Spatial segregation of striped marlin (Kajikia audax) by size in the eastern Pacific Ocean

Striped marlin (Kajikia audax) is an epipelagic fish distributed in oceanic and coastal waters of the Pacific Ocean. This species is usually found in warm and coastal waters with high primary productivity. The main goal of this study was to describe the spatial segregation of striped marlin by average Eye‐Fork length (EFL) in the eastern Pacific Ocean (EPO) and its relationship with environmental variables using EFL data obtained from tuna purse‐seining and Generalized Additive Models (GAMs). The model suggested that larger individuals of striped marlin were more likely to be found in waters with high Chlorophyll‐a concentration (>2 mg/m³) and with temperatures lower than 25°C, within a region known as the “cold tongue” and the Humboldt current system, while smaller individuals were more likely to be found in warmer and low productive areas within a region known as the “warm pool of the EPO.” We observed that set type caused a large variation on average EFL of striped marlin; larger fish were captured in sets associated with floating objects (natural and manmade), while smaller fish were captured in sets associated with dolphins. Despite this, our findings suggest that striped marlin has a latitudinal gradient in average EFL; larger individuals occurred predominantly south of 10°N, while smaller ones occurred predominantly in coastal waters between 10°N and 20°N, thus demonstrating a spatial segregation of the species affected by its maturity stage.     

Comparison of image and rapid field assessments of riparian zone condition in Australian tropical savannas

Suitable methods for measuring and monitoring the condition of riparian environments are being investigated by government agencies responsible for maintaining these environments in Australia. The objective of this work was to compare two riparian condition assessment approaches, the Tropical Rapid Appraisal of Riparian Condition (TRARC) method developed for rapid on-ground assessment of the environmental condition of savanna riparian zones and an image based riparian condition monitoring scheme. Measurements derived from these two approaches were compared and correlated. The sample representativeness of the TRARC method was evaluated and the cost-effectiveness and suitability for multi-temporal analysis of the two approaches were assessed. Two high spatial resolution multi-spectral QuickBird satellite images captured in 2004 and 2005 and coincident field data covering sections of the Daly River in the Northern Territory, Australia were used in this work. Both field and image data were processed to map indicators of riparian zone condition including percentage canopy cover, organic litter on the ground, canopy continuity, tree clearing, bank stability, and flood damage. Spectral vegetation indices, image segmentation, and supervised classification were used to produce riparian health indicator maps. QuickBird image data were used to examine if the spatial distribution of TRARC transects provided a representative sample of ground based estimates of riparian health indicators. Covering approximately 3% of the study area, the sample mean of the TRARC estimates of individual indicators of riparian zone condition were in most cases within 20% of the global mean derived from the whole imaged riparian area. The cost-effectiveness of the image based approach was compared to that of the ground based TRARC method. Results showed that the TRARC method was more cost-effective at spatial scales from 1 km to 200 km of river in relatively homogeneous riparian zones along rivers with only one channel, while image based assessment becomes more feasible at regional scales (200–2000 km of river). A change detection analysis demonstrated that image data can provide detailed information on gradual change, while the TRARC method is less suited for multi-temporal analysis due to the ranked data format, which inhibits precise detection of change. However, results from both methods were considered to complement each other for single date assessment of riparian zones if used at appropriate spatial scales.     

Analysis of Land use and Land Cover Changes in the Coastal Area of Bangladesh using Landsat Imagery

Coastal land use across the globe has experienced remarkable rapid change over the recent decades because of extraordinary anthropogenic pressure and climate variability and change. Therefore, quantitative information about coastal land use change is imperative for effective management and planning resources for sustainable development. We analysed the quantitative land use and land cover changes during 1989–2000–2010 periods in three important agroecological zones of the most vulnerable coastal region of Bangladesh using Landsat images (Thematic Mapper/Enhanced Thematic Mapper Plus). In the Ganges Tidal Floodplain, the area under shrimp cultivation greatly increased at the rate of 2·05% per annum. The majority of the shrimp area gained from conversion of single cropland. In the Meghna Estuarine Floodplain, decreased mudflat and water bodies were observed, which was predominantly converted into cropland. In Chittagong Coastal Plain, salt pan–shrimp area increased with the expense of single and/or double cropland. In all the study areas, settlement area considerably increased over time. The dynamics of land use change have been attributed to low and unstable food production in the coastal region. The approach adopted in study and the results obtained from the study would likely be useful for policy making and identifying direction for future studies on the coastal land use in Bangladesh. Copyright © 2015 John Wiley & Sons, Ltd.     

High resolution satellite imaging sensors for precision agriculture

The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since the farming technology started developing in the mid to late 1980s. Various types of remote sensors carried on ground-based platforms, manned aircraft, satellites, and more recently, unmanned aircraft have been used for precision agriculture applications. Original satellite sensors, such as Landsat and SPOT, have commonly been used for agricultural applications over large geographic areas since the 1970s, but they have limited use for precision agriculture because of their relatively coarse spatial resolution and long revisit time. Recent developments in high resolution satellite sensors have significantly narrowed the gap in spatial resolution between satellite imagery and airborne imagery. Since the first high resolution satellite sensor IKONOS was launched in 1999, numerous commercial high resolution satellite sensors have become available. These imaging sensors not only provide images with high spatial resolution, but can also repeatedly view the same target area. The high revisit frequency and fast data turnaround time, combined with their relatively large aerial coverage, make high resolution satellite sensors attractive for many applications, including precision agriculture. This article will provide an overview of commercially available high resolution satellite sensors that have been used or have potential for precision agriculture. The applications of these sensors for precision agriculture are reviewed and application examples based on the studies conducted by the author and his collaborators are provided to illustrate how high resolution satellite imagery has been used for crop identification, crop yield variability mapping and pest management. Some challenges and future directions on the use of high resolution satellite sensors and other types of remote sensors for precision agriculture are discussed.     

