Gaoqiang, Zheng

Rumiana, Vatseva

Vekerdy, Zoltán

Xiaoqin, Wang

Yunzhi, Chen

Repository of the Academy's Library

 123  Monitoring Forest Dynamics(1988-2010) in Changting County Using Time Series of Remote Sensing Data Yunzhi Chena,*, Xiaoqin Wang a, Zoltan Vekerdyb, Rumiana Vatsevac, Gaoqiang Zhenga , Li Zhanga a.Department Name, University Name a.Spatial Information Research Center of Fujian Province, Fuzhou University，P.R.China b.Faculty of Geo-Information Science and Earth Observation, University of Twente, The Netherlands c National Institute of geophysics, geodesy and geography, Bulgarian Academy of Sciences, Bulgaria *chenyunzhi@fzu.edu.cn  Abstract— Forest dynamics of Changting County was studied using time series of remote sensing data. MODIS 1 km 16days NDVI (MOD13A2)time series covers 2000-2010 were firstly used to detect large patches of abrupt changes  by algorithm called BFAST( detecting and characterizing Breaks For Additive Seasonal and Trend).The time of  the detected breaks were compared with forest disturbance map produced by visual interpretation of  Landsat time series. The result showed that both commission error and omission of were high. The coarse resolution of the product, poor quality of residual cloudy pixels, noises, affects the performance of the algorithm. With 30m spatial resolution, Landsat time series were more suitable to capture forest disturbance at small scale at biennial interval, but the acquisition date differ greatly between different years, which complicated the detection. To quantify seasonal variation between different Landsat data, adaptive Savitzky-Golay filtering was applied on MODIS NDVI to denoise and extract seasonality parameters. Key seasonality parameters, such as the amplitude of growing was extracted. Which help to judge whether the breaks were affected by seasonal variation or not.. Integrated forest z-score(IFZ) was calculated for each Landsat data and vegetation change  tracker were used to detect forest disturbance. The disturbance map showed that most disturbance located in central and southwest parts, and happened in 1991,1998 and 2010. Sen’s Slope calculation and Mann-kendall test for undisturbed area showed that forest in east increased and declined patches distributed equally. Validation of the disturbance map and regularization of time series are necessary to improve the result. Keywords—Forest Dynamics, Time Series, Integrated forest z-score (IFZ), Disturbance, Trend I. INTRODUCTION Understanding forest dynamics over space and time is crucial to ecological study  Recent study[1-3] show that time series remote sensing data was able to capture forest changes at different spatial and different frequency. Besides widely used MODIS time series, Landsat  time series are used widely since 2008.  In order to locate and time change quickly, we adapt a two-step plan. Firstly, MODIS 1km 16days NDVI products were used to locate large patches happening      abrupt change at high frequency or showing obvious trend over long period. secondly, 30m resolution Landsat time series were used to locate the change at finer scale with longer history but with lower frequency.  II. DATA SOURCES Changting County was choosed as study area. The spatial extend of  Changting County is Lat: 25°18′40″～26°02′05″,Lon: 116°00′45″～116°39′20″. The center part of the county is residential area and cultivated land. Area around this area are mainly forest land. Trees were planted widely to improve the ecological condition for water soil management reason, among trees planted, Pinus massoniana are pioneer species because they are more adapted to local soil barren environment, red bayberry, Lespedeza bicolor Turcz, PaspalumwettsteiniiHackel were also planted. A. MODIS 16-day 1km NDVI MODIS 16-day NDVI products from collection 5, (MOD13A2) tiled h28v06 covered the period from 01-Jan-2001 to 2-Oct-2012 were collected, reprojected and subset for further information extraction. The study period consists of 247 MODIS compositing periods   Fig. 1  MODIS 1km 16days NDVI coering Changting County   B. Landsat TM/ETM Altogether 11 Landsat TM/ETM from 1988 to 2010 were used. Most of the Landsat TM/ETM were acquired in autumn, a few were acquired in winter. Fig. 2  Landsat TM on Oct 16,1988 coering Changting County TABLE I  LANDSAT TM/ETM USED1 19881016 2 19911009 3 19981113 4 20011231 5 20041012 6 20061103 7 20061221 8 20081210 9 20090111 10 20091026 11 20101029 III. .PROCESS, APPLIED METHODSA. Change detection for MODIS time seriesDetecting and characterizing Breaks For Additive Seasonal and Trend(BFAST)[4] were used to detect abrupt change and trend in MODIS time series. The main idea of BFAST was to  decompose the original time series into trend, seasonal, and remainder 124      components(Fig 3).From Yt, Tt, St, et symbolized original, trend, seansonal, remainder time serial respectively. From St, seasonal variation was obvious, the magnitude is about 0.25. Three breaks existed in trend component, NDVI value after first break increased rapidly the keep stable for longtime to an average   level at 0.6, NDVI value after the second break increased rapidly and decreased to 0.6, NDVI value after third breaks decreased about 0.2 and increased to 0.6, this break can be explained by the disturbance event followed by recovery. From the et components, poor quality caused by residual cloudy pixels, noises, affects the performance of the algorithm, at many cases, the magnitude of the reminder component were more higher than the breaks in trend or seasonal variation.  