Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data

Boles, Stephen; Xiao, Xiangming; Liu, Jiyuan; Zhang, Qingyuan; Munkhtuya, Sharav; Chen, Siqing; Ojima, Dennis S.

doi:10.1016/j.rse.2004.01.016

Cited by 130 publications

(65 citation statements)

References 35 publications

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“…Additional to the well-known Normalised Difference Vegetation Index (NDVI) [46] and Enhanced Vegetation Index (EVI) [47], we calculated the Land Surface Water Index (LSWI). A number of studies have used LSWI time series for land cover mapping [48,49]. The LSWI is sensitive to equivalent water thickness (g H 2 O/m 2 ) and is therefore useful for detecting canopy water content or water stress, in contrast to canopy greenness.…”

Section: Modis Data and Processingmentioning

confidence: 99%

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

et al. 2016

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Tropical environments present a unique challenge for optical time series analysis, primarily owing to fragmented data availability, persistent cloud cover and atmospheric aerosols. Additionally, little is known of whether the performance of time series change detection is affected by diverse forest types found in tropical dry regions. In this paper, we develop a methodology for mapping forest clearing in Southeast Asia using a study region characterised by heterogeneous forest types. Moderate Resolution Imaging Spectroradiometer (MODIS) time series are decomposed using Breaks For Additive Season and Trend (BFAST) and breakpoints, trend, and seasonal components are combined in a binomial probability model to distinguish between cleared and stable forest. We found that the addition of seasonality and trend information improves the change model performance compared to using breakpoints alone. We also demonstrate the value of considering forest type in disturbance mapping in comparison to the more common approach that combines all forest types into a single generalised forest class. By taking a generalised forest approach, there is less control over the error distribution in each forest type. Dry-deciduous and evergreen forests are especially sensitive to error imbalances using a generalised forest model i.e., clearances were underestimated in evergreen forest, and overestimated in dry-deciduous forest. This suggests that forest type needs to be considered in time series change mapping, especially in heterogeneous forest regions. Our approach builds towards improving large-area monitoring of forest-diverse regions such as Southeast Asia. The findings of this study should also be transferable across optical sensors and are therefore relevant for the future availability of dense time series for the tropics at higher spatial resolutions.

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Section: Modis Data and Processingmentioning

confidence: 99%

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

et al. 2016

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“…[13] The function P scale accounts for effects of leaf age on canopy photosynthesis, using EVI and LSWI to identify the green-up (leaf expansion) and senescence phases [Xiao et al, 2002[Xiao et al, , 2004aBoles et al, 2004]. For evergreen classes, P scale is assumed to be 1 for the whole year.…”

Section: Gross Ecosystem Exchangementioning

confidence: 99%

A satellite‐based biosphere parameterization for net ecosystem CO₂ exchange: Vegetation Photosynthesis and Respiration Model (VPRM)

Pathmathevan

Wofsy

Matross³

et al. 2008

Global Biogeochemical Cycles

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315

395

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We present the Vegetation Photosynthesis and Respiration Model (VPRM), a satellite‐based assimilation scheme that estimates hourly values of Net Ecosystem Exchange (NEE) of CO2 for 12 North American biomes using the Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI), derived from reflectance data of the Moderate Resolution Imaging Spectroradiometer (MODIS), plus high‐resolution data for sunlight and air temperature. The motivation is to provide reliable, fine‐grained first‐guess fields of surface CO2 fluxes for application in inverse models at continental and smaller scales. An extremely simple mathematical structure, with minimal numbers of parameters, facilitates optimization using in situ data, with finesse provided by maximal infusion of observed NEE and environmental data from networks of eddy covariance towers across North America (AmeriFlux and Fluxnet Canada). Cross validation showed that the VPRM has strong prediction ability for hourly to monthly timescales for sites with similar vegetation. The VPRM also provides consistent partitioning of NEE into Gross Ecosystem Exchange (GEE, the light‐dependent part of NEE) and ecosystem respiration (R, the light‐independent part), half‐saturation irradiance of ecosystem photosynthesis, and annual sum of NEE at all eddy flux sites for which it is optimized. The capability to provide reliable patterns of surface flux for fine‐scale inversions is presently limited by the number of vegetation classes for which NEE can be constrained by the current network of eddy flux sites and by the accuracy of MODIS data and data for sunlight.

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“…(2b)), and fw is reflected by land water index (WI) that was calculated by satellite-derived land surface water index (LSWI) (Xiao et al, 2002;Boles et al, 2004) with consideration of the phenology effect on ε max (P s ) (Xiao et al, 2005b). The P s in the model is calculated as follow:…”

Section: The Vpm Modelmentioning

confidence: 99%

Comparison of four light use efficiency models for estimating terrestrial gross primary production

Zhang

Zhou

Fan

et al. 2015

Ecological Modelling

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a b s t r a c tLight use efficiency (LUE) models that with different structures (i.e., methods to address environmental stresses on LUE) have been widely used to estimate terrestrial gross primary production (GPP) because of their theoretical soundness and practical conveniences. However, a systematic validation of those models with field observations across diverse ecosystems is still lacking and whether the model can be further improved by structural optimization remains unclear. Using GPP estimates at global 51 eddy covariance flux towers that cover a wide climate range and diverse vegetation types, we evaluated the performances of the four major LUE models (i.e., Carnegie-Ames-Stanford approach (CASA), Global Production Efficiency Model (GLO-PEM), Vegetation Photosynthesis Model (VPM), and Eddy Covariance-Light Use Efficiency (EC-LUE)) and examined the possible further improvement of the better-performed model(s) via model structural optimization. Our results showed that the GLO-PEM, VPM, and EC-LUE exhibited the similar capabilities in simulating GPP (explained around 68% of the total variations) and overall performed better than CASA (58%). Nevertheless, the EC-LUE and VPM were the optimal ones because they required less model inputs than the GLO-PEM. For the two optimal models, we found that the minimum method is better than the multiplication approach to integrate multiple environmental stresses on LUE. Moreover, we found that the VPM can be further improved by incorporating the constraint of water vapor deficit (VPD s ). We suggested that a modified VPM by using minimum method and adding VPD s may be the best model in estimating large-scale GPP if high-quality remote sensing data available, otherwise, the modified models with the water stress reflected by VPD s only is optimal.

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Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data

Cited by 130 publications

References 35 publications

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

A satellite‐based biosphere parameterization for net ecosystem CO₂ exchange: Vegetation Photosynthesis and Respiration Model (VPRM)

Comparison of four light use efficiency models for estimating terrestrial gross primary production

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Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data

Cited by 130 publications

References 35 publications

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

A satellite‐based biosphere parameterization for net ecosystem CO2 exchange: Vegetation Photosynthesis and Respiration Model (VPRM)

Comparison of four light use efficiency models for estimating terrestrial gross primary production

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Product

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About

A satellite‐based biosphere parameterization for net ecosystem CO₂ exchange: Vegetation Photosynthesis and Respiration Model (VPRM)