Weed Management in Rice-Wheat Cropping System through Remote Sensing： A Review

The productivity of the flee-wheat rotation of the Indo-Gangetie Plains is critical to India＇s food security. Severe weed competition resulting from the change in morphological characteristics of the modem high yielding varieties （HYV） led to intensive use of herbicides for weed control. Continuous and indiscriminate use of herbicides led to pollution as well as build up of resistant biotypes. This necessitates the need based use of herbicides. Identification of predominant weeds in rice-wheat cropping system through remote sensing and Geographic Information System （GIS） would help in reducing the load of herbicide on agro-eco system by reducing spray volume, application time and non-target spraying. This will help in developing site-specific weed management technique by geating only those areas with herbicide where weed densities exceed the economic threshold and may reduce application rate of herbicides in patches where weed densities are low. This paper reviews the literature on role of remote sensing for weed management not just to review their empirical findings, but to evaluate the role they have played and have the potential to play-in actual for weed management in flee-wheat cropping system （food bowl of India）.     

基于树影与快鸟图像的单木树高提取

利用黑龙江省塔河地区2008年的快鸟影像,研究了基于高空间分辨率的卫星影像的单木树高提取方法.在试验区内,实测孤立木的树高、胸径和冠幅,并在遥感图像对应位置上一一标记.在对快鸟影像进行裁剪、几何校正等处理的基础上,提取孤立木树冠顶点区域在遥感图像上的灰度值,然后建立灰度值与其树高之间的回归模型,其决定系数达到0.88,...     

遥感技术在湟中县第2次土地调查中的应用

分析了遥感技术在第2次土地调查中的优势和重要作用,介绍了遥感技术在湟中县第2次土地调查中应用的技术路线、工作流程及存在的主要问题。     

英文广告仿拟翻译

英文广告仿拟翻译,遵循最佳关联原则,综合考虑影响广告效果的诸多因素,可分别采用仿译与创译策略,以实现最佳广告效果。     

基于无人机热红外图像的核桃园土壤水分预测模型建立与应用

为了解北方核桃园区的土壤水分状况,实现优化水资源配置的目的。该文于2016和2017年采用固定式热红外成像仪(A310 f)连续观测得到核桃主要生长季节午后(13:00和14:00)的冠层温度,并同步观测温度、湿度、辐射、风速、降雨量和0~80 cm不同土层深度的土壤体积含水量。并于2017年8月11日利用无人机热成像系统(TC640)对连续灌溉区域和干旱胁迫区域进行了图像采集。结果表明,40~60 cm土层深度可能是核桃树主要吸收水分的区域。冠层温度普遍高于空气温度,其变化范围在0~5℃之间,冠气温差与土壤含水量呈负相关,与太阳辐射呈正向关系,其中,土壤含水量的贡献值达到了75%。利用2017年13:00时的冠层与空气温差数据来建立的土壤水分预测模型,R2=0.64;同时,利用14:00时的实测数据对所建立模型进行验证,R2=0.61,表明该模型具有一定的拟合精度。最后,将模型用于诊断核桃区域水分状况,证明了其具有较好的实际应用效果。该研究首次将固定式热成像设备与无人机热成像系统相结合来研究树木的冠层温度,并成功实现了从理论模型到实际应用,从单株水平到区域尺度的转换。     

基于分量替换高分辨率遥感图像融合方法的对比研究

基于分量替换的高分辨率遥感图像融合是一种十分重要的融合方法,但对该方法的融合原理的深入分析在国内还鲜有报道。为此对分量替换融合方法进行原理探究和实验对比分析。首先从线性代数的角度来阐述分量替换融合算法的实质,并对两种典型的构造可替换波段的分量替换方法（基于光谱响应函数模拟低分辨率全色波段的Gram-Schmidt融合法（GS1）和基于多元一次线性回归拟合低分辨率全色波段的Gram-Schmidt（GS2）融合法进行原理说明;其次,选取QuickBird全色与多光谱图像数据,进行三种有代表性的基于分量替换的融合方法（PCA,GS1,GS2）的对比分析。通过对比融合前后典型地物的光谱特性变化来评价融合影像的光谱保真性也是该研究的一大特色。结果表明:3种融合方法都具有很高的光谱保真性,GS2融合方法具有最优的光谱保真性,PCA和GS1融合算法次之,尤其是GS1融合处理后的图像存在部分光谱失真的现象,GS2算法的光谱保真性明显优于GS1。从基于分量替换融合方法的实质可以诠释出造成GS1融合图像光谱失真的根本原因,GS1融合算法使用的只是实验室理想环境下所获取的名义上的光谱响应特征。传感器的实际成像过程受到诸多因素的影响,如传感器在轨工作环境、大气、观测角度不同等的影响。GS1算法单纯通过不同波段光谱响应函数的线性组合来模拟低分辨率全色波段并不十分准确,GS2直接利用MS和Pan波段像元灰度值进行线性回归,克服了上述不确定性问题。通过以上对比研究发现,如何利用多光谱数据准确地模拟低分辨率全色波段,直接影响到融合后影像的光谱保真性,是目前高分辨率遥感图像融合的关键技术。