Mapping recent agricultural developments in China from satellite data

Adv. Space 5cc.

Vol.2, No.8, pp.111—125, 1983

Printed in Great

Britain.

0273—1177/83/080111—15$07.50/Q

All rights reserved.

Copyright © COSPAR

MAPPING RECENT AGRICULTURAL DEVELOPMENTS IN CHINA FROM SATELLITE DATA R. Welch and C. W. Pannell Department ofGeography, University ofGeorgia, Athens, GA 30602, U.S.A. ABSTRACT Landsat data have been employed to study and map agricultural developments in three regions of China: 1) Pearl River delta; 2) Men River basin; and 3) Xinjiang Autonomous Region. Manual interpretation procedures used in conjunction with multi—date Landsat images and collateral information permitted rice yields to be estimated for the Pearl River delta in 1978. A combination of manual and computer—assisted analyses datakm2of study Northeast 2 of agricultural land ofin Landsat a 184,500 area China revealed that more than 15,000 km had been reclaimed from rangeland and marshland. These analyses also indicated a shift in cropping practices, with the foodcrops wheat and corn replacing cash crops such as soybeans. In the arid west, Landsat image data provided valuable input to a geographic information system (GIS). It appears the GIS approach will prove useful for evaluating agricultural land potential in the remote areas of China. INTRODUCTION Over the last 10 years it has been possible to derive information about recent agricultural land developments in the Peoples Republic of China from Landsat images and computer compatible tapes (CCTs) [1]. Inasmuch as land development in China is related to the food supply for about 22 percent of the world’s population, it is a topic of considerable importance. The specific objectives of this paper, therefore, are to illustrate the various uses of Landsat data for the assessment of agricultural developments in three regions of China the southeast, northeast and arid west (Figure 1), and to evaluate the significance of the information gained from these studies. The methodologies for analyzing Landsat data were different for each of the three areas. In the southeast, for example, manual interpretation techniques were employed with multi-date Landsat image data to map seasonal agricultural patterns and to estimate rice production in the Pearl River delta. For the Nen River basin in Northeast China, the focus was on the distribution of marginal lands reclaimed for agricultural purposes and on the shift in cropping practices from cash crops (soybeans) to food crops (corn, wheat and millet). Both manual and computer-assisted interpretation techniques proved useful for these studies. In a third study area centered on UrUmqi in the arid Xinjiang Autonomous Region (A.R.) of western China, Landsat data were employed to develop a geographic information system (GIS) approach for the assessment of potential agricultural lands. SEASONAL AGRICULTURAL PATTERNS IN THE PEARL RIVER DELTA” The Pearl River delta is formed by the merging of the deltas of the Xi, Bei, and Dong Rivers and is the largest and most productive alluvial plain in South China. Average annual temperature is about 22°Cand yearly rainfall is more than 1500 mm. About 80 percent of the precipitation occurs between April to September and flooding is common in the summer months [2]. Because of the favorable climate, agriculture is a major activity throughout the year. According to Wu [3] and Leeniing [4],the predominant cropping pattern is two crops of wet rice during the summer, followed by a dry crop of vegetables, winter wheat or rapeseed in the winter (Table 1; Figure 2). There is some effort to alternate cropping patterns and fields may be left fallow after the November rice harvest or in the spring. In order to study the agricultural patterns of the Pearl River delta, seven Landsat scenes recorded during 1978 were selected for analysis. The choice of image data sets was determined partly by the availability of good quality coverage of the area and partly by the a priori knowledge of the crop calendar (Figure 2).

1/The contributions of Dr. C. P. Lo, University of Hong Kong, to this work on the Pearl River delta are gratefully acknowledged. JASR 2/8—H

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Developments

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Visual analyses of false color composite images were undertaken with the aid of a multispectral additive color viewer to delineate field and crop patterns. Maps at 1:500,000 scale which depict agricultural land use in the summer and winter seasons were then prepared from the Landsat data (Figures 3 and 4). Area statistics for the land use/cover classes derived from these maps are presented in Table 2. TABLE 2 Land Use in the Pearl River Delta, 1978 (as interpreted from Landsat images) Land Use Type 1.

Early Summer, 1978

Agriculture 2

Wet Cropland Dry Cropland Fallow Land Intensive Market Gardens Mulberry-Fish Ponds Eroded Land and Bare Soil Forest Land 2.

