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耕地生產(chǎn)力隱性退化遙感監(jiān)測與影響因素分析
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自然資源部城市國土資源監(jiān)測與仿真重點(diǎn)實(shí)驗(yàn)室開放基金項(xiàng)目(KF-2020-05-026)和國家自然科學(xué)基金項(xiàng)目(41801202)


Remote Sensing Monitoring of Recessive Degradation for Cultivated Land Productivity and Its Influencing Factors
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    以江蘇省永久耕地為例,,基于2001—2019年中分辨率成像光譜儀(Moderate resolution imaging spectroradiometer,,MODIS)遙感影像,開展耕地生產(chǎn)力隱性退化遙感監(jiān)測和影響因素分析,。BFAST(Breaks for additive seasonal and trend)算法用于建模歷史時期耕地生產(chǎn)力變化的預(yù)期行為,,并以此為基準(zhǔn)判斷監(jiān)測時期耕地生產(chǎn)力是否存在隱性退化風(fēng)險?;诘乩硖綔y器,,從3個準(zhǔn)則層的8項(xiàng)指標(biāo)變量對耕地生產(chǎn)力隱性退化進(jìn)行了主導(dǎo)影響因素探測和因子交互分析。研究結(jié)果表明:江蘇省存在生產(chǎn)力隱性退化的耕地比例為21.9%,,具有顯著的空間差異,。西北地區(qū)的徐州市、宿遷市的耕地生產(chǎn)力隱性退化比例最高,,分別為47.2%和43.4%,,且表現(xiàn)出聚集性。東南地區(qū)的蘇州市,、無錫市和南通市的耕地生產(chǎn)力隱性退化比例較低,,均不足10%。因子探測分析表明外流人口數(shù)量,、種植業(yè)從業(yè)人員數(shù)量和農(nóng)業(yè)機(jī)械化總動力3項(xiàng)指標(biāo)對江蘇省耕地生產(chǎn)力隱性退化的解釋力最強(qiáng),。多因子交互耦合后,人口因素與生產(chǎn)條件解釋力增強(qiáng)最為顯著,。耕地生產(chǎn)力隱性退化的地域分異類型劃分為生產(chǎn)條件約束型,、產(chǎn)出效益約束型和人口因素約束型。農(nóng)業(yè)機(jī)械化總動力,、農(nóng)業(yè)產(chǎn)值和外流人口數(shù)量分別為3種約束類型的首要因素,。從地域空間來看,人口因素約束型地區(qū)在江蘇省占比最大,,主要集中于蘇北地區(qū),。對于不同約束類型區(qū)域分別提出加強(qiáng)高標(biāo)準(zhǔn)農(nóng)田建設(shè)、實(shí)施惠農(nóng)政策,、減緩勞動力析出等相應(yīng)的政策建議,。

    Abstract:

    While sticking to the arable-land red line, the recessive degradation of arable land productivity caused by farmers decisions on the use of cultivated land should not be ignored, such as the reduction of the intensity and efficiency of cultivated land use. Taking permanent cultivated land in Jiangsu Province as an example, remote sensing monitoring and influencing factor analysis of recessive degradation were carried out for cultivated land productivity, based on the moderate resolution imaging spectroradiometer(MODIS)remote sensing images from 2001 to 2019. The breaks for additive seasonal and trend algorithm(BFAST)was introduced to model the expected behavior of cultivated land productivity trend in the historical period, in order to identify the recessive degradation of cultivated land productivity in the monitoring period. Based on the Geodetor tool, the dominant influencing factors and factor interaction analysis of the recessive degradation of cultivated land productivity were carried out from three criteria levels and eight index variables. The results indicated that the proportion of recessive degradation for cultivated land productivity in Jiangsu Province was 21.9%, with a significant spatial difference. The highest proportion of recessive degradation of cultivated land productivity appeared in the northwest area, located in Xuzhou and Suqian, which was 47.2% and 43.4%, respectively; besides, their spatial distribution showed aggregation. The recessive degradation rate of cultivated land productivity in Suzhou, Wuxi and Nantong in the southeast region was low, and it was less than 10%. Factor detection analysis showed that the three indicators of population loss, plantation employees and total power of agricultural mechanization had the strongest explanation for recessive degradation of cultivated land production in Jiangsu Province. Through the interaction detector, the explanatory power of population factors and production conditions was improved most significantly. The regional differentiation of recessive degradation for cultivated land production was divided into three types, including production condition constraint, economic benefit constraint and population constraint. The total power of agricultural mechanization, agricultural output value and population loss were the primary factors of the three constraint types respectively. From the perspective of regional space, the population constraint areas accounted for the largest proportion in Jiangsu Province, mainly concentrated in the north. According to different constraints types of recessive degradation for cultivated land production, the corresponding policy suggestions were put forward, such as strengthening the construction of well-facilitated farmland, implementing benefiting-agriculture policies and slowing down the diversion of labor.

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李卓,查思含,霍偉,王林林,郭文華,孫丹峰.耕地生產(chǎn)力隱性退化遙感監(jiān)測與影響因素分析[J].農(nóng)業(yè)機(jī)械學(xué)報,2022,53(4):363-371. LI Zhuo, ZHA Sihan, HUO We, WANG Linlin, GUO Wenhua, SUN Danfeng. Remote Sensing Monitoring of Recessive Degradation for Cultivated Land Productivity and Its Influencing Factors[J]. Transactions of the Chinese Society for Agricultural Machinery,2022,53(4):363-371.

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  • 收稿日期:2021-11-28
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  • 在線發(fā)布日期: 2022-01-12
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