深度学习支持下地块尺度农田宜机化遥感评价方法

摘要:

农业机械化是推进农业现代化与保障国家粮食安全的关键支撑，而农田宜机化水平是决定农机作业效率的重要基础。然而，现有研究多集中于宜机化的必要性论证与成本效益分析，缺乏一套系统的精细化评价方法，难以科学性与有针对性的指导实施大区域农田宜机化改造工作。该研究基于多源中、高分辨率遥感数据和深度学习语义分割算法，自动提取农田地块与道路等基础设施的空间分布信息，为农田宜机化提供关键数据基础，同时结合数字高程模型与水旱分布数据，从地块形状、规模、平整度以及道路通达度4个维度建立综合评价体系，并以当阳市为研究区域，实现地块尺度农田宜机化水平精细化评估。结果表明：1）语义分割模型在地块提取与道路识别中表现良好，农田地块与道路的面积准确度分别达到0.92和0.79，边界匹配度分别达到0.85和0.86；共识别出农田地块46.8万个，道路总长度9 747.23 km；2）研究区农田宜机化水平空间分布差异显著，高宜机化区集中分布于东南部平原，在中部平原与低丘地带呈分散分布，西部丘陵区的宜机化水平较低；研究区宜机化分级结果显示，宜机化良好的农田面积占比达67.3%，乡镇尺度归因分析表明，除草埠湖镇外，地块形状与规模是各乡镇宜机化水平的共性制约因素，丘陵山区乡镇还普遍面临道路通达度不足与地块平整度较差的问题。该研究构建的评价方法能够有效、精细地表征地块尺度的农田宜机化水平，为大范围农田宜机化动态监测与差异化改造策略制定提供数据支持和决策依据。

Abstract:

The adaptability level of farmland to agricultural machinery (i.e., the degree to which farmland facilitates agricultural machinery) can determine the efficiency and cost of mechanized operations in modern agriculture. However, existing research has focused mostly on the machinery adaptability or cost-benefit analysis after farmland consolidation. It is lacking in a systematic and refined evaluation of national food security. It is often required to implement the high adaptability of the large-scale farmland machinery. This study aims to automatically extract the spatial distribution of farmland parcels and roads using multi-source medium- and high-resolution remote sensing data and the deep learning semantic segmentation network HRNet (High-Resolution Network). A multi-task learning was employed to simultaneously predict the farmland, parcel boundaries, and roads. Parcel boundaries were also refined using conditional probability post-processing. Thereby, fine-scale data were provided to assess the adaptability level of farmland machinery. A 30 m digital elevation model (DEM) was combined with the paddy/dryland distribution data from a random forest classifier. An evaluation system was established from four dimensions: parcel shape, parcel size, surface evenness, and road accessibility. Seven indicators were obtained to determine the weight of each indicator. Criteria Importance Through Intercriteria Correlation (CRITIC) and the parcel-level Farmland Machinery Adaptability Index (FMAI) were subsequently calculated to validate in Dangyang City, Hubei Province. The results show that the HRNet semantic segmentation model performed well in both parcel and road extraction, with an area F1 score of 0.92 for farmland and 0.79 for roads, as well as an edge F1-score (F1-edge) of 0.85 for parcel boundaries and 0.86 for roads. A total of 468 000 farmland parcels and 9747.23 km of roads were identified in the study area. The spatial distribution of farmland machinery adaptability exhibited significant heterogeneity: High-adaptability areas (FMAI > 77.3) were concentrated in the southeastern Juzhang River alluvial plain in the central plains and low hilly areas, while the western hilly and mountainous regions generally shared the low adaptability levels. The grading results indicated that the area proportion of grades 1 and 2 (good adaptability) accounted for 67.3% of the total farmland area, but their parcel number proportion was only 38.6%, indicating the fragmented parcels generally had the lower adaptability grades. Township-scale attribution analysis revealed that the irregular parcel shape and small parcel size were common constraints to machinery adaptability over all townships, except for Caobuhu Town. The additional constraints were attributed to the insufficient road accessibility (road accessibility score 62-70) and low surface evenness (evenness score 78-85) in hilly and mountainous townships (e.g., Wangdian and Miaoqian). The evaluation can effectively and finely characterize the parcel-level farmland machinery adaptability and its limiting factors. The finding can provide data support and decision-making for dynamic monitoring of large-scale machinery adaptability, differentiated consolidation, and high-standard construction in farmland.

图 1 研究区域遥感影像与样本

Figure 1. Remote sensing imagery and samples of the study area

图 2 数字高程模型、坡度与水田旱地分布数据

Figure 2. Digital elevation model, slope and distribution of paddy and dryland

图 3 高分辨率特征网络结构

Figure 3. Architecture of the HRNet

图 4 研究区地块与道路提取结果

Figure 4. Extraction results of parcels and roads in the study area

图 5 研究区农田宜机化指数空间分布

Figure 5. Spatial distribution of farmland machinery adaptability index in the study area

图 6 各乡镇不同农田宜机化等级的面积分布

Figure 6. Area distribution of different farmland machinery adaptability grades in various townships

图 7 各乡镇农田宜机化评价指标面积加权均值对比

Figure 7. Area-weighted mean values of farmland machinery adaptability indicators across townships

图 8 不同比例误差下的宜机化评价指标均值变化

Figure 8. Changes in the mean values of farmland machinery adaptability indicators under different scale errors

图 9 不同比例误差下不同宜机化等级的农田地块数量占比变化

Figure 9. Changes in the share of farmland parcels with different mechanization levels under different scale errors

