Perspective of Chinese GF-1 high-resolution satellite data in agricultural remote sensing monitoring

High-resolution satellite data have been playing an important role in agricultural remote sensing monitoring. However,the major data sources of high-resolution images are not owned by China. The cost of large scale use of high resolution imagery data becomes prohibitive. In pace of the launch of the Chinese "High Resolution Earth Observation Systems",China is able to receive superb high-resolution remotely sensed images(GF series) that equalizes or even surpasses foreign similar satellites in respect of spatial resolution,scanning width and revisit period. This paper provides a perspective of using high resolution remote sensing data from satellite GF-1 for agriculture monitoring. It also assesses the applicability of GF-1 data for agricultural monitoring,and identifies potential applications from regional to national scales. GF-1's high resolution(i.e.,2 m/8 m),high revisit cycle(i.e.,4 days),and its visible and near-infrared(VNIR) spectral bands enable a continuous,efficient and effective agricultural dynamics monitoring. Thus,it has gradually substituted the foreign data sources for mapping crop planting areas,monitoring crop growth,estimating crop yield,monitoring natural disasters,and supporting precision and facility agriculture in China agricultural remote sensing monitoring system(CHARMS). However,it is still at the initial stage of GF-1 data application in agricultural remote sensing monitoring. Advanced algorithms for estimating agronomic parameters and soil quality with GF-1 data need to be further investigated,especially for improving the performance of remote sensing monitoring in the fragmented landscapes. In addition,the thematic product series in terms of land cover,crop allocation,crop growth and production are required to be developed in association with other data sources at multiple spatial scales. Despite the advantages,the issues such as low spectrum resolution and image distortion associated with high spatial resolution and wide swath width,might pose challenges for GF-1 data applications and need to be addressed in future agricultural monitoring.     

Multi-temporal analysis of terrestrial laser scanning data to detect basal stem rot in oil palm trees

Terrestrial laser scanning technology is an advanced active remote sensing ranging method that is well suited for yielding high-resolution scans of the morphology of a tree, which is an indicator of the health of the plant. The Ganoderma boninense fungus causes basal stem rot (BSR), which threatens the oil palm industry in Malaysia. To date, the current practice of inspection in a plantation is conducted every 6 months. Monitoring the progress with a closer gap is required to comprehend if any changes can be seen earlier than 6 months. Therefore, the objectives of this study were to identify the most suitable parameters of the oil palm trees to detect the BSR disease based on temporal laser scanning data and to identify suitable time frames for monitoring the progress of the symptoms of the disease. Terrestrial laser scanning data was used to analyse changes in the crown and frond metrics of oil palm trees with two different scan durations i.e., 2- and 4-months after the first scan. This spatio-temporal data is important in the precision agriculture field for better oil palm management, to understand the disease development for long-term solutions and also to provide a fast response so that appropriate treatment can be given to the palm as early as possible. Statistical analyses, i.e., the Kruskal–Wallis test with α?=?0.05 and the Wilcoxon post-hoc test, were conducted to determine significant differences in the parameters at different points in time. The results show that crown strata number 17 (850 cm from the top) and the crown area were the most suitable parameters to be used. Furthermore, the oil palm trees with BSR can be detected by comparing the 4-month scan or the second 2-month scan. The results have shown that the effect of Ganoderma boninense infection can be differentiated at the later stage. In conclusion, the changes can be measured at 4-months after the first inspection, thus helping to preventing crop losses.

     

Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data

Precision Agriculture - Proximal and remote sensors have proved their effectiveness for the estimation of several biophysical and biochemical variables, including yield, in many different crops....     

海南省油棕园土壤养分调查与评价

【目的】调查海南省主要油棕园土壤养分状况,通过评价土壤肥力,为海南省油棕种植区土壤养分的合理、科学管理提供依据。【方法】选择海南省海口、陵水、儋州、定安、文昌等市县的油棕种植园采集油棕根际表层（10～20cm）土壤,测定土壤pH、有机质、碱解氮、有效磷、速效钾、交换性钙、交换性镁、有效铜、有效锌、有效铁、有效锰、有效硼等12个指标,并进行差异显著性分析。【结果】调查样点的pH较适合油棕的生长;油棕根际土壤的有机质和氮磷钾等主要营养含量较高的为儋州市和定安县,其次为海口市,最低的为文昌市和陵水县;海南省油棕园土壤交换性钙普遍供应充足,交换性镁除文昌市椰子所含量较低外,其他调查样点不存在缺乏现象;调查样点土壤不缺乏铜、锌、铁3种微量元素,除文昌市和陵水市土壤有效锰含量较低外,其他样点处于中等以上含量水平;整个海南省调查样点油棕地存在缺硼现象。【结论】应通过施肥,提高文昌市和陵水县土壤肥力水平,满足该地区油棕生长的需要;重视硼肥的施用,以改善海南省油棕园缺硼现象。     

