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Article(id=1311999896803177041, tenantId=1146029695717560320, journalId=1311385739772133495, issueId=1311999647825101743, articleNumber=null, orderNo=null, doi=10.6041/j.issn.1000-1298.2025.12.038, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1724947200000, receivedDateStr=2024-08-30, revisedDate=1731945600000, revisedDateStr=2024-11-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1790734751177, onlineDateStr=2026-09-30, pubDate=1766592000000, pubDateStr=2025-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790734751177, onlineIssueDateStr=2026-09-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790734751177, creator=13701087609, updateTime=1790734751177, updator=13701087609, issue=Issue{id=1311999647825101743, tenantId=1146029695717560320, journalId=1311385739772133495, year='2025', volume='56', issue='12', pageStart='1', pageEnd='818', issueExtLink='null', onlineDate='null', pubDate='1766592000000', pubDateStr='2025-12-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790734691815, creator='13701087609', updateTime=1790738411040, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1312015247465083866, tenantId=1146029695717560320, journalId=1311385739772133495, issueId=1311999647825101743, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1312015247465083867, tenantId=1146029695717560320, journalId=1311385739772133495, issueId=1311999647825101743, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=417, endPage=424, ext={EN=ArticleExt(id=1311999898342486610, articleId=1311999896803177041, tenantId=1146029695717560320, journalId=1311385739772133495, language=EN, title=Evaluation of Suitability of Abandoned Cultivated Land for Recultivation Based on AHP-CRITIC-TOPSIS, columnId=1311999800569066336, journalTitle=Transactions of the Chinese Society for Agricultural Machinery, columnName=Agricultural Informatization Engineering, runingTitle=null, highlight=null, articleAbstract=

In order to explore the suitability of abandoned cultivated land for recultivation and improve the utilization rate of cultivated land, taking Lishu County, Jilin Province as the study area, firstly based on the Google Earth Engine platform and the 2022 Sentinel-2 image data of Lishu County, a total of 46 features such as spectral features, vegetation features, texture features, and terrain features were calculated, and random forest algorithm was used to construct the optimal feature set, and finally 39 features were screened to participate in the classification, so as to obtain the scope of abandoned cultivated land. Then the AHP-CRITIC-TOPSIS model was used to construct an evaluation model of the suitability of abandoned cultivated land for recultivation from three dimensions: natural, soil and socio-economic conditions, and the suitability was divided into four grades: highly suitable, moderately suitable, barely suitable and unsuitable, so as to put forward a feasible scheme for the rehabilitation of abandoned cultivated land. The results showed that the accuracy of the classification results of land use types in Lishu County in 2022 was relatively accurate, and the overall classification accuracy and Kappa coefficient were above 95%, indicating that the classification results were consistent with the actual land types and the overall classification effect was good. The area of abandoned cultivated land not suitable for recultivation in Lishu County was 1 673.73 hm2, which was mainly located in the southeast of the study area, and the area of abandoned cultivated land suitable for recultivation was 1 071.67 hm2, which was mainly located in the low-altitude plain area in the northwest of the study area. At present, the implementation of the national cultivated land protection policy indeed improved the use of cultivated land in rural areas, but the problems of abandonment and low utilization efficiency of rural cultivated land were still prominent, and the results can provide a scientific basis for improving the quality of cultivated land and ensuring food security in Lishu County and surrounding areas.

, authors=Hailing JIANG, Xinhui FENG, Shuhan ZHANG, Hailin YU, Jingyi ZHANG, authorsList=Hailing JIANG, Xinhui FENG, Shuhan ZHANG, Hailin YU, Jingyi ZHANG, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311999903098827381, articleId=1311999896803177041, tenantId=1146029695717560320, journalId=1311385739772133495, language=CN, title=基于AHP-CRITIC-TOPSIS的撂荒耕地复耕适宜性评价, columnId=1311999800749421410, journalTitle=农业机械学报, columnName=农业信息化工程, runingTitle=null, highlight=null, articleAbstract=

为探析撂荒耕地复耕适宜性，以提高耕地利用率，本文以吉林省梨树县为研究区，基于谷歌地球引擎平台和梨树县2022年Sentinel-2影像数据，通过计算光谱特征、植被特征、纹理特征、地形特征等共46个特征，运用随机森林算法构建最优特征集，最终筛选39个特征参与分类，得出撂荒耕地范围。采用AHP-CRITIC-TOPSIS模型，分别从自然、土壤、社会经济条件3个维度构建撂荒耕地复耕适宜性评价模型，并将适宜程度划分为高度适宜、中度适宜、勉强适宜和不适宜4个等级，从而提出撂荒耕地复耕可行性方案。研究结果表明，2022年梨树县土地利用类型分类结果精度较高，总体分类精度以及Kappa系数均在95%以上，表明分类结果与实际地类一致程度高，整体分类效果好。梨树县撂荒耕地中不适宜复耕的撂荒耕地面积为1 673.73 hm2，主要位于研究区东南部地区；高度适宜复耕撂荒耕地面积为1 071.67 hm2，主要位于研究区西北部海拔较低的平原地区。目前国家耕地保护政策的落实对农村耕地利用情况确有改善，但农村耕地抛荒、利用效率低等问题依旧突出，研究结果可为梨树县以及周边地区提高耕地质量、保证粮食安全提供科学依据。

