image is mostly related to the spatial variability of the NDVI values, as shown in other studies (e.g., [27,28]). …”

Section: Principal Components Analysismentioning

confidence: 93%

“…PCA has proven to be particularly effective (when applied to NDVI time series) at identifying seasonal changes of the land cover and revealing their relationship to different factors-mainly climate variability and human activities factors [25][26][27][28][29][30]. The type of information captured by each PC image will vary according to the characteristics of the input NDVI time series (temporal resolution and length of the time series, spatial resolution and extent) and the PCA method used (either unstandardized if the PC images are calculated using the covariance matrix, or standardized if the correlation matrix is used instead).…”

Section: Principal Components Analysismentioning

confidence: 99%

“…The type of information captured by each PC image will vary according to the characteristics of the input NDVI time series (temporal resolution and length of the time series, spatial resolution and extent) and the PCA method used (either unstandardized if the PC images are calculated using the covariance matrix, or standardized if the correlation matrix is used instead). However, some common outcomes are observed when the area under analysis is large enough to include different types of land cover: The first PC image captures the major element of variability in the NDVI time series (which for large areas corresponds to the spatial variability of the NDVI values, and is correlated to the integrated NDVI over all seasons), whilst the other PC images capture the seasonal and intra-seasonal variability of NDVI, each related to a particular factor or a combination of factors [25][26][27][28][29][30].…”

Section: Principal Components Analysismentioning

confidence: 99%

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A Remote Sensing Approach for Regional-Scale Mapping of Agricultural Land-Use Systems Based on NDVI Time Series

et al. 2017

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Abstract:In response to the need for generic remote sensing tools to support large-scale agricultural monitoring, we present a new approach for regional-scale mapping of agricultural land-use systems (ALUS) based on object-based Normalized Difference Vegetation Index (NDVI) time series analysis. The approach consists of two main steps. First, to obtain relatively homogeneous land units in terms of phenological patterns, a principal component analysis (PCA) is applied to an annual MODIS NDVI time series, and an automatic segmentation is performed on the resulting high-order principal component images. Second, the resulting land units are classified into the crop agriculture domain or the livestock domain based on their land-cover characteristics. The crop agriculture domain land units are further classified into different cropping systems based on the correspondence of their NDVI temporal profiles with the phenological patterns associated with the cropping systems of the study area. A map of the main ALUS of the Brazilian state of Tocantins was produced for the 2013-2014 growing season with the new approach, and a significant coherence was observed between the spatial distribution of the cropping systems in the final ALUS map and in a reference map extracted from the official agricultural statistics of the Brazilian Institute of Geography and Statistics (IBGE). This study shows the potential of remote sensing techniques to provide valuable baseline spatial information for supporting agricultural monitoring and for large-scale land-use systems analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Principal Components Analysismentioning

confidence: 93%

Section: Principal Components Analysismentioning

confidence: 99%

Section: Principal Components Analysismentioning

confidence: 99%

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A Remote Sensing Approach for Regional-Scale Mapping of Agricultural Land-Use Systems Based on NDVI Time Series

et al. 2017

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“…Localised catastrophes could be verified, and variance-covariance analyzed components. In prior investigations, NDVI has a moderate-to-high accuracy in forecasting crop production [3][4][5][6][7][8][9][10][11], but boundary areas (transition from drought to normal conditions) have not been analyzed. In the present study, the NDVI is stable in the transition zone, and strong enough to detect statistically significant differences in plant growth (irrigated vs. non-irrigated), even early in the plant growth cycle (Figure 3).…”

Section: Discussionmentioning

confidence: 99%

Remote sensing and US crop insurance program integrity: data mining satellite and agricultural data

Little¹,

Schucking²,

Gartrell³

et al. 2007

WIT Transactions on Information and Communication Technologies, Vol 38

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The objective of this investigation is to (1) integrate remote sensing data into an existing data warehouse of the US crop insurance program 1990 to 2007, (2) test remote sensing correlations with crop production, and (3) use remotely sensed time series data to assess variation in crop production. Previously (2000 to 2007) data mining of the data warehouse was based upon probabilistic and algorithmic approaches to identification of possible fraud, waste, or abuse. The value of adding satellite data warehouse to data mining resources is provision of (1) an additional empirical metric, (2) objective data on vegetative health, (3) measurable metrics for capturing the variance of plant health, and (4) a means for measuring the covariance of location and production. Refinement of data mining through the addition of satellite data for routine use to reduce fraud, waste, and abuse will ultimately diminish the frequency of false positives.

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Forest phenoclusters for Argentina based on vegetation phenology and climate

Silveira

Radeloff

Pastur

et al. 2022

Ecological Applications

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Forest biodiversity conservation and species distribution modeling greatly benefit from broad-scale forest maps depicting tree species or forest types rather than just presence and absence of forest, or coarse classifications. Ideally, such maps would stem from satellite image classification based on abundant field data for both model training and accuracy assessments, but such field data do not exist in many parts of the globe. However, different forest types and tree species differ in their vegetation phenology, offering an opportunity to map and characterize forests based on the seasonal dynamic of vegetation indices and auxiliary data. Our goal was to map and characterize forests based on both land surface phenology and climate patterns, defined here as forest phenoclusters. We applied our methodology in Argentina (2.8 million km 2 ), which has a wide variety of forests, from rainforests to cold-temperate forests. We calculated phenology measures after fitting a harmonic curve of the enhanced vegetation index (EVI) time series derived from 30-m Sentinel 2 and Landsat 8 data from 2018-2019. For climate, we calculated land surface temperature (LST) from Band 10 of the thermal infrared sensor (TIRS) of Landsat 8, and precipitation from Worldclim (BIO12). We performed stratified X-means cluster classifications followed by hierarchical clustering. The resulting clusters separated well into 54 forest phenoclusters with unique combinations of vegetation phenology and climate characteristics. The EVI 90th percentile was more important than our climate and other phenology measures in providing separability among different forest phenoclusters. Our results highlight the potential of combining remotely sensed phenology measures and climate data to improve broad-scale forest mapping for different management and conservation goals, capturing functional rather than structural or compositional characteristics between and within tree species. Our approach results in classifications that go beyond simple forest-nonforest in areas where the lack of detailed ecological field data precludes tree species-level classifications, yet

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Comparison of single-year and multiyear ndvi time series principal components in cold temperate biomes

Cited by 68 publications

References 13 publications

A Remote Sensing Approach for Regional-Scale Mapping of Agricultural Land-Use Systems Based on NDVI Time Series

A Remote Sensing Approach for Regional-Scale Mapping of Agricultural Land-Use Systems Based on NDVI Time Series

Remote sensing and US crop insurance program integrity: data mining satellite and agricultural data

Forest phenoclusters for Argentina based on vegetation phenology and climate

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