Method of evaluating diversity of carrot roots using a self-organizing map and image data

Carrot cultivars differ in nutritional value, and the quality of individual pieces may differ from the average, which can be troublesome for companies that process carrots. What is needed is a tool for quick confirmation of the existence and definition of the nature of the differences between the carrot roots. The aim of the experiment was to test whether the simple image parameters, such as colour, will determine the variation within populations of carrot and what kind of information about this differentiation may be provided by a self-organizing map. We indicated that the image data in combination with the self-organization algorithm can provide useful information about carrot roots. However analysis of each individual neural neuron could produce too many summaries to be useful. Therefore, the map was segmented into clusters using k-means method. Segmentation of the self-organizing map allowed to make a comprehensive evaluation of the roots. From the practical point of view such segmentation could help in sorting material, for example, taking into account suitability for processing. The proposed method is sensitive to changes in the processing features of the raw material and is able to locate it in the appropriate area of the topological map. This makes the processor can quickly check whether the raw material he received meets the standards established by a food processing plant. This information is important for the processing industry, in which final product parameters depend on the quality of raw material.     

NOAA卫星可见光和红外资料在干旱监测中的应用

利用NOAA卫星AVHRR通道1、2计算NDVI和通道4的亮温T4,建立模型方程W=aeNUV1 bT4 c监测辽西地区土壤干旱情况.结果表明,土壤深度20cm左右是该模型适用的最佳层次,且模型的监测结果与实测值相对误差较小.     

茶园双人修剪机的验收、使用与保养

讨论购机验收的必要性，介绍了茶园修剪机使用保养的常规作法及其注意事项。     

Tropical deforestation and habitat fragmentation in the Amazon: satellite data from 1978 to 1988

Landsat satellite imagery covering the entire forested portion of the Brazilian Amazon Basin was used to measure, for 1978 and 1988, deforestation, fragmented forest, defined as areas less than 100 square kilometers surrounded by deforestation, and edge effects of 1 kilometer into forest from adjacent areas of deforestation. Tropical deforestation increased from 78,000 square kilometers in 1978 to 230,000 square kilometers in 1988 while tropical forest habitat, severely affected with respect to biological diversity, increased from 208,000 to 588,000 square kilometers. Although this rate of deforestation is lower than previous estimates, the effect on biological diversity is greater.     

基于GIS的农业科技示范园区土壤和土地资源管理研究

应用GIS（地理信息系统）技术建立浙江省衢县白水畈农业科技示范园区土壤和土地资源的信息化管理。园区1：1万土壤图和1：1万土地利用现状图经扫描输入计算机，交替运用ArcView和Are／Info软件，对土壤图和土地利用现状图进行了屏幕数字化，建立拓朴关系，经编辑、坐标转换等处理，建立空间数据库。园区34个土壤剖面的测定值和描述性状、51个耕层土样的测定值和调查统计资料由键盘输入计算机，建立了属性数据库。通过空间数据库和属性数据库的联接，实现土壤和土地资源及其相应属性的互相查询关系。在此基础上分析了园区土壤和土地资源的空间分布特征，获得各类土壤和土地资源的面积。并探讨了GIS在土壤和土地资源综合利用和管理方面的应用。     

利用GIS和SPSS技术综合评价长葛市农业土壤肥力

运用GIS和SPSS技术 ,对河南省长葛市主要农业土壤肥力进行综合评价。首先选取与土壤肥力密切相关的 7个因素 ,建立综合评价指标体系 ,结合长葛市土壤的理化性质特点 ,确定各指标的评价指数。并根据各个因素对土壤肥力影响的大小不同 ,建立判断矩阵 ,确定其权重。然后 ,对长葛市主要的 9种农业土壤的肥力进行了综合评价 ,评价结果表明 ,两合土、褐土化两合土和潮黄土为一等地 ,砂壤土、潮垆土、立黄土为二等地 ,褐土化砂土、盐化潮土、灰质黑老土为三等地。并根据评价结果 ,提出了土壤合理利用的建议。     

