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Yiting Wang
College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China

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Journal article
Published: 01 April 2021 in IEEE Transactions on Geoscience and Remote Sensing
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To improve our capacity to map long-term vegetation dynamics in heterogeneous landscapes, this study proposed a new prior knowledge-based spatiotemporal enhancement method, namely, PK-STEM, to fuse MODIS and Landsat FPAR products following the remote sensing trend surface framework. PK-STEM uses historical Landsat FPAR images as prior knowledge and fuses them with new satellite-derived FPAR data. PK-STEM can work in three modes: 1) using only MODIS data; 2) using only Landsat data; and 3) using both MODIS and Landsat data. This study retrieved FPAR from Landsat images using a scaling-based method and tested the performance of PK-STEM in a regional application. For the entire year of 2012, we compared the performance of PK-STEM in different modes and with that of two typical spatiotemporal fusion methods, the enhanced spatial and temporal adaptive reflectance model (ESTARFM) and unmixing-based linear mixing growth model (LMGM). Then, a long time series FPAR data set at 30-m resolution and eight-day intervals was generated for 13 years (2000-2012). Our results show that PK-STEM in mode III is the most robust and accurate (root mean squared error (RMSE) = 0.062; mean R = 0.851) among the three modes and more accurate than ESTARFM (mean RMSE = 0.065; mean R = 0.776) and LMGM (mean RMSE = 0.074; mean R = 0.734). For the 12 years (2000-2011), PK-STEM also achieves high accuracies with mean RMSE = 0.066 and R = 0.938. PK-STEM is very flexible with a continual update mechanism and is efficient for long time series applications.

ACS Style

Yiting Wang; Guangjian Yan; Donghui Xie; Ronghai Hu; Hu Zhang. Generating Long Time Series of High Spatiotemporal Resolution FPAR Images in the Remote Sensing Trend Surface Framework. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -15.

AMA Style

Yiting Wang, Guangjian Yan, Donghui Xie, Ronghai Hu, Hu Zhang. Generating Long Time Series of High Spatiotemporal Resolution FPAR Images in the Remote Sensing Trend Surface Framework. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-15.

Chicago/Turabian Style

Yiting Wang; Guangjian Yan; Donghui Xie; Ronghai Hu; Hu Zhang. 2021. "Generating Long Time Series of High Spatiotemporal Resolution FPAR Images in the Remote Sensing Trend Surface Framework." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-15.

Journal article
Published: 14 January 2021 in Remote Sensing
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Despite its wide applications, the spatiotemporal fusion of coarse- and fine-resolution satellite images is limited primarily to the availability of clear-sky fine-resolution images, which are commonly scarce due to unfavorable weather, and such a limitation might cause errors in spatiotemporal fusion. Thus, the effective use of limited fine-resolution images, while critical, remains challenging. To address this issue, in this paper we propose a new phenological similarity strategy (PSS) to select the optimal combination of image pairs for a prediction date. The PSS considers the temporal proximity and phenological similarity between the base and prediction images and computes a weight for identifying the optimal combination of image pairs. Using the PSS, we further evaluate the influence of input data on the fusion accuracy by varying the number and temporal distribution of input images. The results show that the PSS (mean R = 0.827 and 0.760) outperforms the nearest date (mean R = 0.786 and 0.742) and highest correlation (mean R = 0.821 and 0.727) strategies in both the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the linear mixing growth model (LMGM), respectively, for fusing Landsat 8 OLI and MODIS NDVI datasets. Furthermore, base images adequately covering different growth stages yield better predictability than simply increasing the number of base images.

ACS Style

Yiting Wang; Donghui Xie; Yinggang Zhan; Huan Li; Guangjian Yan; Yuanyuan Chen. Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection. Remote Sensing 2021, 13, 266 .

AMA Style

Yiting Wang, Donghui Xie, Yinggang Zhan, Huan Li, Guangjian Yan, Yuanyuan Chen. Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection. Remote Sensing. 2021; 13 (2):266.

Chicago/Turabian Style

Yiting Wang; Donghui Xie; Yinggang Zhan; Huan Li; Guangjian Yan; Yuanyuan Chen. 2021. "Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection." Remote Sensing 13, no. 2: 266.

