Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology

Walker, Jessica J.; Beurs, Kirsten M. de; Wynne, Randolph H.; Gao, Feng

doi:10.1016/j.rse.2011.10.014

Cited by 252 publications

(199 citation statements)

References 30 publications

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“…Therefore, the Landsat/MODIS image fusion approach [49][50][51] could be applied to obtain inundation maps at the Landsat image scale in future studies. The fusion approach can also be applied for the monitoring and detection of the paddy rice area at the Landsat image scale.…”

Section: Resultsmentioning

confidence: 99%

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Dao

Liou

2015

Remote Sensing

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Cambodia is one of the most flood-prone countries in Southeast Asia. It is geographically situated in the downstream region of the Mekong River with a lowland floodplain in the middle, surrounded by plateaus and high mountains. It usually experiences devastating floods induced by an overwhelming concentration of rainfall water over the Tonle Sap Lake's and Mekong River's banks during monsoon seasons. Flood damage assessment in the rice ecosystem plays an important role in this region as local residents rely heavily on agricultural production. This study introduced an object-based approach to flood mapping and affected rice field estimation in central Cambodia. In this approach, image segmentation processing was conducted with optimal scale parameter estimation based on the variation of objects' local variances. The inundated area was identified by using Landsat 8 images with an overall accuracy of higher than 95% compared to those derived from finer spatial resolution images. Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products were utilized to identify the paddy rice field based on seasonal inter-variation between vegetation and water index during the transplanting stage. The rice classification result was well correlated with the statistical data at a commune level (R 2 = 0.675). The flood mapping and affected rice estimation results are useful to provide local governments with valuable information for flooding mitigation and post-flooding compensation and restoration.

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Section: Resultsmentioning

confidence: 99%

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Dao

Liou

2015

Remote Sensing

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“…Nine RapidEye scenes, each captured at a date bracketed by two consecutive RapidEye images used for ESTARFM processing, were used to compare pixel values in the predicted image with the corresponding pixels in the reference RapidEye image, on a band by band basis (following Walker et al [10]). White and light grey coloured acquisition dates in Figure 2 indicate the image pairs used for cross-comparison.…”

Section: Accuracy Assessment Of Estarfm Imagesmentioning

confidence: 99%

“…Image fusion aims at enhancing information included in the images and at improving reliability of the prediction. The outcome mitigates or exceeds the limitations of each dataset used as input [10,14]. The result may be a combined time series that exhibits both a high temporal and high spatial resolution [8].…”

Section: Introductionmentioning

confidence: 97%

“…Medium to low spatial resolution instruments such as MODIS, SPOT-Vegetation and AVHRR cover the earth on a daily basis over a wide field of view. This frequency is suitable for monitoring land cover dynamics at large scales [10]. Spatial resolution ranges from 250 m to 1000 m, which is however not satisfactory for tracking changes (such as vegetation dynamics) and spatio-temporal patterns at ecologically relevant resolutions, especially in heterogeneous landscapes [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…However, performance deteriorates in complex mixtures of small patched land cover types such as small-scale agriculture [9]. Several studies successfully tested the fusion of Landsat and MODIS in different environments [8,10,11,[16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Using RapidEye and MODIS Data Fusion to Monitor Vegetation Dynamics in Semi-Arid Rangelands in South Africa

et al. 2015

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Image time series of high temporal and spatial resolution capture land surface dynamics of heterogeneous landscapes. We applied the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) algorithm to multi-spectral images covering two semi-arid heterogeneous rangeland study sites located in South Africa. MODIS 250 m resolution and RapidEye 5 m resolution images were fused to produce synthetic RapidEye images, from June 2011 to July 2012. We evaluated the performance of the algorithm by comparing predicted surface reflectance values to real RapidEye images. Our results show that ESTARFM predictions are accurate, with a coefficient of determination for the red band 0.80 < R 2 < 0.92, and for the near-infrared band 0.83 < R 2 < 0.93, a mean relative bias between 6% and 12% for the red band and 4% to 9% in the near-infrared band. OPEN ACCESSRemote Sens. 2015, 7 6511Heterogeneous vegetation at sub-MODIS resolution is captured adequately: A comparison of NDVI time series derived from RapidEye and ESTARFM data shows that the characteristic phenological dynamics of different vegetation types are reproduced well. We conclude that the ESTARFM algorithm allows us to produce synthetic remote sensing images at high spatial combined with high temporal resolution and so provides valuable information on vegetation dynamics in semi-arid, heterogeneous rangeland landscapes.

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Modeling multidecadal surface water inundation dynamics and key drivers on large river basin scale using multiple time series of Earth‐observation and river flow data

Heimhuber

Tulbure

Broich

2017

Water Resources Research

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Periodically inundated floodplain areas are hot spots of biodiversity and provide a broad range of ecosystem services but have suffered alarming declines in recent history. Despite their importance, their long‐term surface water (SW) dynamics and hydroclimatic drivers remain poorly quantified on continental scales. In this study, we used a 26 year time series of Landsat‐derived SW maps in combination with river flow data from 68 gauges and spatial time series of rainfall, evapotranspiration and soil moisture to statistically model SW dynamics as a function of key drivers across Australia's Murray‐Darling Basin (∼1 million km2). We fitted generalized additive models for 18,521 individual modeling units made up of 10 × 10 km grid cells, each split into floodplain, floodplain‐lake, and nonfloodplain area. Average goodness of fit of models was high across floodplains and floodplain‐lakes (r2 > 0.65), which were primarily driven by river flow, and was lower for nonfloodplain areas (r2 > 0.24), which were primarily driven by rainfall. Local climate conditions were more relevant for SW dynamics in the northern compared to the southern basin and had the highest influence in the least regulated and most extended floodplains. We further applied the models of two contrasting floodplain areas to predict SW extents of cloud‐affected time steps in the Landsat series during the large 2010 floods with high validated accuracy (r2 > 0.97). Our framework is applicable to other complex river basins across the world and enables a more detailed quantification of large floods and drivers of SW dynamics compared to existing methods.

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Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology

Cited by 252 publications

References 30 publications

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Using RapidEye and MODIS Data Fusion to Monitor Vegetation Dynamics in Semi-Arid Rangelands in South Africa

Modeling multidecadal surface water inundation dynamics and key drivers on large river basin scale using multiple time series of Earth‐observation and river flow data

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