电缆式地层测压资料在西湖凹陷油气田中的应用

随着西湖凹陷油气田勘探开发的不断深入,以三低(低电阻率/低对比度、低孔隙度和低渗透率)油气藏为主要特征的复杂油气藏所占比例逐渐增大,电缆式地层测压资料在三低油气藏评价中发挥着越来越重要的作用.在收集整理西湖凹陷油气田大量地层测压资料应用实例的基础上,总结了形成研究区超压的影响因素及超压点判断的方法;归纳了研究区电缆式地层测压资料的分类,测压点主要分为5大类:有效点、超压点、干点、致密点、坐封失败点;研究了电缆式地层测压资料的3类具体应用:计算地层流体密度及气油比,根据压力剖面确定流体界面,评价地层渗透率;并提出了新的油气藏气油比评价方法及流度与渗透率的转化关系.电缆式地层测压资料在储层流体性质识别及确定流体界面方面取得了较好的应用效果,对电缆式地层测压资料在西湖凹陷的后续应用具有重要的借鉴意义.     

Automated detection and identification of white-backed planthoppers in paddy fields using image processing

A survey of the population densities of rice planthoppers is important for forecasting decisions and efficient control. Traditional manual surveying of rice planthoppers is time-consuming, fatiguing, and subjective. A new three-layer detection method was proposed to detect and identify white-backed planthoppers(WBPHs, Sogatella furcifera(Horváth)) and their developmental stages using image processing. In the first two detection layers, we used an Ada Boost classifier that was trained on a histogram of oriented gradient(HOG) features and a support vector machine(SVM) classifier that was trained on Gabor and Local Binary Pattern(LBP) features to detect WBPHs and remove impurities. We achieved a detection rate of 85.6% and a false detection rate of 10.2%. In the third detection layer, a SVM classifier that was trained on the HOG features was used to identify the different developmental stages of the WBPHs, and we achieved an identification rate of 73.1%, a false identification rate of 23.3%, and a 5.6% false detection rate for the images without WBPHs. The proposed three-layer detection method is feasible and effective for the identification of different developmental stages of planthoppers on rice plants in paddy fields.     

Mapping radiation interception in row-structured orchards using 3D simulation and high-resolution airborne imagery acquired from a UAV

This study was conducted to model the fraction of intercepted photosynthetically active radiation (fIPAR) in heterogeneous row-structured orchards, and to develop methodologies for accurate mapping of the instantaneous fIPAR at field scale using remote sensing imagery. The generation of high-resolution maps delineating the spatial variation of the radiation interception is critical for precision agriculture purposes such as adjusting management actions and harvesting in homogeneous within-field areas. Scaling-up and model inversion methods were investigated to estimate fIPAR using the 3D radiative transfer model, Forest Light Interaction Model (FLIGHT). The model was tested against airborne and field measurements of canopy reflectance and fIPAR acquired on two commercial peach and citrus orchards, where study plots showing a gradient in the canopy structure were selected. High-resolution airborne multi-spectral imagery was acquired at 10?nm bandwidth and 150?mm spatial resolution using a miniaturized multi-spectral camera on board an unmanned aerial vehicle (UAV). In addition, simulations of the land surface bidirectional reflectance were conducted to understand the relationships between canopy architecture and fIPAR. Input parameters used for the canopy model, such as the leaf and soil optical properties, canopy architecture, and sun geometry were studied in order to assess the effect of these inputs on canopy reflectance, vegetation indices and fIPAR. The 3D canopy model approach used to simulate the discontinuous row-tree canopies yielded spectral RMSE values below 0.03 (visible region) and below 0.05 (near-infrared) when compared against airborne canopy reflectance imagery acquired over the sites under study. The FLIGHT model assessment conducted for fIPAR estimation against field measurements yielded RMSE values below 0.08. The simulations conducted suggested the usefulness of these modeling methods in heterogeneous row-structured orchards, and the high sensitivity of the normalized difference vegetation index and fIPAR to background, row orientation, percentage cover and sun geometry. Mapping fIPAR from high-resolution airborne imagery through scaling-up and model inversion methods conducted with the 3D model yielded RMSE error values below 0.09 for the scaling-up approach, and below 0.10 for the model inversion conducted with a look-up table. The generation of intercepted radiation maps in row-structured tree orchards is demonstrated to be feasible using a miniaturized multi-spectral camera on board UAV platforms for precision agriculture purposes.     

A fast and robust method for plant count in sunflower and maize at different seedling stages using high-resolution UAV RGB imagery

Precision Agriculture - Acquiring the crop plant count is critical for enhancing field decision-making at the seedling stage. Remote sensing using unmanned aerial vehicles (UAVs) provide an...     

