Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from Landsat and MODIS data

Guerschman, Juan Pablo; Scarth, Peter; McVicar, Tim R.; Renzullo, Luigi J.; Malthus, Tim J.; Stewart, Jane; Rickards, Jasmine; Trevithick, Rebecca

doi:10.1016/j.rse.2015.01.021

Cited by 137 publications

(128 citation statements)

References 56 publications

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“…Okin (2007), Okin et al (2013), and Guerschman et al (2015) used and compared variations of linear spectral unmixing techniques, including traditional SMA, multiple endmember SMA (MESMA), and relative spectral mixture analysis (RSMA) for resolving fractional cover using MODIS data [13][14][15]. Scarth et al (2010) also applied similar techniques to Landsat data So far, remote sensing is the unique means to acquire vegetation cover at large scales that might otherwise be costly and labor-intensive [2][3][4].…”

Section: Discussionmentioning

confidence: 99%

A 2001–2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region

et al. 2017

Data

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Fractional covers of photosynthetic and non-photosynthetic vegetation are key indicators for land degradation surveillance in the dryland of China. However, there are no available, well validated, and multispectral-based products. Aiming for this, we selected the Beijing and Tianjin Sandstorm Source Region as the study area, and utilized the linear spectral mixture model for generating the fractional cover of PV, NPV, and bare soil, with endmember spectra retrieved from the field measured endmember spectral library, SummaryFractional cover of vegetation plays a key role in resisting wind and water erosion, thus it has been widely used as the indicator for land degradation monitoring and assessment. From a functional perspective, vegetation can be categorized as photosynthetic (green leaves) and non-photosynthetic (wood, senescent material, and litter) material [1]. Unlike the well-developed multiple datasets of the fractional cover of PV, the datasets of the fractional cover of NPV are relatively rare, mainly because of the difficulty in differentiating NPV from the soil background, particularly when multispectral sensors are considered. However, NPV is common and widely distributed in the drylands due to the scarce, variable rainfall and low soil fertility. Therefore, simultaneously acquiring the fractional cover of PV and NPV would provide new insights for land degradation surveillance and land management.In this context, an archive of fractional cover of PV and NPV was computed for the Beijing and Tianjin Sandstorm Source Region (BTSSR), with monthly temporal resolution covering 2001

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Section: Discussionmentioning

confidence: 99%

A 2001–2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region

et al. 2017

Data

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“…Con la finalidad de poder analizar los distintos métodos, se realiza una posterior validación del modelo obtenido (Guerschman et al, 2015). Para ello, existen diversas técnicas, entre las cuales destacamos los métodos de validación cruzada.…”

Section: Introductionunclassified

Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos

et al. 2015

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Resumen: Los métodos de regresión no paramétricos son una gran herramienta estadística para obtener parámetros biofísicos a partir de medidas realizadas mediante teledetección. Pero los resultados obtenidos se pueden ver afectados por los datos utilizados en la fase de entrenamiento del modelo. Para asegurarse de que los modelos son robustos, se hace uso de varias técnicas de validación cruzada. Estas técnicas permiten evaluar el modelo con subconjuntos de la base de datos de campo. Aquí, se evalúan dos tipos de validación cruzada en el desarrollo de modelos de regresión no paramétricos: hold-out y k-fold. Los métodos de regresión lineal seleccionados fueron: Linear Regression (LR) y Partial Least Squares Regression (PLSR). Y los métodos no lineales: Kernel Ridge Regression (KRR) y Gaussian Process Regression (GPR). Los resultados de la validación cruzada mostraron que LR ofrece los resultados más inestables, mientras KRR y GPR llevan a resultados más robustos. Este trabajo recomienda utilizar algoritmos de regresión no lineales (como KRR o GPR) combinando con la validación cruzada k-fold con un valor de k igual a 10 para hacer la estimación de una manera robusta.Palabras clave: Hold-Out, k-fold, validación cruzada, aprendizaje automático, procesos gausianos, regresión de Kernel Ridge. Analysis of cross-validation methods for robust retrieval of biophysical parametersAbstract: Non-parametric regression methods are powerful statistical methods to retrieve biophysical parameters from remote sensing measurements. However, their performance can be affected by what has been presented during the training phase. To ensure robust retrievals, various cross-validation sub-sampling methods are often used, which allow to evaluate the model with subsets of the field dataset. Here, two types of cross-validation techniques were analyzed in the development of non-parametric regression models: hold-out and k-fold. Selected non-parametric linear regression methods were least squares Linear Regression (LR) and Partial Least Squares Regression (PLSR), and nonlinear methods were Kernel Ridge Regression (KRR) and Gaussian Process Regression (GPR). Cross-validation results showed that LR performed most unstable, while KRR and GPR led to more robust results. This work recommends using a nonlinear regression algorithm (e.g., KRR, GPR) in combination with a k-fold cross-validation technique with k=10 to realize robust retrievals.

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“…Confidence in the underlying LFGC imagery used in this study is first demonstrated. An assessment of both the MODIS and Landsat Fractional Ground Cover products by (Guerschman et al, 2015) utilised a subset of the field data used in this study and reported strong relationships between field data and LFGC imagery. LFGC imagery used in this study, in particular the NPV fraction, is an example of a remotely sensed image technique that more accurately depicts ground cover vegetation in arid environments than those used in previous studies (Hobbs, 1995, Holm et al, 2003.…”

Section: Decreasing Trend Assessmentmentioning

confidence: 94%

“…Each of the time-series in each field site image subset was also assessed and only a very small proportion (<1%) was fond to have no significant downward trend with a confidence level of 98% as detailed in table 2. As (Guerschman et al, 2015) states, monitoring vegetation continuously across large landscapes requires robust remote sensing techniques underpinned by accurate field data to calibrate and assess each technique. Confidence in the underlying LFGC imagery used in this study is first demonstrated.…”

Section: Decreasing Trend Assessmentmentioning

confidence: 99%

Assessing Landsat Fractional Ground-Cover Time Series Across Australia’s Arid Rangelands: Separating Grazing Impacts From Climate Variability

Barnetson

Phinn

Scarth

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Suitable measures of grazing impacts on ground cover, that enable separation of the effects of climatic variations, are needed to inform land managers and policy makers across the arid rangelands of Australia. This work developed and tested a time-series, changepoint detection method for application to time series of vegetation fractional cover derived from Landsat data to identify irregular and episodic ground-cover growth cycles. Utilising the High Performance Computing power of the Google Cloud Compute Engine these cycles were segmented to distinguish grazing impacts from that of climate variability. A measure of grazing impact was developed using a multivariate technique to quantify the rate and degree of ground cover change. The method was successful in detecting both long term and short term growth cycles. Growth cycle detection was assessed against rainfall surplus measures indicating a relationship with high rainfall periods. During periods of ground cover decline, grazing utilisation was observed across four major grasslands. Ground cover change associated with grazing impacts was also assessed against field measurements of ground cover indicating a relationship between both field and remotely sensed ground cover. Cause and effects between grazing practices and ground cover resilience can now be explored in isolation to climatic drivers. This is important to the long term balance between ground cover utilisation and overall landscape function and resilience.

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Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from Landsat and MODIS data

Cited by 137 publications

References 56 publications

A 2001–2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region

A 2001–2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region

Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos

Assessing Landsat Fractional Ground-Cover Time Series Across Australia’s Arid Rangelands: Separating Grazing Impacts From Climate Variability

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