The Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA 2014)
Proceedings of EORSA 2014 Editors: Qihao WENG Paolo GAMBA George XIAN Guangxing Wang Jianjun Zhu
Co-Organizers: •
Central South University of Forestry and Technology University, China. Central South University, China.
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Hunan University of Science and Technology, China.
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Indiana State University, USA.
Technical Sponsors: •
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IEEE Geoscience and Remote Sensing Society
International Society for Photogrammetry and Remote Sensing •
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Group on Earth Observations
Natural Science Foundation of China
GROUPON _� EARTH OBSERVATIONS NSFC
IEEE Catalog Number: CFP1439E-ART ISBN: 978-1-4799-4184-1
Table of Contents
A Practical Semi-automatic Approach for Optical and SAR Data Co-registration Hongsheng Zhang, Hui Lin, and Yuanzhi Zhang
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1
An Improved Multipath Forward Scattering Model of GNSS-Reflectometry Xuerui Wu, and Shuanggen Jin
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5
Accurately Calibrate Kinect Sensor Using Indoor Control Field Popo Gui, Qin Ye, Hongmin Chen, Tinghui Zhang, and Chun Yang
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9
Validation of MODIS Vegetation Continuous Fields in Two Areas in Mexico Yon Gao, Jean Fram;ois Mas, Jaime Paneque-Galvez, Margaret Skutsch, Adrian Ghilardi, Jose Antonio Navarrete pacheco, and Ignacio Paniagua
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14
Land-Ocean Leakage Errors in Satellite Gravity Measurements Using Forward Modeling Fang Zou, Shuanggen Jin, and Guiping Feng
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19
Vegetation Phenology Monitoring With Seawinds Scatterometer in Eastern Asia Linlin Lu, Qingting Li, Cuizhen Wang, and Huadong Guo
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24
Ground Subsidence Monitoring in Western Region of Shanghai Maglev by Cosmo-Skymed Ascending and Descending SAR Images Jingwen Zhao, and Jicang Wu
28
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Classifying the Landsat Thermal Data to Detect Urban Sprawl Tendency with the Multilayer Level Set Approach Yishuo Huang, and Chih-Ping Peng
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33
Spatial-temporal Change Analysis of Evapotranspiration in the Heihe River Basin Nona Yon, Weiwei Zhu, Xueliang Feng, and Sheng Chang
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38
Hyperspectrallmage Pansharpening for Photo Analysis by Ratio Enhancement Qizhi Xu, Yun Zhang, and 80 Li
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42
Study on Extra-high Voltage Power Line Scatterers in Time Series SAR Sha Li, Tao Li, Mingzhou Wang, Ailing Hou, Wenhao Wu, Kan Xu, and Yon Liu
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47
The Spatio-temporal Dynamic Analysis of Salt Marsh Vegetations in Chongming Dongtan Based on Remote Sensing Data Jie Yu, Yi Lin, Chaoyang Hu, and Yuguan Zhang
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52
Remote Sensing Monitoring of Wind-preventing and Sand-fixing Effects of Coastal Protection Forests: a Case Study of Haitan Island, Fujian, China Xianli Peng, Feng Ding, Wen/eng Wu, and Xin Zhang
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IX
57
Characteristics of Urban Heat Island (UHI) Source and Sink Areas in Urban Region of Shenyang Li-guang Li, Hong-bo Wang, Zi-qi Zhao, Fu Cai, Xian-li Zhao, and Shen-Iai Xu
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62
3D Coseismic Deformation of Wenchuan MsB.O Earthquake with D-lnSAR Technology and the Thrust Fault Movement Model Linying Guo, and Jicang Wu
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67
Surface Urban Heat Island Variation on Prevailing Wind Direction Belt Base on TM/ETM+ Data in Shenyang, China Hongbo Wang, Ziqi Zhao, Liguang Li, Fu Cai, Xianli Zhao, Shenlai Xu, Peng Jiang, Deqin Li, and Yi Lin
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71
Fast Retrieval of Land Surface Emissivity from Landsat Data through IDL Programming Xin Zhang, Feng Ding, Xianli Peng, Wen/eng Wu, and Pengyu Fan
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76
Prospecting Specularite-Haematite Resources with ETM+ and Field Data, Marampa Iron Occurrence, Northern Sierra Leone Lamin R. Mansaray, Lei Liu, Jun Zhou, Zhimin Ma, and Desmond Alie
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81
Automatic Extraction Method Study of Independent Features Based on Elevation Projection of Point Clouds and Morphological Characters of Ground Object Shen Gao, and Qingwu Hu..................................................................................................................................86
Multi-scale Object-oriented Building Extraction Method of Tai'an City from High Resolution Image Chaokui Li, Xiaojiao Dong, and Qiang Zhang....................................................................................................91