Modelling and monitoring sows’ activity types in farrowing house using acceleration data

This article suggests a method for classifying sows’ activity types performed in farrowing house. Five types of activity are modeled using multivariate dynamic linear models: high active (HA), medium active (MA), lying laterally on one side (L1), lying laterally on the other side (L2) and lying sternally (LS). The classification method is based on a Multi-Process Kalman Filter (MPKF) of class I. The performance of the method is validated using a Test data set. Results of activity classification appear satisfying: 75-100% of series are correctly classified within their activity type. When collapsing activity types into active (HA and MA) vs. passive (L1, L2, LS) categories, results range from 96 to 100%. In a second step, the suggested method is applied on series collected for 19 sows around the onset of farrowing, including 9 sows that received bedding materials (57 sow days in total) and 10 sows that received no bedding material (61 sow days in total). Results indicate that there is a marked (i) increase of active behaviours (HA and MA, p < 0.001) and (ii) decrease of lying laterally (L1 and L2) behaviours starting 20-16 h before the onset of farrowing; during the last 24 h before parturition, the averaged time spent lying laterally in a row decreases and the number of changes of activity types for HA and MA increases. These behavioural changes occur for sows both with and without bedding material, but are more marked when bedding material is provided. Straightforward perspectives for applications of this classification method for monitoring activity types are, e.g. automatic detection of farrowing and detection of health disorders.     

Oestrus detection in dairy cows from activity and lying data using on-line individual models

Automated monitoring and detection of oestrus in dairy cows is attractive for reasons of economy in dairy farming. While high performance detection has been shown possible using high-priced progesterone measurements, detection results were less reliable when only low-cost sensor data were available. Aiming at improving detection scheme reliability with the use of low-cost sensor data, this study combines information from step count and leg tilt sensors. Introducing a lying balance for the individual animal, a novel change detection scheme is derived from observed distributions of the step count data and the lying balance. Detection and hypothesis testing are based on generalised likelihood ratio optimisation combined with time-wise joint probability windowing based on the duration of oestrus and oestrus intervals. It is shown to be essential that cow-specific parameters and test statistics are derived on-line from data to cope with behaviours of individuals. Performance is validated on 18 sequences of data where definite proof of prior oestrus was available in form of subsequent pregnancy. These data were extracted from data sequences from 44 dairy cows over an 8 months period. The results show sensitivity 88.9% and error rate 5.9.%, which is very satisfactory when only cheap sensor data are used.     

Maize acreage estimation using ENVISAT MERIS and CBERS-02B CCD data in the North China Plain

Crop acreage estimation is a key aspect to forecast crop production. Maize acreage estimation becomes more and more important because the fast production changes every year due to the dynamics of the prices. This paper focuses on maize acreage estimation in the North China Plain using ENVISAT MERIS and CBERS-02B CCD data of 2008. Firstly, adaptive maximum likelihood classification of CBERS-02B CCD images based on ground survey provided reliable maize area fraction image (AFI). CBERS derived AFIs (as reference AFI) were used to train a 3-layer back-propagation neural network, this was then used to the whole MERIS data to generate MERIS AFIs (AFIe). To estimate maize acreage, the maize AFI from MERIS was masked with cropland dataset and maize acreages were estimated by zonal statistic of maize AFI at district level. The statistical results were also modified using a non-arable coefficient to remove the effects of non-arable factors. The results showed a close relationship between estimated and statistical maize acreage (R² ≈ 0.88). At province level, the estimation error is approximately 8%. This method is valuable for wide-scale, regional crop acreage estimation at the early stage of growing season. The study gives suggestions about high resolution image acquisition, spatial distribution and cropland datasets.     

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.     

新疆北部天然草地产草量遥感监测预测模型研究

应用１９９１～１９９４年乌鲁木齐、阜康试验区不同草地类型的光谱观测资料和乌鲁木齐、阜康、阿勒泰试验区牧草产量资料及ＮＯＡＡ／ＡＶＨＲＲ资料,建立了新疆北部各地天然草场光谱监测模型、卫星遥感监测与预测模型,为建立草地遥感监测业务服务系统奠定基础．     

Discrimination between organically and conventionally grown winter wheat farm pair samples using the copper chloride crystallisation method in combination with computerised image analysis

Organic and conventional winter wheat farm pair grain samples were tested with the copper chloride crystallisation method and submitted to computerised image analyses followed by pattern recognition and classification with multivariate statistical tools.Appropriate discriminant analyses (DA) models were established. Depending on the analysed region of interest up to 100% of “unknown” samples could be correctly predicted using the DA models.