, authors=姜海玲, 冯馨慧, 张舒涵, 于海淋, 张竞以, authorsList=姜海玲, 冯馨慧, 张舒涵, 于海淋, 张竞以, authorCompany=null, correspAuthors=null, authorNote=

姜海玲（1986—），女，副教授，博士，主要从事高光谱遥感基础理论及应用研究，E-mail：

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姜海玲（1986—），女，副教授，博士，主要从事高光谱遥感基础理论及应用研究，E-mail：

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姜海玲（1986—），女，副教授，博士，主要从事高光谱遥感基础理论及应用研究，E-mail：

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(in Chinese), articleTitle=Basic farmland demarcation coupling comprehensive quality and spatial connectivity of cultivated land, refAbstract=null)], funds=[Fund(id=1311999908295570094, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999896803177041, awardId=20240701167FG, language=CN, fundingSource=吉林省科技发展计划项目(20240701167FG), fundOrder=null, country=null), Fund(id=1311999908358484655, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999896803177041, awardId=41701424, language=CN, fundingSource=国家自然科学基金青年基金项目(41701424), fundOrder=null, country=null), Fund(id=1311999908413010608, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999896803177041, awardId=JJKH20230502KJ, language=CN, fundingSource=吉林省教育厅科学技术研究项目(JJKH20230502KJ), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1311999903346291318, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999896803177041, xref=null, 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figureFileSmall=91sLs1tmmh35WW4fWciTSQ==, figureFileBig=9tDTqQ2e1a11gb6JdmOT8w==, tableContent=null), ArticleFig(id=1311999907599315621, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999896803177041, language=CN, label=图6, caption=撂荒耕地复耕综合适宜性分区, figureFileSmall=91sLs1tmmh35WW4fWciTSQ==, figureFileBig=9tDTqQ2e1a11gb6JdmOT8w==, tableContent=null), ArticleFig(id=1311999907670618790, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999896803177041, language=EN, label=Tab. 1, caption=

Construction of suitability index system for rehabilitation of abandoned cultivated land

, figureFileSmall=null, figureFileBig=null, tableContent=
目标层准则层层指标指标层内容指标指向性
撂荒耕地复耕适宜性评价自然条件AA1坡度-
A2坡向-
土壤条件BB1pH值-
B2有机质含量+
B3图斑面积+
B4土壤质地-
社会经济条件CC1与道路距离-
C2与水源距离-
C3与居民点距离-
C4人口数+
), ArticleFig(id=1311999907737727655, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999896803177041, language=CN, label=表1, caption=

撂荒耕地复耕适宜性指标体系构建

, figureFileSmall=null, figureFileBig=null, tableContent=
目标层准则层层指标指标层内容指标指向性
撂荒耕地复耕适宜性评价自然条件AA1坡度-
A2坡向-
土壤条件BB1pH值-
B2有机质含量+
B3图斑面积+
B4土壤质地-
社会经济条件CC1与道路距离-
C2与水源距离-
C3与居民点距离-
C4人口数+
), ArticleFig(id=1311999907821613736, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999896803177041, language=EN, label=Tab. 2, caption=

Evaluation index system judgment matrix

, figureFileSmall=null, figureFileBig=null, tableContent=
准则层自然条件A土壤条件B社会经济条件C权重
自然条件A1320.52
土壤条件B1/311/30.14
社会经济条件C1/2310.34
), ArticleFig(id=1311999907892916905, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999896803177041, language=CN, label=表2, caption=

评价指标体系判断矩阵

, figureFileSmall=null, figureFileBig=null, tableContent=
准则层自然条件A土壤条件B社会经济条件C权重
自然条件A1320.52
土壤条件B1/311/30.14
社会经济条件C1/2310.34
), ArticleFig(id=1311999907993580202, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999896803177041, language=EN, label=Tab. 3, caption=

Comprehensive weight value of evaluation index of suitability of abandoned cultivated land for recultivation in Lishu County

, figureFileSmall=null, figureFileBig=null, tableContent=
准则层指标层综合权重
条件准则权重指标指标权重
自然条件A0.52坡度A10.110.19
坡向A20.110.19
土壤条件B0.14pH值B10.130.06
有机质含量B20.090.04
图斑面积B30.110.05
土壤质地B40.080.04
社会经济条件C0.34与道路距离C10.090.10
与水源距离C20.120.14
与居民点距离C30.070.09
人口数C40.090.10
), ArticleFig(id=1311999908073271979, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999896803177041, language=CN, label=表3, caption=

