不同滤波算法对反演叶面积指数的影响

目的使用离散型激光雷达数据反演叶面积指数（LAI）的过程中,数据预处理的关键步骤为激光雷达滤波。穿透指数（LPI）作为反演过程中的重要变量,需要根据点云的类型计算,从而直接受到滤波精度的影响。因此,滤波算法的精度能间接影响到反演LAI的精度。虽然滤波算法不断改进,滤波精度逐渐提高,应用在越来越多的场景,但关于不同滤波算法对反演LAI精度影响的探讨较少。方法本文通过对机载LiDAR滤波算法历史、发展和现状的调研,最终选择混合滤波算法（Hybrid）、自适应不规则三角网滤波算法（ATIN）、形态学滤波算法（Morph）和基于坡度滤波算法（Slope）为研究对象;分别使用这4种算法,得到点云中的地面点;根据Beer-Lambert定律,反演帽儿山国家森林公园落叶松林和榆树林的LAI;以经过评估的精度更高的Hybrid算法为标准,计算另外3种算法的滤波精度和LPI偏差;对比分析LAI反演模型的平均精度;最后,通过分析不同误差来源的影响强度,确定了反演LAI时较好的滤波算法。结果在最佳的采样半径下,经过Hybrid、ATIN、Morph和Slope滤波算法处理,LAI反演模型的平均精度,在落叶松林,R2分别为:0.900 3、0.876 3、0.892 5、0.877 0;RMSE分别为:0.105 6、0.134 5、0.109 7、0.133 2;在榆树林,R2分别为:0.914 4、0.903 0、0.887 2、0.900 0;RMSE分别为:0.269 0、0.201 7、0.189 4、0.207 0。在落叶松林,I类误差较大的Morph算法,能保证较高的模型精度;而II类误差较大的Slope和ATIN算法对应的反演模型精度较低。结论经不同滤波算法处理得到的LAI反演模型精度存在差异,经混合滤波算法处理其对应的LAI反演模型精度更高,形态学滤波算法的滤波精度较低,对应的反演模型精度较高;滤波算法导致的I、II类误差中,II类误差对LAI反演模型的影响更大。      

Comparative analysis of GF-1,HJ-1,and Landsat-8 data for estimating the leaf area index of winter wheat

Using simultaneously collected remote sensing data and field measurements,this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1(GF-1) wide field of view(WFV) camera,environment and disaster monitoring and forecasting satellite(HJ-1) charge coupled device(CCD),and Landsat-8 operational land imager(OLI) data for estimating the leaf area index(LAI) of winter wheat via reflectance and vegetation indices(VIs). The accuracies of these LAI estimates were then assessed through comparison with an empirical model and the PROSAIL radiative transfer model. The effects of radiation calibration,spectral response functions,and spatial resolution on discrepancies in the LAI estimates between the different sensors were also analyzed. The results yielded the following observations:(1) The correlation between reflectance from different sensors is relative good,with the adjusted coefficients of determination(R2) between 0.375 to 0.818. The differences in reflectance are ranging from 0.002 to 0.054. The correlation between VIs from different sensors is high with the R2 between 0.729 and 0.933. The differences in the VIs are ranging from 0.07 to 0.156. These results show the three sensors' images can all be used for cross calibration of the reflectance and VIs.(2) The four VIs from the three sensors are all demonstrated to be highly correlated with LAI(R2 between 0.703 and 0.849). The linear models associated with the 2-band enhanced vegetation index(EVI2),which feature the highest R2(higher than 0.746) and the lowest root mean square errors(RMSE)(less than 0.21),were selected to estimate the winter wheat LAI. The accuracy of the estimated LAI from Landsat-8 was the highest,with the relative errors(RE) of 2.18% and an RMSE of 0.13,while the HJ-1 was the lowest,with the RE of 2.43% and the RMSE of 0.15.(3) The inversion errors in the different sensors' LAI estimates using the PROSAIL model are small. The accuracy of the GF-1 is the highest with the RE of 3.44%,and the RMSE of 0.22,whereas that of the HJ-1 is the lowest with the RE of 4.95%,and the RMSE of 0.26.(4) The effects of the spectral response function and radiation calibration for the different sensors are small and can be ignored,but the effects of spatial resolution are significant and must be taken into consideration in practical applications.     