Fig. 3 decomposing of original time series byWe check time of main breaks by BFAST on MODIS time series (MTS) and visual interpretation on Landsat time series (LTS) by overlaying both, symbolizing time with same colour system and with different style. Solid pixels were result by MTS, hollowed polygons were result by LTS, both matched when the polygons overlay the solid pixels were same colour. From Fig.4, the probability of consistency was very low. Commission error and omission of were high. Fig. 4 time of main breaks  BFAST    125   by BFAST on MTS(solid) and visual interpretation on  LTS(hollow) As Watts,et al(2013)[5] claimed, user-defined parameters had effect on the timing and number of abrupt changes it detect. To improve the result, parameters such as h, number of breaks were tuned, and result seem to be better  as Fig.5 showed. Large patches of breaks in 2001 diminished, commission errors was decreased, map of breaks time seem to be more reasonable. Excluding breaks in the central cultivated area, breaks of forested area, especially those separate  patches of change is more reliable, in these areas, breaks can be seen both from Fig.4 and Fig.5. The omission errors may be caused by the coarse resolution of MODIS data, which were not suitable to capture small patches of breaks caused by human activity.   Fig. 5 time of main breaks  by BFAST with parameter tuning With 30m spatial resolution, Landsat time series were more suitable to capture forest disturbance at small scale at biennial interval, but the acquisition date differ greatly between different years, which complicated the detection.To quantify seasonal variation between different Landsat data, adaptive Savitzky-Golay filtering was applied on MODIS NDVI to denoise and extract seasonality parameters. Key seasonality parameter, such as amplitude(Fig.6) of growing seanson was extracted and analysed. Which help to judge whether the breaks were affected by seasonal variation or not. From Fig.6, amplitude of  most vegetation growing season were below 0.3 about half of the average NDVI, if breaks detected higher than this value, evidence of breaks was enough. Considering autumn is ideal season for image acquisition, images in autumn are preferred, if no autumn image in some year, images in winter was used as substitution.  Fig. 6 Amplitude of growing season from MTS B. Change detection for Landsat TM/ETM time series By BFAST algorithm, changes in MODIS NDVI may caused by cultivated vegetation or forest, It’s hard to distinguish between these two types of change. Huang, et al(2010)[6] adopted a parameter called integrated forest z-score (IFZ) to describe the likelihood a pixel being forest. The formula to calculated IFZ is listed as equation  (1~2): )7,5,3( =−= iSDbbFZiiii                         (1) ∑==NBiiFZNBIFZ12)(1     (2) In  equation (1), i means  band order, bi is DN value of band  i  of  the pixel, ib , iSD is mean and standard derivation of forest sample in band i, iFZ  is forest score of band i. Band 3,band 5,band 7, i.e., the red , two SWIR bands is used to calculate individual forest score. In equation (2), NB means the number of bands, in this case , NB = 3. IFZ is square root mean of  the three iFZ . To calculate IFZ, forest samples is collected from each image to calculate correspondingib , iSD .  IFZ  measure the degree of a pixel deviate from the forest samples, that is the lower the IFZ, the higher likelihood to forest sample.  For forest samples, IFZ value is lower than other land cover types. In Fig.7, forest land is dark, residual area is bright, and cultivated land is grey.  126   Fig. 7 IFZ image    To determine the threshold for forest, statistics on IFZ for forest polygon samples  is carried out. From Tab. V, mean IFZ value scale is 0.8 to 0.9,  standard derivations of  IFZ are about 0.5, that means for most forest pixels,  IFZ value will be fall in the scale 0~2. Most sample with max value below 5, but some higher than 10. In order to balance the commission error and omission error, the value of 3 is set to be the threshold to differentiate between forest and other land cover type. IFZ value greater or equal to 3 is considered as HIGH IFZ pixel, denoted HIFZ, and IFZ value less than 3 is considered as LOW IFZ, denoted LIFZ TABLE II STATISTICS ON IFZ FOR FOREST SAMPLES Image date ifzmin  ifzmax  ifzmean  ifzstd  19881016 0.076  11.016  0.835  0.551  19911009 0.066  4.384  0.85  0.517  19981113 0.065  3.914  0.863  0.504  20011231 0.02  4.087  0.862  0.507  20041012 0.139  3.578  0.899  0.438  20061103 0.091  4.159  0.877  0.48  20061221 0.027  4.858  0.862  0.506  20081210 0.092  4.221  0.865  0.501  20090111 0.057  4.761  0.853  0.521  20091026 0.079  4.393  0.862  0.507  20101029 0.091  4.528  0.858  0.514   Temporal profile for 28 forest samples being disturbed in Oct 16,1988 confirmed (Fig.8) the fact when being disturbed, most forest samples’ IFZ value is above 3, some got to 6, but a few is around 2, descend in the following 3~5 years, being stable at 1.5 to  2. IFZ temporal variation will help to monitoring forest dynamics.   