6139 km 6388 2582 --

860 77 9508

6540 km2 7362 198 686 846 --

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Winter, 1978

Total

109 109

109 109

25772

25771

From Figure 3, it is evident that rice crops occupy much of the alluvial area in summer with fallowing common in the coastal regions of the delta. This suggests poor soil fertility due to saline conditions and reduced growth rates in the wet paddy lands along the coast. By late summer most of this fallow land is being used for rice cultivation. In December, a high percentage of the land is utilized for the winter crop rotation with little land fallowed (Figure 4). According to Miu and Chen; and Wu [6],wheat, rapeseed or vegetables are the preferred winter crops. If Figures 3 and 4 are compared with a soils map of the delta (Figure 5), close correlations between the land use patterns and soil conditions are evident. Such correlations have permitted rice yields to be estimated. For example, yields for fields in the various soils regions are known (Table 3), and from the analyses of the Landsat images it was possible to map three categories of paddy field quality (Figure 6): 1) good; 2) medium; 3) poor. TABLE 3

Estimated Rice Yields in Different Types of Fields

Field type 1. 2. 3.

Saline field Sandy field Enclosed field

Source:

Mean yield, 1978 (metric ton/ha) 2.63 6.75 13.13

Derived from Miu and Chen, 1966

This categorization of fields was based on their distance from the sea, the availability of irrigation water, and the infrared reflectance patterns recorded on the seasonal Landsat images. In general, the fields farthest from the sea have been farmed longest and have the best irrigated fields and soils [7].

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118

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The area for each category of paddy field was then measured from Figure 6 and used in conjunction with the data in Table 3 to produce an estimated rice yield of 5,871,736 metric tons for 1978 (Table 4). This figure is consistent with other reports for Ihe Guangdong Providence in 1978 [8]. TABLE 4

Estimated Yield of Rice Crop in the Pearl River Delta, Study Area, 1978

Type of Field

Area (ha)

Good Medium Poor

Estimated Avg. Yield (metric tons/ha)

Estimated Rice Production (metric tons)

286,777 289,535 57,792

13.13 6.75 2.63

3,765,382 1,954,361 151 ,993

634,104

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Total (for early and late rice crop)

NEW AGRICULTURAL LANDS IN THE MEN RIVER BASIN OF N.E. CHINA~’ 2 in Heilongjiang and dilin The Men River Basin study area(Figure occupies km Provinces of Northeast China 1). approximately It is an area184,500 characterized by poorly drained wetlands, bogs, ponds, and saline marshes, and the drainage of these wetlands is one of the main challenges to land reclamation and agricultural production. Only on the older farmlands which occupy higher ground are the agriculturally productive chernozems and brown soils well developed. Prior to 1947 the farms of the Men River Basin were generally small and probably averaged less than 5 ha. A large percentage of the farmland was allocated to the important cash crop, soybeans, with gaoliang (grain sorghum) and millet the principal food crops. Spring wheat was limited to small areas in the far north. With the establishment of communist government control in 1947, colonization resumed and large tracts of wasteland were converted to farmland. Between 1953 and 1975, nearly 300 large—scale and over 700 small-scale state farms were established in the Nen River basin and along the lower reaches of the Sungari River. By 1975, 470 reclamation areas had been established in Heilongjiang Province alone and newly reclaimed land in this province was reported to exceed 13,000 km2 [9]. Cropping practices were also reported to have changed with soybean production declining in favor of spring wheat and corn. A land use/cover map of the Nen River study area at a scale of 1:1,500,000 was produced from manual interpretations of Landsat MSS images in order to determine the extent and location of new agricultural land developments [10]. Based on area statistics compiled from this map, it was apparent that more than 15,000 km2 of rangeland and wetland had been converted to agricultural land in recent years (Table 5). TABLE 5

Areas of the Various Land Use Classes % of Nen River Basin Study Area

Category

Area in km2

Wetland Rangeland Forest Water Urban New Agricultural Land* Clear-Cut and/or Burned Land Intensive Market Garden* Extensive Field Cropland*

26,500 25,200 46,400 1,330 165 15,300 2,325 785 66,500

14.4 13.7 25.1 0.7 0.1 8.3 1.3 0.4 36.0

184,505 82,585

100.0 44.7

Total Area Total Farmland*

Crop identification studies of the new lands were concentrated on two state farms with distinct field patterns (Figure 7). The largest of the two farm study areas occupies approximately 288 km2 on the west bank of the Nen River (47°50’NLatitude, l23°50’ELongitude) and 2/This section was adapted from Welch, Lo and Pannell (1979), Footnote 1.