Toward Accurate XC02 Level 2 Measurements by Combining Different C02 Derivations from GOSAT and SCIAMACHY Yingying Jing, Jiancheng Shi, and Tianxing Wang............................................................................................96
The Distributed Storage Strategy Research of Remote Sensing Image Based on Mongo DB Chaokui Li, and Yang W u ...................................................................................................................................101
Towards Real Time Quarter-Hour Monitoring of the Urban Thermal Environment at Sharpened Spatial Resolution Iphigenia Keramitsoglou, Chris T. Kiranoudis, and Panagiotis Sismanidis .................................................105
Mass Concentration Variations Characteristics of PM10 and PM2.S in Guangzhou (China) Runping Liu, and Fenglei Fan............................................................................................................................111
Building Detection with LiDAR Point Clouds Based on Regional Multi-Return Density Analyzing Lelin Li, and Jinping Zhang ...............................................................................................................................116
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Wildfire Evacuation Trigger Buffers for Sensitive Areas: EVITA project Chris Kiranoudis, Emmanoui/ Zachariadis, /phigenia Keramitsog/ou, Kelly Saini, Olga Kaka/iagou, and Epameinontas K/eitsikas
1 21
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Spatiotemporal Change Analysis of Urban Land Surface Component Based on V-I-S Model - A case study in Guangzhou Wei Fan, Runping Liu, and Feng/ei Fan
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1 26
Change and Comparison of Urban Land Use in China's 3 Terrain Ladders Bin Quan, Xianzhao Liu, Shi Lei, Tao Guo, Hui Song, and linning Xie
131
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A Comparative Analysis of Data Fusion Techniques Based On Landsat TM and ALOS PALSAR Data Panpan Zhao, Lijuan Liu, Dengsheng Lu, and Huaqiang Du
136
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A SVM Ensemble Approach Combining Pixel-based and Object-based Features for the Classification of High Resolution Remotely Sensed Imager Chun Liu, Liang Hong, Sensen Chu, and lie Chen
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140
Boreal Forest Height Inversion Using E-SAR POLinSAR Data Based Coherence Optimization Methods and Three-Stage Algorithm Qinghua Xie, lianjun Zhu, Changcheng Wang, and Haiqiang Fu
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145
Research on Road Information Extraction from High Resolution Imagery Based on Global Precedence Hao Chen, Lill Yin, and Li Ma
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151
A New Regional Shape Index for Classification of High Resolution Remote Sensing Images Sensen Chu, Liang Hong, Chun Liu, and lie Chen
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156
Monitoring Ecological Management of Three-North Shelterbelt Program: a Case Study in Zhongyang County, China Xiaohui Wang, Hongbo lu, Yongfu Chen, and Erxue Chen
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161
A Study of Natural Recovery of Vegetation in Wenchuan Earthquake Affected Zone via Remote sensing Kaiguo Yuan, and Tan Liu
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166
Building Scattering Centers Analysis with Polarimetric SAR Interferometry Based on ESPRIT Algorithm Ning Li, Changcheng Wang, Haiqiang Fu, Qinghua Xie, and Wenxiu Xiong
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171
Adding a New Dimension to Global Urban Observations - Inventory of Human Settlements Pattern and Urban Morphology Using VHR SAR Data of the Tandem-X Mission Thomas Esch, Mattia Marconcini, Andreas Fe/bier, Wieke He/dens, and Achim Roth XI
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176
Sensitivity of Vegetation toward Precipitation in Dry Land of China Using Satellite Images Anmin Fu, Rong Fu, Tao Sun, and Xiangji Kong
180
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Monitoring Remediation of Temporary Land Use in Road Construction Using Remote Sensing Technique Changbing Liu, Jianbo Hu, Aijing Li, and Dongchang Li
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185
Remote Sensing Image Classification with Small Training Samples Based On Grey Theory Dongshui Zhang, Xinbao Chen Yongshun Han, Lixia Cong, Qinmin Wang, and Xiaoqin Wang
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190
Multi-Scale Simulation and Accuracy Assessment Of Forest Carbon Using Landsat And MODIS Data Enping Yan Guangxing Wang, Hui Lin, and Hua Sun
195
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Selection of Sensitive Bands for Classification of Tree Species Based on Pigment Content and Hyperspectral Data Zhuo Zang, Hui Lin, and Guangxing Wang
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200
Development of High-Resolution Population Database from ASTER-Derived Urban Area Map Hiroyuki Miyazaki, Koki /wao, and Ryosuke Shibasaki
206
