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Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS
Agricultural Sciences in China
September 2011
2011, 10(9): 1419-1430
Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS WANG Da-cheng1, 2, LI Cun-jun2, SONG Xiao-yu2, WANG Ji-hua1, 2, YANG Xiao-dong2, HUANG Wen-jiang2, WANG Jun-ying3 and ZHOU Ji-hong3 Institute of Agricultural Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310029, P.R.China Beijing Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China 3 Agricultural Technology Extension Station in Beijing, Beijing 100029, P.R.China 1 2
Abstract It is very important to provide reference basis for winter wheat quality regionalization of cultivation area. The aim of this article was based on factors affecting wheat quality and setting realistic spatial models in each part of the land for assessment of land suitability potentials in Beijing, China. The study employed artificial neural network (ANN) analysis to select factors and evaluate the relative importance of selected environment factors on wheat grain quality. The spatial models were developed and demonstrated their use in selecting the most suitable areas for the winter wheat cultivation. The strategy overcomes the non-accurate traditional statistical methods. Satellite images, toposheet, and ancillary data of the study area were used to find tillable land. These categories were formed by integrating the various layers with corresponding weights in geographical information system (GIS). An integrated land suitability potential (LSP) index was computed considering the contribution of various parameters of land suitability. The study demonstrated that the tillable land could be categorized into spatially distributed agriculture potential zones based on soil nutrient and assembled weather factors using RS and GIS as not suitable, marginally suitable, moderately suitable, suitable, and highly suitable by adopting the logical criteria. The sort of land distribution map made by the factors with their weights showed more truthfulness. Key words: LSP, ANN, suitable areas, wheat, RS and GIS
INTRODUCTION Wheat (Tritium aestivum L.) is not only a significant component of major staple foods, but also of a principal crop in northern China. The land units in each part of the field are demarcated through its properties, position, and usage, and each of them have their own potentials and limitations. It is possible to grade land units according to their qualities (FAO 1990). Land suitability potential (LSP) evaluation is an important step to detect the environmental limitation in sustainable land Received 14 October, 2010
use planning. It deals with the assessment of land performances for the specific use that is crop production. Wheat quality (grain protein content is a key index) characteristics are usually influenced by genotype, environmental factors, and interactions between genotype and environment. In Beijing, China, most land is suitable for wheat and belongs to strong gluten wheat region in China (He et al. 2002). Due to growing the same varieties in different areas in Beijing, the quality of their own products displays marked difference. The main reason is ecological factors distribution featured by inhomogeneity. The strong environmental effects
Accepted 7 December, 2010
WANG Da-cheng, Ph D, Tel: +86-10-62754134, Fax: +86-10-51503750, E-mail: [email protected]; Correspondence WANG Ji-hua, Professor, Tel: +86-1051503488, Fax: +86-10-51503750, E-mail: [email protected] © 2011, CAAS. All rights reserved. Published by Elsevier Ltd. doi:10.1016/S1671-2927(11)60135-1
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on grain protein content can be manipulated by an appropriate choice factor (Guo et al. 2005). If these ecological factors are evaluated in terms of their eco-environmental factors, they will collectively determine their suitability for cultivation. Therefore, the producers planning division and details in current district from big divisions foundation of Beijing farmland to thinning partitions, can get information of somewhere suit for high-quality wheat and where is inappropriate planting. Thus, local wheat production with professional and technical personnel guiding production and quality can be guaranteed. Over the past decades, many studies have done on assessment of land suitability potentials for selecting certain cultivation areas. The studies of Li and Fan (2002) and Cao et al. (2004) mostly focused on the overall meteorological factors on large surface areas and analyzing system from the mathematics modeling and the analysis method, such as the influence of climate on crop quality. The application have been reported for climatic factor influencing wheat quality in Inner Mongolia, China (Li and Fan 2002). Cao et al. (2004) studied the relationship between temperature, sunshine and quality of spring-sown wheat; and a number of methods have been proposed for modeling the spatial distribution of land suitability potentials for selecting crop cultivation areas. Besides the factors studies, the simplest approach is Thiessen polygon method which amounts at drawing around each gage a polygon of influence with the boundaries at a distance halfway between gage. Although the Thiessen polygon method is essentially used for estimation of area rainfall, it has been applied to the interpolation of point measurements. Similarly, inverse distance interpolation that makes factor as a weighted average of surrounding values and the weights being reciprocal to the square distance from the unsampled location is also widely applied in many regions. However, for both Thiessen polygon method and inverse distance interpolation weighting method, they have not considered topographic variables such as significance level influences on quality formation of wheat, which rather is believed to be important factors especially in weather and soil factors (Krishna 1996).
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In order to overcome this deficiency, some researchers develop the relationships between factors and a range of topographic variables and used regression analysis and GIS techniques to model crop area spatial pattern (Ramalho-Filho et al. 1997; Pirbalouti and Golparvar 2008). But, the method heavily depends on the accuracy of regression model and rarely considers spatial relationships among sample points. Multivariate geostatistics which is based on the theory of regionalized variables is recently increasingly preferred because it allows one to capitalize on the spatial correlation between neighboring observations to predict attributes values at unsampled locations, and also can be complemented by ancillary attributes. Several authors have shown that the multivariate geostatistical prediction provides better estimates of importance factors than conventional methods (Hammond and Walker 1984; Kalogirou 2002; Rockstrom et al. 2002). Some studies have been carried out through close examination of the indicators of land suitability evaluation in a watershed of Karnataka, India (Kasturirangan 1995; Shim et al. 2002; Bandyopadhyay et al. 2009; Sathish and Niranjana 2010). Under ArcGIS platform, assessment was made to quantify spatial patterns of precipitation in Chongqing tobacco planting region, China. Based on agro-ecological characterization of land suitability assessment for selected crops in vellore district, the agro-land suitability map was generated by matching the crop requirement details with the land qualities (Sys 1972, 1985). However, few attempts have been made so far to examine the precise factors and their weight influence on wheat quality and the model they got can not promote to other regions (Zhang et al. 2001; Zhao et al. 2004; Wang et al. 2010). The major objective of this project was to find out important factors influencing the quality of winter wheat in Beijing area and evaluate suitable land based on ecoenvironmental factors for winter wheat cultivation. The study employed artificial neural network (ANN) to evaluate the relative importance of soil and weather factors on grain quality in Beijing, and perform land suitability analyses remote sensing (RS) and geographic information systems (GIS) data tools. These techniques would provide a good platform for data generation, integration, processing, and analyses.