Agricultural Developments in China

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was designated as Study Area I. This farm area was reclaimed from marshland in recent years, and Landsat ililages revealed new roads and channels had been extended into the marsh to support further reclamation efforts. It is an ideal site for crop identification, with fields of regular configuration averaging ha. ofAnother Area 2 located about 20 kmabout to the200south Study farm Area area I was (Study selected II) of approximately 88 km to test the possibilities for signature extension. The principal aids to identification of crops include crop calendars, reflectance spectra, and images recorded on dates which maximize the reflectance differences between the crops [12,13]. Figure 8 indicates that identification of specific crops is handicapped by the overlap in the growing seasons. In particular, soybeans, corn, and millet (of which there are early and late sown varieties) are grown throughout the summer months and are unlikely to be uniquely identified by simple correlation of the crop calendar with image data. A closer inspection of Figure 8, however, reveals that spring wheat and early sown millet are harvested by the end of duly, by which time corn and soybeans are at a mature stage of growth with full canopies and maximum reflectance in the near infrared (Figure 9). Fields of late sown millet are still in the early stages of growth with the exposed underlying soil modifying the normal reflectance curves for green vegetation. These reflectance spectra, while subject to wide variations due to stage of plant growth, proportion of vegetation to soil exposure, moisture content, solar angle, etc. provide an additional basis for discrimination of crops by both manual and computer assisted classification techniques [14].

Wavelength

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Fig. 9 Generalized spectral reflectance curves for wheat, soybeans, corn/millet, and dry disturbed soil. Based on the crop calendars, Landsat image data were selected for three dates in the 1975 growing season: May 2, August 1 , and October 1. Bands 5 and 7 images were subsequently obtained for each of these dates, and a computer compatible tape (CCT) for August 1 . The August 1 image data proved most useful for crop identification and classifications were undertaken using both supervised and unsupervised techniques. The results of classifications for the two large farms (Study Areas I and II) are tabulated in Table 6, and it evident that the percentage of land allocated to various crops is essentially the same in both Study Areas I and II; that is approximately 15 percent to soybeans, 20 percent to corn/millet, and 65 percent to wheat/millet. The area in soybeans is less than anticipated, and it is probable that the food requirements of an expanding population and the post—World War II shift of foreign markets has resulted in the conversion of land from cash crop (soybeans) to food crop (wheat and corn) production. Wheat is reported to have replaced millet as a principal food crop, and it is probable that spring wheat is the dominant crop on these farms. Corn is often alternated with soybeans and accounts for up to 25 percent of the farmland, which indicates that it has become a major crop in Northeast China. These percentages, while related directly to the farms studied in detail, appear to be valid for other large state farms in Northeast China and are supported by statements in the literature [15].

K. We I cli and C .W. P,innc 11

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TABLE 6

Developments

in China

121

Comparison of Cropland Statistics for Study Areas I and II Study Area I

Soybean Corn/Millet Harvested Wheat/Millet Plowed Field Total Cropland

(%)

Study Area II (%)

14 23 62 1

13 20 65 2

100

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EVALUATION OF POTENTIAL CROPLAND IN XINdIANG AUTONOMOUS REGION THROUGH THE USE OF A GEOGRAPHIC INFORMATION SYSTEM~S/ 2 (51 x 52 km) in the Xinjiang The for on study km 10). Segments of the Tian Shan A. R.area and selected is centered the occupies city of approximately Uriimqi (Figures2600 1 and and related outlier ranges (the Bogda Feng) with elevations to 5400 in are located to the south and east. These highlands form an approximately east-west oriented ecological divide. During the months of April through September, winds are predominantly from the northwest and bring moisture to the north slopes of the mountains. For example, annual precipation at UrUmqi averages about 276 mm/year with a pronounced summer maximum of about 25-35 mm/month. Precipitation at the higher elevations (-‘2000 m) is more than double that at UrUmqi (elevation 760 in), and approximately 200 km southeast of UrUmqi in the rainshadow of the Bogda Range, is the Turpan Depression with an average precipitation of only 25 nm/year. Other important physical characteristics of this area include variations in local relief that range from comparatively level and low areas in the plain around UrUmqi to the steep slopes of the Bogda Range at elevations above 3000 in. The aridity of the area has limited cultivation mainly to irrigated fields, and oasis agriculture is largely dependent on water supplies drawn from the neighboring mountain streams. Herding is also very important on the grasslands found at elevations of 1000-1600 in. Because of the diverse characteristics of the physical landscape (plains, desert, and nountains) and the rapid transition from intense human activity near Iir’Umqi to the sparsely populated basin to the southeast, the study area provides a unique opportunity to test the possibility of assessing land potential with the aid of a GIS. Thi; methodology may permit the development of a predictive model for evaluating other areas ir~ western China. In order to assess land potential in the UrUmqi area, data on land use/cover, elevation, precipitation, slope, and soils were required. In addition, a topographic map base to which the various data sets could be registered was essential to the study. The only topographic map of this remote region available to the investigators was an Operational Navigation Chart (ONC F—7) of 1:100,000 scale with contour intervals of 500 and 1000 feet [17]. This map was photographically enlarged to 1:250,000 scale and the enlargement served as the base to which all data sets were registered (Figure 11). The methods for generating the various data sets are discussed in the following paragraphs. Attempts to classify land use/cover by digital

