Tree Species (Genera) Identification with GF-1 Time-Series in A Forested Landscape, Northeast China

Forests are the most important component of terrestrial ecosystem; the accurate mapping of tree species is helpful for the management of forestry resources. Moderate- and high-resolution multispectral images have been commonly utilized to identify regional tree species in forest ecosystem, but the accuracy of recognition is still unsatisfactory. To enhance the forest mapping accuracy, this study integrated the land surface phenological metrics and text features of forest canopy on tree species identification based on Gaofen-1 (GF-1) wide field of view (WFV) and time-series images (36 10-day NDVI data), conducted at a forested landscape in Harqin Banner, Northeast China in 2017. The dominant tree species include Pinus tabulaeformis, Larix gmelinii, Populus davidiana, Betula platyphylla, and Quercus mongolica in the study region. The result of forest mapping derived from a 10-day dataset was also compared with the outcome based upon a commonly utilized 30-day dataset in tree species identification. The results indicate that tree species identification accuracy is significantly (p < 0.05) improved with higher temporal resolution (10-day, 79.4%) of images than commonly used monthly data (30-day, 76.14%), and the accuracy can be further increased to 85.13% with a combination of the information derived from principal component analysis (PCA) transformation, phenological metrics (standing for the information of growing season) and texture features. The integration of higher dimensional NDVI data, vegetation growth dynamics and feature of canopy simultaneously will be beneficial to map tree species at the landscape scale.

[1]  Shankar Chakraborty,et al.  A modified principal component analysis-based utility theory approach for optimization of correlated responses of EDM process , 2012 .

[2]  Jiyuan Liu,et al.  Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data , 2002 .

[3]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[4]  O. Mutanga,et al.  Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry , 2009 .

[5]  R. Pu,et al.  A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species , 2012 .

[6]  Martin Kappas,et al.  Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery , 2017, Sensors.

[7]  Ranga B. Myneni,et al.  Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS , 2006 .

[8]  P. Defourny,et al.  Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery , 2006 .

[9]  Mark A. Friedl,et al.  Sensitivity of vegetation phenology detection to the temporal resolution of satellite data , 2009 .

[10]  P. Beck,et al.  Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI , 2006 .

[11]  Guangxing Wang,et al.  Up-scaling methods based on variability-weighting and simulation for inferring spatial information across scales , 2004 .

[12]  B. Cabezudo,et al.  Use of monocharacteristic growth forms and phenological phases to describe and differentiate plant communities in Mediterranean-type ecosystems , 2002, Plant Ecology.

[13]  Jennifer N. Hird,et al.  Noise reduction of NDVI time series: An empirical comparison of selected techniques , 2009 .

[14]  Lorenzo Bruzzone,et al.  Fusion of Hyperspectral and LIDAR Remote Sensing Data for Classification of Complex Forest Areas , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Pieter Kempeneers,et al.  Data Fusion of Different Spatial Resolution Remote Sensing Images Applied to Forest-Type Mapping , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Frédéric Achard,et al.  Forest classification of Southeast Asia using NOAA AVHRR data , 1995 .

[17]  Yuji Murayama,et al.  Pixel-based and object-based classifications using high- and medium-spatial-resolution imageries in the urban and suburban landscapes , 2015 .

[18]  Soe W. Myint,et al.  A support vector machine to identify irrigated crop types using time-series Landsat NDVI data , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[19]  Barbara Koch,et al.  Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[20]  F. Giannetti,et al.  Forest species discrimination in an Alpine mountain area using a fuzzy classification of multi-temporal SPOT (HRV) data , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[21]  Bogdan Zagajewski,et al.  Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images , 2017 .

[22]  Yang Shao,et al.  Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points , 2012 .

[23]  P. Jönsson,et al.  Mapping fractional forest cover across the highlands of mainland Southeast Asia using MODIS data and regression tree modelling , 2007 .

[24]  Sassan Saatchi,et al.  Modeling distribution of Amazonian tree species and diversity using remote sensing measurements , 2008 .

[25]  Cho‐ying Huang,et al.  Retrieving multi-scale climatic variations from high dimensional time-series MODIS green vegetation cover in a tropical/subtropical mountainous island , 2014, Journal of Mountain Science.

[26]  Wei Ren,et al.  An Improved Approach Considering Intraclass Variability for Mapping Winter Wheat Using Multitemporal MODIS EVI Images , 2019, Remote. Sens..

[27]  Heather Reese,et al.  Tree Species Classification with Multi-Temporal Sentinel-2 Data , 2018, Remote. Sens..

[28]  Alicia K. Birky,et al.  NDVI and a simple model of deciduous forest seasonal dynamics , 2001 .

[29]  Jonas Ardö,et al.  Disentangling the effects of climate and people on Sahel vegetation dynamics , 2008 .

[30]  Zhaohui Xue,et al.  Spatio-temporal analysis of phenology in Yangtze River Delta based on MODIS NDVI time series from 2001 to 2015 , 2018, Frontiers of Earth Science.

[31]  Louis-Vincent Fichet,et al.  Suitability of Global Forest Change data to report forest cover estimates at national level in Gabon , 2014 .

[32]  D. Roberts,et al.  The impact of spatial resolution on the classification of plant species and functional types within imaging spectrometer data , 2015 .

[33]  M. Friedl,et al.  Land Surface Phenology from MODIS: Characterization of the Collection 5 Global Land Cover Dynamics Product , 2010 .

[34]  Xiaolin Zhu,et al.  Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology , 2020, Remote. Sens..

[35]  Adrien Michez,et al.  Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[36]  D. Hoare,et al.  Phenological description of natural vegetation in southern Africa using remotely-sensed vegetation data , 2004 .

