arXiv subjects

Inder Tecuapetla-Gómez

Publications and source records attributed to Inder Tecuapetla-Gómez.

6 recordsLinked to original sources

arXiv2024

Phenology curve estimation via a mixed model representation of functional principal components: Characterizing time series of satellite-derived vegetation indices

Vegetation phenology consists of studying synchronous stationary events, such as the vegetation green up and leaves senescence, that can be construed as adaptive responses to climatic constraints. In this paper, we propose a method to estimate the annual phenology curve from multi-annual observations of time series of vegetation indices derived from satellite images. We fitted the classical harmonic regression model to annual-based time series in order to construe the original data set as realizations of a functional process. Hierarchical clustering was applied to define a nearly homogeneous group of annual (smoothed) time series from which a representative and idealized phenology curve was estimated at the pixel level. This curve resulted from fitting a mixed model, based on functional principal components, to the homogeneous group of time series. Leveraging the idealized phenology curve, we employed standard calculus criteria to estimate the following phenological parameters (stationary events): green up, start of season, maturity, senescence, end of season and dormancy. By applying the proposed methodology to four different data cubes (time series from 2000 to 2023 of a popular satellite-derived vegetation index) recorded across grasslands, forests, and annual rainfed agricultural zones of a Flora and Fauna Protected Area in northern Mexico, we verified that our approach characterizes properly the phenological cycle in vegetation with nearly periodic dynamics, such as grasslands and agricultural areas. The R package sephora was used for all computations in this paper.

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arXiv2021

Modeling satellite-based open water fraction via flexible Beta regression: An application to wetlands in the north-western Pacific coast of Mexico

Carbon sequestration and water filtering are two examples of the several ecosystem services provided by wetlands. Open water mapping is an effective means to measure any wetland extension as these are comprised of many open water bodies. An economical, though indirect, approach towards mapping open water bodies is through applying geo-computational methods to satellite images. In this work we propose the flexible Beta regression (FBR) model to predict open water fraction from measurements of a water index. We focus on observations derived from two MODIS images acquired during the dry season of 2008 in Marismas Nacionales, a wetland located in the north-western Pacific coast of Mexico. A Bayesian estimation procedure is presented to estimate the FBR model; in particular, we provide details of a nested Metropolis-Hastings and Gibbs sampling algorithm to carry out parameter estimation. Our results show that the FBR model produces valid predictors of water fraction unlike the standard model. Our work is complemented by software developed in the R language and available through a GitHub repository.

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arXiv2020

On the potential of BFAST for monitoring burned areas using multi-temporal Landsat-7 images

In this paper, we propose a semi-automatic approach to map burned areas and assess burn severity that does not require prior knowledge of the fire date. First, we apply BFAST to NDVI time series and estimate statistically abrupt changes in NDVI trends. These estimated changes are then used as plausible fire dates to calculate dNBR following a typical pre-post fire assessment. In addition to its statistical guarantees, this method depends only on a tuning parameter (the bandwidth of the test statistic for changes). This method was applied to Landsat-7 images taken over La Primavera Flora and Fauna Protection Area, in Jalisco, Mexico, from 2003 to 2016. We evaluated BFAST's ability to estimate vegetation changes based on time series with significant observation gaps. We discussed burn severity maps associated with massive wildfires (2005 and 2012) and another with smaller dimensions (2008) that might have been excluded from official records. We validated our 2012 burned area map against a high resolution burned area map obtained from RapidEye images; in zones with moderate data quality, the overall accuracy of our map is 92%.

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arXiv2019

Time delay estimation in satellite imagery time series of precipitation and NDVI: Pearson's cross correlation revisited

In order to describe more accurately the time relationships between daily satellite imagery time series of precipitation and NDVI we propose an estimator which takes into account the sparsity naturally observed in precipitation. We conducted a series of simulation studies and show that the proposed estimator's variance is smaller than the canonical's (Pearson-based), in particular, when the signal-to-noise ratio is rather low. Also, the proposed estimator's variance was found smaller than the canonical's one when we applied them to stacks of images (2002-2016) taken on some ecological regions of Mexico. Computations for this paper are based on functions implemented in our new R package geoTS.

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arXiv2018

Fully-Automatic Multiresolution Idealization for Filtered Ion Channel Recordings: Flickering Event Detection

We propose a new model-free segmentation method, JULES, which combines recent statistical multiresolution techniques with local deconvolution for idealization of ion channel recordings. The multiresolution criterion takes into account scales down to the sampling rate enabling the detection of flickering events, i.e., events on small temporal scales, even below the filter frequency. For such small scales the deconvolution step allows for a precise determination of dwell times and, in particular, of amplitude levels, a task which is not possible with common thresholding methods. This is confirmed theoretically and in a comprehensive simulation study. In addition, JULES can be applied as a preprocessing method for a refined hidden Markov analysis. Our new methodolodgy allows us to show that gramicidin A flickering events have the same amplitude as the slow gating events. JULES is available as an R function jules in the package clampSeg.

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arXiv2016

Autocovariance estimation in regression with a discontinuous signal and $m$-dependent errors: A difference-based approach

We discuss a class of difference-based estimators for the autocovariance in nonparametric regression when the signal is discontinuous (change-point regression), possibly highly fluctuating, and the errors form a stationary $m$-dependent process. These estimators circumvent the explicit pre-estimation of the unknown regression function, a task which is particularly challenging for such signals. We provide explicit expressions for their mean squared errors when the signal function is piecewise constant (segment regression) and the errors are Gaussian. Based on this we derive biased-optimized estimates which do not depend on the particular (unknown) autocovariance structure. Notably, for positively correlated errors, that part of the variance of our estimators which depends on the signal is minimal as well. Further, we provide sufficient conditions for $\sqrt{n}$-consistency; this result is extended to piecewise Holder regression with non-Gaussian errors. We combine our biased-optimized autocovariance estimates with a projection-based approach and derive covariance matrix estimates, a method which is of independent interest. Several simulation studies as well as an application to biophysical measurements complement this paper.

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