Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data

Neteler, Markus

doi:10.3390/rs1020333

Cited by 316 publications

(213 citation statements)

References 30 publications

(17 reference statements)

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“…DLST and NLST were used as proxies for both soil and air temperature, which play an important role in shaping tsetse habitat. Low-quality pixels were removed from the raw data using the quality assessment layer and outliers were filtered using a variant of the boxplot algorithm (37). Vegetation indices at 1 km of spatial resolution and with temporal resolution of 16 d (MOD13A2/ MYD13A2) were also downloaded and processed using the quality assessment layer.…”

Section: Methodsmentioning

confidence: 99%

Mapping landscape friction to locate isolated tsetse populations that are candidates for elimination

Bouyer

Dicko

Cecchi

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Tsetse flies are the cyclical vectors of deadly human and animal trypanosomes in sub-Saharan Africa. Tsetse control is a key component for the integrated management of both plagues, but local eradication successes have been limited to less than 2% of the infested area. This is attributed to either resurgence of residual populations that were omitted from the eradication campaign or reinvasion from neighboring infested areas. Here we focused on Glossina palpalis gambiensis, a riverine tsetse species representing the main vector of trypanosomoses in West Africa. We mapped landscape resistance to tsetse genetic flow, hereafter referred to as friction, to identify natural barriers that isolate tsetse populations. For this purpose, we fitted a statistical model of the genetic distance between 37 tsetse populations sampled in the region, using a set of remotely sensed environmental data as predictors. The least-cost path between these populations was then estimated using the predicted friction map. The method enabled us to avoid the subjectivity inherent in the expert-based weighting of environmental parameters. Finally, we identified potentially isolated clusters of G. p. gambiensis habitat based on a species distribution model and ranked them according to their predicted genetic distance to the main tsetse population. The methodology presented here will inform the choice on the most appropriate intervention strategies to be implemented against tsetse flies in different parts of Africa. It can also be used to control other pests and to support conservation of endangered species.area-wide integrated pest management | eradication | vector control | remote sensing | resistance surface

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Section: Methodsmentioning

confidence: 99%

Mapping landscape friction to locate isolated tsetse populations that are candidates for elimination

Bouyer

Dicko

Cecchi

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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“…Surface interpolation approaches using only temperature and no auxiliary data fail to produce realistic results in areas with complex topography [13] when the data points used for interpolation are unevenly distributed, leaving large gaps. Therefore, spatial interpolation methods should not only use the existing LST values, but also auxiliary data as predictors for LST in a statistical model.…”

Section: Reconstruction Of Remotely Sensed Temperature Mapsmentioning

confidence: 99%

“…Even though HANTS was originally developed for NDVI reconstruction, it can, in theory, also be applied to LST or any other time series showing periodicity. Regarding spatial interpolation, previously applied methods to filter and reconstruct MODIS LST values include 2D [19] and 3D [13] spline interpolation. While being a full reconstruction of all layers, the approach of Neteler et al [13] was only developed for a small region with very complex topography.…”

Section: Previous Approachesmentioning

confidence: 99%

Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail

2014

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Abstract:Temperature is a main driver for most ecological processes, and temperature time series provide key environmental indicators for various applications and research fields. High spatial and temporal resolutions are crucial for detailed analyses in various fields of research. A disadvantage of temperature data obtained by satellites is the occurrence of gaps that must be reconstructed. Here, we present a new method to reconstruct high-resolution land surface temperature (LST) time series at the continental scale gaining 250-m spatial resolution and four daily values per pixel. Our method constitutes a unique new combination of weighted temporal averaging with statistical modeling and spatial interpolation. This newly developed reconstruction method has been applied to greater Europe, resulting in complete daily coverage for eleven years. To our knowledge, this new reconstructed LST time series exceeds the level of detail of comparable reconstructed LST datasets by several orders of magnitude. Studies on emerging diseases, parasite risk assessment and temperature anomalies can now be performed on the continental scale, maintaining high spatial and temporal detail. We illustrate a series of applications in this paper. Our dataset is available online for download as time aggregated derivatives for direct usage in GIS-based applications.

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“…Then, LSTs flagged with "average emissivity error ≤0.04 (or >0.04)" or "average LST error >3 K" were eliminated. Finally, abnormally low LSTs, determined by histogram-based quartile statistics [53], were probably caused by undetected clouds and thus filtered out. In this study, Terra nighttime LST, instead of Aqua and/or daytime LST, was chosen because it is more stable and accurate [54,55] and has a weaker angular effect [56] and a larger number of valid pixels [57].…”

Section: Spatio-temporal Predictorsmentioning

confidence: 99%

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

et al. 2016

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Near surface air temperature (Ta) is one of the most critical variables in climatology, hydrology, epidemiology, and environmental health. In situ measurements are not efficient for characterizing spatially heterogeneous Ta, while remote sensing is a powerful tool to break this limitation. This study proposes a mapping framework for daily mean Ta using an enhanced empirical regression method based on remote sensing data. It differs from previous studies in three aspects. First, nighttime light data is introduced as a predictor (besides land surface temperature, normalized difference vegetation index, impervious surface area, black sky albedo, normalized difference water index, elevation, and duration of daylight) considering the urbanization-induced Ta increase over a large area. Second, independent components are extracted using principal component analysis considering the correlations among the above predictors. Third, a composite sinusoidal coefficient regression is developed considering the dynamic Ta-predictor relationship. This method was performed at 333 weather stations in China during 2001-2012. Evaluation shows overall mean error of −0.01 K, root mean square error (RMSE) of 2.53 K, correlation coefficient (R 2 ) of 0.96, and average uncertainty of 0.21 K. Model inter-comparison shows that this method outperforms six additional empirical regressions that have not incorporated nighttime light data or considered predictor independence or coefficient dynamics (by 0.18-2.60 K in RMSE and 0.00-0.15 in R 2 ).

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Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data

Cited by 316 publications

References 30 publications

Mapping landscape friction to locate isolated tsetse populations that are candidates for elimination

Mapping landscape friction to locate isolated tsetse populations that are candidates for elimination

Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

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