基于LANDSAT的辽宁省朝阳市1985-2020年退耕还林工程动态监测

帅艳民; 霍燃; 曲歌; 邵聪颖; 田艳君

doi:10.13961/j.cnki.stbctb.2023.03.015

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基于LANDSAT的辽宁省朝阳市1985-2020年退耕还林工程动态监测

试验研究

- 基于LANDSAT的辽宁省朝阳市1985-2020年退耕还林工程动态监测
- Dynamic Monitoring of Grain for Green Program at Chaoyang City, Liaoning Province Based on Landsat Images During 1985-2020
- 水土保持通报 2023年43卷第3期页码：113-122
- 作者机构：
  
  1. 辽宁工程技术大学测绘与地理科学学院,辽宁,阜新,123000
  2. 浙江师范大学地理与环境科学学院,浙江,金华,321004
  3. 中国科学院中亚生态与环境研究中心,新疆,乌鲁木齐,830011
  4. 中国科学院大学,北京,100049
- 作者简介：
- 基金信息：
  
  辽宁工程技术大学项目“智慧农业遥感监测创新培育团队”(LNTU20TD-23)
- DOI：10.13961/j.cnki.stbctb.2023.03.015
  中图分类号： P23;F301.21
- 纸质出版：2023
- 稿件说明：
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帅艳民, 霍燃, 曲歌, 等. 基于LANDSAT的辽宁省朝阳市1985-2020年退耕还林工程动态监测[J]. 水土保持通报, 2023,43(3):113-122.

Shuai Yanmin, Huo Ran, Qu Ge, et al. Dynamic Monitoring of Grain for Green Program at Chaoyang City, Liaoning Province Based on Landsat Images During 1985-2020[J]. Bulletin of Soiland Water Conservation, 2023, 43(3): 113-122.
帅艳民, 霍燃, 曲歌, 等. 基于LANDSAT的辽宁省朝阳市1985-2020年退耕还林工程动态监测[J]. 水土保持通报, 2023,43(3):113-122. DOI： 10.13961/j.cnki.stbctb.2023.03.015.

Shuai Yanmin, Huo Ran, Qu Ge, et al. Dynamic Monitoring of Grain for Green Program at Chaoyang City, Liaoning Province Based on Landsat Images During 1985-2020[J]. Bulletin of Soiland Water Conservation, 2023, 43(3): 113-122. DOI： 10.13961/j.cnki.stbctb.2023.03.015.

摘要

[目的

]

分析辽宁省朝阳市退耕还林工程作用下的土地覆被情况，跟踪监测年际间地类变动趋势，探讨退耕还林工程对朝阳市荒山、耕地、林地和草地的影响，为朝阳市土地资源合理规划利用提供科学支撑。[方法

]

基于1985—2020年逐期Landsat影像，利用C5.0算法提取该地区地表覆被时空分布信息，借助土地利用动态度、土地利用转移矩阵等分析36 a间山田林草的动态变化及其驱动因素。[结果

]

①36 a间朝阳市荒山绿化面积达2 745.5 km

，退耕还绿的面积约933 km

，工程成效较为显著。②退耕区和还林区主要分布在坡度＞25°和海拔超过500 m的地区，二者变化主要表现在内部结构的转换，即增加海拔低于500 m和坡度＜25°的耕地比例，基本符合退耕还林政策要求。[结论

]

5个阶段内朝阳市的山田林草在年际间都存在相互转换的情况，相比前3个阶段，后两个阶段在政策实施的加持下“逆过程”现象减少，工程实施方面仍需加强监督，精确理解国家政策和项目技术规程，因地制宜地贯彻有关政策。

Abstract

[Objective] The land cover during the grain for green project (GGP) at Chaoyang City

Liaoning Province were analyzed

and the trend of inter annual land class changes were tracked and monitored

and the impact of the project on barren hills

cultivated land

woodlands

and grasslands at Chaoyang City were discussed

in order to provide scientific support for the rational planning and utilization of land resources at Chaoyang City.[Methods] We used the C5.0 algorithm with Landsat images from 1985 to 2020 to extract the spatial-temporal distribution information of land cover in the region. The dynamic changes and driving factors of forest and grassland in a mountainous area during the 36-year study period were determined by means of land use dynamic attitude and a land use transfer matrix.[Results] ① During the past 36 years

the afforestation area of barren hills at Chaoyang City reached 2 745.5 km2

and the afforestation area of cultivated land was about 933 km2. GGP had achieved remarkable benifits. ② The returned farmland and reforestation areas were mainly located in areas with slopes >25° and altitudes over 500 m. The changes were mainly reflected in the transformation of the internal structure

that is

increasing the proportion of arable land with elevations below 500 m and slopes <25°

basically meeting the requirements of the GGP policy.[Conclusion] The barren hills

cultivated land

forests and grassland of Chaoyang City in the five GGP stages were transformed from one year to another. Compared with the first three GGP stages

the "reverse process" phenomenon in the latter two GGP stages was reduced in response to policy adjustments and implementation. In terms of project implementation

it will still be necessary to strengthen supervision

accurately understand national policies and project technical regulations

and implement policies according to local conditions.