新疆喀纳斯国家自然保护区植被叶面积指数观测与遥感估算

叶面积指数(Leaf Area Index,LAI)是重要的植被结构参数,调控着植被与大气之间的物质与能量交换,在生态环境脆弱的我国西北部开展植被LAI的研究对阐明该地区植被对气候变化和人类活动的响应特征具有重要的科学意义。利用LAI-2200和TRAC仪器观测了新疆喀纳斯国家级自然保护区森林和草地的有效叶面积指数(LAI_e)和真实LAI,构建了其遥感估算模型,生成了研究区LAI_e和LAI的空间分布图。在此基础上,分析了LAI随地形因子(海拔、坡度、坡向)的变化特征,探讨了将其应用于估算研究区森林生物量密度的可行性,并评估了研究区MODIS LAI产品的精度。结果表明:研究区阔叶林、针阔混交林、针叶林、草地LAI_e的平均值分别为4.40、3.18、2.57、1.76,LAI的平均值分别为4.76、3.93、3.27、2.30。LAI_e和LAI的高值主要集中分布在湖泊和河流附近;植被LAI随海拔、坡度和坡向的变化表现出明显的垂直地带性的特点。LAI随海拔和坡度的增加呈现先增加后减小的变化趋势,坡向对针叶林和草地LAI的影响明显,但对阔叶林和针阔混交林LAI的影响较弱;森林生物量密度(BD)随LAI增加而线性增加(BD=44.396LAI-25.946,R²=0.83),研究区森林生物量密度平均值为120.3 t/hm²,估算的总生物量为5.0×10⁶t;MODIS LAI产品与利用TM数据生成的LAI之间具有一定的相似性(森林R²=0.42,草地R²=0.53),但森林和草地的MODIS LAI产品分别比利用TM数据生成的LAI偏低16.5%和24.4%。     

基于MISR数据大兴安岭地区叶面积指数反演及尺度转换验证研究

叶面积指数(LAI)是气候研究和生态研究中重要的植被冠层结构参数,遥感技术为快速获取大面积叶面积指数提供了有效途径。大兴安岭地区是我国重要的生态功能区,本文以大兴安岭为研究区域,根据森林林分特征,采用基于物理过程的4-Scale几何光学模型,利用多角度MISR遥感数据反演该区域叶面积指数数据。几何光学模型特点在于参数具有物理意义,考虑地面反射的热点效应,模型反演过程不依赖于样本数据适用于大区域反演研究,MISR数据提供同一区域多角度遥感数据,有效解决了单一观测角度植被指数和叶面积指数函数关系饱和点低的问题。由于地面验证数据空间尺度无法满足MISR数据的空间分辨率,本文采用TM数据对样地实测叶面积指数数据进行尺度转换,针对不用坡向叶面积指数空间异质性进行分析,讨论不同空间分辨率验证数据的合理性,研究表明大兴安岭区域使用600m空间分辨率验证数据对MISR数据反演结果检验最优,该分辨率下叶面积指数变化随空间尺度变化趋于稳定,并较好地避免了2种遥感数据几何配准带来的误差。结果表明:4-Scale几何光学模型适用于我国大兴安岭地区森林叶面积指数反演,实验中MISR数据反演叶面积指数的平均绝对误差为25.6%、均方根误差为0.622。本研究为大兴安岭地区叶面积指数大区域快速定量反演提供了研究基础。      

高CO2浓度下冬小麦的高光谱特征及其与叶面积指数和SPAD值的反演

以冬小麦为研究对象,利用开顶式气室试验,开展以环境CO2浓度为对照(CK)和比CK处理的CO2浓度高200μmol·mol-1(T)处理的试验,测定冬小麦主要生育期冠层光谱反射率、叶面积指数(LAI)和SPAD值,分析LAI、SPAD值与原始光谱反射率、光谱特征参数的相关性,并探究最优回归反演模型.结果表明,高CO2浓...     

Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation

To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations,winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index(LAI) values. The performances of the calibrated crop environment resource synthesis for wheat(CERES-Wheat) model for two different assimilation scenarios were compared by employing ensemble Kalman filter(EnKF)-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors,assimilation stages and temporal-spatial scales. Overall,the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehensively consider several factors in the initial conditions and observations. When using this strategy,an adjusted coefficients of determination(R~2) of 0.84,a root mean square error(RMSE) of 323 kg ha~(–1),and a relative errors(RE) of 4.15% were obtained at the field plot scale and an R~2 of 0.81,an RMSE of 362 kg ha~(–1),and an RE of 4.52% were obtained at the pixel scale of 30 m×30 m. With increasing observation errors,the accuracy of the yield estimates obviously decreased,but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution,the accuracy of the crop yield estimates decreased; however,the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time,especially in operational systems used for regional crop yield estimates.     

Phenotyping a diversity panel of quinoa using UAV-retrieved leaf area index,SPAD-based chlorophyll and a random forest approach

Given its high nutritional value and capacity to grow in harsh environments, quinoa has significant potential to address a range of food security concerns. Monitoring the development of phenotypic traits during field trials can provide insights into the varieties best suited to specific environmental conditions and management strategies. Unmanned aerial vehicles (UAVs) provide a promising means for phenotyping and offer the potential for new insights into relative plant performance. During a field trial exploring 141 quinoa accessions, a UAV-based multispectral camera was deployed to retrieve leaf area index (LAI) and SPAD-based chlorophyll across 378 control and 378 saline-irrigated plots using a random forest regression approach based on both individual spectral bands and 25 different vegetation indices (VIs) derived from the multispectral imagery. Results show that most VIs had stronger correlation with the LAI and SPAD-based chlorophyll measurements than individual bands. VIs including the red-edge band had high importance in SPAD-based chlorophyll predictions, while VIs including the near infrared band (but not the red-edge band) improved LAI prediction models. When applied to individual treatments (i.e. control or saline), the models trained using all data (i.e. both control and saline data) achieved high mapping accuracies for LAI (R²?=?0.977–0.980, RMSE?=?0.119–0.167) and SPAD-based chlorophyll (R²?=?0.983–0.986, RMSE?=?2.535–2.861). Overall, the study demonstrated that UAV-based remote sensing is not only useful for retrieving important phenotypic traits of quinoa, but that machine learning models trained on all available measurements can provide robust predictions for abiotic stress experiments.

     

种植密度和施肥量对南麦618农艺性状、叶绿素含量及产量的影响

[目的]探讨小麦品种南麦618在四川东北部丘陵地区的最佳种植密度和施肥量,为科学制定其高产高效栽培技术提供参考依据.[方法]采用2因素3水平完全随机区组设计,种植密度3个水平分别为2.18×106株/ha(A1)、2.48×106株/ha(A2)和2.78×106株/ha(A3),施肥量3个水平分别为N 33.0 kg/ha+P 25.7 kg/ha(B1)、N 66.0 kg/ha+P 51.3 kg/ha(B2)和N 99.0 kg/ha+P 77.0 kg/ha(B3).生育期内分别测定南麦618的相关农艺性状、旗叶叶绿素含量和单茎绿叶面积,成熟时各小区单独收获计算产量.[结果]随着种植密度加大,南麦618的单位面积有效穗数增加,成穗率下降,穗粒数和千粒重呈不同程度的下降趋势,穗长变短,产量、收获指数呈上升趋势,旗叶叶绿素含量降低,单茎绿叶面积减小.随着施肥量的增加,南麦618的单位面积有效穗数增加,成穗率下降,穗粒数和千粒重呈不同程度的上升趋势,穗长变长,产量、收获指数呈上升趋势,旗叶叶绿素含量升高,单茎绿叶面积增大.种植密度和施肥量的交互作用对穗粒数和产量有极显著影响(P<0.01),有效穗数与产量显著相关(P<0.05).综合分析,A2B3处理的农艺性状最优,光合性能最佳,产量最高(5.49×103 kg/ha).[结论]在川东北丘陵区种植南麦618时,种植密度以2.48×106株/ha、施肥量以N 99.0 kg/ha+P 77.0 kg/ha为宜.     

