Available online at www.sciencedirect.com Procedia ProcediaEnvironmental EnvironmentalSciences Sciences7 (2011) (2011)110–115 1–11 1st Conference on Spatial Statistics 2011 Crop Area Estimation from UAV Transect and MSR Image Data Using Spatial Sampling Method: a Simulation Experiment Yaozhong Pana,b, Jinshui Zhanga,b, Kejian Shena* b a Beijing Normal University, Beijing, 19 Xinjiekou Wai Street, Beijing 100875,China State Key Laboratory of Earth Surface Process and Resource Ecology College of Resources Science and Technology, 19 Xinjiekou Wai Street, Beijing 100875,China Abstract This paper proposed a spatial sampling method, this method had proved that using stratified random sampling, choosing area-scale index as auxiliary variable, using simulated MSR image and UAV image can improve the crop area estimation accuracy over large area, the crop area estimation accuracy can be more than 95% under a 95% CI © 2010 2011 Published peer-review underunder responsibility of Spatial Statistics 2011 © Published by by Elsevier ElsevierLtd Ltd.Selection Selectionand and/or peer-review responsibility of Spatial Statistics 2011 Keywords: Crop Area Estimation ; UAV Transect ; MSR image ;sampling ; classifiation accuracy Introduction It has been proved that using remote sensing to estimate the regional distribution of individual crop types is critical to a wide range of end-users, including government agencies, farmers, and researches [1] Medium spatial resolution (MSR) remote sensing data are now widely used to estimate crop area and distributions [2-4] With the development of remote sensing techniques, MSR image data (such as HJ-1A/B image data) can guarantee that enough data acquisition and accepted survey cost over large area However, remote sensing image have some problem, such as mixed pixel, different crop have same spectrum, which lead to the crop area classification accuracy difficult to over 90% [5] HJ satellites image a given point on the earth's surface once every days [6] The spatial resolution of CCD data on HJ-1A/B satellite is 30 m, the width is700 kilometer The developing spatial sampling techniques, which integrate the strengths of both statistics and remote * Corresponding author Tel.: 010-58805750 fax: 010-58805750 E-mail address: ashenkejian@126.com 1878-0296 © 2011 Published by Elsevier Ltd Selection and peer-review under responsibility of Spatial Statistics 2011 doi:10.1016/j.proenv.2011.07.020 Yaozhong Pan et al / Procedia Environmental Sciences (2011) 110–115 sensing, are being widely used This method need large sample size which is cannot available by present investigation technology So, the selection of sample is facing challenges The Unmanned Aerial Vehicle (UAV) has the characteristics of flexibility, high resolution, can guarantee that the dynamic data acquisition, real-time and current situation, can be used as an effective way to guarantee enough sample size Which have been used to numerous applications in natural resources management [7] This study chooses MSR image data (take simulated HJ image for example) and UAV transect o simulate complex agriculture area in large area, uses spatial sampling technique, in order to validate whether they can improve the crop area estimation accuracy over large area This study researches the factors of crop area estimation, such as the length of UAV transect, the effect of sample size to crop area estimation Study area and data source 2.1 Study area Study area is located in Jiangsu province(Fig a), area is 60h54 km2, planting structure of this study area is complex, such as intercropping, scattered cultivated land When we use remote sensing to estimate the crop area, the complex planting structure will cause a series of problem, for example, mixed pixel cause classification error, the same period of crop, such as rice, cotton, soybean, vegetable, cannot be classified accurately in MSR image data Fig.1 (a) RGB432; (b) classification of RapidEye image 2.2 Data source 2.2.1 RapidEye image and Classification of RapidEye image The raw Remotely-sensed data (Fig.1.a) is RapidEye image with the m spatial resolution, which was acquired on September 30, 2010, image size is 60h54km2, which is located in Jiangsu Province RapidEye image was classified by Object-based method The final classification of the RapidEye image are shown in (Fig.1.b), the RapidEye image was classified as rice (826.31km2 ), tree (145.92km2 ), water (209.99 km2 ), construction (776.20 km2 ) and unclassified (1281.58 km2) 2.2.2 Simulation of UAV image and HJ image The