Ecosystem functional assessment based on the “optical type” concept and self-similarity patterns: An application using MODIS-NDVI time series autocorrelation

Huesca, Margarita; Miguel, S. Merino de; Eklundh, Lars; Litago, Javier; Cicuéndez, Víctor; Rodríguez-Rastrero, Manuel; Ustin, Susan L.; Palacios-Orueta, Alicia

doi:10.1016/j.jag.2015.04.008

Cited by 18 publications

(23 citation statements)

References 63 publications

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“…"optical types") is adapted in Huesca et al (2015) to more closely link remote sensing data with ecosystem dynamics. In this work the "optical types" concept is implemented on the basis of MODIS NDVI time-series dynamics over a Mediterranean broadleaf forest region.…”

Section: The Special Issuementioning

confidence: 99%

Advances in remote sensing of vegetation function and traits

Houborg

Fisher

Skidmore

2015

International Journal of Applied Earth Observation and Geoinfor

119

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Section: The Special Issuementioning

confidence: 99%

Advances in remote sensing of vegetation function and traits

Houborg

Fisher

Skidmore

2015

International Journal of Applied Earth Observation and Geoinfor

119

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“…Additionally, the relative and absolute thresholds used to determine the beginning and end of the growing seasons can be difficult to establish (e.g. what point of the curve is the 'start'), and the frequency component of vegetative responses are not captured [Huesca et al, 2015]. This is problematic for many types of semi-arid vegetation, which respond strongly to precipitation events.…”

Section: Phenology and Time-seriesmentioning

confidence: 99%

“…Finely resolving patterns and changes in vegetative growth requires high temporal resolution and statistical methods to observe patterns in vegetation responses [Huesca et al, 2015]. Such approaches are diverse and varied.…”

Section: Phenology and Time-seriesmentioning

confidence: 99%

“…This additional order of dimensionality enables greater distinction between pixels, and potentially the ability to group pixels that are 'more similar' over time. Huesca et al [2015] apply the "Optical Types" framework in this way. This approach effectively links Optical Types with temporal dynamics.…”

Section: Phenology and Time-seriesmentioning

confidence: 99%

“…Although the RF models are able to relate the training data and satellite imagery, a focused effort to examine the relationship between the 'optical type' concept proposed by Ustin and Gamon (2010) or similar measurements and the community classes would add surety to the classification model [Cingolani et al, 2008;Huesca et al, 2015]. Other data fusion techniques (such as in Chapter 2) may also improve classification.…”

Section: Model Developmentmentioning

confidence: 99%

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Remote Sensing Time-Series Analysis, Machine Learning, and K-Means Clustering Improves Dryland Vegetation and Biological Soil Crust Classification

Enterkine¹

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Dryland and semi-arid vegetation communities, although appearing to the casual observer as relatively simplistic and homogeneous, are in fact the opposite. Upon further inspection, semi-arid vegetation is highly complex and heterogeneous at almost any scale. The same holds true for biological soil crust. Growing concern about global changes in climate, nutrient cycles, and land use have required increasing scrutiny of our understanding of these communities and all of their constituents, as we seek to improve forecasting models and inform land management decisions. This thesis aims to provide insight to the paradigm of how we create and interpret vegetation classifications in a semi-arid ecosystem. In the first chapter, I examine the potential of new remote sensing imaging platforms in combination with machine learning algorithms and cloud computing as they apply to time-series analyses for vegetation classification. The results of this indicate that sinusoidal approximations ("Harmonic Models") of vegetation indices are able to predict vegetation cover with nearly the same accuracy as monthly composites, and that a combination of both perform no better than either. Additionally, I examine how assigning classes to training data (e.g. species-level, plant functional type) influence the classification accuracy, interpretability, and potential uses. Stricter class membership requirements at increasingly aggregated scales (e.g. PFT) lead to greater accuracies. Finding the implication of this conclusion unsatisfactoryat the extreme, everything was either cheatgrass or bare, the shrub class succumbing almost entirely to vii errors of omission-I investigated approaching other methods to assign classes to field data that captured more of the realities of semi-arid vegetation within our study area. To this extent, k-means clustering was used to determine what community classes were present in the field data. The outcome of this approach is a class where each potential constituent cover has a known distribution. Overall accuracies were found to be lower for this approach. However, the classification outcomes quantify overlapping distributions of cover types (e.g. 'sagebrush' or 'shrub') between classes. These accuracies are assessed using 'fuzzy' confusion matrices. This enables more information to be preserved through the remote sensing classification process, and reserves more interpretation for the map user than a typical 'hard' classification. Importantly the distributions of cover types are likely most representative of field conditions and thus more useful to land managers making holistic decisions about restoration or fuel management. For the second chapter, I delved deeper into the potentials of new remote sensing and computing platforms to predict biological soil crust cover. The growing field of research on biological soil crust points to potentially significant implications for nutrient and water cycling, in addition to positive effects on native vascular vegetation. However, spatial data are lacking due to remot...

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Remotely sensed phenological heterogeneity of restored wetlands: linking vegetation structure and function

Dronova

Taddeo

Hemes

et al. 2021

Agricultural and Forest Meteorology

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Seasonal phenological dynamics of vegetation hold important clues on ecosystem performance towards management goals, like carbon uptake, and thus should be considered in projections of their targeted services. However, in wetlands spatio-temporal heterogeneity due to mixing of open water, soil, green and dead vegetation makes it difficult to generalize ecosystem functioning across different regions. Remote sensing observations can provide spatially-explicit, cost-effective phenology indicators; however, little is known about their capacity to indicate the links between wetland ecosystem structure and function. Here we assessed this potential by comparing one-year Enhanced Vegetation Index (EVI) from satellite products at high (5m; RapidEye) and low (30m; Landsat) spatial resolutions with eddy covariance time series of net carbon exchange, field digital camera (phenocam) greenness and water temperature among three floristically similar restored wetlands in California, USA. Phenological timing differed by wetland site: depending on satellite, the range in site-median start of greening was up to 28 days, end of greening-up to 73 days, start of senescence-up to 79 days, and end of senescence-up to 10 days. Key transition dates from satellite inputs agreed with seasonal changes in net carbon exchange, phenocam greenness and water temperatures, suggesting that phenological contrasts could result in part from site differences in vegetation configuration and litter affecting the exposure of canopy, soil and water to sunlight and thus sub-canopy microclimate and ecosystem functioning. Yet, the agreement between satellite inputs was non-systematic, with the greatest disparities at the more heterogeneous, less vegetated site. Phenological model fitting uncertainty increased with greater spatial resolution, highlighting the tradeoff between the accuracy of representing vegetation and the complexity of local seasonal variation. These findings highlight the sensitivity of satellite-derived phenology to structural and functional heterogeneity of ecosystems and call for more rigorous spatiallyexplicit analyses to inform assessments of restoration and management outcomes.

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Ecosystem functional assessment based on the “optical type” concept and self-similarity patterns: An application using MODIS-NDVI time series autocorrelation

Cited by 18 publications

References 63 publications

Advances in remote sensing of vegetation function and traits

Advances in remote sensing of vegetation function and traits

Remote Sensing Time-Series Analysis, Machine Learning, and K-Means Clustering Improves Dryland Vegetation and Biological Soil Crust Classification

Remotely sensed phenological heterogeneity of restored wetlands: linking vegetation structure and function

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