[37]  Surjya K. Pal,et al.  Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique , 2012 .

[38]  D. Roberts,et al.  Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales , 2005 .

[39]  M. Schaepman,et al.  Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006 , 2009 .

[40]  Cho-ying Huang,et al.  Impacts of vegetation onset time on the net primary productivity in a mountainous island in Pacific Asia , 2013 .

[41]  Jibo Yue,et al.  How up-scaling of remote-sensing images affects land-cover classification by comparison with multiscale satellite images , 2018, International Journal of Remote Sensing.

[42]  Xiaojuan Xu,et al.  Trend Evolution of Vegetation Phenology in China during the Period of 1981-2016 , 2020, Remote. Sens..

[43]  J. Hernández‐Stefanoni,et al.  Predicting Tropical Dry Forest Successional Attributes from Space: Is the Key Hidden in Image Texture? , 2012, PloS one.

[44]  B. Datt,et al.  On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multi-temporal classification , 2005 .

[45]  S. Franklin,et al.  Aerial Image Texture Information in the Estimation of Northern Deciduous and Mixed Wood Forest Leaf Area Index (LAI) , 1998 .

[46]  M. Westoby.  Selective forces exerted by vertebrate herbivores on plants. , 1989, Trends in ecology & evolution.

[47]  Rajendra Prasad,et al.  Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data , 2015 .

[48]  S. Reed,et al.  Effects of canopy tree species on belowground biogeochemistry in a lowland wet tropical forest , 2013 .

[49]  Benoit Rivard,et al.  Intra- and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery , 2006 .

[50]  Craig A. Coburn,et al.  A multiscale texture analysis procedure for improved forest stand classification , 2004 .

[51]  Zhao-Liang Li,et al.  Scale Issues in Remote Sensing: A Review on Analysis, Processing and Modeling , 2009, Sensors.

[52]  Miaogen Shen,et al.  Changes in autumn vegetation dormancy onset date and the climate controls across temperate ecosystems in China from 1982 to 2010 , 2015, Global change biology.

[53]  Clement Atzberger,et al.  How much does multi-temporal Sentinel-2 data improve crop type classification? , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[54]  Dirk Pflugmacher,et al.  Forest Stand Species Mapping Using the Sentinel-2 Time Series , 2019, Remote. Sens..

[55]  Benjamin W. Heumann,et al.  AVHRR Derived Phenological Change in the Sahel and Soudan, Africa, 1982 - 2005 , 2007 .

[56]  Erxue Chen,et al.  Classification of Land Cover, Forest, and Tree Species Classes with ZiYuan-3 Multispectral and Stereo Data , 2019, Remote. Sens..

[57]  Xiaolin Zhu,et al.  Accurate mapping of forest types using dense seasonal Landsat time-series , 2014 .

[58]  Tetsuji Ota,et al.  Influence of using texture information in remote sensed data on the accuracy of forest type classification at different levels of spatial resolution , 2011, Journal of Forest Research.

[59]  S. Kuntz,et al.  Applications of image texture in forest classification , 1994 .

[60]  R. Hill,et al.  Mapping tree species in temperate deciduous woodland using time‐series multi‐spectral data , 2010 .

[61]  Lorenzo Bruzzone,et al.  A Novel Context-Sensitive Semisupervised SVM Classifier Robust to Mislabeled Training Samples , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[62]  Maribeth Price,et al.  Comparing Classification Results of Multi-Seasonal TM against AVIRIS Imagery – Seasonality more Important than Number of Bands , 2012 .

[63]  David A Clausi.  An analysis of co-occurrence texture statistics as a function of grey level quantization , 2002 .

[64]  Thomas R. Loveland,et al.  Integrating remotely sensed land cover observations and a biogeochemical model for estimating forest ecosystem carbon dynamics , 2008 .

[65]  Patrick Hostert,et al.  Cross-border comparison of land cover and landscape pattern in Eastern Europe using a hybrid classification technique , 2006 .

[66]  Peter E. Thornton,et al.  BGC-model parameters for tree species growing in central European forests , 2005 .

[67]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[68]  Clement Atzberger,et al.  First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe , 2016, Remote. Sens..

[69]  Onisimo Mutanga,et al.  Multi-phenology WorldView-2 imagery improves remote sensing of savannah tree species , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[70]  Per Jönsson,et al.  TIMESAT - a program for analyzing time-series of satellite sensor data , 2004, Comput. Geosci..

[71]  Hua Chen,et al.  Forest classification based on MODIS time series and vegetation phenology , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[72]  R. Hall,et al.  Incorporating texture into classification of forest species composition from airborne multispectral images , 2000 .

[73]  L. Tang,et al.  Drone remote sensing for forestry research and practices , 2015, Journal of Forestry Research.

[74]  Dirk Tiede,et al.  Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data , 2018, Remote. Sens..

[75]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[76]  G. Dedieu,et al.  Global-Scale Assessment of Vegetation Phenology Using NOAA/AVHRR Satellite Measurements , 1997 .

[77]  Christopher Conrad,et al.  Estimating the fractional cover of growth forms and bare surface in savannas. A multi-resolution approach based on regression tree ensembles. , 2013 .

[78]  Michael A. Lefsky,et al.  Review of studies on tree species classification from remotely sensed data , 2016 .

[79]  Cláudia Maria de Almeida,et al.  Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil , 2017, Remote. Sens..

[80]  A. Alessandri,et al.  On the coupling between vegetation and rainfall inter‐annual anomalies: Possible contributions to seasonal rainfall predictability over land areas , 2008 .

[81]  Jurandy Almeida,et al.  Plant Species Identification with Phenological Visual Rhythms , 2013, 2013 IEEE 9th International Conference on e-Science.