我国农户大麦生产技术效率及其影响因素分析——基于12个省份大麦种植户的调查数据

本文基于12个省份大麦种植户的调查数据,采用随机前沿生产函数模型,对农户大麦生产技术效率及其影响因素进行了分析。结果表明:样本农户大麦生产的平均技术效率为0.86,存在显著的技术效率损失;样本农户大麦生产技术效率不存在非常显著的省际差异;大麦生产各投入要素中,农资投入的平均产出弹性最大,人工投入的平均产出弹性最小;户主受教育程度、更换大麦品种频率和对大麦种子的满意度对技术效率有显著的正向影响,大麦种植面积对技术效率有显著的负向影响;东部地区样本农户的技术效率显著低于中部地区样本农户。最后,根据研究结论提出了强化科技支撑、加快良种良法推广和加强政策支持等提高大麦生产技术效率的建议。     

基于CBERS-02B卫星影像光谱信息的水稻种植面积提取方法——以南京市溧水县为例

利用水稻成熟期的影像,在进行光谱信息的统计和分析的基础上,对比水稻与其他植被在各波段DN(digital number)值以及NDVI(normalized difference vegetable index)值的数值差异,总结出水稻与其他植被反射光谱特征,认为成熟期的水稻在红光、绿光、蓝光波段DN值以及NDVI值上具有异于其他植被的特征,并通过波段运算扩大其数值差异,建立水稻像元提取模型,确定阈值,从而进行水稻种植面积的提取,精度可达91.95%。该方法能满足小区域的水稻种植面积提取和制图,并能够满足一定的精度要求。     

Extended-altitude, aerial mapping of crop NDVI using an active optical sensor: A case study using a Raptor™ sensor over wheat

Recently reported testing of active, optical crop sensors in low-level aircraft have demonstrated a new class of airborne sensing system that can be deployed under any ambient illumination conditions, including at night. A second-generation, high-powered, light-emitting diode system has been assembled and tested over a 80 ha field of wheat (Triticum aestevum) by mapping the normalised difference vegetation index (NDVI) at altitudes ranging from 15 to 45 m above the canopy; significantly higher altitudes than existing systems. Comparisons with a detailed on-ground NDVI survey indicated the aerial sensor values were highly correlated to the on-ground sensor (0.79 < R² < 0.85), with close to unity slope and zero offset. The maximum average deviation between aerial and on-ground NDVI values was 0.04. Sample calculations involving two exemplar algorithms, one for estimating monoculture pasture biomass and the other for estimating wheat yield, indicate such deviations to have no significant effect on prediction accuracy. The subsequent NDVI maps proved to be invariant to sensor height over the 15-45 m altitude range meaning this new sensor configuration can be deployed over undulating crops and pastures and in areas with nearby obstacles such as trees and buildings.     

多效唑对稻麦苗期根系活力和叶片IAA氧化酶、过氧化物酶活性的影响

通过水培试验研究了不同浓度多效唑处理对苗期稻麦根系活力和叶片IAA氧化酶、过氧化物酶活性的影响。结果表明,在5~200m g/L浓度范围内,多效唑处理能显著提高水稻和小麦苗期根系活力和叶片IAA氧化酶、过氧化物酶的活性。多效唑浓度为20m g/L时,根系活力达到峰值;浓度为100m g/L时,IAA氧化酶和过氧化物酶的活性达到峰值。     

Low altitude remote sensing technologies for crop stress monitoring: a case study on spatial and temporal monitoring of irrigated pinto bean

Site-specific crop management is a promising approach to maximize crop yield with optimal use of rapidly depleting natural resources. Availability of high resolution crop data at critical growth stages is a key for real-time data-driven decisions during the production season. The goal of this study was to evaluate the possibility of using small unmanned aerial system (UAS)-based remote sensing technologies to monitor the crop stress of irrigated pinto beans (Phaseolus vulgaris L.) with varied irrigation and tillage treatments. A small UAS with onboard multispectral and infrared thermal imaging sensors was used to collect data from bean field plots on three growth stages in 2015 and 2016, respectively. Indicators including green normalized vegetation index (GNDVI), canopy cover (CC, ratio of ground covered by crop canopy to the total plot area) and canopy temperature (CT, °C) of crops were extracted from imaging data and correlated with ground-reference crop yield and leaf area index (LAI) estimated with a handheld ceptometer. Results show that GNDVI, CC and CT were able to differentiate crops with full and deficit irrigation treatments at each of the three growth stages in both years. Developed indicators were strongly correlated with to the crop yield with Pearson correlation coefficients (r) of approximate 0.71 and 0.72 for GNDVI and CC, respectively, in the early growth stage (54 days after planting) in both years. Canopy temperature showed even stronger correlation (r > 0.8) with yield at early growth stage. Performance of small UAS-based imagery-based indicators in crop stress monitoring and crop yield estimation was better than or comparable to that of the ground-based LAI estimates, indicating the potential of such remote sensing tool in rapid crop stress monitoring and management.     

