利用地形、土壤和作物信息辅助提高东北漫岗地数字高程模型精度的新方法

【目的】SRTM DEM是可免费访问公开可用的数字高程模型,但是当前SRTM DEM的垂直精度不能满足精细农业对地形数据的需求,提高其垂直精度,为精准农业等领域提供数据基础。【方法】以黑龙江省海伦东兴农机合作社为研究区,采集实际地面高程数据,获取SPOT-6、Sentinel-2A遥感影像和SRTM DEM。提取归一化湿度指数（NDMI）、归一化植被指数（NDVI）、土壤亮度（TCB）、潜在太阳辐射（PSR）等变量分析地形对其影响关系。利用极限学习机（ELM）和反向传播神经网络（BPNN）提高SRTM DEM水平空间分辨率和垂直精度。使用实际地面高程点进行精度验证,与基于无人机和光学立体像对（ZY-3）生成的DEM进行对比。【结果】SRTM、NDVI、NDMI、TCB与改进后高程的灰色关联度在90%以上,是改进SRTM DEM的重要辅助信息。在整个研究区,BPNN方法的RMSE_P为0.98,R²_P为0.98,ELM的RMSE_P为1.00,R²_P为0.90。在平坦区,BPNN方法的RMSE_P为0.84,ELM的RMSE_P为1.00;在起伏区,BPNN方法的RMSE_P为0.99,ELM的RMSE_P为0.94。该方法获得的DEM的垂直精度高于ZY-3光学立体像对生成的 DEM的垂直精度,为提高SRTM的水平空间分辨率和垂直精度提供了新思路。【结论】引入SRTM、NDVI、NDMI、TCB辅助信息有利于提高SRTM DEM的空间分辨率和垂直精度,获得高精度的DEM。BPNN方法获得的数字高程模型的精度整体上高于ELM方法, BPNN方法更加适用于平坦区高精度DEM的获取,ELM方法更加适用于起伏区。     

Evaluation of an algorithm for automatic detection of broad-leaved weeds in spring cereals

Lack of automatic weed detection tools has hampered the adoption of site-specific weed control in cereals. An initial object-oriented algorithm for the automatic detection of broad-leaved weeds in cereals developed by SINTEF ICT (Oslo, Norway) was evaluated. The algorithm (“WeedFinder”) estimates total density and cover of broad-leaved weed seedlings in cereal fields from near-ground red–green–blue images. The ability of “WeedFinder” to predict ‘spray’/‘no spray’ decisions according to a previously suggested spray decision model for spring cereals was tested with images from two wheat fields sown with the normal row spacing of the region, 0.125 m. Applying the decision model as a simple look-up table, “WeedFinder” gave correct spray decisions in 65–85% of the test images. With discriminant analysis, corresponding mean rates were 84–90%. Future versions of “WeedFinder” must be more accurate and accommodate weed species recognition.     

基于高分六号卫星的植被生态景观监测分析

随着国产高分卫星对地观测系统的建立和完善,精细化生态遥感监测分析需求已提上议事日程。笔者以新疆维吾尔自治区巴音郭楞蒙古自治州和静县为例,利用GIS和遥感技术对高分6号卫星数据进行归一化植被指数(NDVI)反演,生成植被指数覆盖度等级,结合土地利用类型,对研究区植被生态景观现状进行分析评价;并且用DEM数据提取高程、坡度2个主要的地形因子对NDVI值的空间分布进行了相关分析。结果显示：（1）和静县植被覆盖度等级分布都具有明显的空间异质性,总体而言,以中度和较低覆盖植被为主;（2）和静县植被指数在低海拔与高海拔地区较低,在中海拔地区(2500~3200 m)植被指数达到最大,最大值为0.48;（3）坡度对植被影响较大。在10°左右,植被长势较好,植被指数达到最大。     

地形调节植被指数及其在森林动态监测中的应用

森林一般分布在复杂地形山区,消除地形影响成为提高森林遥感监测精度必须解决的基本问题之一。本文提出一种无需DEM数据支持、仅依据光学遥感影像近红外波段与红光波段数据就能有效消除山区地形影响的地形调节植被指数（TAVI）。以1998年与2008年两期LandsatTM影像为实证研究数据,进行TAVI抗地形影响性能验证并采用...     