关键词

Keywords

references

Ramankutty N, Foley J A, Olejniczak N J. People on the land:Changes in global population and croplands during the 20th century[J]. Ambio a Journal of the Human Environment, 2002,31(3):251-257.

Young A. Is there really spare land? A critique of estimates of available cultivable land in developing countries[J]. Environment Development and Sustainability, 1999,1(1):3-18.

陈永富.退耕还林工程定量监测与评价研究[D].北京林业大学,2006.

汪滨,张志强.黄土高原典型流域退耕还林土地利用变化及其合理性评价[J].农业工程学报,2017,33(7):235-245,316.

Zhou Decheng, Zhao Shuqing, Zhu Chao. The grain for green project induced land cover change in the Loess Plateau:A case study with Ansai County, Shanxi Province, China[J]. Ecological Indicators, 2012,23:88-94.

杨亮彦,黎雅楠,范鸿建.榆林市土地利用/覆被时空格局变化及退耕还林工程对其的效应[J].自然资源情报,2022(9):37-44.

田璐,许月卿,孙丕苓.退耕还林还草工程对土地利用/覆被变化及景观格局的影响:以张家口市为例[J].中国农业大学学报,2015,20(4):205-213.

Bai Xuefeng, Wang Bin, Qi Ying. The effect of returning farmland to grassland and coniferous forest on watershed runoff:A case study of the Naoli River basin in Heilongjiang Province, China[J]. Sustainability, 2021,13(11):6264.

李登科,王钊.退耕还林后陕西省植被覆盖度变化及其对气候的响应[J].生态学杂志,2020,39(1):1-10.

张玉东,谭红兵.黄土高原典型干旱区退耕还林后植被覆盖变化研究[J].生态科学,2017,36(1):139-146.

刘逸滨,刘宝元,成城,等.退耕还林草20年来榆林市植被覆盖度时空变化及影响因素分析[J].水土保持学报,2022,36(2):197-208,218.

Liu Zuying, Wang Bing, Zhao Yusen, et al. Monitoring and evaluation of the effects of grain for green project in the upper and middle reaches of China's Yangtze River[J]. The Journal of Applied Ecology, 2018,29(8):2463-2469.

刘祖英,王兵,赵雨森,等.长江中上游地区退耕还林成效监测与评价[J].应用生态学报,2018,29(8):2463-2469.

汪芳甜,安萍莉,蔡璐佳,等.基于RS与GIS的内蒙古武川县退耕还林生态成效监测[J].农业工程学报,2015,31(11):269-277.

孙智辉,雷延鹏,卓静,等.延安北部丘陵沟壑区退耕还林(草)成效的遥感监测[J].生态学报,2010,30(23):6555-6562.

U.S. Geological Survey. Landsat Surface Reflectance Climate Data Records[R] Reston, VA, USA:U.S. Geological Survey, 2014.

U.S. Geological Survey. Landsat Surface Reflectance Climate Data Records[R] Reston, VA, USA:U.S. Geological Survey, 2019.

辽宁省统计局.辽宁统计年鉴[M].北京:中国统计出版局,2007-2017.

Malthouse E C, Mulhern F J. Book review:Handbook of data mining and knowledge discovery[J]. Journal of Marketing Research, 2003,40(3):372-374.

徐涵秋.利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J].遥感学报,2005,9(5):589-595.

Haralick R M, Shanmugam K, Dinstein I. Textural features for image classification[J]. IEEE Transactions on Systems, Man and Cybernetics, 1973,SMC-3(6):610-621.

Deng Chengbin, Wu Changshan. BCI:A biophysical composition index for remote sensing of urban environments[J]. Remote Sensing of Environment, 2012,127:247-259.

Rouse J W, Haas R H, Deering D W. Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation[R]. Nasa/gsfct Type Final Report, 1973.

侯西勇,邸向红,侯婉,等.中国海岸带土地利用遥感制图及精度评价[J].地球信息科学学报,2018,20(10):1478-1488.

盛辉,万红,崔建勇,等.基于TM影像的城市热岛效应监测与预测分析[J].遥感技术与应用,2010,25(1):8-14.

付建新,曹广超,郭文炯.1980-2018年祁连山南坡土地利用变化及其驱动力[J].应用生态学报,2020,31(8):2699-2709.

马彩虹,任志远,李小燕.黄土台塬区土地利用转移流及空间集聚特征分析[J].地理学报,2013,68(2):257-267.

于泽民,郭建英.我国的退耕还林研究进展[J].内蒙古环境科学,2009,21(S1):119-123.

王丹,夏建新,郭泺,等.基于RS和GIS的黔东南地区退耕还林工程成效监测[J].应用基础与工程科学学报,2015,23(S1):30-40.

郑雪,姚顺波,鲁亚楠.退耕还林工程对粮食生产的影响:以山西省为例[J].水土保持通报,2020,40(2):239-246,254.

杨亮彦,黎雅楠,范鸿建.榆林市土地利用/覆被时空格局变化及退耕还林工程对其的效应[J/OL].自然资源情报,2022,(9):37-44.

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