Journal article
Published: 19 March 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Accurate estimation of the fine-resolution fraction of absorbed photosynthetically active radiation (FPAR) across broad spatial extents and long time periods requires efficient and applicable methods. The existing methods can hardly provide a balance between accuracy, simplicity, and transferability through space and time. Within the remote-sensing trend-surface conceptual framework, this article proposes a scaling-based method to efficiently retrieve FPAR from fine-resolution satellite data using coarse-resolution FPAR products as a reference. The method was particularly developed and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) FPAR product and Landsat imagery. First, necessary prior knowledge related to FPAR retrieval and scaling theories was used to explicitly linearize the complex relationship between MODIS FPAR and Landsat surface reflectance. Second, the explicit linear model for FPAR estimation was trained through one-pair image learning for each date to estimate FPAR from Landsat imagery in real time. Both homogeneous and heterogeneous cases were considered. The method was validated at ten selected worldwide sites from the Validation of Land European Remote Sensing Instruments (VALERI) program and derived an overall root mean squared error (RMSE) of 0.133. A long time series of FPAR data set at the 30-m resolution was generated at the regional scale (approximately 2000 km²) for 13 years (2000-2012). The results were accurate (RMSE = 0.072) and MODIS-consistent, which were significantly better than those of the normalized difference vegetation index (NDVI) downscaling-based and regression tree methods. The scaling-based method provides accurate, MODIS-consistent and spatially consistent FPAR estimates in real time, is highly transferrable through space and time, and allows for future extension of FPAR estimates to the era of the Landsat series satellites.

ACS Style

Yiting Wang; Guangjian Yan; Ronghai Hu; Donghui Xie; Wei Chen. A Scaling-Based Method for the Rapid Retrieval of FPAR From Fine-Resolution Satellite Data in the Remote-Sensing Trend-Surface Framework. IEEE Transactions on Geoscience and Remote Sensing 2020, 1 -14.

AMA Style

Yiting Wang, Guangjian Yan, Ronghai Hu, Donghui Xie, Wei Chen. A Scaling-Based Method for the Rapid Retrieval of FPAR From Fine-Resolution Satellite Data in the Remote-Sensing Trend-Surface Framework. IEEE Transactions on Geoscience and Remote Sensing. 2020; (99):1-14.

Chicago/Turabian Style

Yiting Wang; Guangjian Yan; Ronghai Hu; Donghui Xie; Wei Chen. 2020. "A Scaling-Based Method for the Rapid Retrieval of FPAR From Fine-Resolution Satellite Data in the Remote-Sensing Trend-Surface Framework." IEEE Transactions on Geoscience and Remote Sensing , no. 99: 1-14.

Conference paper
Published: 03 November 2016 in 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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Spatial scale effects, defined as the phenomenon that the estimates at multiple resolution are inconsistent, have aroused wide concerns in remote sensing studies. But very few studies have paid attention to the effects of scale on phenological studies. This paper investigated the scale effects in estimating phenological transitional dates from remote sensing data. A prior-knowledge vegetation index (VI) time series at 30 m resolution was composed, based on which the time series at 240 m, 480 m and 960 m were derived. The green-up onset and dormancy onset dates were then estimated from the VI time series using a double-logistic plant growth model. The derived estimates at multiple resolutions were compared and the effects of spatial scales were verified. Landscape heterogeneities were found to be related to spatial scale effects. The changes in the estimated green-up onset dates and dormancy onset dates exhibited different patterns with the coarsening of spatial resolution.

ACS Style

Yiting Wang; Donghui Xie; Ronghai Hu; Guangjian Yan. Spatial scale effect on vegetation phenological analysis using remote sensing data. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2016, 1329 -1332.

AMA Style

Yiting Wang, Donghui Xie, Ronghai Hu, Guangjian Yan. Spatial scale effect on vegetation phenological analysis using remote sensing data. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2016; ():1329-1332.

Chicago/Turabian Style

Yiting Wang; Donghui Xie; Ronghai Hu; Guangjian Yan. 2016. "Spatial scale effect on vegetation phenological analysis using remote sensing data." 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 1329-1332.