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Analysis of the Land Degradation Pattern in Zoige Plateau Marsh Wetland Based on MODIS data Wen/an Feng, and Haozhe Zhong
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211
Multi-view Oblique Aerial Image Sparse Matching Zhenchao Zhang, Chenguang Dai, De/in Mo, and Mingyan Zhao
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216
Study on Daily Mean Temperature Modeling Kun Qin, and Junhuan Peng
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221
Sparse Coding Based Airport Detection from Medium Resolution Landsat-7 Satellite Remote Sensing Images Gong Cheng, Junwei Han, Peicheng Zhou, Xiwen Yao, Dingwen Zhang, and Lei Guo
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226
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231
Visualization Methods by Using Graphs For Geodynamics Xinbao Chen, Chaokui Li, Dongshui Zhang, and Ning Qian
Spectral Unmixing of MODIS Data Based on Improved Endmember Purification Model: Application to Forest Type Identification Li Chen, Hui Lin, Guangxing Wang, Hua Sun and Enping Yan
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234
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239
On-Orbit Calibration of Domestic APS Star Tracker Jun/eng Xie, Hongzhao Tang, Xianhui Dou, and Jingqi Long
XII
Evaluation of Near-infrared Spectral Sensitivity in Ground-based XC02 Retrieval using Ground based observations Xiuchun Qin, Liping Lei, Da Liu, Lijie Guo, Kawasaki Masahiro, and Masafumi Ohashi
243
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The Geometric and Cartographic Characteristics of the ZY-3 Images and Their Usage in the Production of Cartographic Data and GIS Applications in Brazil Lucio Muratori De Alencastro Gra(:a, Eduardo Henrique Geraldi AraUjo, Erivaldo Antonio Da Silva, Mauro Issamu Shikawa and Paulo De Oliveira Camargo
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247
Remote Sensing Aided Geographically Weighted Regression Modeling of Urban Air Pollution in California Bin Zou, Yu Guo, Yuqi Tang, Qihao Weng, and Shan Xu
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251
A Unified Spatial-Temporal-Spectral Fusion Model Using Landsat and MODIS Imagery Bin Chen, and Bing Xu
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256
A New Dual-baseline Polarimetric SAR Interferometry Vegetation Height Inversion Using Complex Least Squares Adjustment Haiqiang Fu, Jianjun Zhu, Changcheng Wang, Qinghua Xie, and Rang Zhao
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261
Forest Biomass Estimation using Fourier-based Textural Ordination of High Resolution Airborne Optical Image Shili Meng, Yang Pang, and Zhongjun Zhang
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266
Spatial Statistics of Atmospheric Signal in Repeatpass InSAR Yunmeng Cao, Zhiwei Li, Jianchao Wei, Wenjun Zhan, Jianjun Zhu, and Changcheng Wang
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271
A Hierarchical Structural Features Analysis Technique to Reduce Registration Noise for Change Detection on VHR Images Chen Zhong, Bo Li, and Qizhi Xu
276
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Study on Characteristic of Land Use Change and Driving Force in Changsha City Guanghui Yu, and Ting Wang
280
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Comparison of Object-Based and Pixel-Based Methods For Urban Land-Use Classification From Worldview-2 Imagery Yanchen Wu, Yinghai Ke, Huili Gong, Beibei Chen, and Lin Zhu
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284
Automatic Path Planning and Navigation with Stereo Cameras Shenlu Jiang, Zhonghua Hong, Yun Zhang, Yanling Han, Ruyan Zhou, and Bo Shen
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289
Spatio-temporal Analysis of the Land Subsidence in the UK using Independent Component Analysis Bin Liu, Wujiao Dai, Wei Peng, and Xiaolin Meng
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XIII
294
Texture Based Information Extraction from Remote Sensing Images Using Object Based Classification Approach Kuldeep and Pradeep Garg
299
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Estimation of Subpixel Land Surface Temperature Using Landsat TM Imagery: A Case Examination on Heterogeneous Urban Area Guanhua Guo, Zhi!eng Wu, and Yingbiao Chen
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304
Multi-scale Analysis of Landscape Pattern of Hunan Province Lili Yin, Hao Chen, and Wei Li
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Phenologically-Tuned Karst Rocky Desertification Monitoring and Ecological Responses Assessment Using Satellite Image Time Series Xiangjian Xie, Peijun Du, Zhaohui Xue, Samat Alim, and Jieqiong Luo
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A Dual Mode FPGA Implementation of Real-time Target Detection for Hyperspectrallmagery Bin Yang, Minhua Yang, Lianru Gao, and Bing Zhang
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China Wetland Extraction and Classification Using MODIS Data Liwei Xing, Zhenguo Niu, and Demin Zhou
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High-Voltage Transmission Towers Detection Using Hybrid Polarimetric SAR Data Lei Xie, Hong Zhang, Chao Wang, Bo Zhang and Fan Wu XIV