森林植被遥感监测影像最佳分辨率选择

遥感影像分辨率的高低直接影响着森林植被监测的精度、成本和效率,故选择适合森林植被监测的影像最佳分辨率具有重要的应用价值。针对森林植被监测影像最佳分辨率选择方法及结果缺乏的问题,从林业实际应用出发,提出了基于1个步长的变异函数分析空间变异并综合考虑监测精度、成本和效率来确定森林植被监测影像最佳分辨率方法。基于最新的国产高分二号（GF-2）全色影像,利用1个步长的变异函数对湖南常宁洋泉镇林区3种典型分布类型森林植被进行拟合分析,初步确定适合森林植被监测的影像最低分辨率。然后对重采样形成的不同尺度多光谱影像分别进行监督分类,并对结果进行定量定性分析,结合影像成本和数据处理时间,找到适合不同类型森林植被监测的影像最佳分辨率。研究表明:不同分布类型的森林植被,适合遥感监测的影像最佳分辨率不同:①小冠幅森林植被3.2 m;②大冠幅森林植被16.0 m;③混合冠幅森林植被8.0 m。该森林植被遥感监测影像最佳分辨率确定方法和结果可为其他区域森林植被遥感监测影像最佳分辨率确定提供借鉴。     

DEM重采样坡度衰减分析

通过数字高程模型(DEM)重采样发现,随着水平分辨率的降低,地形也随着趋向平缓,坡度信息不断丢失,地面表达能力逐步下降.利用1∶10 000数字地形图生成5m水平分辨率DEM,以此为基准重采样生成10、20、40m水平分辨率的DEM,提取各水平分辨率DEM的坡度.对提取的5m水平分辨率DEM的坡度进行邻域分析,提取10、20、40m水平分辨率DEM的最大坡度,以此求取其与直接由相对应水平分辨率DEM提取的坡度的差值,并对坡度差值进行频率统计分析,同时分析了剖面曲率在重采样过程中的变化.结果表明,随着DEM水平分辨率的降低,坡度平均值不断降低,较小坡度的频率逐渐增大,较大坡度的频率逐渐减小.DEM水平分辨率较高时,坡度差值分布集中于较小坡度差值处,且范围集中;DEM水平分辨率越低,坡度差值分布曲线愈加分散,且集中于较大坡度差值处.DEM水平分辨率越高,剖面曲率曲线分布越广,DEM水平分辨率越低,曲线分布越窄,且变得集中,其中剖面曲率较高部分损失严重.     

基于Quick Bird的阿其克苏河植被覆盖度研究

[目的]利用高分辨率Quick Bird影像,并结合实地的植被和土地类型调查,对艾比湖地区阿其克苏河植被覆盖进行归一化植被指数( NDVI)遥感分类,利用遥感影像处理软件(如Envi软件、Arcgis软件、Erdas软件)提取了该区的植被覆盖等信息.[方法]基于NDVI图对该区进行植被覆盖度反演,并结合实际测量数据提出了荒漠区NDVI分类标准和植被覆盖等级划分标准.[结果]通过NDVI分类图和植被等级覆盖图,发现该地区植被大多集中于河滩及其两岸;而且离水源越近,植被覆盖度越高,NDVI值也越高,这与荒漠区植被分布特点吻合.[结论]植被覆盖度反演结果与实地测量对比分析有很好的切合度.     

Seven samurai to protect “our” food: the reform of the food safety regulatory system in Japan after the BSE crisis of 2001

Using the case of food safety governance reform in Japan between 2001 and 2003, this paper examines the relationship between science and trust. The paper explains how the discovery of the first BSE positive cow and consequent food safety scandals in 2001 politicized the role of science in protecting the safety of the food supply. The analysis of the Parliamentary debate focuses on the contestation among legislators and other participants over three dimensions of risk science, including “knowledge,” “objects,” and “beneficiaries.” The metaphor of “seven samurai” and the relationally situated roles of “samurai,” “bandits,” and “beneficiaries” are used to show that in the process of policy making certain moral and ethical expectations on a new expert institution for food safety were contested and negotiated to frame responsibilities and commitments of social actors for creating the food system based on trust.

