The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform

Tianyu Yang - One of the best experts on this subject based on the ideXlab platform.

  • Deriving Regional Crown Closure Using Spectral Mixture Analysis Based on Up-Scaling Endmember Extraction Approach and Validation
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
    Co-Authors: Haijing Tian, Yuxing Zhang, Yongfeng Dang, Xiliang Ni, Yunfei Xu, Min Xu, Xiaowen Li, Haibing Xiang, Tianyu Yang
    Abstract:

    This paper investigates the retrieval of forest Crown Closure (CC) from the Landsat Thematic Mapper (TM) data and aerial images with a linear spectral mixture analysis (SMA) method. Anshan is selected as the study area. Two endmember extraction methods were used in this paper: 1) traditional image-based method and 2) up-scaling method. (When we get the fractions of components from a coregistered 0.6-m spatial resolution image, the linear spectral mixture model is applied to unmix the TM image and obtain the required endmembers.) For both methods, four fraction images (sunlit canopy, shaded canopy, sunlit background, shaded background) were calculated by linear spectral mixture model and used to derive CC. Results showed that CC can be fitted best with sum of fractions of sunlit canopy and shaded canopy at S-shaped curve and the up-scaling endmember extraction method is better than traditional image-based endmember extraction method. Finally, the up-scaling endmember extraction method was used to map forest CC in Anshan forested region. The measured forest CC distribution map was used to validate the estimated map. Results show that the estimated CC and measured CC have little difference and the estimated CC is slightly lower. The majority of Anshan forest CC values were between 0.4 and 0.8.

Haijing Tian - One of the best experts on this subject based on the ideXlab platform.

  • Deriving Regional Crown Closure Using Spectral Mixture Analysis Based on Up-Scaling Endmember Extraction Approach and Validation
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
    Co-Authors: Haijing Tian, Yuxing Zhang, Yongfeng Dang, Xiliang Ni, Yunfei Xu, Min Xu, Xiaowen Li, Haibing Xiang, Tianyu Yang
    Abstract:

    This paper investigates the retrieval of forest Crown Closure (CC) from the Landsat Thematic Mapper (TM) data and aerial images with a linear spectral mixture analysis (SMA) method. Anshan is selected as the study area. Two endmember extraction methods were used in this paper: 1) traditional image-based method and 2) up-scaling method. (When we get the fractions of components from a coregistered 0.6-m spatial resolution image, the linear spectral mixture model is applied to unmix the TM image and obtain the required endmembers.) For both methods, four fraction images (sunlit canopy, shaded canopy, sunlit background, shaded background) were calculated by linear spectral mixture model and used to derive CC. Results showed that CC can be fitted best with sum of fractions of sunlit canopy and shaded canopy at S-shaped curve and the up-scaling endmember extraction method is better than traditional image-based endmember extraction method. Finally, the up-scaling endmember extraction method was used to map forest CC in Anshan forested region. The measured forest CC distribution map was used to validate the estimated map. Results show that the estimated CC and measured CC have little difference and the estimated CC is slightly lower. The majority of Anshan forest CC values were between 0.4 and 0.8.

  • temporal changing analysis of forest Crown Closure of anshan city based on spectral mixture analysis
    International Geoscience and Remote Sensing Symposium, 2013
    Co-Authors: Haijing Tian, Chunxiang Cao, Daming Bao, Yongfeng Dang
    Abstract:

    Spectral mixture analysis method was selected for mapping forest canopy Closure in different periods and analyzing the dynamic changes of forest canopy Closure with Landsat TM data and field sample data. And sunlit canopy, shaded canopy, sunlit background and shaded background are selected as the four components. In order to conduct the research, Anshan city is selected as the study area. We derive the forest canopy Closure of Anshan city in 1990, 2000, 2006, then analysis the dynamic changes of Anshan forest canopy Closure during these periods. The result shows that Anshan forest canopy Closure shows a downward trend from 1995 to 2000, and shows an upward trend from 2000 to 2006.

Yongfeng Dang - One of the best experts on this subject based on the ideXlab platform.