表 1 精度评价指标

Table 1 Accuracy assessment metrics

指标类别

Metrics category 评价指标

Assessment metrics 计算公式

Formulation 适用对象

Object 说明

Explanation
面积准确度

Area accuracy 精确率Precision$ {\mathrm{Precision}}=\dfrac{{\mathrm{TP}}}{{\mathrm{TP+FP}}} $农田
道路 TP、TN、FP和FN分别表示准确识别的目标像素数量、准确识别的非目标像素数量、目标错分的像素数量以及非目标错分的像元数量
召回率Recall$ {\mathrm{Recall}}=\dfrac{{\mathrm{TP}}}{{\mathrm{TP+FN}}} $
面积综合得分F1$ F1=\dfrac{2\text{×}{\mathrm{Precision}}\text{×}{\mathrm{Recall}}}{{\mathrm{Precision}}+{\mathrm{Recall}}} $
边界匹配度

Edge accuracy 完整度Com$ \mathrm{Com}=\dfrac{L_{\mathrm{pred}}\cap L\mathrm{{truth}}}{L{\mathrm{truth}}} $地块边界
道路 L pred 与 L truth 分别表示模型提取的线状地物和真实的线状地物，∩表示两者在彼此缓冲区内的重叠长度，本研究缓冲区大小设置为2.5 m
精准度Corr$ \mathrm{Corr}=\dfrac{L_{\mathrm{truth}}\cap L_{\mathrm{pred}}}{L_{\mathrm{pred\mathrm{ }}}} $
边界综合得分F1-edge$ F1-\text{edge}=\dfrac{2\text{×}\mathrm{Com}\text{×}\mathrm{Corr}}{\mathrm{Com}+\mathrm{Corr}} $

表 2 农田宜机化评价指标赋分标准

Table 2 Scoring standards of farmland machinery adaptability indicators

一级指标

Primary indicators 二级指标

Secondary indicators 赋分Score
10 20 30 40 50 60 70 80 90 100
地块形状

Parcel shape 地块长宽比-水田 PAR-P(1.0, 1.5](1.5, 2.0](2.0, 2.5](2.5, 3.0]>5.0(3.0, 5.0]
地块长宽比-旱地 PAR-D(1.0, 1.5](1.5, 2.0](2.0, 2.5](2.5, 3.0](3.0, 10.0](10.0, 20.0]>20.0
地块宽度 PW/m[0, 5](5, 10](10, 15](15, 20](20, 25]>25
地块与最小包围盒面积比

PMBAR<0.1[0.1, 0.2)[0.2, 0.3)[0.3, 0.4)[0.4, 0.5)[0.5, 0.6)[0.6, 0.7)[0.7, 0.8)[0.8, 0.9)>0.9
地块规模

Parcel size 面积-水田 Area-P/hm 2(0, 0.03](0.03, 0.06](0.06, 0.13](0.13, 0.20](0.20, 0.33]>0.33
面积-旱地 Area-D/hm 2(0, 0.03](0.03, 0.06](0.06, 0.33](0.33,0.66](0.66, 1.33](1.33, 3.33]>3.33
地块平整度

Surface levelness 坡度 Slope/(°)>25[25, 15)[15, 10)[10, 6)[6, 2)[2, 0]
农田道路通达度

Farmland road accessibility 地块与道路距离 RD/m>100[50, 100)[20, 50)[10, 20)[5, 10)<5
道路宽度 RW/m 0(0, 1.5](1.5, 2.0](2.0, 3.0](3.0, 4.0](4.0, 5.0](5.0, 6.0]>6.0

表 3 模型精度评价

Table 3 Model accuracy evaluation

指标

Metrics 面积准确度Area accuracy 边界匹配度Edge accuracy
Precision Recall F1 Com Corr F1-edge
农田 0.908 0.946 0.927---
地块边界---0.897 0.828 0.859
道路 0.811 0.775 0.792 0.848 0.878 0.862

表 4 农田宜机化指标权重

Table 4 Weights for farmland machinery adaptability indicators

指标

Indicator 地块形状Parcel shape 地块规模

Parcel size 地块平整度

Surface levelness 农田道路通达度

Farmland road accessibility
地块长宽比

PAR 地块宽度

PBW 地块最小包围盒面积比

PMBAR 最近道路距离

RD 道路宽度

RW
主观权重Objective weight 0.083 0.083 0.083 0.250 0.250 0.125 0.125
客观权重Subjective weight 0.177 0.134 0.097 0.141 0.097 0.186 0.168
综合权重Comprehensive weight 0.107 0.081 0.058 0.256 0.176 0.169 0.153

表 5 研究区农田宜机化分级统计结果

Table 5 Grading statistics result of farmland machinery adaptability in the study area

宜机化等级

Grading 宜机化指数

FMAI 数量统计

Number statistics 面积统计

Area statistics 宜机化指标统计

Machinery adaptability indicators statistics
农田地块数

Number of

parcels 数量占比

Percentage of

total number/%农田面积

Farmland area/hm2 面积占比

Percentage of

total number/%地块形状均值

Mean of

P-shape 地块规模均值

Mean of

P-size 地块平整度均值

Mean of SL 农田道路通

达度均值

Mean of FRA
一级 77.3～100 73297 15.65 24869.00 42.40 76.37 74.98 94.06 85.82
二级 67.3～77.3 107234 22.90 14581.13 24.86 68.43 56.32 85.65 81.03
三级 57.3～67.3 108130 23.09 9448.59 16.11 64.92 44.87 84.66 61.54
四级 47.1～57.3 103400 22.08 7495.62 12.78 64.58 43.70 81.61 35.75
五级 20.7～47.1 76243 16.28 2257.48 3.85 57.30 25.67 74.53 24.70
合计 468304 100 58651.82 100 66.20 48.82 84.04 58.11

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