梨树县撂荒耕地复耕适宜性评价指标综合权重

, figureFileSmall=null, figureFileBig=null, tableContent=
准则层指标层综合权重
条件准则权重指标指标权重
自然条件A0.52坡度A10.110.19
坡向A20.110.19
土壤条件B0.14pH值B10.130.06
有机质含量B20.090.04
图斑面积B30.110.05
土壤质地B40.080.04
社会经济条件C0.34与道路距离C10.090.10
与水源距离C20.120.14
与居民点距离C30.070.09
人口数C40.090.10
), ArticleFig(id=1311999908148769452, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999896803177041, language=EN, label=Tab. 4, caption=

Ranking of evaluation results

, figureFileSmall=null, figureFileBig=null, tableContent=
评价指标正理想解距离D+负理想解距离D-相对接近度T排序结果
坡度A11.2893.8810.7511
坡向A21.7313.2230.6513
pH值B11.6723.0090.6434
有机质含量B23.6691.1710.24210
图斑面积B32.4661.4210.2729
土壤质地B42.4142.4630.5057
与道路距离C12.0062.9040.5845
与水源距离C22.4752.7670.5286
与居民点距离C32.8511.9590.4078
人口数C41.4383.4660.7072
), ArticleFig(id=1311999908211684013, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999896803177041, language=CN, label=表4, caption=

评价结果排序

, figureFileSmall=null, figureFileBig=null, tableContent=
评价指标正理想解距离D+负理想解距离D-相对接近度T排序结果
坡度A11.2893.8810.7511
坡向A21.7313.2230.6513
pH值B11.6723.0090.6434
有机质含量B23.6691.1710.24210
图斑面积B32.4661.4210.2729
土壤质地B42.4142.4630.5057
与道路距离C12.0062.9040.5845
与水源距离C22.4752.7670.5286
与居民点距离C32.8511.9590.4078
人口数C41.4383.4660.7072
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基于AHP-CRITIC-TOPSIS的撂荒耕地复耕适宜性评价
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姜海玲 , 冯馨慧 , 张舒涵 , 于海淋 , 张竞以
农业机械学报 | 农业信息化工程 2025,56(12): 417-424
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农业机械学报 |农业信息化工程 2025 , 56 (12) : 417 -424
基于AHP-CRITIC-TOPSIS的撂荒耕地复耕适宜性评价
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姜海玲 , 冯馨慧, 张舒涵, 于海淋, 张竞以
作者信息
  • 吉林师范大学地理科学与旅游学院，四平　136000
作者简介:

姜海玲（1986—），女，副教授，博士，主要从事高光谱遥感基础理论及应用研究，E-mail：

Evaluation of Suitability of Abandoned Cultivated Land for Recultivation Based on AHP-CRITIC-TOPSIS
Hailing JIANG , Xinhui FENG, Shuhan ZHANG, Hailin YU, Jingyi ZHANG
Affiliations
  • College of Geography and Tourism, Jilin Normal University, Siping 136000, China
出版时间: 2025-12-25 doi: 10.6041/j.issn.1000-1298.2025.12.038
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摘要
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为探析撂荒耕地复耕适宜性，以提高耕地利用率，本文以吉林省梨树县为研究区，基于谷歌地球引擎平台和梨树县2022年Sentinel-2影像数据，通过计算光谱特征、植被特征、纹理特征、地形特征等共46个特征，运用随机森林算法构建最优特征集，最终筛选39个特征参与分类，得出撂荒耕地范围。采用AHP-CRITIC-TOPSIS模型，分别从自然、土壤、社会经济条件3个维度构建撂荒耕地复耕适宜性评价模型，并将适宜程度划分为高度适宜、中度适宜、勉强适宜和不适宜4个等级，从而提出撂荒耕地复耕可行性方案。研究结果表明，2022年梨树县土地利用类型分类结果精度较高，总体分类精度以及Kappa系数均在95%以上，表明分类结果与实际地类一致程度高，整体分类效果好。梨树县撂荒耕地中不适宜复耕的撂荒耕地面积为1 673.73 hm2，主要位于研究区东南部地区；高度适宜复耕撂荒耕地面积为1 071.67 hm2，主要位于研究区西北部海拔较低的平原地区。目前国家耕地保护政策的落实对农村耕地利用情况确有改善，但农村耕地抛荒、利用效率低等问题依旧突出，研究结果可为梨树县以及周边地区提高耕地质量、保证粮食安全提供科学依据。