规模农业对农产品质量安全的影响分析——以黑龙江垦区为例

生产经营方式与农产品质量安全有密切的关系,发展以规模农业为主要方向的现代农业是确保农产品质量安全的关键举措之一。基于规模农业对提升农产品质量安全水平、实现全链条质量跟踪和高效监督的积极意义,结合黑龙江垦区现代化规模农业发展的实践,分析规模农业发展对农产品质量安全带来的潜在风险。结果表明,现代农业技术、高度集约化生产、农业产业化经营对公众健康、环境及质量追溯能产生积极的影响,可实现对农产品质量安全的高效监督。但是,在农业技术研究落后于实践需求、集约化生产和组织化监管不完善的条件下,对公众健康安全、环境污染、农产品质量与追溯等则会产生潜在危害和负面影响。研究表明,在以转变农业经营观念为主导的现代农业可持续发展路径选择和实践中,规模农业发展模式在保障我国农产品质量安全中的作用将逐步增强。因此,在发展规模农业的背景下,提出以完善现代农业技术体系、推进可持续的高度集约化经营、提升生产主体执行标准化能力、把握规模与效率的关系等规避规模农业风险和保障农产品安全的具体对策。     

麦红吸浆虫在小麦穗上的分布和危害特点

通过调查麦红吸浆虫幼虫在小麦穗上的分布和危害特点 ,进一步明确 :麦穗上麦红吸浆虫幼虫聚集分布的田间分布格局与虫口密度无关 ;麦红吸浆虫在麦穗上的分布和危害与麦穗大小和受害部位有一定的关系。就麦穗大小而言 ,以中型穗上虫量最多 ,受害最重 ;大型穗次之 ;小型穗上虫量最少 ,受害最轻。就麦穗受害部位而言 ,以中部虫量最多 ,受害最重 ;下部次之 ;上部虫量最少 ,受害最轻。     

光谱与纹理信息结合的黄河三角洲土壤盐渍化信息提取——以垦利区为例

土壤盐渍化是制约黄河三角洲农业发展的关键问题，及时准确地掌握土壤盐渍化信息对土地资源保护和开发利用具有积极意义。本研究以黄河三角洲核心区域垦利区2019年4月17日的Sentinel-2遥感影像为数据源，在ENVI和e Cognition软件支持下，利用灰度共生矩阵法提取遥感影像的二阶矩、对比度、熵、相关性等纹理特征信息，结合归一化植被指数（NDVI）、盐分指数（SI）等光谱特征信息，通过预设分类规则实现对黄河三角洲垦利区的盐渍土分类。结果表明，加入二阶矩、对比度、熵、相关性4个纹理特征统计量，再结合光谱信息对垦利区盐渍土进行分类，总体分类精度为92.4%，Kappa系数为0.89，相较于仅利用光谱信息的分类方法，总分类精度提高了10.5个百分点；各分类类别的生产者精度与使用者精度较仅依靠光谱信息分类的分类结果均明显提高，其中中度盐渍土的分类效果最好，其生产者精度与使用者精度最高，分别为95.0%、95.9%。本研究提出利用遥感光谱结合纹理特征实现滨海区盐渍土信息的提取方法，提高了盐渍土分类精度，为准确掌握研究区土壤盐渍化信息提供了新途径。     