© 2011, CAAS. All rights reserved. Published by Elsevier Ltd.
Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS
MATERIALS AND METHODS Study region Beijing is located at the northern tip of the North China Plain, near the meeting point of the Xishan Mountain and Yanshan Mountain ranges (115°25´-117°30´E, 39°38-40°51´N), in the warm temperature zone and has a continental climate, annual rainfall averages nearly 700 mm, most of it comes in July and August, 2010, the frost-free period is 185 d, the average temperature throughout the whole year is 11.7°C, the average temperatures of the hottest month in July and the coldest month in January range from 27-4.6°C, covers more than 16 800 ha, mountainous country area is 10 317.5 ha, accounting for 62% of Beijing’s total area; plain area 6 390.3 ha, accounting for 38% of the total area. In present study, the total number of sampling sites was 263 and every sample spot we have chosen were in plain, and plain is main terrain in study area, the main soil types in Beijing are alluvial and cinnamon soils, both of them regarded as carry on the same influence of the quality formation.
Preparations of data Soil nutrient Soil samples were collected from all wheat sampling locations at 0-20 cm sampling depth and analyzed at NERCITA laboratories for soil organic matters, total N, available phosphorus, and available potassium. Meteorological data and combination Meteorological data including average temperature, the minimum temperature, the maximum temperature, average sunshine time, and rainfall were collected from eight meteorological stations (Miyun, Huairou, Shunyi, Daxing, Pinggu, Tonghzou, Fangshan, and Changping, Beijing, China) in 2010. Through the data integration, meteorological data and protein of wheat formation in high correlation were processed in following ways: (1) according to certain periods (10 or 5 d) to integrate operation, especially in the most key periods of wheat quality (such as during later seed filling period) to every 5 d; (2) addition and subtraction, such as the accumulated temperature ( 0°C, 10°C), range of temperature, illumination time and so on in some growth
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periods. After integration, various meteorological factor values in each sampling sites were created by ArcGIS through space data interpolation command in Arc/Info workstation (ESRI, Redlands, CA, USA). For grain protein before harvesting, 5 kg kernels were collected from each sampling location on regular grids of about 3 m×5 m. The total number of sampling sites was 263 with the same cultivar of Zhongyou 206 (Fig. 1) in Beijing. The fertilizer and water management were carried out as per local recommendations. These samples were later analyzed for grain protein content using a foss INFRATEC 1229 NIR grain analyzer (Perstorp Analytical Inc., Silver Springs, MN, USA). In order to obtain current distribution on basic farmland information of Beijing based on field sampling and sampling visual interpretation, we utilized SPOT imagery (spatial resolution of 2.5 m pixel size), BJ-1 satellite imagery (spatial resolution of 4 m pixel size), history air photos (0.4 m pixel size), toposheet, and ancillary data of the study area simultaneously. The results are shown in Fig. 2.
Statistical analyses It was carried out with four main steps for assessment
Fig. 1 The distribution map of wheat surveyed in 2010.
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Fig. 2 Distribution of basic farmlands in Beijing.
of land suitability potentials by: (1) obtaining critical factors employed by ANN which used to build a wheat protein prediction, to evaluate the relative importance of selected soil and weather factors in grain quality; (2) generating spatial data layers for the land suitability model; (3) identifying spatial data layers and for classes within each spatial data, one without weight and the other with weight generated form ANN; (4) producing land suitable regionalization map for agriculture purposes with each one of the polygons in the final thematic layer was qualitatively visualized into one of them with five categories: not suitable, marginally suitable, moderately suitable, suitable, and, highly suitable. The flow chart of winter wheat land suitability potential assessment is shown in Fig. 3. Factor analysis Fifty-six factors from temperature, sunshine, rainfall, soil, and meteorological transformation period were combined to reduce data redundancy and further analyze the important variables selected by factor analysis in ANN and factors primaries are shown in Table 1. Impact of the selected soil and meteorological variables on protein of wheat was evaluated using back propagation ANNs in STATISTICA 6.0 (StatSoft, Tulsa, OK, USA). The basic structure of a feed-forward, back-propagation network consists of an input, hidden,
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and output layers (Fig. 4). The hidden layer controls the complexity of the relationship between input and output variables. The more neurons (nodes or units) included in the hidden layer, the more complex or nonlinear are the relationships. The basic processing element of a neural network is a neuron, which consists of seven major components: weighting factors, summation function, transfer function, scaling and limiting processes, output function, error function, back-propagated value, and learning function (Anderson and McNeill 1992). When the neurons are repeatedly exposed to historical data (or training data), the mathematical constructs in each neuron can be repeatedly adjusted using special training algorithms to a configuration that sufficiently minimizes the overall error function (StatSoft 2002). Because the selection subset was actually part of the training process, another independent testing subset was used to evaluate the prediction performance of the final ANN models (StatSoft 2004). The tested network types included multilayer perceptron (MLP), radial basis function (RBF), and linear networks. Only the results of MLP and RBF models were used, the best model selected was MLP 15:15-7-1 (Fig. 4) Once the model was selected, sensitivity analysis was performed to evaluate the relative importance of each variable in explaining wheat quality. In this analysis, each input variable was treated in turn as if it was not available and the average value of that variable was used. A sensitivity ratio was calculated by dividing the total network error when the variable was treated as not available by the total network error when the actual values of the variable were used. If the ratio was greater than 1.0, then the variable made an important contribution to quality variability. The higher the ratio, the more important the variable (StatSoft 2004). Layers defined specific suitability level of the factors By means of expert opinion and literatures, a specific suitability level per factor for wheat on condition of natural and using the whole plastic-film mulching on double ridges technical was defined. Then factor maps were constructed in the ArcGIS. Parameters from field surveys were computed using GPS and processed in GIS by inverse distance weighted technique (IDW) (ArcGIS 9.2) with the relevant logical conditions and the basis of factors influencing quality of winter wheat each layer maps qualitatively visualized into five cat-
© 2011, CAAS. All rights reserved. Published by Elsevier Ltd.
Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS
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Fig. 3 Methodology for delineating the land suitability potential.
© 2011, CAAS. All rights reserved. Published by Elsevier Ltd.
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Table 1 Descriptive statistics of 15 influencing factors Influence factor Accumulated temperature from 20 May to 10 June (°C) Average temperature from 26 May to 30 May (°C) Average temperature from 1 June to 5 June (°C) Accumulated temperature from 1 March to 10 June (°C) Illumination time from 20 May to 25 May (h) Rainfall from 20 May to 10 June (mm) Range of temperature from 1 May to 10 June (°C) Range of temperature from 20 May to 10 June (°C) Illumination time from 6 June to 10 June (h) Available nitrogen content of soil (mg kg -1) Maximum temperature from 1 May to 10 June (°C) Accumulated temperature from 1 June to 5 June (°C) Average temperature from 1 May to 10 June (°C) Organic matter in soil (g kg-1) Number of days which the temperature above 32°C (d)
Fig. 4 The neural network for calculating wheat protein.
egories which influenced the quality of suitable level of winter wheat, 1-good, 2-fair, 3-moderate, 4-poor, and 5-not suitable (Fig. 4). Classes 1 and 2 were assigned good and fair categories, due to the adequate accumulated temperature at early growth stage, illumination time during later stage of wheat growth, and adequate rainfall. Classes 3 and 4 were categorized as moderate and average due to slightly short of growing condition in some stages such as less rainfall in flowering stage leading to the decrease in glutelin content and less available nitrogen content of soil than classes 1 and 2. Class 5 had relatively opposite poorer environmental conditions because wheat quality was remarkably influenced by water stress at flowering period and was kept under the poor category. The ARC/INFO GIS software package was used for creation of digital database, data integration and analysis. All thematic maps were digitized in continuous mode, in vector format, and the digitized values then edited. The different polygons in the thematic maps were labelled separately. Unique attributes were assigned for
Max
Min
Mean
SD
662.1 23.84 27.04 1 658.3 50.4 79.7 25.6 25.1 34.77 41.07 29 135.2 23.79 45.19 13
618.6 21.98 25 1 483.4 41.3 22.8 19 19 17.77 5.14 25.9 125 22.05 2.33 7
641.1 22.84 25.98 1 569.69 45.41 46.575 23.26 22.94 28.56 16.55 27.36 129.94 22.89 22.75 11
17.45 0.72 0.79 72.19 2.94 22.34 2.01 1.78 5.83 7.87 0.98 3.95 0.69 10.65 2.07
all the features of different thematic maps. Initially, each one of the polygons in the final thematic layer was qualitatively visualized into one of the categories, and then normalized each layer because the numerical value range and physical meaning of extracted features were different, masking to form a image with some resolution in ENVI 4.3. In terms of their importance with respect to land suitable for agriculture purposes, each map showed five levels: not suitable, marginally suitable, moderately suitable, suitable, and highly suitable in terms of their importance with respect to land suitable for agriculture purposes. Then, suitable weights were assigned to each thematic feature after considering their characteristics. Knowledge based weight assignment was carried out for each feature, and they were integrated and analyzed using the weighted aggregation method (ESRI 1989). Development of spatial model In this method, the total weights of the final integrated polygons were derived as sums or products of the weights assigned to the different layers, according to their suitability. The equation used in GIS for the assessment of land suitability potential (LSP) for agricultural purposes was: The spatial model for selecting the most suitable areas for wheat cultivation was used for tillable areas in Beijing. Performance evaluation application is shown as below: LSP1=12 direct proportion factors-3 inverse relation factors=b1+b2+b3+b4+b5+b6+b7+b8+b9+b 10+b11+b12-b13-b14-b15 (model 1) LSP2=12 direct proportion factors and influence ratio-3 inverse relation factors=b1×1.158+b2× 1.133+b3×1.101+b4×1.097+b5×1.090+b6×
© 2011, CAAS. All rights reserved. Published by Elsevier Ltd.
Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS
1.0 8 1 + b 7 × 1 . 0 4 9 + b 8 × 1 . 0 4 1 + b 9 × 1 . 0 3 6 + b 1 0 × 1.023+b11×0.987+b12×0.984-b13×1.158-b14×1.123b15×1.08 (model 2) .
RESULTS By using the above model, the land suitability map was prepared and shown in Figs. 6 and 7. Changping, Tongzhou, and the east of Shunyi were more or less agriculturally suitable, most of those areas were of the best quality for winter wheat. As known from experience, plenty of water and fewer hills around these areas were the main reason. However, most area of Fangshan, in the Southeast of Yanqing and the south of Miyun were not satisfied for quality wheat. In addition, most arable land (56.2-63.7%) was in three middle levels (marginally suitable, moderately suitable, suitable). In particular, by using the model for weight, the agriculture land suitability map was prepared and is shown in Fig. 7. The analysis showed that the most area in Beijing was more or less agriculturally suitable. However, about 3.9% (100.347 ha) of the area was rated as not suitable, a very large portion of the area (61.5%) comes under moderate to average, 16.1% rated marginally suitable, 27.2% (about 699.856 ha) of the area was rated as highly suitable. These conditions were determined based on ground observations by field experts (ground truth team that consisted of a local agronomist, water specialist, soil scientist, and a remote sensing expert). The precise locations of these areas were recorded using a GPS. During survey, 78 points were picked up as random sample and the best accuracies were obtained for approach 2 (variable weights for layers) with accuracy of 73.4% for not suitable and 84.6% for highly suitable. The confusion occurred mostly between close classes (e.g., marginal and moderate suitable and most suitable).