撂荒耕地  /  AHP-CRITIC-TOPSIS  /  复耕适宜性  /  评价

In order to explore the suitability of abandoned cultivated land for recultivation and improve the utilization rate of cultivated land, taking Lishu County, Jilin Province as the study area, firstly based on the Google Earth Engine platform and the 2022 Sentinel-2 image data of Lishu County, a total of 46 features such as spectral features, vegetation features, texture features, and terrain features were calculated, and random forest algorithm was used to construct the optimal feature set, and finally 39 features were screened to participate in the classification, so as to obtain the scope of abandoned cultivated land. Then the AHP-CRITIC-TOPSIS model was used to construct an evaluation model of the suitability of abandoned cultivated land for recultivation from three dimensions: natural, soil and socio-economic conditions, and the suitability was divided into four grades: highly suitable, moderately suitable, barely suitable and unsuitable, so as to put forward a feasible scheme for the rehabilitation of abandoned cultivated land. The results showed that the accuracy of the classification results of land use types in Lishu County in 2022 was relatively accurate, and the overall classification accuracy and Kappa coefficient were above 95%, indicating that the classification results were consistent with the actual land types and the overall classification effect was good. The area of abandoned cultivated land not suitable for recultivation in Lishu County was 1 673.73 hm2, which was mainly located in the southeast of the study area, and the area of abandoned cultivated land suitable for recultivation was 1 071.67 hm2, which was mainly located in the low-altitude plain area in the northwest of the study area. At present, the implementation of the national cultivated land protection policy indeed improved the use of cultivated land in rural areas, but the problems of abandonment and low utilization efficiency of rural cultivated land were still prominent, and the results can provide a scientific basis for improving the quality of cultivated land and ensuring food security in Lishu County and surrounding areas.

