Relationship between land cover patterns and surface temperature in urban areas

Rhee, Jinyoung; Park, Seonyoung; Lu, Zhenyu

doi:10.1080/15481603.2014.964455

Cited by 85 publications

(49 citation statements)

References 52 publications

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“…500, 1000) are grown based on two randomizations including (1) a randomly selected subset of the training samples for each tree and (2) a randomly selected subset of input variables at each node of the tree. This way, RF overcomes the well-known limitation of CART, in that results are sensitive to the configuration and quality of training data (Lawrence and Wright, 2001;Rhee et al, 2014;Guo and Du, 2017). Thus, RF has recently gained popularity in remote sensing classification and regression Li et al, 2014;Liu et al, 2015;Lu et al, 2013;Park et al, 2016;Yoo et al, 2012).…”

Section: Deterministic and Probabilistic Approaches For CI Detectionmentioning

confidence: 99%

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Lee

Han

et al. 2017

Atmos. Meas. Tech.

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Abstract. The detection of convective initiation (CI) is very important because convective clouds bring heavy rainfall and thunderstorms that typically cause severe socio-economic damage. In this study, deterministic and probabilistic CI detection models based on decision trees (DT), random forest (RF), and logistic regression (LR) were developed using Himawari-8 Advanced Himawari Imager (AHI) data obtained from June to August 2016 over the Korean Peninsula. A total of 12 interest fields that contain brightness temperature, spectral differences of the brightness temperatures, and their time trends were used to develop CI detection models. While, in our study, the interest field of 11.2 µm T b was considered the most crucial for detecting CI in the deterministic models and the probabilistic RF model, the trispectral difference, i.e. (8.6-11.2 µm)-(11.2-12.4 µm), was determined to be the most important one in the LR model. The performance of the four models varied by CI case and validation data. Nonetheless, the DT model typically showed higher probability of detection (POD), while the RF model produced higher overall accuracy (OA) and critical success index (CSI) and lower false alarm rate (FAR) than the other models. The CI detection of the mean lead times by the four models were in the range of 20-40 min, which implies that convective clouds can be detected 30 min in advance, before precipitation intensity exceeds 35 dBZ over the Korean Peninsula in summer using the Himawari-8 AHI data.

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Section: Deterministic and Probabilistic Approaches For CI Detectionmentioning

confidence: 99%

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Lee

Han

et al. 2017

Atmos. Meas. Tech.

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“…It has been widely used for various remote sensing applications for both classification and regression [66][67][68]. Compared to the CART algorithm, RF uses a bootstrap aggregating technique to improve model performance.…”

Section: Machine Learning-based Downscaling Modelsmentioning

confidence: 99%

Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches

Park

et al. 2016

Remote Sensing

Self Cite

128

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This study presented a MODIS 8-day 1 km evapotranspiration (ET) downscaling method based on Landsat 8 data (30 m) and machine learning approaches. Eleven indicators including albedo, land surface temperature (LST), and vegetation indices (VIs) derived from Landsat 8 data were first upscaled to 1 km resolution. Machine learning algorithms including Support Vector Regression (SVR), Cubist, and Random Forest (RF) were used to model the relationship between the Landsat indicators and MODIS 8-day 1 km ET. The models were then used to predict 30 m ET based on Landsat 8 indicators. A total of thirty-two pairs of Landsat 8 images/MODIS ET data were evaluated at four study sites including two in United States and two in South Korea. Among the three models, RF produced the lowest error, with relative Root Mean Square Error (rRMSE) less than 20%. Vegetation greenness related indicators such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), and vegetation moisture related indicators such as Normalized Difference Infrared Index-Landsat 8 OLI band 7 (NDIIb7) and Normalized Difference Water Index (NDWI) were the five most important features used in RF model. Temperature-based indicators were less important than vegetation greenness and moisture-related indicators because LST could have considerable variation during each 8-day period. The predicted Landsat downscaled ET had good overall agreement with MODIS ET (average rRMSE = 22%) and showed a similar temporal trend as MODIS ET. Compared to the MODIS ET product, the downscaled product demonstrated more spatial details, and had better agreement with in situ ET observations (R 2 = 0.56). However, we found that the accuracy of MODIS ET was the main control factor of the accuracy of the downscaled product. Improved coarse-resolution ET estimation would result in better finer-resolution estimation. This study proved the potential of using machine learning approaches for ET downscaling considering their effectiveness and ease of implementation. Future research includes development of the spatial-temporal fusion models of Landsat data and MODIS ET in order to increase temporal resolution of downscaled ET.

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“…Machine learning is a novel approach used in various remote sensing applications, including land cover/land use classification [46][47][48][49][50][51][52], change detection [53,54], geological mapping [55], vegetation mapping [56][57][58][59], hydrological studies [60][61][62] and atmospheric studies [63,64]. In this study, two rule-based machine learning approaches-decision tree (DT) and random forest (RF)-were used for the classification of open water, sea ice and melt pond from the TerraSAR-X dual-polarization [65], was used to carry out the DT-based classification.…”

Section: Machine Learning Approaches For Melt Pond Retrievalmentioning

confidence: 99%

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Han

Kim

et al. 2016

Remote Sensing

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Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approaches-decision trees (DT) and random forest (RF)-in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15ˆ15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the classification of open water, sea ice and melt ponds in the RF model. The average of the co-polarization phase difference and the alpha angle in a mid-incidence angle were also identified as the important variables in the RF model. The melt pond fraction and sea ice concentration retrieved from the RF-derived melt pond map showed root mean square deviations of 2.4% and 4.9%, respectively, compared to those from the reference melt pond maps. This indicates that there is potential to accurately monitor melt ponds on multiyear sea ice in the summer season at a local scale using high-resolution dual-polarization SAR data.

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Relationship between land cover patterns and surface temperature in urban areas

Cited by 85 publications

References 52 publications

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

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