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Analysis of RapidEye Imagery for Agricultural Land Cover and Land Use Mapping Huiyong Sang, Jixian Zhang, Liang Zhai, Chengji Qiu, and Yucai Xue
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Underdeveloped Village Extraction from High Spatial Resolution Optical Image Based on G LCM Textures and Fuzzy Classification Xiaoli Liang, Liwei Li, Gang Cheng, and Lianru Gao
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Selection of Atmospheric Correction Method and Estimation of Chlorophyll-A (Chi-A) In Coastal Waters of Hong Kong Majid Nazeer, and Janet Elizabeth Nichol
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Remote Sensing Measure of Severity on the Zhalong Wetlands and Consequent Ecological Effects Zhiqiang Wang, Li Jiang, 80 Kong, Hao Chen and Tongguo Zhang
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The Extraction of Plantation with Texture Feature in High Resolution Remote Sensing Image Gong Chen Shouzhen Liang, and Jingsong Chen
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Vegetation Chlorophyll Content Estimation Based on Model Simulation and a Li image Jingjing Ma, Jinguo Yuan, Sha Zhang, and Yujia Zhang
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Feature Assessment in Object-based Forest Classification using Airborne LiDAR Data and High Spatial Resolution Satellite Imagery Zhenyu Zhang, Xiaoye Liu, and Wendy Wright
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xv
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413
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452
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Land Cover Classification Analysis about Water and Elevation in East Dongting Wetland XVI
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Zijuan Zhu, Huaiqing Zhang, and Hua Liu
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Wetland Information Extraction of the East Dongting Lake Using Mean Shift Segmentation Jia Hu, Huaiqing Zhang, ChengXing Ling, Hui Lin, Hua Sun, and Guangxing Wang
XVII
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2014 Third International Workshop on Earth Observation and Remote Sensing Applications
Multi-scale simulation and accuracy assessment of forest carbon using Landsat and MODIS data Enping Yan1, Hui Lin1, Guangxing Wang1,2 & Hua Sun1 Research Center of Forest Remote Sensing & Information Engineering, Central South University of Forestry & Technology, Changsha 410004, China 2 Department of Geography, Southern Illinois University, Carbondale, IL 62901 USA Yan:
[email protected]; Lin:
[email protected]; Wang:
[email protected] 1
Abstract—Forest ecosystems have a great potential in mitigation of carbon concentration in the atmosphere. Thus, generating its spatially explicit estimates at national, regional and global scale becomes very important. In Southern China, mapping forest carbon is often conducted by combining ground plot data from national forest inventory and remotely sensed images from Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) with variable spatial resolutions. However, the inconsistency of sample plot sizes with spatial resolutions of images will lead to a great challenge for mapping and accuracy assessment of forest carbon. In this study, an image based spatial co-simulation was used to map forest carbon by directly combining a total of 56 sample plots with plot size of 25.8 m × 25.8 m and a Landsat Thematic Mapper ™ image at a spatial resolution of 30 m × 30 m for You County of Hunan with a area of 4.82×105 hm2. An image based spatial block co-simulation was then employed to combine and scale up the plot and TM image data to 225 m × 225 m, 450 m × 450 m and 900 m × 900 m to create forest carbon maps at multiple spatial resolutions. Moreover, MODIS images, including MOD13Q1, MOD09A1 and MOD15A2 with three spatial resolutions corresponding to those above, were applied to map forest carbon for this County. The obtained map from TM image at the spatial resolution of 30 m × 30 m was validated using a dataset of 26 sample plots that were not used for simulation, while the accuracy of the MODIS derived maps was assessed using the higher resolution TM image derived estimates. The results showed that the coefficient of determination between the TM image derived forest carbon estimates and the observations was 0.81 with a root mean square error of 8.8 T/ha. The determination coefficient between the TM and MODIS derived estimates varied from 0.78 to 0.82. Moreover, all the TM and MODIS derived estimates had similar spatial distributions and patterns to those of the sample plot data. But, compared with the maps from TM image, the MODIS derived forest carbon maps were more smoothed. This study, to some extent, overcame some of significant gaps that currently exists in mapping and accuracy assessment of forest carbon using remotely sensed data when the ground data have different spatial resolutions from used images. Keywords- Accuracy assessment; forest carbon mapping; Landsat; MODIS; sample plot; spatial co-simulation
I.