Fig. 8 IFZ temporal profile for forest samples being disturbed 时序Landsat TM/ETM时序IFZ计算Frequency(IFZ<3 )= 11 森林Frequency(IFZ<3 )<=3非森林Frequency(IFZ>=3 )=1And T∈（6,8,9） 森林Frequency(IFZ>=3 )=2And T1∈（6,8,9）and(IFZT1+1 >=3)是否是否是排除6,8,9的森林干扰Frequency(IFZ>=3 ）>= 3判断干扰的时间干扰的频率、时空分布对IFZ<=3，计算森氏斜率判断MK显著性森林变化趋势否否 Fig. 9 forest dynamics monitoring based on temporal Landsat IFZ  A flowchart for forest dynamics monitoring was put forward based on temporal variation of IFZ. It contain following steps: 1. Time series Landsat TM/ETM  data as input. 2 IFZ calculation for each image and layer stack by date order. 3. calculate frequency of  LIFZ. If  the frequncy equal to 11, the pixel is classified as forest. If  the frequency is less than 3, the pixel is classified as nonforest. 4. Calculate  frequency of HIFZ. If the frequency equal to 1, considering the fact that forest will not be recovered  in less than one year, pixels with HIFZ in Nov 3,2006  and LIFZ in Dec 21,2006, or HIFZ in Dec 10,2008  and LIFZ in Jan 11,2009, or  HIFZ in Jan 11,2009 and LIFZ in Oct 26,2009 would not be classified as disturbance,  count the disturbance frequency, and date  the start of each disturbance. 5. For pixels classified as forest, calculate Sen’s Slope and carried Mann Kendall test, to determine area with significant trends. From Fig.10, disturbance happened mostly in south west, areas around resident area.   ifzmean1ifzmean2 127    Fig. 10  Distribution of Changting County’s  forest disturbance  From Fig.11, disturbance happened more often in area around the resident area. As Fig.12 shown, forest in the central, northwest, southeast part  of Changting county recovered,  forest in southwest part of the county declined. More forest in recovery than in  declination.     Fig. 11 Distribution of Changting County’s  forest disturbance frequency   Fig. 12 Distribution of Changting County’s  forest trend IV.  CONCLUSIONS Two different type of time series data ,including MODIS 16-day 1km NDVI products from collection 5 (MOD13A2), and Landsat TM/ETM 30m data were used in forest dynamics monitoring in Changting county. BFAST and VCT algorithm were carried out on MODIS and Landsat TM/ETM time series respectively. BFAST result show that the algorithm is more sensitive to user defined parameter,  and MODIS 1km data was too coarse for human induced disturbance detection. Using temporal variation of integrated forest z-score, VCT is more capable to capture disturbance in Changting county, which located mostly in south west, areas around resident area, happened more often in area around the resident area. Trend showed that more forest in recovery than in  declination.    ACKNOWLEDGMENT                                                                    The research and the publication was supported by the European Union project IGIT titled as „Integrated geo-spatial information technology and its application to resource and environmental management towards the GEOSS” (grant agreement PIRSES-GA-2009-247608), National Science and Technology Support Program(2013BAC08B01). REFERENCES [1]. S. Eckert,, F. Hüsler,  et al.. "Trend analysis of MODIS NDVI time series for detecting land degradation and  128  regeneration in Mongolia." Journal of Arid Environments 113(0) ,2015,pp 16-28. [2]. Z. Zhu,. and C. E. Woodcock. "Continuous change detection and classification of land cover using all available Landsat data." Remote Sensing of Environment 144(0) ,2014,pp: 152-171. [3]. Y. Setiawan,, K. Yoshino, et al.. "Characterizing the dynamics change of vegetation cover on tropical forestlands using 250&#xa0;m multi-temporal MODIS EVI." International Journal of Applied Earth Observation and Geoinformation 26(0), 2014,pp132-144. [4]. J.Verbesselt, R. Hyndman, et al. "Phenological change detection while accounting for abrupt and gradual trends in satellite image time series." Remote Sensing of Environment 114(12), 2010,pp 2970-2980. [5]. L.M. Watts, and S.W. Laffan , Sensitivity of the BFAST algorithm to MODIS satellite and vegetation index. 20th International Congress on Modelling and Simulation, Adelaide, Australia, 1–6 December 2013 [6]. C.Huang,, S. N. Goward, et al.. "An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks." Remote Sensing of Environment 114(1) ,2010,pp183-198.     1  IGIT 2015     Tamás Jancsó – Péter Engler (eds)  Integrated geo-spatial information technology and its application to resource and environmental management towards GEOSS           2015 2     IGIT 2015 – International Conference   16- 17 January, 2015 Project No: 247608          Publisher:  Nyugat-magyarországi Egyetem Kiadó, Sopron ISBN 978-963-334-211-4     

Monitoring forest dynamics (1988-2010) in Changting County using time series of remote sensing data

Abstract

Similar works

Full text

Available Versions

Repository of the Academy's Library