Journal article
Published: 07 April 2016 in Remote Sensing
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The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical biophysical parameter in eco-environmental studies. Scaling of FAPAR from the field observation to the satellite pixel is essential for validating remote sensing FAPAR product and for further modeling applications. However, compared to spatial mismatches, few studies have considered temporal mismatches between in-situ and satellite observations in the scaling. This paper proposed a general methodology for scaling FAPAR from the field to the satellite pixel considering the temporal variation. Firstly, a temporal normalization method was proposed to normalize the in-situ data measured at different times to the time of satellite overpass. The method was derived from the integration of an atmospheric radiative transfer model (6S) and a FAPAR analytical model (FAPAR-P), which can characterize the diurnal variations of FAPAR comprehensively. Secondly, the logistic model, which derives smooth and consistent temporal profile for vegetation growth, was used to interpolate the in-situ data to match the dates of satellite acquisitions. Thirdly, fine-resolution FAPAR products at different dates were estimated from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data using the temporally corrected in-situ data. Finally, fine-resolution FAPAR were taken as reference datasets and aggregated to coarse resolution, which were further compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) FAPAR product. The methodology is validated for scaling FAPAR from the field to the satellite pixel temporally and spatially. The MODIS FAPAR manifested a good consistency with the aggregated FAPAR with R2 of 0.922 and the root mean squared error of 0.054.

ACS Style

Yiting Wang; Donghui Xie; Song Liu; Ronghai Hu; Yahui Li; Guangjian Yan. Scaling of FAPAR from the Field to the Satellite. Remote Sensing 2016, 8, 310 .

AMA Style

Yiting Wang, Donghui Xie, Song Liu, Ronghai Hu, Yahui Li, Guangjian Yan. Scaling of FAPAR from the Field to the Satellite. Remote Sensing. 2016; 8 (4):310.

Chicago/Turabian Style

Yiting Wang; Donghui Xie; Song Liu; Ronghai Hu; Yahui Li; Guangjian Yan. 2016. "Scaling of FAPAR from the Field to the Satellite." Remote Sensing 8, no. 4: 310.

Journal article
Published: 05 February 2016 in IEEE Transactions on Geoscience and Remote Sensing
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Scale effect, which is caused by a combination of model nonlinearity and surface heterogeneity, has been of interest to the remote sensing community for decades. However, there is no current analysis of scale effect in the ground-based indirect measurement of leaf area index (LAI), where model nonlinearity and surface heterogeneity also exist. This paper examines the scale effect on the indirect measurement of LAI. We built multiscale data sets based on realistic scenes and field measurements. We then implemented five representative methods of indirect LAI measurement at scales (segment lengths) that range from meters to hundreds of meters. The results show varying degrees of deviation and fluctuation that exist in all five methods when the segment length is shorter than 20 m. The retrieved LAI from either Beer's law or the gap-size distribution method shows a decreasing trend with increasing segment lengths. The length at which the LAI values begin to stabilize is about a full period of row in row crops and 100 m in broadleaf or coniferous forests. The impacts of segment length on the finite-length averaging method, the combination of gap-size distribution and finite-length methods, and the path-length distribution method are relatively small. These three methods stabilize at the segment scale longer than 20 m in all scenes. We also find that computing the average LAI of all of the short segment lengths, which is commonly done, is not as good as merging these short segments into a longer one and computing the LAI value of the merged one.

ACS Style

Guangjian Yan; Ronghai Hu; Yiting Wang; Huazhong Ren; Wanjuan Song; Jianbo Qi; Ling Chen. Scale Effect in Indirect Measurement of Leaf Area Index. IEEE Transactions on Geoscience and Remote Sensing 2016, 54, 3475 -3484.

AMA Style

Guangjian Yan, Ronghai Hu, Yiting Wang, Huazhong Ren, Wanjuan Song, Jianbo Qi, Ling Chen. Scale Effect in Indirect Measurement of Leaf Area Index. IEEE Transactions on Geoscience and Remote Sensing. 2016; 54 (6):3475-3484.