INTRODUCTION
Forests play an important role in mitigation of global warming and carbon concentration in the atmosphere [1]. Accurately mapping forest carbon at local, regional and global scale has great significance for policy decision making to 978-1-4673-1946-1/12/$31.00 ©2014 IEEE.
reduce greenhouse effect and to develop carbon marketing. Numerous methods have been developed to estimate forest carbon at different spatial and temporal scales, including process based model, converting forest biomass directly obtained from forest inventory data into carbon, and combining national forest inventory plot data and remotely sensed images [2-5]. Among the aforementioned methods, combination of plot and image data has the greatest potential for generating spatial distribution of forest carbon, especially for the countries where forest inventory programs have been operated. Moreover, many spatial interpolation methods including regression modeling, neural network and K-nearest neighbors have been widely used to combine the plot and image data and to map forest carbon density. However, these methods lack the ability to directly aggregate the spatial data and their uncertainties from a finer spatial resolution to a coarser one when the plot data have inconsistent spatial resolutions with the used remotely sensed images [6, 7]. On the other hand, although MODIS data have been widely used to map forest carbon at regional and global scales [8], several challenges exist. First, MODIS products have variable spatial resolutions that are often coarser than 200 m × 200 m, while the spatial resolutions of sample plots are often much finer than those, which leads to the difficulty to combine the spatial data. Second, we also lack of the coarser spatial resolution plot data that can be applied to directly assess the accuracy of the MODIS derived forest carbon maps. Thus there is an urgent need to develop a method that can overcome the gaps. For the purpose, Wang et al used a geostatistical technology and developed image-based spatial co-simulation and block cosimulation algorithms to combine sample plot and image data and derive spatial distribution of forest carbon at any spatial resolutions and at the same time to scale up the spatial data from smaller units to larger blocks [6,7]. These algorithms are based on spatial autocorrelation of variables and crosscorrelation between them and provide a methodological framework for mapping forest carbon using sample plot and remotely sensed data that have different spatial resolutions. However, because of the different spatial resolutions between the plot and image data, these methods cannot be used to directly conduct accuracy assessment of forest carbon estimates that have spatial resolutions coarser than that of plot data. The objective of this study is to test the methods developed by Wang et al. for simulation of forest carbon and to further
2014 Third International Workshop on Earth Observation and Remote Sensing Applications develop an approach for accuracy assessment of the obtained forest carbon estimates when the used plot data and remotely sensed images have inconsistent spatial resolutions. In this study, Landsat TM images were first combined with forest inventory plot data using image-based spatial co-simulation and block co-simulation to map forest carbon at multiple spatial resolutions. The obtained maps were validated using a set of sample plot data and then used as reference to assess the quality of forest carbon maps using MODIS images at multiple spatial resolutions. II.
MATERIALS AND METHODS
A. Study area This study was conducted in You County, Hunan Province of South China (Fig. 1). Its latitude ranges from 27° 04′ N to 27° 06′ N, and the longitude varied from 113° 04′ E to 113° 43′ E. It is characterized by a typical subtropical climate with average annual temperature 17.8℃ and annual precipitation of 1411 mm. The typical forest types include Chinese fir and Pinus massoniana planations, evergreen broadleaf forests, deciduous and evergreen broad-leaf mixed forests, bamboo and shrubs [9].
converted to forest carbon using the biomass-to-carbon coefficient for various species [11] (Fig. 1, right). The biomass of fruit forests and shrub were calculated by the average biomass method with the average biomass of 23.7 thm-2 and 19.76 thm-2 respectively, and were converted to forest carbon using a biomass-to-carbon coefficient of 0.5. In addition, for the species without models, the models of similar tree species were used. The plot forest carbon values varied from 0 to 43.02 T/ha. 2) Remotely sensed images: A Landsat5 TM scene dated on August 21, 2009 was acquired, covering 100% of the study area - You County. This image consists of six bands at spatial resolution of 30m including band 1, band 2, band 3, band 4, band 5 and band 7. After the radiometric correction and atmospheric correction, the image was geo-referenced to the Universal Transverse Mercator (UTM) projection using a topographic map, with the root mean square error (RMSE) less than one pixel. Due to a relative simple topography, its topographic correction was omitted. Moreover, three 2009’s MODIS products including MOD13Q1 、 MOD09A1 and MOD15A2 were used for the simulation of forest carbon at the spatial resolutions of 225 m × 225 m, 450 m × 450 m and 900 m × 900 m. C. Image transformations
Figure 1. Locations of the study area (left: shadowed) and spatial distribution and forest carbon values (right) of sample plots
B. Data sets 1) Forest inventory plots: In You County, a total of 82 national forest inventory permanent sample plots were allocated based on a grid of 4 km × 8 km, and measured repeatedly at an interval of five years during the past 50 years [10], with a plot size of 28.5 m × 28.5 m (about 0.08 ha) (Fig. 1, right). In this study, the sample plot data collected in the summer of 2009 were used, consistent with the available Landsat TM images in time. Within each plot, the same methods were used to measure tree diameter at breast height (DBH, 1.3 m) and height (H). Out of 82 plots, 56 plots were randomly selected and used for developing the simulations and the rest 26 plots for assessing accuracy of estimates. In this study, a regression model method was used to calculate forest biomass for its convenience of accuracy assessment and repeatability. Based on the measurements of DBH and H, the tree above-ground biomass (trunk, branch, bark, leaf) and under-ground biomass were calculated using empirical regression models published by the state forestry administration for various species [11]. The values of tree biomass were summed to obtain plot biomass that was further 978-1-4673-1946-1/12/$31.00 ©2014 IEEE.