abandonment of cultivated land  /  AHP-CRITIC-TOPSIS  /  evaluation of the suitability  /  recultivation
姜海玲, 冯馨慧, 张舒涵, 于海淋, 张竞以. 基于AHP-CRITIC-TOPSIS的撂荒耕地复耕适宜性评价. 农业机械学报, 2025 , 56 (12) : 417 -424 . DOI: 10.6041/j.issn.1000-1298.2025.12.038
Hailing JIANG, Xinhui FENG, Shuhan ZHANG, Hailin YU, Jingyi ZHANG. Evaluation of Suitability of Abandoned Cultivated Land for Recultivation Based on AHP-CRITIC-TOPSIS[J]. Transactions of the Chinese Society for Agricultural Machinery, 2025 , 56 (12) : 417 -424 . DOI: 10.6041/j.issn.1000-1298.2025.12.038
正文
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0　引言
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耕地作为粮食生产基础资源，也是保障粮食安全、维护社会经济稳定的基础和保障[1]。近年来，党中央、国务院高度重视耕地保护问题，提出要扎紧耕地保护的“篱笆”，守住18亿亩耕地红线，但部分地区耕地“非粮化”“非农化”现象依旧突出[2-3]。我国耕地撂荒现象主要出现于20世纪80年代中后期，土地抛荒现象成因为：随着我国经济高速发展，非农就业机会增多，大量农村劳动力向城镇转移，导致农业有效劳动力投入不足，进而影响耕地利用[4]。由于耕地细碎化，分散的耕地资源增加农业生产成本、降低农业产出，导致农民更倾向于从事经济收益更高的非农生产活动[5-6]。
耕地等农业生产资源减少会严重影响区域粮食产量[7-8]，2021年吉林省人民政府办公厅印发《吉林省坚决制止耕地“非农化”行为防止耕地“非粮化”稳定粮食生产工作方案》，指出为全面落实党中央、国务院决策部署，坚决制止耕地“非农化”行为、防止耕地“非粮化”，采取“长牙齿”的硬措施，落实最严格的耕地保护制度，切实稳定粮食生产，严防死守耕地红线和国家粮食安全生命线，坚决扛起耕地保护和粮食安全的政治责任。因此，探索撂荒耕地复耕适宜性，对于维持耕地资源可持续利用具有重要意义。
耕地撂荒现象最早出现在20世纪初的欧洲[9]，发生区域主要是西欧山区、广阔的草原以及东欧土壤贫瘠、难开发的区域。HE等[10]利用Landsat时间序列的时空片段来检测耕地撂荒的程度和具体时间。WU等[11]利用随机森林分类法生成时序农田概率数据集，再采用LandTrendr算法提取时间序列耕地概率数据集。20世纪中后期，我国开始出现耕地撂荒现象，学者们对此进行了探究。徐中春等[12]借助CiteSpace软件对我国耕地撂荒进行定量分析。秦小敏等[13]应用决策树分类法提取了凉山州孙水河流域的撂荒地分布数据。肖文菊等[14]利用Sentinel-2数据构建归一化植被指数时序曲线，基于振幅差异以及最佳振幅分割阈值选取，构建撂荒地识别规则。目前国内学者们大多针对不同尺度耕地撂荒进行研究，而缺少针对撂荒耕地分布特征研究以及改善措施。综合我国农业大国基本国情，需要充分落实耕地利用优先序，该复耕要限期恢复耕种，坚持分类处置，化解存量，有序推进撂荒耕地恢复耕种。
从国内外研究现状来看，现有的主要评价方法有熵权法[15]、层次分析法[16]、模糊综合评价法[17]、客观赋权法[18]、灰色预测模型[19]等。这些方法主观性强，使得综合评价结果存在一定偏差，不够精确。为解决上述不足，本文以吉林省梨树县为研究区，基于谷歌地球引擎（Google Earth Engine，GEE）平台的Sentinel-2影像，通过随机森林算法[20]进行特征优选，最终提取撂荒耕地空间分布范围。综合考虑自然条件、土壤条件和社会经济条件，基于AHP法（Analytic hierarchy process）和CRITIC法（Criteria importance though intercrieria correlation）确定综合权重，最后引入TOPSIS法（Technique for order preference by similarity to an ideal solution）构建撂荒耕地复耕适宜性评价模型。划分撂荒耕地复耕适宜性等级，揭示其复耕适宜性，以期为耕地可持续发展提供科学依据。
1　研究区概况
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梨树县，隶属于吉林省四平市，地处松辽平原腹地，属于北温带半湿润大陆季风性气候，四季分明，雨热同季，作物生长期日照、降水较充足。东、北两面有蜿蜒的东辽河环绕，西、南有条子河、招苏台河、兴开河、叶赫河，这使得梨树县境内拥有充足的水资源，耕地得以高效利用。地势南高北低，东南部有大黑山余脉斜贯，北部为低洼平原，中部是波状台地，梨树县沃壤无垠，土质肥沃，水源充足，宜于耕作。梨树县研究区位置示意图如图1所示。
2　材料与方法
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2.1　数据来源