  • Deriving Regional Crown Closure Using Spectral Mixture Analysis Based on Up-Scaling Endmember Extraction Approach and Validation
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
    Co-Authors: Haijing Tian, Yuxing Zhang, Yongfeng Dang, Xiliang Ni, Yunfei Xu, Min Xu, Xiaowen Li, Haibing Xiang, Tianyu Yang
    Abstract:

    This paper investigates the retrieval of forest Crown Closure (CC) from the Landsat Thematic Mapper (TM) data and aerial images with a linear spectral mixture analysis (SMA) method. Anshan is selected as the study area. Two endmember extraction methods were used in this paper: 1) traditional image-based method and 2) up-scaling method. (When we get the fractions of components from a coregistered 0.6-m spatial resolution image, the linear spectral mixture model is applied to unmix the TM image and obtain the required endmembers.) For both methods, four fraction images (sunlit canopy, shaded canopy, sunlit background, shaded background) were calculated by linear spectral mixture model and used to derive CC. Results showed that CC can be fitted best with sum of fractions of sunlit canopy and shaded canopy at S-shaped curve and the up-scaling endmember extraction method is better than traditional image-based endmember extraction method. Finally, the up-scaling endmember extraction method was used to map forest CC in Anshan forested region. The measured forest CC distribution map was used to validate the estimated map. Results show that the estimated CC and measured CC have little difference and the estimated CC is slightly lower. The majority of Anshan forest CC values were between 0.4 and 0.8.

  • temporal changing analysis of forest Crown Closure of anshan city based on spectral mixture analysis
    International Geoscience and Remote Sensing Symposium, 2013
    Co-Authors: Haijing Tian, Chunxiang Cao, Daming Bao, Yongfeng Dang
    Abstract:

    Spectral mixture analysis method was selected for mapping forest canopy Closure in different periods and analyzing the dynamic changes of forest canopy Closure with Landsat TM data and field sample data. And sunlit canopy, shaded canopy, sunlit background and shaded background are selected as the four components. In order to conduct the research, Anshan city is selected as the study area. We derive the forest canopy Closure of Anshan city in 1990, 2000, 2006, then analysis the dynamic changes of Anshan forest canopy Closure during these periods. The result shows that Anshan forest canopy Closure shows a downward trend from 1995 to 2000, and shows an upward trend from 2000 to 2006.

Yulong Lu - One of the best experts on this subject based on the ideXlab platform.

  • Retrieval of Canopy Closure and LAI of Moso Bamboo Forest Using Spectral Mixture Analysis Based on Real Scenario Simulation
    IEEE Transactions on Geoscience and Remote Sensing, 2011
    Co-Authors: Huaqiang Du, Guo-mo Zhou, Xiaojun Xu, Hongli Ge, Yufeng Zhou, Yulong Lu
    Abstract:

    This paper investigates the retrievals of the canopy Closure and leaf area index (LAI) of the Moso bamboo forest from the Landsat Thematic Mapper data using a constrained linear spectral unmixing method. A new approach for endmember collection based on the real scenario simulation of the Moso bamboo forest is developed. Four fraction images (i.e., sunlit canopy, shaded canopy, sunlit background, and shaded background) are calculated and used to develop the canopy Closure and LAI. The results show that the predicted Crown Closure, which was inverted from the sunlit and shaded canopies, has a good agreement with the observed Crown Closure (R2 = 0.725). The accuracy assessment indicates that the root mean square error (rmse) and the relative root mean square error (rmse_r) are 10% and 13.37% for the predicted Crown Closure, respectively. The LAI has the highest correlation coefficient with the shaded background, and it can be fitted by an exponential model (R2 = 0.497). The linear relationship between the predicted and observed LAI values is significant at a level of 99% (P

Guo-mo Zhou - One of the best experts on this subject based on the ideXlab platform.

