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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (2) : 202-212     DOI: 10.6046/gtzyyg.2020183
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Change of cultivated land and its driving factors in Alar reclamation area in the past thirty years
SONG Qi(), FENG Chunhui, GAO Qi, WANG Mingyue, WU Jialin, PENG Jie()
College of Plant Science, Tarim University, Alar 843300, China
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Abstract  

The clarification of the dynamic change trend of cultivated land and its driving factors is an important basis for ensuring national food security, rationally developing and utilizing soil and water resources and adjusting land use structure. Taking Alar reclamation area in southern Xinjiang as an example and based on Landsat satellite remote sensing images, population, GDP and other data of seven important periods from 1990 to 2019, the authors selected the best algorithm to interpret remote sensing images by comparing the accuracy of five classification algorithms comprising SAM-CRF, ANN-CRF, MDC-CRF, MLC-CRF and SVM-CRF. Next, the characteristics of cultivated land area change, type transformation and spatial dynamic change were analyzed by using the interpretation results, and then the main driving factors, action path and intensity of cultivated land area change were discussed. The results show that the SVM-CRF algorithm has the highest classification accuracy among the five classification algorithms, with the overall accuracy of 0.95 and the Kappa coefficient of 0.94. The overall accuracy of the other four algorithms is between 0.65 and 0.89, and the Kappa coefficient is between 0.58 and 0.86. The area of cultivated land in the study area has continued to increase in the past three decades, and the net increase in cultivated land area is 729.97 km 2 (312.21%). Cultivated land transfer-in and transfer-out has shown a trend of outward expansion and inward contraction, respectively. Total population, GDP, Total Investment in Fixed Assets, gross agricultural product and cotton price are the main driving factors for the change of cultivated land area,among which GDP has the greatest direct impact on the change of cultivated land area, while cotton price has the least impact. Except that GDP has a negative effect on cultivated land area, the other four factors have a positive effect on cultivated land area, and the overall performance of the five factors is a positive effect.

Keywords change of cultivated land      driving factors      remote sensing      Landsat      land use/cover     
ZTFLH:  TP79  
Corresponding Authors: PENG Jie     E-mail: tarimsongqi@163.com;pjzky@163.com
Issue Date: 21 July 2021
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Qi SONG
Chunhui FENG
Qi GAO
Mingyue WANG
Jialin WU
Jie PENG
Cite this article:   
Qi SONG,Chunhui FENG,Qi GAO, et al. Change of cultivated land and its driving factors in Alar reclamation area in the past thirty years[J]. Remote Sensing for Land & Resources, 2021, 33(2): 202-212.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020183     OR     https://www.gtzyyg.com/EN/Y2021/V33/I2/202
Fig.1  Geographic position of study area
Fig.2  Satellite remote sensing images of Alar reclamation area from 1990 to 2019
类型 包含地类 假彩色影像特征 假彩色影像 实地照片
耕地 棉地、玉米地、小麦地、高粱地等 鲜红色,形状规则,一般为矩形,均匀分布
林草地 胡杨、草场、柽柳等 红色,形状不规则,一般分布在河流及道路两侧
园地 苹果地、葡萄地、枣地、香梨地等 暗红色,形状相对规则,连片分布
水体 河流、水库、沟渠等 蓝色,形状规则,有明显边界线
建设用地 城镇、学校、乡村、工矿等各类建设用地 白色、浅蓝色,形状规则,连片分布
沙地 沙漠、沙质土壤 淡黄色,形状不规则,有清晰纹理,成片分布
盐碱地 受到盐渍化污染的土壤 白色,形状不规则,有明显白色斑块,成片分布
其他 湿地、裸石、荒地等 棕色或黑色,形状不规则,多分布于水体及荒地附近
Tab.1  Classification system and interpretation signs of Alar reclamation area
Fig.3  Number and distribution of sample points for each category
类型 SAM—CRF ANN—CRF MDC—CRF MLC—CRF SVM—CRF
UA/% PA/% UA/% PA/% UA/% PA/% UA/% PA/% UA/% PA/%
耕地 73.85 74.15 91.56 77.87 92.47 83.30 94.47 89.90 98.41 99.77
林草地 34.50 45.87 65.20 68.33 72.24 69.86 83.59 77.21 97.33 94.89
园地 55.63 63.23 76.44 84.08 81.09 85.30 92.05 87.91 96.89 97.64
水体 85.48 72.13 88.65 79.47 90.66 82.84 91.67 85.69 92.81 93.34
建设用地 42.13 47.57 63.30 71.29 67.70 72.92 83.15 86.12 96.43 90.40
沙地 78.89 48.73 85.02 76.09 88.11 86.38 89.11 88.49 95.32 89.49
盐碱地 60.37 87.37 73.51 90.31 81.59 91.57 82.23 92.71 95.63 93.74
其他 52.60 72.03 78.61 77.82 81.57 83.96 88.46 88.19 91.93 98.95
Kappa 0.58 0.77 0.82 0.86 0.94
OA 0.64 0.81 0.84 0.89 0.95
Tab.2  Accuracy comparison of SAM—CRF,ANN—CRF,MDC—CRF, MLC—CRF and SVM—CRF classification results in 2019
Fig.4  The classification results of each method were compared
Fig.5  Local region comparison of SVM and SVM—CRF algorithm results