Chicago/Turabian Style

Guangjian Yan; Ronghai Hu; Yiting Wang; Huazhong Ren; Wanjuan Song; Jianbo Qi; Ling Chen. 2016. "Scale Effect in Indirect Measurement of Leaf Area Index." IEEE Transactions on Geoscience and Remote Sensing 54, no. 6: 3475-3484.

Journal article
Published: 04 February 2015 in Journal of Mountain Science
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Regional Landslide Susceptibility Zonation (LSZ) is always challenged by the available amount of field data, especially in southwestern China where large mountainous areas and limited field information coincide. Statistical learning algorithms are believed to be superior to traditional statistical algorithms for their data adaptability. The aim of the paper is to evaluate how statistical learning algorithms perform on regional LSZ with limited field data. The focus is on three statistical learning algorithms, Logistic Regression (LR), Artificial Neural Networks (ANN) and Support Vector Machine (SVM). Hanzhong city, a landslide prone area in southwestern China is taken as a study case. Nine environmental factors are selected as inputs. The accuracies of the resulting LSZ maps are evaluated through landslide density analysis (LDA), receiver operating characteristic (ROC) curves and Kappa index statistics. The dependence of the algorithm on the size of field samples is examined by varying the sizes of the training set. The SVM has proven to be the most accurate and the most stable algorithm at small training set sizes and on all known landslide sizes. The accuracy of SVM shows a steadily increasing trend and reaches a high level at a small size of the training set, while accuracies of LR and ANN algorithms show distinct fluctuations. The geomorphological interpretations confirm the strength of SVM on all landslide sizes. Our results show that the strengths of SVM in generalization capability and model robustness make it an appropriate and efficient tool for regional LSZ with limited landslide field samples.

ACS Style

Yiting Wang; Arie Christoffel Seijmonsbergen; Willem Bouten; Qing-Tao Chen. Using statistical learning algorithms in regional landslide susceptibility zonation with limited landslide field data. Journal of Mountain Science 2015, 12, 268 -288.

AMA Style

Yiting Wang, Arie Christoffel Seijmonsbergen, Willem Bouten, Qing-Tao Chen. Using statistical learning algorithms in regional landslide susceptibility zonation with limited landslide field data. Journal of Mountain Science. 2015; 12 (2):268-288.

Chicago/Turabian Style

Yiting Wang; Arie Christoffel Seijmonsbergen; Willem Bouten; Qing-Tao Chen. 2015. "Using statistical learning algorithms in regional landslide susceptibility zonation with limited landslide field data." Journal of Mountain Science 12, no. 2: 268-288.

Conference paper
Published: 31 October 2008 in Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics
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With the recent availability of commercial high resolution remote sensing panchromatic imagery from sensors such as IKONOS and QUICKBIRD, it is possible to extract individual objects such as buildings from the imagery. However, traditional extraction methods cannot get the desired accuracy, because knowledge is not utilized. In this paper, we put forward a texture-based approach to get building information from the panchromatic imagery. Firstly, the image is segmented based on texture of variogram feature. Building corner structure knowledge is also combined to detect and connect building edges. Then we fill interiors of buildings through seed filling algorithm. In the final stage, point noises and linear noises are eliminated from the imagery through area or shape index value. The accuracy assessment adopted in this paper is GIS overlay analysis, which shows that 93.9% of building information is extracted correctly. The result indicates that the approach supplies another new technique for interpreting high spatial resolution remotely sensed imagery.© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

ACS Style

Yiting Wang; Xinliang Li; Wuming Zhang; Liqiang Zhang. Building extraction of urban area from high resolution remotely sensed panchromatic data of urban area. Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics 2008, 7144, 71441 .

AMA Style

Yiting Wang, Xinliang Li, Wuming Zhang, Liqiang Zhang. Building extraction of urban area from high resolution remotely sensed panchromatic data of urban area. Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics. 2008; 7144 ():71441.

Chicago/Turabian Style

Yiting Wang; Xinliang Li; Wuming Zhang; Liqiang Zhang. 2008. "Building extraction of urban area from high resolution remotely sensed panchromatic data of urban area." Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics 7144, no. : 71441.