classification of Rapideye image was used to simulated UAV image, we assumed that the rice on the classification of the RapidEye image is the classification of UAV image Then area of rice can be assumed to be the real area of rice, which can be used to appraisal accuracy 111 112 Yaozhong Pan et al / Procedia Environmental Sciences (2011) 110–115 To simulate HJ image, we resample the rice and the simulated same period of crop on the classification of the Rapideye image to 30 m resolution, then we think that the simulated HJ image had considered the classification error of mixed pixel Methodology The flow of this experiment includes: (1) Construction of sample frame (2) sampling design (Stratified random sampling, Sampling results appraisal), (3) analysis (Relationship between sample size and the classification accuracy of HJ image 3.1 Construction of sample frame Considering the characteristics of UAV, we assumed the width of UAV image is 0.9km, and we simulated different length of UAV transect which vary from 5km to 30km (Table 1), finally we created fishnet with different length of UAV transect on HJ image The length of transect was defined as follows Table 1, Simulation of UAV transect UAV transect 5kmh0.9km 10kmh0.9km 15kmh0.9km 20kmh0.9km 25kmh0.9km 30kmh0.9km RapidEye image 1kmh0.18km 2kmh0.18km 3kmh0.18km 4kmh0.18km 5kmh0.18km 6kmh0.18km Population 18000 9000 6000 4500 3600 3000 3.2 Stratified random sampling 3.2.1 Samples size determination To validate the requirement of sample size under different length of UAV transect, we sampled 0.5%,1%,.2%,.3%, 4%, 5% of the Population to each kind of UAV transect 3.2.2 Stratified variable definition and optimal stratification We choose stratified random sampling to estimate rice area, which had been confirmed to be en efficient method [8] Determinate stratified variable: we choose area-scale index as a stratified variable, because area-scale index is a better auxiliary variable in stratified sampling [9] Determinate the number of layers: Although stratified sampling is more accurate, it does not mean the more layers the better accuracy According to many researches, when the number of layers larger than a certain number, the increase of precision will be small Unless the correlation coefficient between estimate of target variable and stratification of actual variable is larger than 0.95, there should not be more than layers [10] Determinate the layer boundaries: The cumulative equivalent frequency with square root method is used to get the optimal stratification This method was proposed by Dalenius and Hodges [11] 3.2.3 Samples allocation When sample size n (for example 1%) is fixed, we use Neyman allocation method to calculate the sample size of each layer, which is calculated as follow: 113 Yaozhong Pan et al / Procedia Environmental Sciences (2011) 110–115 ( n u N i u Si ) ni m ¦ ( N i u Si ) i (1) Where n represents the sample size; ni represents the sample size of layer i; Ni represents the capacity of layer i; Si represents the standard deviation of layer i; m represents the number of layers 3.3 Area estimation m _ ¦ ( X i u Ni ) Pˆ i (2) Where Pˆ represents overall point estimation; X i represents the sample mean of layer; Ni represents the capacity of layer i; m represents the number of layers 3.4 Sampling results appraisal We appraisal Sampling results by relative error, which is defined as follow: r (3) Pˆ P / P Where r represents relative error, Pˆ represents overall point estimation, P represents true value Results 4.1 The estimate accuracy with different length of UAV transect Table 2, Sample size of different length of UAV transect 5kmh0.9km 10kmh0.9km 15kmh0.9km 20kmh0.9km 25kmh0.9km 30kmh0.9km N 18000 9000 6000 4500 3600 3000 Sample size0.5% 90 45 30 22 18 15 Sample size 1% 180 90 60 45 36 30 Fig.3 (a) sample size 0.5%; (b) sample size 1% With each kind of UAV transect, we sampled 0.5% and 1% of the population (Table 2) in the UAV image and sampled 20 times to estimate rice area, under 95% CI, the results are shown in Fig We can 114 Yaozhong Pan et al / Procedia Environmental Sciences (2011) 110–115 see that all the relative error is less than 5% Considering the characteristic of UAV, we chose 30kmh 0.9km UAV transect as more suitable sample unit to validate the influence of classification error to sampling 4.2 The effect of classification accuracy to sample size When the length of UAV transect is fixed, we validate the sample size acquirement under different classification accuracy, for example, when classification accuracy of