类别 		预测类别
总计
UA/%
耕地 林草地 园地 水体 建设用地 沙地 盐碱地 其他
真实类别 耕地 20 896 207 34 8 15 52 16 6 21 234 98.41
林草地 22 10 129 167 26 0 4 25 34 10 407 97.33
园地 22 235 8 867 1 0 8 10 9 9 152 96.89
水体 0 0 0 17 579 0 1 334 7 20 18 940 92.81
建设用地 0 0 0 8 2 485 68 10 6 2 577 96.43
沙地 0 0 0 677 0 17 152 132 33 17 994 95.32
盐碱地 0 51 0 9 193 418 16 855 99 17 625 95.63
其他 4 52 13 525 56 130 926 19 440 21 146 91.93
总计 20 944 10 674 9 081 18 833 2 749 19 166 17 981 19 647 119 075
PA/% 99.77 94.89 97.64 93.34 90.40 89.49 93.74 98.95 OA/% 95.24
Tab.3  Evaluation of land type accuracy in Alar reclamation area
Fig.6  1990—2019 land use/cover in Alar reclamation area
Fig.7  The proportion of each land type in Alar reclamation area from 1990 to 2019
Fig.8  Average annual change rate of land use types in Alar reclamation area from 1990 to 2019
年份 1990年 合计
耕地 林草地 园地 水体 建设用地 沙地 盐碱地 其他
2019年 耕地 — 94.91 32.95 11.37 1.27 54.97 312.99 345.74 854.20
林草地 52.49 — 17.74 15.94 0.92 57.43 260.87 258.29 663.68
园地 47.67 59.68 — 2.43 0.52 17.96 73.15 113.17 314.58
水体 1.08 12.18 0.39 — 0.09 15.20 67.03 46.20 142.17
建设用地 10.59 12.90 1.82 0.29 — 1.38 8.03 22.55 57.56
沙地 0.12 4.93 0.15 4.48 0.02 — 105.39 49.29 164.38
盐碱地 3.13 4.09 0.61 5.05 0.13 351.32 — 109.86 474.19
其他 9.17 40.13 3.49 35.34 0.53 88.52 177.01 — 354.19
合计 124.25 228.82 57.15 74.90 3.48 586.78 1 004.47 945.10 —
Tab.4  Transformation matrix in all parts of Alar reclamation area from 1990 to 2019(km2)
Fig.9  Spatial dynamic change chart of cultivated land from 1990 to 2019
年份 总人口/
万人 		非农业人
口/万人 		GDP/
亿元 		全社会固定资
产投资/亿元 		第一产
业/亿元 		农业生产
总值/亿元 		棉花价格/
(元·kg-1) 		年均气
温/℃ 		年均降
水量/mm 		耕地面
积/km2
1990年 -1.49 -0.97 -0.79 -0.74 -0.91 -0.82 -1.78 1.05 -0.65 -1.48
1994年 -0.95 -0.87 -0.72 -0.72 -0.79 -0.84 -0.57 0.42 -1.25 -0.95
2000年 -0.16 -0.81 -0.66 -0.67 -0.73 -0.75 -0.29 -0.85 -1.22 -0.40
2006年 0.19 -0.11 -0.47 -0.47 -0.46 -0.46 0.46 -1.17 0.77 0.12
2010年 0.24 0.09 -0.20 -0.25 0.18 0.10 1.25 -0.85 0.89 0.55
2015年 0.65 1.17 1.19 1.13 1.15 1.49 0.15 1.37 0.55 0.84
2019年 1.52 1.50 1.66 1.71 1.56 1.27 0.79 0.04 0.91 1.31
Tab.5  Change of cultivated land area in Alar reclamation area from 1990 to 2019 and the value after standardization of relevant indexes
相关系数 总人口/
万人 		非农业人
口/万人 		GDP/
亿元 		全社会固定资
产投资/亿元 		第一产
业/亿元 		农业生产
总值/亿元 		棉花价格/
(元·kg-1) 		年均气
温/℃ 		年均降
水量/mm 		耕地面
积/km2
总人口/万人 —
非农业人口/万人 0.91 —
GDP/亿元 0.86 0.97 —
全社会固定资产投资/亿元 0.85 0.97 0.99 —
第一产业/亿元 0.89 0.99 0.99 0.98 —
农业生产总值/亿元 0.83 0.97 0.97 0.96 0.98 —
棉花价格/
(元·kg-1) 		0.82 0.65 0.50 0.49 0.62 0.55 —
年均气温/℃ -0.22 0.17 0.31 0.31 0.23 0.33 -0.54 —
年均降水量/mm 0.74 0.79 0.64 0.63 0.72 0.70 0.75 -0.17 —
耕地面积/km2 0.98 0.93 0.85 0.84 0.91 0.87 0.87 -0.17 0.81 —
Tab.6  Variable correlation coefficient matrix of the driving force of farmland change
因子 直接作用 间接作用
总人口/
万人 		GDP/
亿元 		全社会固
定资产投
资/亿元 		农业生
产总值/
亿元 		棉花
价格/
(元·kg-1)
总人口/万人 0.57** -1.29** 0.95** 0.52** 0.21**
GDP/亿元 -1.51** 0.49** 1.12** 0.61** 0.13**
全社会
固定资
产投资/
亿元 		1.12** 0.48** -1.51** 0.61** 0.12**
农业生
产总值/
亿元 		0.63** 0.47** -1.46** 1.07** 0.14**
棉花价
格/(元·kg-1) 		0.26** 0.47** -0.76** 0.54** 0.34**
Tab.7  The path coefficient of driving factor to cultivated land area
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