techniques were frustrated by the rugged terrain which caused deep shadows obscuring large portions of the study area. Thus, land use/cover classes were mapped from visual analyses of Landsat MSS images displayed as false color composites on the additive color viewer. These class boundaries were then transferred to the 1:250,000 enlargement of ONC F-7. Next, a grid of 1000 x 1000 m (4 x 4 mm) cells (corresponding to 100 ha) was registered to the map using well-defined control points. Each of the 2652 grid cells was then assigned a code number from 1 to 10 representing one of the classes. Thus, each grid cell could be referenced by an X, Y, Z value. This layer of data was then stored in an IBM 370/158 computer disk file. Other layers of data, including elevation, slope, precipitation, soils, vegetation, and proximity to streams and roads were compiled from various sources. The layers of inforiiiation stored in the computer can be manipulated according to pre-defined algorithms. One algorithm used in this study maps the grid cells which are common to a predefined set of controlling variables. In this instancr, agricultural land appears to be controlled by precipitation, soil type, and slope. Of these, precipitation varies with elevation, and soil boundaries are flexible. Because the relatively level areas of fertile soil are already used for agricultural purposes, expansion of agricultural land may require farming the steeper slopes. In Figure 12, each 100 ha grid cell in the study area is represented by a symbol keyed to precipitation values, and only those cells with slopes of less than 5 percent common to

3/This section was adapted from Welch, et al (1981), Footnote 16.

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123

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desertic or chestnut and brown soils (the soils suitable for agriculture) are shown on the left-hand map. From this map, it is evident that the rangeland iiimiediately east of UrUmqi is potentially suitable for agriculture. In fact, assuming the data on soils and precipitation are reasonably accurate, an additional 28,000 ha of rangeland can be potentially converted to agricultural land. If slopes to 20 percent can be utilized, as shown on the right-hand map in Figure 12, it is possible to increase the net agricultural land by another 13,000 ha. Unfortunately, the exact requirements for irrigation cannot be determined, nor can the impact of land conversion on pastoral activities. However, it is clear that a GIS approach does offer considerable potential for assessing agricultural lands in remote areas of China. CONCLUSION Mapping agricultural land use developments in three different regions of China from Landsat images has led to the following overall assessments. In tropical south China, cropping patterns are intensive and multi—seasonal wherever slope and soil conditions permit. Emphasis is on rice production in alluvial lowlands and on a winter rotation of wheat or vegetables. Slope land at lower elevations accounts for roughly half of the cultivated land and is used for non-irrigated crops. Correlation of collateral information on soils and crops with data derived from the analyses of Landsat imates permits rice yields to be estimated. In the Nen2 River in northeast China manual interpretations Landsat images indicated of newbasin farmland in the 184,500 km2 study area had been ofreclaimed from marshland 15,300 km and rangeland, with perhaps an additional 37,000 km2 suitable for future reclamation. Classification of crops on two large state farms proved possible through the use of a combination of manual and computer assisted procedures conducted with the aid of crop calendars, reflectance spectra, and image data in photographic and CCT formats. Three crop categories could be identified: (1) soybeans, (2) corn/millet, and (3) harvested wheat/millet. These analyses indicate that at the time of the study, emphasis was being placed on food crops such as wheat and corn, which to some extent have replaced soybeans, millet, and gaoliang as the principal crops in the northeast. Attempts are being made to expand agricultural production in the remote land areas of China. The GIS approach, which permits variables to be stored in a computer as layers of iriforination registered to a specific map reference, provides a powerful nechanisni for modeling the variables controlling agricultural land productivity in the arid and remote regions of western China. ACKNOWLEDGMENTS These studies of China were supported by the National Science Foundation grant NSF SES 8040696. REFERENCES 1.

Welch, R., C. W. Pannell and C. P. Lo, Annals of the Association of American Geogra-ET1 w299 310 m501 310 p~j~6S,595 (1975) Welch, R., H. C. Lo and C. W. Pannell, Photogrametric Engineering and Remote Sensing 45, 1211 (1979) Welch, R. and C. W. Pannell

,

Photogrammetric Record 10, 575 (1982)

2.

Atlas Publishers (Ditu Chubanshe), Provincial Atlas of the People’s Rep~~ic of China Beijing, 1977.

3.

Wu, Youwen, Geographical Knowledge (Dili zhishi) 4, 1 (1979)

4.

Leeming, Frank, Asian SurveIlY, 540 (1979)

5.

American Plant Studies Delegation, Plant Studies in the People’s Republic of China: Trip Report of the American Plant Studies Delegation, National Academy of Sciences, Washington, B. C., 1975.

6.

Miu, Hung-chi (Hongqi Miao) and Hua—tsai Chen (Huacai Chen), Acta Geographic Sinica (Dili Xuebao) 32, 74 (1964) Wu, ~cit.

7.

No. 3.

Miu and Chen,

~

cit. No. 6.

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,

Agricultural

Developments

itt China

8.

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