  • Multi-scale Crown Closure retrieval for moso bamboo forest using multi-source remotely sensed imagery based on geometric-optical and Erf-BP neural network models
    Journal of remote sensing, 2015
    Co-Authors: Cong Wang, Guo-mo Zhou, Shaobo Sun, Ning Han, Guolong Gao
    Abstract:

    This article focuses on retrieving the multi-scale Crown Closure CC of Moso bamboo forest using Systeme Pour l’Observation de la Terre SPOT5 and Landsat Thematic Mapper TM satellite remotely sensed imagery based on the geometric-optical model and the artificial neural network ANN model. CC at local scale was first retrieved using the Li-Strahler geometric-optical model LSGM and images from an unmanned aerial vehicle UAV. Then, multi-scale CC was retrieved using the Erf-BP model a kind of back-propagation BP feed-forward neural network, which takes a Gaussian error function Erf as an activation function of the hidden layer based on a combination of SPOT5 and Landsat TM images. The results show that by combining multi-source remotely sensed data, the CC of Moso bamboo forest can be retrieved at the local region, township area, and county scale with high accuracy using the Erf-BP model. Estimated values have a linear relationship with the observed values at a significance level of 0.05. The highest accuracy of the retrieval of CC referred to as LSGM-UAV-CC was observed at the local region based on LSGM and UAV, with the coefficient of determination R2 of 0.63, followed by that at the township area with an R2 of 0.0.55 based on LSGM-UAV-CC and SPOT5 data using the Erf-BP model Erf-BP-SPOT5-CC, and that at the county scale with an R2 of 0.54 based on Erf-BP-SPOT5-CC and Landsat TM data using the Erf-BP model Erf-BP-TM-CC.

  • Retrieval of Crown Closure of moso bamboo forest using unmanned aerial vehicle (UAV) remotely sensed imagery based on geometric-optical model
    Journal of Applied Ecology, 2015
    Co-Authors: Cong Wang, Guo-mo Zhou, Shaobo Sun, Guolong Gao
    Abstract:

    This research focused on the application of remotely sensed imagery from unmanned aerial vehicle (UAV) with high spatial resolution for the estimation of Crown Closure of moso bamboo forest based on the geometric-optical model, and analyzed the influence of unconstrained and fully constrained linear spectral mixture analysis (SMA) on the accuracy of the estimated results. The results demonstrated that the combination of UAV remotely sensed imagery and geometric-optical model could, to some degrees, achieve the estimation of Crown Closure. However, the different SMA methods led to significant differentiation in the estimation accuracy. Compared with unconstrained SMA, the fully constrained linear SMA method resulted in higher accuracy of the estimated values, with the coefficient of determination (R2) of 0.63 at 0.01 level, against the measured values acquired during the field survey. Root mean square error (RMSE) of approximate 0.04 was low, indicating that the usage of fully constrained linear SMA could bring about better results in Crown Closure estimation, which was closer to the actual condition in moso bamboo forest.

  • Retrieval of Canopy Closure and LAI of Moso Bamboo Forest Using Spectral Mixture Analysis Based on Real Scenario Simulation
    IEEE Transactions on Geoscience and Remote Sensing, 2011
    Co-Authors: Huaqiang Du, Guo-mo Zhou, Xiaojun Xu, Hongli Ge, Yufeng Zhou, Yulong Lu
    Abstract:

    This paper investigates the retrievals of the canopy Closure and leaf area index (LAI) of the Moso bamboo forest from the Landsat Thematic Mapper data using a constrained linear spectral unmixing method. A new approach for endmember collection based on the real scenario simulation of the Moso bamboo forest is developed. Four fraction images (i.e., sunlit canopy, shaded canopy, sunlit background, and shaded background) are calculated and used to develop the canopy Closure and LAI. The results show that the predicted Crown Closure, which was inverted from the sunlit and shaded canopies, has a good agreement with the observed Crown Closure (R2 = 0.725). The accuracy assessment indicates that the root mean square error (rmse) and the relative root mean square error (rmse_r) are 10% and 13.37% for the predicted Crown Closure, respectively. The LAI has the highest correlation coefficient with the shaded background, and it can be fitted by an exponential model (R2 = 0.497). The linear relationship between the predicted and observed LAI values is significant at a level of 99% (P