Non-governmental organizations,strategic bridge building,and the “scientization” of organic agriculture in Kenya

This paper contributes to the growing social science scholarship on organic agriculture in the global South. A “boundary” framework is used to understand how negotiation among socially and geographically disparate social worlds (e.g., non-governmental organizations (NGOs), foreign donors, agricultural researchers, and small-scale farmers) has resulted in the diffusion of non-certified organic agriculture in Kenya. National and local NGOs dedicated to organic agriculture promotion, training, research, and outreach are conceptualized as “boundary organizations.” Situated at the intersection of multiple social worlds, these NGOs engage in “strategic bridge building” and “strategic boundary-work.” Strategic bridge building involves the creation and use of “boundary objects” and “hybrid forms” that serve as meeting grounds for otherwise disconnected social worlds. Strategic boundary-work involves efforts to “scientize,” and thereby legitimize, organic agriculture in the eyes of foreign donors, potential research collaborators, the Kenyan state, and farmers. Examples of strategic bridge building and boundary-work are presented in the paper. The Kenyan case illustrates that different social actors can unite around a shared objective – namely, the promotion and legitimization of organic agriculture as an alternative to the Green Revolution (GR) technological package.

Delineating productivity zones in a citrus grove using citrus production,tree growth and temporally stable soil data

The productivity of a citrus grove with variation in tree growth was mapped to delineate zones of productivity based on several indicator properties. These properties were fruit yield, ultrasonically measured tree canopy volume, normalized difference vegetation index (NDVI), elevation and apparent electrical conductivity (EC_a). The spatial patterns of soil series, soil color and EC_a, and their correspondence with the variation in yield emphasized the importance of variation in the soil in differentiating the productivity of the grove. Citrus fruit yield was positively correlated with canopy volume, NDVI and EC_a, and yield was negatively correlated with elevation. Although all the properties were strongly correlated with yield and were able to explain the productivity of the grove, citrus tree canopy volume was most strongly correlated (r = 0.85) with yield, explaining 73% of its variation. Tree canopy volume was used to classify the citrus grove into five productivity zones termed as ‘very poor’, ‘poor’, ‘medium’, ‘good’ and ‘very good’ zones. The study showed that productivity of citrus groves can be mapped using various attributes that directly or indirectly affect citrus production. The productivity zones identified could be used successfully to plan soil sampling and characterize soil variation in new fields.     

Shadow effect on multi-spectral image for detection of nitrogen deficiency in corn

The efficiency of side-dressing, a more efficient of nitrogen application method than uniform application in either late Fall or early Spring, relies heavily on the capability of nitrogen deficiency detection on a sprayer. To determine the site-specific yield potential for corn, multi-spectral image analysis including aerial- and ground-based images has been used. Some acceptable calibration relationships between the multi-spectral reflectance and SPAD readings have been found from previous study. In sunny weather conditions there was a shadow in the image made by corn leaf itself. This research investigated the shadow effect on the image for detecting corn nitrogen deficiency based on corn canopy reflectance information. The results indicated that the reflectance of red channel in shadow area showed strong inverse correlation, so the vegetation index NDVI using red and NIR channels also showed strong correlation (R² = 77) compared to the whole leaf and bright area. And the reflectance (green and red) and vegetation index(G_NDVI, NDVI, and ratio) in shadow area showed more consistent correlations than others using these image analysis methods.     

DEM分辨率对大兴安岭林区水土流失评价结果的影响

为揭示数字化高程模型(DEM)分辨率对水土流失评价结果的影响,基于地理信息系统(GIS)和遥感技术(RS),利用不同分辨率(10、20、30、40、50、60、70、80、90、100 m)DEM,提取坡度因子(S)和坡长因子(L),并复合成地形因子(L_S),将L_S因子、降雨侵蚀力、土壤可侵蚀性、植被覆盖和管理与水土保持措施5个主要影响水土流失的因子,带入修正的通用土壤流失方程,得到不同分辨率下的土壤侵蚀量。分析不同DEM分辨率下,地形因子及水土流失评价结果的变化规律。结果表明:在研究区域内,土壤流失量对分辨率变化最为敏感,对L_S因子的敏感度大于S因子和L因子;DEM分辨率小于40 m时,土壤侵蚀量变化较小;当分辨率为50 m时,发生剧变,当分辨率超过60 m后逐渐趋于平稳。随着DEM分辨率的降低,其对水土流失评价结果影响也越来越小,因此可以认为最适宜水土流失评价的DEM分辨率应为50~60 m。利用50 m分辨率DEM计算的研究区域水土流失主要以微度和轻度侵蚀为主,占研究区域总面积的90%以上,水土流失较严重情况主要位于山脊和人类干扰较强烈地区。     

