Synthetic RapidEye data used for the detection of area-based spruce tree mortality induced by bark beetles

Latifi, Hooman; Dahms, Thorsten; Beudert, Burkhard; Heurich, Marco; Kübert, Carina; Dech, Stefan

doi:10.1080/15481603.2018.1458463

Cited by 26 publications

(21 citation statements)

References 59 publications

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“…By using TPS, residual distribution not only ensures that the re-aggregated fused fine-resolution image exactly matches the original coarse resolution image, but also help to improve accuracy of individual subpixels. Previous research also reported better accuracy of FSDAF compared to STARFM and UBDF in various application scenarios [7,[50][51][52]. Please note that FSDAF has some shortcomings.…”

Section: Model Characteristics and Applicable Situationsmentioning

confidence: 83%

Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

Liu

Yin

et al. 2019

Remote Sensing

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In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications.

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Section: Model Characteristics and Applicable Situationsmentioning

confidence: 83%

Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

Liu

Yin

et al. 2019

Remote Sensing

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“…RF generates numerous independent trees to overcome the limitations of a single-decision (or regression) tree method, such as the dependency on a single tree and the problem of overfitting the training data, resulting in better performance than single CARTs (Kim et al, 2015;Lee et al, 2016;Liu et al, 2018). A multitude of independent trees are ensembled to reach a solution by majority voting for classification or averaging for regression (e.g., Amani et al, 2017;Im et al, 2016;Latifi et al, 2018). RF provides information on how a variable contributes to model development using out-of-bag (OOB) data that are not used in training a model (Sonobe et al, 2017;.…”

Section: Machine Learning Approach (Random Forest; Rf)mentioning

confidence: 99%

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Park

Shin

et al. 2019

Atmos. Chem. Phys.

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Abstract. Long-term exposure to particulate matter (PM) with aerodynamic diameters < 10 (PM10) and 2.5 µm (PM2.5) has negative effects on human health. Although station-based PM monitoring has been conducted around the world, it is still challenging to provide spatially continuous PM information for vast areas at high spatial resolution. Satellite-derived aerosol information such as aerosol optical depth (AOD) has been frequently used to investigate ground-level PM concentrations. In this study, we combined multiple satellite-derived products including AOD with model-based meteorological parameters (i.e., dew-point temperature, wind speed, surface pressure, planetary boundary layer height, and relative humidity) and emission parameters (i.e., NO, NH3, SO2, primary organic aerosol (POA), and HCHO) to estimate surface PM concentrations over South Korea. Random forest (RF) machine learning was used to estimate both PM10 and PM2.5 concentrations with a total of 32 parameters for 2015–2016. The results show that the RF-based models produced good performance resulting in R2 values of 0.78 and 0.73 and root mean square errors (RMSEs) of 17.08 and 8.25 µg m−3 for PM10 and PM2.5, respectively. In particular, the proposed models successfully estimated high PM concentrations. AOD was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed, solar radiation, and dew-point temperature. The use of aerosol information derived from a geostationary satellite sensor (i.e., Geostationary Ocean Color Imager, GOCI) resulted in slightly higher accuracy for estimating PM concentrations than that from a polar-orbiting sensor system (i.e., the Moderate Resolution Imaging Spectroradiometer, MODIS). The proposed RF models yielded better performance than the process-based approaches, particularly in improving on the underestimation of the process-based models (i.e., GEOS-Chem and the Community Multiscale Air Quality Modeling System, CMAQ).

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“…), early detection of tree disturbances (Latifi et al. ), monitoring agro‐ecosystems (Lobell et al. ; Pan et al.…”

Section: Introductionmentioning

confidence: 99%

“…Garonna et al 2014;Hamunyela et al 2013;Heumann et al 2007;Karkauskaite et al 2017;Ma et al 2013;Piao et al 2006;Ricotta and Avena 2000;Wang et al 2016). Only a few studies present regional-based plant phenology mapping (1 m-30 m pixel size), which is used for the quantification of browning trends in boreal forests (Sulla-Menashe et al 2018), early detection of tree disturbances (Latifi et al 2018), monitoring agro-ecosystems (Lobell et al 2003;Pan et al 2015) or evaluating green-up shift (Debinski et al 2000;Fisher et al 2006;Vrieling et al 2017). In most cases, multi-temporal spectral phenology curves are indirectly used for habitat type discrimination (F€ orster et al 2011; F eret et al 2015), tree species identification (Dymond et al 2002;Hill et al 2010), crop type classification (Peña- Barrag an et al 2011;Foerster et al 2012) or estimation of floristic diversity (Vina et al 2016).…”

Section: Introductionmentioning

confidence: 99%

The colors of heath flowering – quantifying spatial patterns of phenology in Calluna life‐cycle phases using high‐resolution drone imagery

Neumann

Behling

Schindhelm

et al. 2019

Remote Sens Ecol Conserv

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Recent developments in high‐resolution ecosystem mapping using unmanned aerial vehicles (UAV) open up promising perspectives for the monitoring of fine‐scale vegetation patterns and ecological functioning. In this study, we examine the potential of UAV imagery to track the structural composition and related phenological traits of the dwarf shrub Calluna vulgaris on a former military training area. On a European Natura 2000 heathland site, habitat management is shown to be evaluated on the basis of flowering dynamics and reproductive recovery as a proxy of functional changes that are generated after fire destruction. In particular, we utilize true color camera information and digital surface models to determine the spatiotemporal evolution of Calluna life‐cycle phases and flowering phenology 2 years after controlled burning. A stepwise methodological framework is presented that extracts Calluna pixels, spatially separates juvenile and mature life‐cycle phases, differentiates between generative and vegetative shoot extension and finally quantifies proportions of flowers, fruits and vegetative growth in two consecutive peak flowering periods. We show that Calluna life‐cycle phases can be spatially differentiated in pioneer, building and mature phases in UAV imagery. In the juvenile phase, regeneration from germination is low (<10%) and sprouting from the stem base dominates recovery patterns after fire management. The study further reveals that the distribution of flowering and fruiting during peak flowering time is spatially heterogeneous with high variations between years. In the fire unaffected mature phase, proportions of flowers and fruits are reduced indicating a decrease in reproductive capacity of unmanaged Calluna stands. A phenological phase shift is detected from flowering to fruiting that is related to an increase of Calluna growth height. The study shows that the functional patterns of growth and reproduction can be mapped for heathland ecosystems using simple true color drone cameras.

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Synthetic RapidEye data used for the detection of area-based spruce tree mortality induced by bark beetles

Cited by 26 publications

References 59 publications

Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

The colors of heath flowering – quantifying spatial patterns of phenology in Calluna life‐cycle phases using high‐resolution drone imagery

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