1) Landsat TM (30 m × 30 m): Five groups of images transformation were conducted for TM data, including: a) Six bands and their inversions (TM1 to TM5 and TM7, 1/TM1 to 1/TM5 and 1/TM7; b) Band ratios (TM12, TM13, ……, TM57, where TM12 = TM1/TM2 and so on, TM357 = (TM3+TM5)/TM7; TM3574 = (TM3+TM5+TM7)/TM4, and TM2357 = (TM2+TM3+TM5)/TM7); c) Vegetation indices (Normalized differential vegetation index (NDVI), Soil Adjusted Vegetation Index (SAVI), Atmospherically Resistant Vegetation Index (ARVI), Enhanced Vegetation Index (EVI)); d) First three components from principal component analysis (PCA); and e) A total of 48 texture variables from grey-level co-occurrence matrix based texture measures (mean, angular second moment, contrast, correlation, dissimilarity, entropy, homogeneity and variance) [12]. 2) MODIS images: Using MOD13Q1 data (225 m×225 m), NDVI, EVI and their texture transformations were conducted. For MOD09A1 product (450 m×450 m), five groups of images transformations mentioned for TM image were calculated and the used bands include band 1, band 2, band 3, band4 , band6, and band7. For MOD15A2 product (900 m × 900 m), two vegetation indices (FPAR and LAI) and their texture transformations were conducted. D. Simulation and up-scaling methods In this study, the study area was divided into grid that consisted of equal size pixels. By combining the sample plot and image data, the expected forest carbon maps at the spatial resolutions mentioned above were generated using an imageaided sequential Gaussian co-simulation and block cosimulation.
2014 Third International Workshop on Earth Observation and Remote Sensing Applications In the sequential Gaussian Co-simulation, it was supposed that a study area consists of N pixels that have similar spatial resolution with the used plot and image data. A random process was defined as a set of dependent random variables z (u ) , one for each location u in the study area. A random path to estimate each of the pixels was then determined using a random function. Following this path, at each location the conditional cumulative distribution fucntion (CCDF) (mean and variance) of the random variable was created using a unbiased cokriging estimator, that is, by weighting the sample plot data, collocated image values, and previously estimates if any. The weights were calculated based on spatial variability (variogram) of the random process. The greater the spatial variability, the smaller the weight. From this distribution, a value was randomly drawn as a realization at this location. Through a large number of random paths, the above process was repeated, which led to many realizations for each pixel. In this study, a total of 500 random paths was used. Across the realizations, a sample mean and variance of forest carbon were calculated for each location. This algorithm is based on the assumption that forest carbon has a Gaussian distribution and if not, normal score transformation of sample plot and image data should be made. For the details of this algorithm and its computer programs, readers can refer to [6, 7] and [13]. In the block co-simulation, it was supposed that a block is divided into m smaller pixels consistent with the sample plots and images pixels in size. Within a block, each of smaller pixels was estimated using unbiased cokriging estimator. From the estimates of smaller pixels, the CCDF was derived and the simulation was conducted for the block. This simulation was similar to the one mentioned above. In this study, it was assumed that the size of sample plots was similar to the spatial resolution of TM image pixels, that is, 30 m × 30 m. Thus, the forest carbon maps were directly generated based on the sample plot and TM image data using the spatial co-simulation. Moreover, the TM images and sample plot data were combined and scaled up to to the spatial resolutions of 225 m × 225 m, 450 m × 450 m and 900 m × 900 m using the spatial block co-simulation. At the same time, the TM derived forest carbon estimates at the spatial resolution of 30 m × 30 m were also aggregated to these cpoarser spatial resolutions using a window averaging and the aggregated estimates were then combined with the MODIS images using the spatial co-simulation at the spatial resolutions of 225 m × 225 m, 450 m × 450 m and 900 m × 900 m. E) Accuracy assessment In this study, Pearson product moment correlation coefficients between the obtained images and the sample plot values were calculated. The coefficients were statistically tested for their significant differences from zero using
rα = tα2 (n − 2 + tα2 ) based on the student’s distribution at a risk level α = 5%, where n is number of sample plots used. The images that had the highest correlation were applied to create forest carbon maps. For the TM image at the spatial resolution 978-1-4673-1946-1/12/$31.00 ©2014 IEEE.
of 30 m × 30 m, the simulated values were compared with the observations of 26 plots that were not used for simulation. The TM image derived estimates were then scaled up to spatial resolutions of 225 m × 225 m, 450 m × 450 m and 900 m × 900 m using a window average and employed as references to compare the forest carbon estimates from the MODIS images at the corresponding spatial resolutions. III.