DISCUSSION This research espoused and illustrated spatial modeling approach for determining suitable levels area in Beijing, the methodology adopted for this study was to map the suitability classes based on the winter wheat suitability
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and crop climatic suitability using the FAO framework for land evaluation. The study has established the application of agro-ecological units for sustainable land use planning. Fifteen different agro-ecological units were selected and ANN analysis was employed to select factors and evaluate the relative importance of selected environment factors on wheat grain quality. The spatial models were developed and their use in selecting the most suitable areas was demonstrated for the winter wheat cultivation. The strategy overcomes the non-accurate traditional statistical methods. Satellite images, toposheet and ancillary data of the study area were used to find tillable land, while these categories were integrated in the various layers with corresponding weights in a geographical information system (GIS). An integrated land suitability potential (LSP) index was computed considering the contribution of various parameters of land suitability. The study demonstrated that a tillable land can be categorized into spatially distributed agriculture potential zones based on the soil nutrient and assembled weather factors using RS and GIS as not suitable, marginally suitable, moderately suitable, suitable, and highly suitable by adopting the logical criteria. The sort of land distribution map made by the factors with their weights showed more truthfulness. The land suitability designates land according to its complex alloy ecological factor capability, regardless of any conceptual interest of planner. The composite effect of physical parameters determines the degree of suitability which can help in further categorizing the land into different classes of development. Moreover, the process of suitability assessment is much dependent upon the prevalent conditions, such as pressure on land. The suitability analysis attempted in this study must be viewed as a basic prioritization of land for agriculture development. Therefore, a multi-disciplinary study (field surveys, ground realities, old maps, and remote sensing imagery) has been undertaken to carry out land use suitability analysis identifying the areas to be used for agriculture purpose. The assessment of physical parameters provides the information about the limitations of the land for agricultural development. Even though the present study indicates that it is possible to land suitability potentials for selecting winter wheat cultivation areas in Beijing employing ANN
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Fig. 5 Factor maps for wheat on condition of use spatially extrapolated. A, illumination time from 6 June to 10 June (h). B, number of days in which the temperature above 32°C (d). C, available nitrogen content of soil (mg kg-1). D, average temperature from 1 May to 10 June (°C). E, rainfall from 20 May to 10 June (mm). F, accumulated temperature from 20 May to 10 June (°C). G, average temperature from 1 June to 5 June (°C). H, range of temperature from 20 May to 10 June (°C). I, average temperature from 26 May to 30 May. J, organic matter in soil (g kg-1). K, accumulated temperature from 1 June to 5 June (°C). L, rainfall from 1 May to 10 June (mm). M, range of temperature from 1 May to 10 June (°C). N, maximum temperature from 1 May to 10 June (°C). O, accumulated temperature from 1 March to 10 June (°C). © 2011, CAAS. All rights reserved. Published by Elsevier Ltd.
Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS
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Fig. 6 Distribution space in Beijing (approach 1, equal weights for layers variable scores within layers).
analysis to evaluate the relative importance of selected soil and weather factors with RS and GIS, this technique is probably still not very practical for completely accurate assessment. The quality formation of winter wheat have been found to depend on atmospheric conditions (e.g., wind speed, hailstone), biotic conditions (e.g., crop variety, leaf area index), and local policy as well as field management strategies. A more refined result will be made if spatial distribution of factors data
from more meteorological stations is used. Therefore, more studies are needed to explore the response characteristics of winter wheat under different levels in the field conditions.
CONCLUSION This strategy overcomes the non-accurate traditional
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Fig. 7 Distribution space in Beijing (approach 2, variable weights for layers variable scores within layers).
statistical methods for important factors and their weights to winter wheat quality, and the detection result was displayed after integrated a climate and soil database with deferent spatial and temporal resolutions in GIS context visualization processing according to the established models. The suitable planting areas of wheat in this research is a kind of potential distribution. The actual evaluation of the suitable planting areas for
wheat is likely to be affected by the other factors. Although there was a slight discrepancy between the potential distribution and the actual distribution, this research has simulated the potentially suitable planting areas of wheat in Beijing. Besides, the weighting factor process has generated valuable information, which could be useful for future specific studies on wheat. These results can be useful for programs sponsored by local
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Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS
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Table 2 Ranking of important factors influencing protein content of winter wheat Influencing factor Illumination time from 6 June to 10 June (h) Number of days which the temperature above 32°C (d) Available nitrogen content of soil (mg kg-1) Average temperature from 1 May to 10 June (°C) Rainfall from 20 May to 10 June (mm) Accumulated temperature from 20 May to 10 June (°C) Average temperature from 1 June to 5 June (°C) Range of temperature from 20 May to 10 June (°C) Average temperature from 26 May to 30 May Organic matter in soil (g kg-1) Accumulated temperature from 1 June to 5 June (°C) Rainfall from 1 May to 10 June (mm) Range of temperature from 1 May to 10 June (°C) Maximum temperature from 1 May to 10 June (°C) Accumulated temperature from 1 March to 10 June (°C)
and federal governments, in order to support production of wheat crops.
Acknowledgements This research was supported by the National Natural Science Foundation of China (40701120), the Beijing Nova Program, China (2008B33), and the Beijing Natural Science Foundation, China (4092016).