本文所有指标数据来源为：pH值、有机质含量、土壤质地数据源自国家青藏高原科学数据中心（https://data.tpdc.ac.cn/）；人口数据来源于第七次人口普查数据；水系及道路数据来源于OSM网站（https://www.openstreetmap.org/）；数字高程（Digital elevation model，DEM）数据来源于地理空间数据云（http://www.gscloud.cn/），分辨率为30 m；坡度及坡向数据源于DEM数据，由ArcGIS空间分析功能计算得出。遥感数据是GEE云计算平台提供的Sentinel-2遥感影像。
2.2　撂荒耕地识别与提取方法
依据梨树县实际情况确定地物分类体系，设置撂荒耕地、有作物耕地、林地、草地、建设用地、水域共6类地物类型，结合Google Earth高分辨率历史影像目视解译随机选取样本共700个，确保样本在研究区域内均匀分布。基于GEE平台利用2022年Sentinel-2影像，选取4类特征因子：光谱特征、植被特征、纹理特征、地形特征，共46个特征因子。利用随机森林算法对特征因子进行重要性优选，筛选重要性高的特征因子参与分类，进而提取梨树县撂荒耕地。
2.3　撂荒耕地复耕适宜性评价方法
2.3.1　指标体系构建
结合梨树县实际情况，参考目前国际上应用最广泛的土地适宜性评价系统，即联合国粮农组织在1976年提出的《土地评价纲要》。将撂荒耕地复耕适宜程度划分为：高度适宜、中度适宜、勉强适宜和不适宜4个等级。参照《资源环境承载能力和国土空间开发适宜性评价指南（试行）》[21]，选取普适性较高、代表性较强、容易获取的指标因子，在此基础上构建的撂荒耕地复耕适宜性评价指标体系包含3个准则层：自然条件、土壤条件、社会经济条件，如表1所示。
2.3.2　评价指标标准化
由于不同评价指标之间不具备可比性，为了保证评价过程规范化和科学化，通过将不同指标依据不同作用划分为正向指标（+）和负向指标（-），采用极差标准法对各项指标数据进行标准化处理，以消除不同指标之间的量纲影响。
（1）正向指标标准化处理计算式为
式中
      x——指标原始值
      xmin——指标最小值
      xmax——指标最大值
（2）负向指标标准化处理计算式为
2.3.3　指标权重确定
采用AHP法对撂荒耕地复耕适宜评价系统的3个准则层进行赋权，采用CRITIC法确定指标层权重，最后将2种方法结合得出指标综合权重，兼顾主观性与客观性[22]。
（1）层次分析法
层次分析法是一种主观赋权法，是将判断矩阵中的各项指标对目标重要性两两比较，并进行打分，从而构建判断矩阵，最终得出各个指标权重，如表2所示。
为确保研究结果准确性，需要进行一致性检验分析。借助SPSSAU软件计算，经过指标排序和不断的调整检验，最终得到最大特征值为3.054，CI值为0.027，对应随机一致性RI表查询到RI值为0.52。因此CR=CI/RI=0.052<0.1，通过一致性检验，表明权重无逻辑性错误。
（2）CRITIC分析法
CRITIC法是一种比熵权法和标准离差法更好的客观赋权法，该方法通过标准差和相关系数来反映样本离散性和因子间冲突性，标准差越大说明离散程度越大，因子权重也就越大；相关系数越大说明因子间冲突性越强，其权重也就越小[23]，如表3所示。
在AHP法与CRITIC法的基础上确定综合权重，综合权重计算式为
式中
      Wi——第i个评价对象综合权重
      WAHP——基于AHP法确定的准则层权重
      WCRITIC——基于CRITIC法确定的指标层权重
综合权重结果表明，坡度、坡向2项指标综合权重较大，这2项指标均由自然条件决定，很难受人为因素影响，因此指标权重较大。可见自然条件是影响梨树县撂荒耕地复耕的根本原因。
2.3.4　TOPSIS法
TOPSIS法能够避免主观性因素影响，方便灵活构建多指标的综合分析应用[24]。分维度评价撂荒耕地复耕适宜性，剖析各指标单元的有效性，TOPSIS法能充分挖掘数据信息，通过检测评价单元与多特性最优解集和最劣解集间的相对贴合程度，进行优劣排序和评价[25]。评价对象到正理想解和负理想解距离计算式为
式中
      ——第i个评价对象与正理想解距离
      ——第i个评价对象与负理想解距离
      ——正理想解
      ——负理想解
      zij——标准化值
评价对象的相对接近度计算式为
式中
      T——第i个评价对象的相对接近度
T越大表明该评价单元离理想解越接近，即T越大评价单元质量越高。
3　结果与分析
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3.1　特征优选及分类结果
通过对特征重要性进行排序，发现特征重要性在92处显著下降，因此选择重要性高于92的特征，排除重要性较低的特征因子。经过筛选，共选取39个特征参与分类。运用随机森林分类法得到2022年梨树县土地利用结果（图2），识别到梨树县耕地总面积为260 357.44 hm2，其中有作物耕地面积为252 745.65 hm2，撂荒耕地面积为7 611.79 hm2，撂荒耕地占比为2.9%。对提取结果进行验证，在GEE中采用withRandom函数，选用80%样本训练，20%样本验证，最终得出分类总体精度和Kappa系数均高于95%，提取效果较为准确。为进一步验证和评价分类精度，将分类提取到的耕地总面积和有作物耕地面积与政府文件中数据进行对比。得出梨树县现有耕地面积为262 600 hm2，与耕地总面积提取结果相比，其相对误差为0.85%；2022年梨树县播种面积为248 093.33 hm2，与有作物耕地面积相比，误差为1.9%。可见土地利用分类结果精度较高，可为后续结果提供理论支撑。