RESULTS
The correlation coefficients of plot forest carbon with the obtained images varied from -0.455 to 0.497 and -0.626 to 0.763 for TM and MOD09A1 images. Based on the significant value at the risk level of 5%, plot forest carbon was significantly correlated with a total of 22 and 33 spectral variables for TM and MOD09A1 images, respectively. For MOD13Q1 and MOD15A2 images, the correlation coefficients ranged from -0.230 to 0.312 and -0.390 to 0.395. Based on the significant value at a risk level 5%, plot forest carbon was significantly correlated with 4 and 10 spectral variables, respectively. The texture measures from MODIS images significantly improved the correlation with plot forest carbon. Because of limited space, most of the correlation results were omitted except for three most correlated variables in Table I. TABLE I.
PEARSON PRODUCT MOMENT CORRELATION COEFFICIENTS OF PLOT FOREST CARBON WITH SPECTRAL VARIABLES
Images Order 1
2
3
variable
Landsat TM
MOD13Q1
MOD09A1
MOD15A2
1/TM3
NDVImean
1/Band1
LAImean
**
0.763
**
0.395**
correlation
0.497**
0.312
variable
1/TM2
NDVI
1/Band4
LAIhom
correlation
0.464**
0.259*
0.743**
-0.390**
variable
1/TM1
NDVIent
1/Band3
LAIdis
correlation
-0.455**
-0.230*
0.700**
0.365**
Note: Correlation is significant at the 0.01 level** and the 0.05 level*
Sample variograms were first calculated using the plot data and then fit using spatial autocorrelation models, including spherical, exponential and Gaussian three models. By comparison, spherical model was selected based on a goodness value - the sum of squares due to error that measures how well the model fits the sample data. The closer to zero the goodness of fit, the better the fit was. Furthermore, the simulation procedures required that the parameters of the variogram models be standardized, that is the nugget plus structure ratio equaled to one unit. The obtained model was: 3
h ⎛ h ⎞ (1) − 0.5 × ⎜ ⎟] 13.64 ⎝ 13.64 ⎠ The 30 m × 30 m TM image based simulation led to a mean value map, a variance map and a probability map for estimates larger than sample mean (Fig. 2). The simulated forest carbon values, their variance and probability had similar spatial distributions to that of sample plots, with larger values in the northeast and southeast. Furthermore, the variances of simulated values also increased as the increasing sampling distances of the plots. In addition, based on the observations of
γ (h) = 0.24 + 0.76 × [1.5 ×
2014 Third International Workshop on Earth Observation and Remote Sensing Applications 26 test plots and their estimates, the coefficient of determination and RMSE for the TM image derived forest carbon map were 0.81 and 8.8 T/ha (Fig. 3).
zero. The simulated maps derived from MODIS images captured the spatial patterns of forest carbon. Also, there were similar spatial variations for the spatial distributions of variances (Fig. 6) and the probabilities for the estimates larger than the sample mean (Fig. 7). Moreover, the accuracy of the MODIS derived estimation maps were assessed using the 26 validation samples from the simulated results based on TM image by calculating the coefficient of determination. The coefficient of determination was 0.85 for MOD09A1 derived results, 0.81 for MOD13Q1, and 0.78 for MOD15A2.
Figure 2. Spatial distribution of (a) simulated forest carbon values; (b) variance; (c) probability for predicted values larger than sample mean using sequential Gaussian co-simulation algotithm and compared with the plot values at spatial resolution of 30m × 30m
Figure 5. Spatial distribution of simulated values for forest carbon at spatial resolution of (a) 225 m × 225 m (b) 450 m × 450 m and (c) 900 m × 900 m using the sequential Gaussian co-simulation and MODIS images
Figure 3. (a) the relationship and (b) residuals between the estimated and observed values of 26 test plots using spatial co-simulation at the spatial resolution of 30 m × 30 m
The plot forest carbon values at the pixel size of 30 m × 30 m were combined with TM image and then scaled up to the spatial resolutions of 225 m × 225 m, 450 m × 450 m and 900 m × 900 m using the spatial block up-scaling co-simulation (Fig. 4). The spatial distributions of the aggregated forest carbon estimates were similar to the map of Fig. 2a at the spatial resolution of 30 m × 30 m, which implied the up-scaling algorithm captured the spatial distribution of forest carbon within the study area.