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Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Error
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0.973 0.952 0.925 0.922 0.916 0.908 0.881 0.874 0.870 0.857 0.856 0.8754 0.841 0.839 0.833
1.158 1.133 1.101 1.097 1.090 1.081 1.049 1.041 1.036 1.020 1.019 1.016 1.014 1.009 1.007
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September 2011
2011, 10(9): 1419-1430
Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS WANG Da-cheng1, 2, LI Cun-jun2, SONG Xiao-yu2, WANG Ji-hua1, 2, YANG Xiao-dong2, HUANG Wen-jiang2, WANG Jun-ying3 and ZHOU Ji-hong3 Institute of Agricultural Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310029, P.R.China Beijing Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China 3 Agricultural Technology Extension Station in Beijing, Beijing 100029, P.R.China 1 2
Abstract It is very important to provide reference basis for winter wheat quality regionalization of cultivation area. The aim of this article was based on factors affecting wheat quality and setting realistic spatial models in each part of the land for assessment of land suitability potentials in Beijing, China. The study employed artificial neural network (ANN) analysis to select factors and evaluate the relative importance of selected environment factors on wheat grain quality. The spatial models were developed and demonstrated their use in selecting the most suitable areas for the winter wheat cultivation. The strategy overcomes the non-accurate traditional statistical methods. Satellite images, toposheet, and ancillary data of the study area were used to find tillable land. These categories were formed by integrating the various layers with corresponding weights in geographical information system (GIS). An integrated land suitability potential (LSP) index was computed considering the contribution of various parameters of land suitability. The study demonstrated that the tillable land could be categorized into spatially distributed agriculture potential zones based on soil nutrient and assembled weather factors using RS and GIS as not suitable, marginally suitable, moderately suitable, suitable, and highly suitable by adopting the logical criteria. The sort of land distribution map made by the factors with their weights showed more truthfulness. Key words: LSP, ANN, suitable areas, wheat, RS and GIS
INTRODUCTION Wheat (Tritium aestivum L.) is not only a significant component of major staple foods, but also of a principal crop in northern China. The land units in each part of the field are demarcated through its properties, position, and usage, and each of them have their own potentials and limitations. It is possible to grade land units according to their qualities (FAO 1990). Land suitability potential (LSP) evaluation is an important step to detect the environmental limitation in sustainable land Received 14 October, 2010
use planning. It deals with the assessment of land performances for the specific use that is crop production. Wheat quality (grain protein content is a key index) characteristics are usually influenced by genotype, environmental factors, and interactions between genotype and environment. In Beijing, China, most land is suitable for wheat and belongs to strong gluten wheat region in China (He et al. 2002). Due to growing the same varieties in different areas in Beijing, the quality of their own products displays marked difference. The main reason is ecological factors distribution featured by inhomogeneity. The strong environmental effects
Accepted 7 December, 2010
WANG Da-cheng, Ph D, Tel: +86-10-62754134, Fax: +86-10-51503750, E-mail: [email protected]; Correspondence WANG Ji-hua, Professor, Tel: +86-1051503488, Fax: +86-10-51503750, E-mail: [email protected] © 2011, CAAS. All rights reserved. Published by Elsevier Ltd. doi:10.1016/S1671-2927(11)60135-1
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on grain protein content can be manipulated by an appropriate choice factor (Guo et al. 2005). If these ecological factors are evaluated in terms of their eco-environmental factors, they will collectively determine their suitability for cultivation. Therefore, the producers planning division and details in current district from big divisions foundation of Beijing farmland to thinning partitions, can get information of somewhere suit for high-quality wheat and where is inappropriate planting. Thus, local wheat production with professional and technical personnel guiding production and quality can be guaranteed. Over the past decades, many studies have done on assessment of land suitability potentials for selecting certain cultivation areas. The studies of Li and Fan (2002) and Cao et al. (2004) mostly focused on the overall meteorological factors on large surface areas and analyzing system from the mathematics modeling and the analysis method, such as the influence of climate on crop quality. The application have been reported for climatic factor influencing wheat quality in Inner Mongolia, China (Li and Fan 2002). Cao et al. (2004) studied the relationship between temperature, sunshine and quality of spring-sown wheat; and a number of methods have been proposed for modeling the spatial distribution of land suitability potentials for selecting crop cultivation areas. Besides the factors studies, the simplest approach is Thiessen polygon method which amounts at drawing around each gage a polygon of influence with the boundaries at a distance halfway between gage. Although the Thiessen polygon method is essentially used for estimation of area rainfall, it has been applied to the interpolation of point measurements. Similarly, inverse distance interpolation that makes factor as a weighted average of surrounding values and the weights being reciprocal to the square distance from the unsampled location is also widely applied in many regions. However, for both Thiessen polygon method and inverse distance interpolation weighting method, they have not considered topographic variables such as significance level influences on quality formation of wheat, which rather is believed to be important factors especially in weather and soil factors (Krishna 1996).
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In order to overcome this deficiency, some researchers develop the relationships between factors and a range of topographic variables and used regression analysis and GIS techniques to model crop area spatial pattern (Ramalho-Filho et al. 1997; Pirbalouti and Golparvar 2008). But, the method heavily depends on the accuracy of regression model and rarely considers spatial relationships among sample points. Multivariate geostatistics which is based on the theory of regionalized variables is recently increasingly preferred because it allows one to capitalize on the spatial correlation between neighboring observations to predict attributes values at unsampled locations, and also can be complemented by ancillary attributes. Several authors have shown that the multivariate geostatistical prediction provides better estimates of importance factors than conventional methods (Hammond and Walker 1984; Kalogirou 2002; Rockstrom et al. 2002). Some studies have been carried out through close examination of the indicators of land suitability evaluation in a watershed of Karnataka, India (Kasturirangan 1995; Shim et al. 2002; Bandyopadhyay et al. 2009; Sathish and Niranjana 2010). Under ArcGIS platform, assessment was made to quantify spatial patterns of precipitation in Chongqing tobacco planting region, China. Based on agro-ecological characterization of land suitability assessment for selected crops in vellore district, the agro-land suitability map was generated by matching the crop requirement details with the land qualities (Sys 1972, 1985). However, few attempts have been made so far to examine the precise factors and their weight influence on wheat quality and the model they got can not promote to other regions (Zhang et al. 2001; Zhao et al. 2004; Wang et al. 2010). The major objective of this project was to find out important factors influencing the quality of winter wheat in Beijing area and evaluate suitable land based on ecoenvironmental factors for winter wheat cultivation. The study employed artificial neural network (ANN) to evaluate the relative importance of soil and weather factors on grain quality in Beijing, and perform land suitability analyses remote sensing (RS) and geographic information systems (GIS) data tools. These techniques would provide a good platform for data generation, integration, processing, and analyses.