3.2　撂荒耕地再利用适宜性综合评价
3.2.1　自然适宜性评价
在自然条件下不适宜复耕的撂荒耕地面积为430.56 hm2，占撂荒耕地总面积的5.7%，主要分布在孟家岭镇及蔡家镇西部，不适宜复耕地区主要受坡度等条件制约，较难开发，这些撂荒耕地再利用的可能性很小；勉强适宜区面积为2 034.63 hm2，占比26.7%；中度适宜区面积为1 784.97 hm2，占比为23.5%；高度适宜区面积为3 361.63 hm2，占比最大为44.1%。自然条件下评价等级较高的撂荒耕地主要分布在孤家子镇、十家堡镇、梨树镇以及小宽镇中部，这部分区域交通便利，土壤条件好，且地势开阔，周围村庄人口密集。若按照高度适宜和中度适宜的撂荒耕地能够实现再利用，则在自然条件下能够实现复垦的撂荒耕地面积为5 146.6 hm2，占撂荒耕地总面积的67.6%，这些耕地可再利用的可能性较高，如图3所示。
3.2.2　土壤适宜性评价
在土壤条件下不适宜复耕的撂荒耕地面积为2 246.75 hm2，占撂荒耕地总面积的29.5%，集中分布在小宽镇、十家堡镇以及蔡家镇中部，不适宜地区主要限制因素为撂荒耕地图斑面积以及土壤有机质含量；勉强适宜区面积为1 583.85 hm2，占比20.8%；中度适宜区面积为2 820.24 hm2，占比37.1%；高度适宜区面积为960.95 hm2，占比12.6%，主要分布在林海镇、榆树台镇中部、万发镇北部、双河乡南部以及梨树镇中部，这些区域土壤条件较好且交通便利，紧邻村庄可以将土地有效利用起来，如图4所示。
3.2.3　社会经济适宜性评价
在社会经济条件下不适宜复耕的撂荒耕地面积为1 973.25 hm2，占撂荒耕地总面积的25.9%，主要呈线性分布在研究区中部以及西部；勉强适宜区面积为3 324.42 hm2，占比43.7%；中度适宜区面积为1 495.35 hm2，占比19.6%，主要围绕勉强适宜撂荒耕地分布；高度适宜区面积为818.77 hm2，占比10.8%，部分村庄的撂荒地块，虽属于自然条件下高度适宜区，但在社会经济条件下属于不适宜开发区，如小宽镇中部撂荒耕地，由此可见社会经济条件是制约耕地撂荒的直接原因，如图5所示。
3.2.4　综合适宜性评价
研究区撂荒耕地总地块为3 717块，其中面积大于0.5 hm2的地块共2 788块，占总地块的75%，呈聚集分布趋势；面积界于0~0.5 hm2的地块共929块，占总地块的25%，呈分散分布趋势。从空间分布上看，撂荒耕地在小宽镇、孟家岭镇以及十家堡镇呈聚集分布特征；通过对指标综合权重进行加权总和，得出综合适宜性得分介于2.64~8.55之间，总体分值较高，平均值5.46，说明综合适宜性可以较好地评价撂荒耕地再利用可能性。
如图6所示，不适宜区总体分值介于2.64~4.73，总面积为1 673.73 hm2，占撂荒耕地总面积的21.9%，主要分布在研究区东南部。该区域地处低山丘陵地区，耕地破碎化严重，难以利用，另外的限制因素为撂荒耕地与道路距离及水源距离，这些区域耕作便利性低、灌溉条件较差、农机作业时间成本高，因此撂荒地复耕难度大。总体上，自然条件以及社会经济条件是不适宜复耕区能否再利用的主要因素。
勉强适宜区总体分值介于4.74~5.61，总面积为2 532.51 hm2，占撂荒耕地总面积的33.3%，主要分布在城区附近，该地区虽地块面积较小，但邻近主城区，劳动力丰富，耕作条件便利。可见尽管细碎地块给农业生产带来一定的难度，但通过合理规划利用，这些地块依旧可以被充分利用。
中度适宜区总体分值介于5.62~6.49，总面积为2 333.88 hm2，占撂荒耕地总面积的30.7%。多数位于小宽镇、孤家子镇、梨树镇等大型村庄，尽管中度适宜区的撂荒耕地土壤条件和地形条件相对较好，具备较强的可复耕性，但由于劳动力外流以及农业收益低等因素，导致其复耕适宜性一般。
在综合条件下高度适宜复耕的区域最小，高度适宜区总体分值介于6.50~8.55，总面积为1 071.67 hm2，占研究区撂荒耕地总面积的14.1%，集中分布在研究区西北部地区，与自然条件下高度适宜区域分布基本一致。通过现有技术改良耕作手段，确保土地正常流转，有助于高度适宜区的撂荒耕地恢复耕种。
3.3　基于TOPSIS法的撂荒耕地复耕适宜评价
由表4可知，采用TOPSIS法确定的梨树县撂荒耕地复耕适宜性评价因子相对接近度T为0.242~0.751。其中，坡度相对接近度最高，为0.751；有机质含量相对接近度最低，为0.242。相对接近度排序前3的指标分别为坡度、人口数、坡向，其中坡度是影响耕地地表径流的重要因素，坡度较小的地区撂荒耕地复耕的可能性更高；随着农村地区人口向城镇化转移，务农劳动力减少，可能导致耕地撂荒现象加剧；处于阴坡耕地，接收到的阳光照射较少，这种情况会影响作物生长速度与产量。由此可见自然条件对撂荒耕地复耕起到突出性作用，是制约撂荒耕地复耕的根本原因，而社会经济条件对于撂荒耕地复耕同样具有较高影响。根据TOPSIS评价结果，从耕地保护、耕地用途管控、加强组织领导以及相关部门协同合作和宣传引导4个方面提出梨树县撂荒耕地复耕的建议。
（1）加强耕地保护。需严格环境监管，治理土壤污染的同时防止新的污染源产生，在农业生产中推广使用低毒、低残留的农药和化肥，减少农业面源污染。通过开展土壤修复、实测土壤配方施肥、完善灌溉排水系统、修整新建田间道路等，提高撂荒耕地利用效率。针对难以利用的撂荒耕地，可以倡导种植不依赖机械生产的作物。
（2）加强耕地用途管控。需严格执行耕地占补平衡管理，加强控制耕地转为林地、草地、园地等其他农用地，防止耕地“非农化”和“非粮化”情况加剧。同时也要避免随意增加经济作物用地的情况发生，一个地区的土地利用现状是长期以来社会、自然、经济综合作用协同发展的结果，具有较强的合理性。在确保土地利用的合理性和可持续性的基础上，逐步增加粮食作物用地的数量，才能更好的保证粮食安全。