Low and high-level visual feature-based apple detection from multi-modal images

Automated harvesting requires accurate detection and recognition of the fruit within a tree canopy in real-time in uncontrolled environments. However, occlusion, variable illumination, variable appearance and texture make this task a complex challenge. Our research discusses the development of a machine vision system, capable of recognizing occluded green apples within a tree canopy. This involves the detection of “green” apples within scenes of “green leaves”, shadow patterns, branches and other objects found in natural tree canopies. The system uses both thermal infra-red and color image modalities in order to achieve improved performance. Maximization of mutual information is used to find the optimal registration parameters between images from the two modalities. We use two approaches for apple detection based on low and high-level visual features. High-level features are global attributes captured by image processing operations, while low-level features are strong responses to primitive parts-based filters (such as Haar wavelets). These features are then applied separately to color and thermal infra-red images to detect apples from the background. These two approaches are compared and it is shown that the low-level feature-based approach is superior (74% recognition accuracy) over the high-level visual feature approach (53.16% recognition accuracy). Finally, a voting scheme is used to improve the detection results, which drops the false alarms with little effect on the recognition rate. The resulting classifiers acting independently can partially recognize the on-tree apples, however, when combined the recognition accuracy is increased.     

利用资源三号卫星提取DEM的精度评价——以荆州区为例

本文运用ENVI软件和资源三号卫星数据,提取了湖北省荆州市荆州区DEM高程数据,并将它与STRM DEM数据进行了比较分析,发现用资源三号卫星提取的高程数据具有空间分辨率高、高程水平变化平稳等优级点,较STRM DEM数据质量高,同时在某些水域或者大面积农田区,有时因其水平色差小的原因,提取误差大。     

基于DEM的数字地形分析

该文在对数字高程模型（DEM）数据来源及结构、数字地形分析及其应用、基于DEM的地形分析中的不确定性和误差分析的基础上,以日本东北地区岩手县早池峰山为研究对象,美国MicroImage公司开发的TNTmips地理信息系统为工具,日本国土地理院发行的“数字地图25000”为基础数据,研究基于DEM的数字地形特征提取与分析方法,以及DEM精度对地形特征的影响. 研究结果表明:①以DEM为基础可提取多种地形特征,如坡度、坡向、坡面形态、流域边界、水流路径等,这些特征在地理信息系统的支持下均可用图形和属性数据来表示;②DEM水平精度对基于DEM提取的数字地形特征影响表现为:低精度的DEM将导致研究区平均坡度变小、坡度标准差变大;同时,DEM精度对不同坡度区域表现为不同的影响,其结果按坡度大致可划分为3种不同类型,即0°～10°、10°～35°以及大于35°; DEM精度对坡向的影响除平坡外变化较小,其中平坡面积随DEM精度的降低而增大;低精度的DEM将导致水文地形信息受损,这将严重影响流域水文模型参数的确立及水文过程模拟分析的精度.      

基于TM影像的代县植被覆盖度动态变化监测

以ERDASIMAGINE系统为操作平台,利用忻州市代县地区2000年和2007年两期TM卫星影像数据,通过对遥感图的几何校正,图像融合,监督分类等处理,对区域植被覆盖状况进行了提取和分析。结果表明：2007年低程度植被覆盖比2000年减少了6834．3hm2,中程度植被覆盖增加了2777．2hm2,高程度植被覆盖增加了4050．8hm2;人类活动和自然因素的变化均是代县地区植被覆盖变化的主要原因。研究表明,利用TM影像并结合erdas计算归一化植被指数NDVI,从而提取植被覆盖度的方法,可以快速准确地对区域植被生长状况进行动态监测。     

Distance,density, local amenities,and suburban development preferences in a rapidly growing East Tennessee county

Changing land-use patterns and amenity-driven migration have brought agriculture back into people’s lives, but there is a disconnection between the realities of production agriculture and romantic images attached to farming. To the extent that “rurality” is attached to farming, people may desire to live in rural places, but they may be unprepared for the realities of living near a working farm. Greater numbers of communities are facing “either/or” outcomes regarding the conversion of “open space” land to residential or commercial uses versus landscape preservation. This study explored the perceptions and preferences of a community regarding the conversion of a hypothetical parcel of open space to a working dairy or to a residential subdivision. Results suggest that the opportunity costs of foregoing open space for residential development are high, with implications for valuing the conservation of traditions that are tied to the land versus conversion of land solely for development purposes.     