© 2011, CAAS. All rights reserved. Published by Elsevier Ltd.
Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS
MATERIALS AND METHODS Study region Beijing is located at the northern tip of the North China Plain, near the meeting point of the Xishan Mountain and Yanshan Mountain ranges (115°25´-117°30´E, 39°38-40°51´N), in the warm temperature zone and has a continental climate, annual rainfall averages nearly 700 mm, most of it comes in July and August, 2010, the frost-free period is 185 d, the average temperature throughout the whole year is 11.7°C, the average temperatures of the hottest month in July and the coldest month in January range from 27-4.6°C, covers more than 16 800 ha, mountainous country area is 10 317.5 ha, accounting for 62% of Beijing’s total area; plain area 6 390.3 ha, accounting for 38% of the total area. In present study, the total number of sampling sites was 263 and every sample spot we have chosen were in plain, and plain is main terrain in study area, the main soil types in Beijing are alluvial and cinnamon soils, both of them regarded as carry on the same influence of the quality formation.
Preparations of data Soil nutrient Soil samples were collected from all wheat sampling locations at 0-20 cm sampling depth and analyzed at NERCITA laboratories for soil organic matters, total N, available phosphorus, and available potassium. Meteorological data and combination Meteorological data including average temperature, the minimum temperature, the maximum temperature, average sunshine time, and rainfall were collected from eight meteorological stations (Miyun, Huairou, Shunyi, Daxing, Pinggu, Tonghzou, Fangshan, and Changping, Beijing, China) in 2010. Through the data integration, meteorological data and protein of wheat formation in high correlation were processed in following ways: (1) according to certain periods (10 or 5 d) to integrate operation, especially in the most key periods of wheat quality (such as during later seed filling period) to every 5 d; (2) addition and subtraction, such as the accumulated temperature ( 0°C, 10°C), range of temperature, illumination time and so on in some growth
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periods. After integration, various meteorological factor values in each sampling sites were created by ArcGIS through space data interpolation command in Arc/Info workstation (ESRI, Redlands, CA, USA). For grain protein before harvesting, 5 kg kernels were collected from each sampling location on regular grids of about 3 m×5 m. The total number of sampling sites was 263 with the same cultivar of Zhongyou 206 (Fig. 1) in Beijing. The fertilizer and water management were carried out as per local recommendations. These samples were later analyzed for grain protein content using a foss INFRATEC 1229 NIR grain analyzer (Perstorp Analytical Inc., Silver Springs, MN, USA). In order to obtain current distribution on basic farmland information of Beijing based on field sampling and sampling visual interpretation, we utilized SPOT imagery (spatial resolution of 2.5 m pixel size), BJ-1 satellite imagery (spatial resolution of 4 m pixel size), history air photos (0.4 m pixel size), toposheet, and ancillary data of the study area simultaneously. The results are shown in Fig. 2.
Statistical analyses It was carried out with four main steps for assessment
Fig. 1 The distribution map of wheat surveyed in 2010.
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Fig. 2 Distribution of basic farmlands in Beijing.
of land suitability potentials by: (1) obtaining critical factors employed by ANN which used to build a wheat protein prediction, to evaluate the relative importance of selected soil and weather factors in grain quality; (2) generating spatial data layers for the land suitability model; (3) identifying spatial data layers and for classes within each spatial data, one without weight and the other with weight generated form ANN; (4) producing land suitable regionalization map for agriculture purposes with each one of the polygons in the final thematic layer was qualitatively visualized into one of them with five categories: not suitable, marginally suitable, moderately suitable, suitable, and, highly suitable. The flow chart of winter wheat land suitability potential assessment is shown in Fig. 3. Factor analysis Fifty-six factors from temperature, sunshine, rainfall, soil, and meteorological transformation period were combined to reduce data redundancy and further analyze the important variables selected by factor analysis in ANN and factors primaries are shown in Table 1. Impact of the selected soil and meteorological variables on protein of wheat was evaluated using back propagation ANNs in STATISTICA 6.0 (StatSoft, Tulsa, OK, USA). The basic structure of a feed-forward, back-propagation network consists of an input, hidden,
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and output layers (Fig. 4). The hidden layer controls the complexity of the relationship between input and output variables. The more neurons (nodes or units) included in the hidden layer, the more complex or nonlinear are the relationships. The basic processing element of a neural network is a neuron, which consists of seven major components: weighting factors, summation function, transfer function, scaling and limiting processes, output function, error function, back-propagated value, and learning function (Anderson and McNeill 1992). When the neurons are repeatedly exposed to historical data (or training data), the mathematical constructs in each neuron can be repeatedly adjusted using special training algorithms to a configuration that sufficiently minimizes the overall error function (StatSoft 2002). Because the selection subset was actually part of the training process, another independent testing subset was used to evaluate the prediction performance of the final ANN models (StatSoft 2004). The tested network types included multilayer perceptron (MLP), radial basis function (RBF), and linear networks. Only the results of MLP and RBF models were used, the best model selected was MLP 15:15-7-1 (Fig. 4) Once the model was selected, sensitivity analysis was performed to evaluate the relative importance of each variable in explaining wheat quality. In this analysis, each input variable was treated in turn as if it was not available and the average value of that variable was used. A sensitivity ratio was calculated by dividing the total network error when the variable was treated as not available by the total network error when the actual values of the variable were used. If the ratio was greater than 1.0, then the variable made an important contribution to quality variability. The higher the ratio, the more important the variable (StatSoft 2004). Layers defined specific suitability level of the factors By means of expert opinion and literatures, a specific suitability level per factor for wheat on condition of natural and using the whole plastic-film mulching on double ridges technical was defined. Then factor maps were constructed in the ArcGIS. Parameters from field surveys were computed using GPS and processed in GIS by inverse distance weighted technique (IDW) (ArcGIS 9.2) with the relevant logical conditions and the basis of factors influencing quality of winter wheat each layer maps qualitatively visualized into five cat-
© 2011, CAAS. All rights reserved. Published by Elsevier Ltd.
Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS
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Fig. 3 Methodology for delineating the land suitability potential.
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Table 1 Descriptive statistics of 15 influencing factors Influence factor Accumulated temperature from 20 May to 10 June (°C) Average temperature from 26 May to 30 May (°C) Average temperature from 1 June to 5 June (°C) Accumulated temperature from 1 March to 10 June (°C) Illumination time from 20 May to 25 May (h) Rainfall from 20 May to 10 June (mm) Range of temperature from 1 May to 10 June (°C) Range of temperature from 20 May to 10 June (°C) Illumination time from 6 June to 10 June (h) Available nitrogen content of soil (mg kg -1) Maximum temperature from 1 May to 10 June (°C) Accumulated temperature from 1 June to 5 June (°C) Average temperature from 1 May to 10 June (°C) Organic matter in soil (g kg-1) Number of days which the temperature above 32°C (d)
Fig. 4 The neural network for calculating wheat protein.
egories which influenced the quality of suitable level of winter wheat, 1-good, 2-fair, 3-moderate, 4-poor, and 5-not suitable (Fig. 4). Classes 1 and 2 were assigned good and fair categories, due to the adequate accumulated temperature at early growth stage, illumination time during later stage of wheat growth, and adequate rainfall. Classes 3 and 4 were categorized as moderate and average due to slightly short of growing condition in some stages such as less rainfall in flowering stage leading to the decrease in glutelin content and less available nitrogen content of soil than classes 1 and 2. Class 5 had relatively opposite poorer environmental conditions because wheat quality was remarkably influenced by water stress at flowering period and was kept under the poor category. The ARC/INFO GIS software package was used for creation of digital database, data integration and analysis. All thematic maps were digitized in continuous mode, in vector format, and the digitized values then edited. The different polygons in the thematic maps were labelled separately. Unique attributes were assigned for
Max
Min
Mean
SD
662.1 23.84 27.04 1 658.3 50.4 79.7 25.6 25.1 34.77 41.07 29 135.2 23.79 45.19 13
618.6 21.98 25 1 483.4 41.3 22.8 19 19 17.77 5.14 25.9 125 22.05 2.33 7
641.1 22.84 25.98 1 569.69 45.41 46.575 23.26 22.94 28.56 16.55 27.36 129.94 22.89 22.75 11
17.45 0.72 0.79 72.19 2.94 22.34 2.01 1.78 5.83 7.87 0.98 3.95 0.69 10.65 2.07
all the features of different thematic maps. Initially, each one of the polygons in the final thematic layer was qualitatively visualized into one of the categories, and then normalized each layer because the numerical value range and physical meaning of extracted features were different, masking to form a image with some resolution in ENVI 4.3. In terms of their importance with respect to land suitable for agriculture purposes, each map showed five levels: not suitable, marginally suitable, moderately suitable, suitable, and highly suitable in terms of their importance with respect to land suitable for agriculture purposes. Then, suitable weights were assigned to each thematic feature after considering their characteristics. Knowledge based weight assignment was carried out for each feature, and they were integrated and analyzed using the weighted aggregation method (ESRI 1989). Development of spatial model In this method, the total weights of the final integrated polygons were derived as sums or products of the weights assigned to the different layers, according to their suitability. The equation used in GIS for the assessment of land suitability potential (LSP) for agricultural purposes was: The spatial model for selecting the most suitable areas for wheat cultivation was used for tillable areas in Beijing. Performance evaluation application is shown as below: LSP1=12 direct proportion factors-3 inverse relation factors=b1+b2+b3+b4+b5+b6+b7+b8+b9+b 10+b11+b12-b13-b14-b15 (model 1) LSP2=12 direct proportion factors and influence ratio-3 inverse relation factors=b1×1.158+b2× 1.133+b3×1.101+b4×1.097+b5×1.090+b6×
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Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS
1.0 8 1 + b 7 × 1 . 0 4 9 + b 8 × 1 . 0 4 1 + b 9 × 1 . 0 3 6 + b 1 0 × 1.023+b11×0.987+b12×0.984-b13×1.158-b14×1.123b15×1.08 (model 2) .
RESULTS By using the above model, the land suitability map was prepared and shown in Figs. 6 and 7. Changping, Tongzhou, and the east of Shunyi were more or less agriculturally suitable, most of those areas were of the best quality for winter wheat. As known from experience, plenty of water and fewer hills around these areas were the main reason. However, most area of Fangshan, in the Southeast of Yanqing and the south of Miyun were not satisfied for quality wheat. In addition, most arable land (56.2-63.7%) was in three middle levels (marginally suitable, moderately suitable, suitable). In particular, by using the model for weight, the agriculture land suitability map was prepared and is shown in Fig. 7. The analysis showed that the most area in Beijing was more or less agriculturally suitable. However, about 3.9% (100.347 ha) of the area was rated as not suitable, a very large portion of the area (61.5%) comes under moderate to average, 16.1% rated marginally suitable, 27.2% (about 699.856 ha) of the area was rated as highly suitable. These conditions were determined based on ground observations by field experts (ground truth team that consisted of a local agronomist, water specialist, soil scientist, and a remote sensing expert). The precise locations of these areas were recorded using a GPS. During survey, 78 points were picked up as random sample and the best accuracies were obtained for approach 2 (variable weights for layers) with accuracy of 73.4% for not suitable and 84.6% for highly suitable. The confusion occurred mostly between close classes (e.g., marginal and moderate suitable and most suitable).