（3）加强组织领导以及相关部门协同合作。根据各部门职能，明确各自在撂荒耕地复耕复种工作中的职责分工，形成工作合力。例如，国土资源部门负责耕地占补平衡的监管和考核，农业部门负责耕地质量保护和提升，林业部门负责林地管理等。各部门分工协作共同承担耕地保护和粮食安全的职责。
（4）加强宣传引导。政府各相关部门应充分利用纸质或电子载体，采取多种形式广泛宣传耕地撂荒治理专项行动的有效做法，做好舆论引导，营造全社会依法依规用地、自觉保护耕地的良好氛围。
4　讨论
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应用随机森林法对Sentinel-2数据进行遥感影像解译，将其分为撂荒耕地、有作物耕地、林地、草地、建设用地、水域6大类，在此基础上对撂荒耕地复耕适宜性进行了主客观结合的评价。研究结果表明农村耕地撂荒现象受多维度因素影响，因此须从经济、政策、自然条件、社会等多维度出发，改善农民生产生活环境、提高耕地质量。
与以往的适宜性评价研究相比，AHP-CRITIC-TOPSIS模型融合了定性认识和定量认识，有效降低了主观性的影响，增加实验结果可信度，使整个评价过程更科学，评价结果更可靠。从自然、土壤、社会经济3个维度构建撂荒耕地复耕适宜性评价体系，划分出4类适宜性等级，并探析10个不同影响指标的综合适宜性评价得分，得到的研究结果全面而有效的为耕地保护政策提供理论依据，有利于政府相关部门对于撂荒耕地采取科学的、精准的应对措施，使得耕地保护政策更加科学、合理且具有针对性。选取梨树县作为研究区，研究结果对梨树县撂荒耕地复耕工作有借鉴意义，为提高耕地质量，保证粮食安全提供科学依据。
本文还存在一些不足：①受到多种因素影响，影像解译精度存在一定误差，可能存在错分漏分的现象，使得最终分类结果与实际情况产生出入。②从研究区域来看，研究范围较小，受数据限制，无法从全省或更大范围角度对不同类型撂荒耕地进行更详细的研究，研究结果不全面。③从时间角度看，仅选取一年撂荒耕地进行研究分析，缺少动态研究，无法对未来耕地趋势进行预测。因此，后续可以扩大研究范围，选取多个典型省进行长时序的撂荒耕地变化趋势研究，合理划定可复耕区域，为提高耕地利用率提供科学理论依据。
5　结论
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（1）基于随机森林分类法，筛选重要特征集，得到最优特征参与分类，得出梨树县2022年撂荒耕地为7 611.79 hm2，占耕地总面积的2.9%，总体精度及Kappa系数均大于95%，说明基于GEE平台的随机森林分类法所提取的地类精度较高，从整体来看，符合对撂荒耕地提取的精度要求。
（2）梨树县耕地撂荒现象是多维度因素影响的结果，包括自然条件、社会经济背景和土壤条件等，这些因素相互交织，形成复杂的撂荒态势。通过主客观结合的AHP-CRITIC-TOPSIS模型对梨树县撂荒耕地进行复耕适宜性评价，得出各指标对撂荒耕地复耕的影响由大到小为：坡度、人口数、坡向、pH值、与道路距离、与水源距离、土壤质地、与居民点距离、图斑面积、有机质含量。
（3）综合分析梨树县撂荒耕地空间分布，得出高度适宜的撂荒耕地面积为1 071.67 hm2，占研究区撂荒耕地总面积的14.1%，集中分布在研究区西北部平原地区，土壤条件良好，可见这些土地具备被进一步开发和利用的潜力；中度适宜区撂荒耕地面积为2 333.88 hm2，占撂荒耕地总面积的30.7%，主要分布在大型村庄内；勉强适宜区撂荒耕地面积为2 532.51 hm2，占撂荒耕地总面积的33.3%，临近主城区，经过合理规划可以被重新利用；不适宜撂荒耕地总面积为1 673.73 hm2，占撂荒耕地总面积的21.9%，主要分布在研究区东南部。
基金
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  • 吉林省科技发展计划项目(20240701167FG)
  • 国家自然科学基金青年基金项目(41701424)
  • 吉林省教育厅科学技术研究项目(JJKH20230502KJ)
文献
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2025年第56卷第12期
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doi: 10.6041/j.issn.1000-1298.2025.12.038
  • 接收时间：2024-08-30
  • 首发时间：2026-09-30
  • 出版时间：2025-12-25
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  • 收稿日期：2024-08-30
  • 修回日期：2024-11-19
基金
吉林省科技发展计划项目(20240701167FG)
国家自然科学基金青年基金项目(41701424)
吉林省教育厅科学技术研究项目(JJKH20230502KJ)
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    吉林师范大学地理科学与旅游学院，四平　136000
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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