山东省冬小麦-夏玉米轮作体系土壤氮素盈余指标体系的构建与评价——以德州市为例

山东省德州市是我国重要的粮食生产基地,大量施用氮肥以及氮素流失是该地氮素污染的重要原因,设定合理的氮素盈余指标体系迫在眉睫。本研究以田间试验研究和文献荟萃为基础,探究德州市冬小麦-夏玉米轮作氮素盈余情况,建立该地氮盈余指标体系,并运用欧氏距离法对德州市当前氮素管理指标进行评价。结果表明:德州市整个冬小麦-夏玉米轮作体系轮作周期氮素盈余量为196.84 kg N·hm~(-2)·a~(-1),小麦季氮素盈余量为111.07 kg N·hm~(-2),玉米季氮素盈余量为85.77 kg N·hm~(-2)。氮素盈余量在30～70 kg N·hm~(-2)·a~(-1)时为德州市整个轮作周期氮素盈余量推荐值;德州市小麦季氮素盈余量推荐值为10～50 kg N·hm~(-2);德州市玉米季氮素盈余量推荐值为20～60 kg N·hm~(-2)。在氮素的损失方面,氨挥发以及淋洗是氮素损失的主要去向。小麦季的氮素污染程度高,玉米季氮素污染程度相对较低。运用欧氏距离法计算德州市氮素管理水平(A值)为0.63,属于中级管理水平。综上,德州市冬小麦-夏玉米轮作体系农田氮素含量较高,从而导致氮素大量损失。加强氮素管理水平,提高氮素利用率是德州市氮素资源管理的长期任务。     

基于地理大数据的省域休闲农业空间格局及其影响因素研究——以浙江省为例

目前,国内外关于休闲农业空间分布的研究大多基于传统调查数据开展。随着网络电子地图的发展,POI(point of interest,兴趣点)数据和通行时间数据为此类研究提供了新的大数据来源,但运用这些数据进行省域休闲农业空间分布研究的实例较少。为此,以高德地图POI数据和百度地图驾车通行时间数据为基础,运用最近邻指数、核密度估计等方法,结合地理探测器分析工具,研究了浙江省休闲农业的空间分布特征及其影响因素。结果表明:休闲农业在浙江省省域尺度上呈多核集聚分布,形成了杭州-湖州核心密集区和安吉县核心密集区,以及其他都市依托型密集区和景区依托型密集区。休闲度假类、田园农业体验类、农家乐类、回归自然类休闲农业项目呈现连片集聚,民俗风情类、村落乡镇类休闲农业项目分布较为均匀,科教旅游类休闲农业项目数量较少。经济发展水平、收入和消费能力、农业发展水平是影响休闲农业密集分布的最主要的社会经济因素。休闲农业分布的都市依托性和景区依托性明显:田园农业体验类等依赖地理区位和地形特征的类型连片集聚,而民俗风情类等与地理区位和地形特征联系较弱的类型则分布较为均匀。整体上,资源禀赋丰富、经济发展和农业发展水平高、居民消费能力较高、客源市场较大、交通较为便捷的区域,休闲农业发展环境较好。     

玉米纹枯病危害损失及防治指标的初步研究

研究结果表明,随着玉米纹枯病病情指数的提高,玉米叶片自下而上干枯严重,产量损失率提高。玉米产量损失率与玉米纹枯病病情指数及病株率呈正相关,相关系数分别为r1=0.9973^※※、r2=0.9732^※※。产量损失率与病情指数及病株率的回归式分别为y=0.39x1-2.41,y=0.43x2-11.94。拟定玉米纹枯病防治指标为病情指数16.4,或病株率为37.1%。     

煤矿区产业转型的机制、模式研究与实证分析——以平朔矿区为例

为探讨煤炭型矿区复垦土地可持续利用与产业转型,本研究以平朔矿区为例,通过对矿区产业结构框架组成关系、矿区传统土地复垦模式以及产业结构特征与演替规律分析,研究了矿区复垦土地与产业转型的机制与模式。研究表明:我国矿区复垦土地可持续利用与产业转型目标导向机制应从"二元主导机制"过渡到"多元并存主导机制",最终实现"多方合作主导机制";平朔矿区产业框架应由粗放型、外延式向精益型、内涵式转变;依产业链不同,平朔矿区产业转型模式可分为两类,一是黑色工业产业链:煤炭开采-煤矸石-电-硅铝-建材工业,可实现矿区资源、能源和废弃物的高效利用,减缓产业转型的环境压力;二是绿色生态产业链:农-林-牧-药-农产品加工-生态旅游,是依托田园综合体的经济技术原理重构"生产、生活、生态"空间、促使复垦土地功能提升的必然途径。