DISCUSSION This research espoused and illustrated spatial modeling approach for determining suitable levels area in Beijing, the methodology adopted for this study was to map the suitability classes based on the winter wheat suitability
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and crop climatic suitability using the FAO framework for land evaluation. The study has established the application of agro-ecological units for sustainable land use planning. Fifteen different agro-ecological units were selected and ANN analysis was employed to select factors and evaluate the relative importance of selected environment factors on wheat grain quality. The spatial models were developed and their use in selecting the most suitable areas was demonstrated for the winter wheat cultivation. The strategy overcomes the non-accurate traditional statistical methods. Satellite images, toposheet and ancillary data of the study area were used to find tillable land, while these categories were integrated in the various layers with corresponding weights in a geographical information system (GIS). An integrated land suitability potential (LSP) index was computed considering the contribution of various parameters of land suitability. The study demonstrated that a tillable land can be categorized into spatially distributed agriculture potential zones based on the soil nutrient and assembled weather factors using RS and GIS as not suitable, marginally suitable, moderately suitable, suitable, and highly suitable by adopting the logical criteria. The sort of land distribution map made by the factors with their weights showed more truthfulness. The land suitability designates land according to its complex alloy ecological factor capability, regardless of any conceptual interest of planner. The composite effect of physical parameters determines the degree of suitability which can help in further categorizing the land into different classes of development. Moreover, the process of suitability assessment is much dependent upon the prevalent conditions, such as pressure on land. The suitability analysis attempted in this study must be viewed as a basic prioritization of land for agriculture development. Therefore, a multi-disciplinary study (field surveys, ground realities, old maps, and remote sensing imagery) has been undertaken to carry out land use suitability analysis identifying the areas to be used for agriculture purpose. The assessment of physical parameters provides the information about the limitations of the land for agricultural development. Even though the present study indicates that it is possible to land suitability potentials for selecting winter wheat cultivation areas in Beijing employing ANN
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Fig. 5 Factor maps for wheat on condition of use spatially extrapolated. A, illumination time from 6 June to 10 June (h). B, number of days in which the temperature above 32°C (d). C, available nitrogen content of soil (mg kg-1). D, average temperature from 1 May to 10 June (°C). E, rainfall from 20 May to 10 June (mm). F, accumulated temperature from 20 May to 10 June (°C). G, average temperature from 1 June to 5 June (°C). H, range of temperature from 20 May to 10 June (°C). I, average temperature from 26 May to 30 May. J, organic matter in soil (g kg-1). K, accumulated temperature from 1 June to 5 June (°C). L, rainfall from 1 May to 10 June (mm). M, range of temperature from 1 May to 10 June (°C). N, maximum temperature from 1 May to 10 June (°C). O, accumulated temperature from 1 March to 10 June (°C). © 2011, CAAS. All rights reserved. Published by Elsevier Ltd.
Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS
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Fig. 6 Distribution space in Beijing (approach 1, equal weights for layers variable scores within layers).
analysis to evaluate the relative importance of selected soil and weather factors with RS and GIS, this technique is probably still not very practical for completely accurate assessment. The quality formation of winter wheat have been found to depend on atmospheric conditions (e.g., wind speed, hailstone), biotic conditions (e.g., crop variety, leaf area index), and local policy as well as field management strategies. A more refined result will be made if spatial distribution of factors data
from more meteorological stations is used. Therefore, more studies are needed to explore the response characteristics of winter wheat under different levels in the field conditions.
CONCLUSION This strategy overcomes the non-accurate traditional
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Fig. 7 Distribution space in Beijing (approach 2, variable weights for layers variable scores within layers).
statistical methods for important factors and their weights to winter wheat quality, and the detection result was displayed after integrated a climate and soil database with deferent spatial and temporal resolutions in GIS context visualization processing according to the established models. The suitable planting areas of wheat in this research is a kind of potential distribution. The actual evaluation of the suitable planting areas for
wheat is likely to be affected by the other factors. Although there was a slight discrepancy between the potential distribution and the actual distribution, this research has simulated the potentially suitable planting areas of wheat in Beijing. Besides, the weighting factor process has generated valuable information, which could be useful for future specific studies on wheat. These results can be useful for programs sponsored by local
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Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS
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Table 2 Ranking of important factors influencing protein content of winter wheat Influencing factor Illumination time from 6 June to 10 June (h) Number of days which the temperature above 32°C (d) Available nitrogen content of soil (mg kg-1) Average temperature from 1 May to 10 June (°C) Rainfall from 20 May to 10 June (mm) Accumulated temperature from 20 May to 10 June (°C) Average temperature from 1 June to 5 June (°C) Range of temperature from 20 May to 10 June (°C) Average temperature from 26 May to 30 May Organic matter in soil (g kg-1) Accumulated temperature from 1 June to 5 June (°C) Rainfall from 1 May to 10 June (mm) Range of temperature from 1 May to 10 June (°C) Maximum temperature from 1 May to 10 June (°C) Accumulated temperature from 1 March to 10 June (°C)
and federal governments, in order to support production of wheat crops.
Acknowledgements This research was supported by the National Natural Science Foundation of China (40701120), the Beijing Nova Program, China (2008B33), and the Beijing Natural Science Foundation, China (4092016).
References
Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Error
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0.973 0.952 0.925 0.922 0.916 0.908 0.881 0.874 0.870 0.857 0.856 0.8754 0.841 0.839 0.833
1.158 1.133 1.101 1.097 1.090 1.081 1.049 1.041 1.036 1.020 1.019 1.016 1.014 1.009 1.007
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