Figure 4. Spatial distribution of simulated forest carbon values at the spatial resolutions of (a) 225 m × 225 m; (b) 450 m × 450 m; (c) 900 m × 900 m using spatial block co-simulation up-scaling algorithm and TM image
The MOD13Q1, MOD09A1 and MOD15A2 derived results also consisted of simulated forest carbon values (Fig. 5), variances of the estimates (Fig. 6) and the probabilities larger than the sample mean (Fig. 7), with spatial resolutions of 225 m × 225 m, 450 m × 450 m, 900 m × 900 m, respectively. The spatial distributions of the simulated values (Fig. 5) were consistent with those of TM derived maps. That is, in the areas where the TM derived values were greater, the corresponding values derived from MODIS were also greater. Generally speaking, the simulated values in eastern were larger than those in the middle and Midwest areas. In addition, the river reservoir area located in northeastern had the estimates of approximate 978-1-4673-1946-1/12/$31.00 ©2014 IEEE.
Figure 6. Spatial distribution of variances of simulated values for forest carbon at spatial resolution of (a) 225 m × 225 m (b) 450 m × 450 m and (c) 900 m × 900 m using the sequential Gaussian co-simulation and MODIS images
Figure 7. Spatial distribution of probability larger than the sample mean for forest carbon at spatial resolution of (a) 225 m × 225 m (b) 450 m × 450 m and (c) 900 m × 900 m using the sequential Gaussian co-simulation algorithm and MODIS images
IV.
DISCUSSION AND CONCLUSIONS
In this study, it was found that the inversion of Landsat TM band 3, 1/TM3, had the highest correlation (0.497) with the plot forest carbon. For MOD09A1 image, the inversion of Band 1 was most correlated with the plot forest carbon. In fact, both Landsat TM band 3 and MOD09A1 band 1 have similar wavelengths, from 0.63 to 0.69 (Landsat TM band 3) and 0.62 to 0.67 (MOD09A1 Band 1), respectively. This finding was supported by the previous study [14]. In addition, for MOD13Q1 and MOD15A2 images, the spectral variables NDVImean and LAImean showed the highest correlation with the plot forest carbon.
2014 Third International Workshop on Earth Observation and Remote Sensing Applications In order to overcome the inconsistency of spatial resolutions that existed between sample plots and various remote sensing images, in this study the spatial co-simulation and block co-simulation developed by Wang et al. [6, 7] were used to map forest carbon at multiple spatial resolutions, including 225 m × 225 m，450 m × 450 m and 900 m × 900 m using Landsat TM and MODIS images. The results showed that the algorithms perfectly captured the spatial distribution and patterns of forest carbon. Theoretically, the higher spatial resolution image MOD13Q1 should lead to more accurate estimates than other MODIS images. However, in this study the MOD13Q1 derived map was associated with low correlation between the estimated and observed values of forest carbon with underestimation for the large observations and overestimation for the small values. In addition, the MOD15A2 product also resulted in lower accuracy of forest carbon estimates. The main reason might be because both MOD13Q1 and MOD15A2 had only two bands and thus they provided less opportunity to capture the information of spatial variability of forest carbon. Compared to MOD13Q1 and MOD15A2, both Landsat TM and MOD09A1 provided the images that showed greater potential to generate accurate forest carbon estimates. This may be mainly because the spectral variables from visible as well as near and middle infrared bands are able to capture spatial variability of forest carbon. This conclusion was consistent with the finding of other studies [15]. The TM derived forest carbon maps at the spatial resolutions of 30 m × 30 m，225 m × 225 m, 450 m × 450 m and 900 m × 900 m showed a range of forest carbon estimates that matched that of the sample plots and accurately captured the spatial distribution and patterns of forest carbon estimates, especially the greater values. The greater forest carbon values suggested that the existence of the hardwood forests in this area. Moreover, compared to the TM image derived maps at the multiple spatial resolutions, especially the 30 m × 30 m TM image derived map, the MODIS images derived forest carbon maps were spatially more smoothed because of coarser spatial resolutions. That is, scaling up the sample plot and TM image data from a finer spatial resolution to a coarser one resulted in more accurate map than using using the corresponding spatial resolution MODIS images. In a word, the spatial co-simulation and block co-simulation algorithms provided great potential to map forest carbon by combining sample plot and image data that have different spatial resolutions and thus overcame some of the gaps that currently exists in the multi-scale mapping and accuracy assessment of forest carbon. ACKNOWLEDGMENT This work was supported by the Ministry of Science and Technology, P. R. China under the national “863” project “Study on key technologies of digital forest resources monitoring” (2012AA102001) and Wang’s funding from the Central South University of Forestry and Technology (Project # 0990).
978-1-4673-1946-1/12/$31.00 ©2014 IEEE.
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