HJ image is 80%, we sampled 0.5%, 1%, 2%, 3%, 4%, 5% of the Population, and under each kind of sample size, we sample test 20times in each kind of sampling proportion Table 3, Simulating method of classification error Classification accuracy HJ image Source of same period of crop 52% Rice+ construction 70% Rice + tree + water 80% Rice + water 85% Rice + tree Simulated method Resample to 30m; Rice/Rice and same period of crop (a) classification accuracy 52%; (b) classification accuracy70% (c˅classification accuracy 80%; (d˅classification accuracy 85% Fig.4 Relationship between sample size and classification accuracy Yaozhong Pan et al / Procedia Environmental Sciences (2011) 110–115 Having proven that the suitable length of transect is 30km h 0.9km ,we research the effect of classification accuracy of HJ image to rice area estimation accuracy In order to simulate classification accuracy, we may assume any class on the classification of RapidEye image to be same period of crop, so we simulated four kind of classification accuracy in Table 3,and resample rice and same period of crop to 30 m We simulated four kind of classification accuracy each kind of classification we sample 0.5%, 1%, 2%, 3%, 4%, 5% sample size, we sample 10 times to each kind of sample size, with a 95% CI More specifically, once the sampled HJ transect were determined, we find the same sampling frame serial number of UAV image and get rice area, which were used to estimate total rice area The results can be seen from Fig 4, if we want the rice area relative error smaller than 5%, when the classification accuracy is 52%, we need sample 5% sample size; when the classification accuracy is 70%, we need sample 3% sample size; when the classification accuracy is 80%, we need sample 2% sample size; when the classification accuracy is 85%, we need sample 2% sample size Conclusion This study proved that using stratified random sampling, choosing area-scale index as auxiliary variable, using MSR image and UAV image, can improve the crop area estimation accuracy over large area, this method can make the crop area estimation accuracy more than 95% under a 95% CI References [1] Lobell, D.B and G.P Asner, Cropland distributions from temporal unmixing of MODIS data Remote Sensing of Environment, 2004 93(3): p 412-422 [2].Langley, S.K., H.M Cheshire and K.S Humes, A comparison of single date and multitemporal satellite image classifications in a semi-arid grassland Journal of Arid Environments, 2001 49(2): p 401-411 [3].Van Niel, T.G and T.R McVicar, Determining temporal windows for crop discrimination with remote sensing: a case study in south-eastern Australia Computers and Electronics in Agriculture, 2004 45(1-3): p 91-108 [4].Brown De Colstoun, E.C., et al., National Park vegetation mapping using multitemporal Landsat data and a decision tree classifier Remote Sensing of Environment, 2003 85(3): p 316-327 [5].Qingbo, Z., TATUS AND TENDENCY FOR DEVELOPMENT IN REMOTE SENSING OF AGRICULTURE SITUATION Journal of China Ag ricultural Resour ces and Regional Planning, 2004(5): p 12-17 [6] http://www.cresda.com/n16/n1130/n1582/8384.html [7].Rango, A and A.S Laliberte, Impact of flight regulations on effective use of unmanned aircraft systems for natural resources applications Journal of Applied Remote Sensing, 2010 4(043539): p 043539 [8].Xiaoqiong, Yang., et al., Spatial sampling design for crop acreage estimation Transactions of the CSAE, 2007(12): p 150155 [9].HU Tan Gao, Zhang.Jinshui,.Pan.Yaozhong., Application of landscape fragmentation in winter wheat area sampling design Journal of Remote Sensing, 2010(6): p 1117-1138 [10].Yuekun, C and H Rong, The Theoretic Structure and Computerized Realization of Stratified Audit Sampling THE THEORY AND PRACTICE OF FINANCE AND ECONOMICS, 2003(5): p 78-80 [11].Dalenius, T and J.L Hodges Jr, Minimum variance stratification Journal of the American Statistical Association, 1959 54(285): p 88-101 115 ... image and UAV image, can improve the crop area estimation accuracy over large area, this method can make the crop area estimation accuracy more than 95% under a 95% CI References [1] Lobell, D.B and. .. estimation accuracy over large area This study researches the factors of crop area estimation, such as the length of UAV transect, the effect of sample size to crop area estimation Study area and. .. simulated HJ image for example) and UAV transect o simulate complex agriculture area in large area, uses spatial sampling technique, in order to validate whether they can improve the crop area estimation