喀斯特石漠化地区遥感影像的决策树分类——以贵州省黔南州长顺县凯佐乡为例

喀斯待石漠化是在自然和人为因素相互作用下土地退化的现象.石漠化地区的遥感影像分类以往采用的是监督分类和非监督分类,但它们单纯地利用像元的亮度特征,分类精度低,往往不能满足实际的应用需要.决策树分类是一种新的遥感影像分类技术.以凯佐乡为研究对象,使用了ASTER影像数据、DEM和岩性数据,通过提取归一化植被指数、比值植被指数、地形坡度等数据建立分类规则,构建决策树.在ENVI软件支持下,获得了研究区的决策树分类影像.通过计算影像分类精度和Kappa系数.得到了以下结论：运用决策树分类法对石漠化地区遥感影像进行分类,可取得较理想的分类效果;实现了石漠化信息的自动化提取;若采用的遥感影像波段更多,DEM分辨率更高并减少数据处理中的误差将能够进一步提高分类精度.     

Early-season crop type mapping using 30-m reference time series

Early-season crop type mapping could provide important information for crop growth monitoring and yield prediction, but the lack of ground-surveyed training samples is the main challenge for crop type identification. Although reference time series based method(RBM) has been proposed to identify crop types without the use of ground-surveyed training samples, the methods are not suitable for study regions with small field size because the reference time series are mainly generated using data set with low spatial resolution. As the combination of Landsat data and Sentinel-2 data could increase the temporal resolution of 30-m image time series, we improved the RBM by generating reference normalized difference vegetation index(NDVI)/enhanced vegetation index(EVI) time series at 30-m resolution(30-m RBM) using both Landsat and Sentinel-2 data, then tried to estimate the potential of the reference NDVI/EVI time series for crop identification at early season. As a test case, we tried to use the 30-m RBM to identify major crop types in Hengshui, China at early season of 2018, the results showed that when the time series of the entire growing season were used for classification, overall classification accuracies of the 30-m RBM were higher than 95%, which were similar to the accuracies acquired using the ground-surveyed training samples. In addition, cotton, spring maize and summer maize distribution could be accurately generated 8, 6 and 8 weeks before their harvest using the 30-m RBM; but winter wheat can only be accurately identified around the harvest time phase. Finally, NDVI outperformed EVI for crop type classification as NDVI had better separability for distinguishing crops at the green-up time phases. Comparing with the previous RBM, advantage of 30-m RBM is that the method could use the samples of the small fields to generate reference time series and process image time series with missing value for early-season crop classification; while, samples collected from multiple years should be further used so that the reference time series could contain more crop growth conditions.     

杉木人工林冠层高度无人机遥感估测

冠层高度是森林资源调查的重要因子。传统的森林树高调查方法存在外业调查难度大,效率低等问题。无人机（UAV）的发展为快速估测森林树高提供了手段。以福建省闽清县的杉木Cunninghamia lanceolata人工林为研究对象,通过Eco Drone-UA无人机遥感系统获取研究区遥感影像,利用Pix4D Mapper软件对航拍多光谱影像进行预处理,构建数字表面模型（DSM）,利用1:10 000地形图生成数字高程模型（DEM）;基于DSM和DEM叠加相减得到树冠高度模型（CHM）,实现杉木树高的提取。结果表明:植被指数和多光谱波段结合随机森林算法能够有效识别真实树冠顶点;利用无人机遥感影像能够实现杉木树高估测,相对误差最小值为0.81%,最大值为23.48%,标准误差为1.48 m,估测精度为90.8%。高程变化对树高估测精度有影响,根据高程大小排序的3组样木实测树高与提取树高的决定系数（R2）分别是0.97,0.84和0.78,标准误差分别是0.67,1.17和1.99 m,在高程较高区域树高估测精度明显高于高程相对较低区域。