Remote sensing of upland vegetation: the potential of high spatial resolution satellite sensors

Aim Traditional methodologies of mapping vegetation, as carried out by ecologists, consist primarily of field surveying or mapping from aerial photography. Previous applications of satellite imagery for this task (e.g. Landsat TM and SPOT HRV) have been unsuccessful, as such imagery proved to have insufficient spatial resolution for mapping vegetation. This paper reports on a study to assess the capabilities of the recently launched remote sensing satellite sensor Ikonos, with improved capabilities, for mapping and monitoring upland vegetation using traditional image classification methods. Location The location is Northumberland National Park, UK. Methods Traditional remote sensing classification methodologies were applied to the Ikonos data and the outputs compared to ground data sets. This enabled an assessment of the value of the improved spatial resolution of satellite imagery for mapping upland vegetation. Post‐classification methods were applied to remove noise and misclassified pixels and to create maps that were more in keeping with the information requirements of the NNPA for current management processes. Results The approach adopted herein for quick and inexpensive land cover mapping was found to be capable of higher accuracy than achieved with previous approaches, highlighting the benefits of remote sensing for providing land cover maps. Main conclusions Ikonos imagery proved to be a useful tool for mapping upland vegetation across large areas and at fine spatial resolution, providing accuracies comparable to traditional mapping methods of ground surveys and aerial photography.     

The spatial pattern of grassland aboveground biomass on Xizang Plateau and its climatic controls

Aims Grassland is the most widely distributed vegetation type on the Xizang Plateau. Accurate remote sensing estimation of the grassland aboveground biomass (AGB) in this region is influenced by the types of vegetation indexes (VIs) used, the grain size (resolution) of the remote sensing data and the targeted ecosystem features. This study attempts to answer the following questions: (i) Which VI can most accurately reflect the grassland AGB distribution on the Xizang Plateau? (ii) How does the grain size of remote sensing imagery affect AGB reflection? (iii) What is the spatial distribution pattern of the grassland AGB on the plateau and its relationship with the climate?Methods We investigated 90 sample sites and measured site-specific AGBs using the harvest method for three grassland types (alpine meadow, alpine steppe and desert steppe). For each sample site, four VIs, namely, Normalized Difference VI (NDVI), Enhanced VI, Normalized Difference Water Index (NDWI) and Modified Soil-Adjusted VI (MSAVI) were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) products with grain sizes of 250 m and 1 km. Linear regression models were employed to identify the best estimator of the AGB for the entire grassland and the three individual grassland types. Paired Wilcoxon tests were applied to assess the grain size effect on the AGB estimation. General linear models were used to quantify the relationships between the spatial distribution of the grassland AGB and climatic factors.Important findings The results showed that the best estimator for the entire grassland AGB on the Xizang Plateau was MSAVI at a 250 m grain size (MSAVI 250 m). For each individual grassland type, the best estimator was MSAVI at a grain size of 250 m for alpine meadow, NDWI at a grain size of 1 km for alpine steppe and NDVI at a grain size of 1 km for desert steppe. The explanation ability of each VI for the grassland AGB did not significantly differ for the two grain sizes. Based on the best fit model (AGB =^-10.80 + 139.13 MSAVI 250 m), the spatial pattern of the grassland AGB on the plateau was characterized. The AGB varied from 1 to 136g m ?2. Approximately 59% of total spatial variation in the AGB for the entire grassland was explained by the combination of the mean annual precipitation (MAP) and mean annual temperature. The explanatory power of MAP was weaker for each individual grassland type than that for the entire grassland. This study illustrated the high efficiency of the VIs derived from MODIS data in the grassland AGB estimation on the Xizang Plateau due to the vegetation homogeneity within a 1×1 km pixel in this region. Furthermore, MAP is a primary driver on the spatial variation of AGB at a regional scale.     

Application of two remote sensing GPP algorithms at a semiarid grassland site of North China

Aims Estimation of gross primary production (GPP) from remote sensing data is an important approach to study regional or global carbon cycle. However, for a given algorithm, it usually has its limitation on applications to a wide range of vegetation types and/or under diverse environmental conditions. This study was conducted to compare the performance of two remote sensing GPP algorithms, the MODIS GPP and the vegetation photosynthesis model (VPM), in a semiarid temperate grassland ecosystem.Methods The study was conducted at a typical grassland site in Ujimuqin of Inner Mongolia, North China, over 2 years in 2006 and 2007. Environmental controls on GPP measured by the eddy covariance (EC) technique at the study site were first investigated with path analysis of meteorological and soil moisture data at a daily and 8-day time steps. The estimates of GPP derived from the MODIS GPP and the VPM with site-specific inputs were then compared with the values of EC measurements as ground truthing at the site. Site-specific ? max (α) was estimated by using rectangular hyperbola function based on the 7-day flux data at 30-min intervals over the peak period of the growing season (May to September).Important findings Between the two remote sensing GPP algorithms and various estimates of the fraction of absorbed photosynthetic active radiation (FPAR), the VPM based on FPAR derived from the enhanced vegetation index (EVI) works the best in predicting GPP against the ground truthing of EC GPP. A path analysis indicates that the EC GPP in this semiarid temperate grassland ecosystem is controlled predominantly by both soil water and temperature. The site water condition is slightly better simulated by the moisture multiplier in the VPM than in the MODIS GPP algorithm, which is a most probable explanation for a better performance of the VPM than MODIS GPP algorithm in this semiarid grassland ecosystem.     

Comparison of leaf area index inversion for grassland vegetation through remotely sensed spectra by unmanned aerial vehicle and field-based spectroradiometer

Aims Remote sensing technology has been proved useful in mapping grassland vegetation properties. Spectral features of vegetation cover can be recorded by optical sensors on board of different platforms. With increasing popularity of applying unmanned aerial vehicle (UAV) to mapping plant cover, the study aims to investigate the possible applications and potential issues related to mapping leaf area index (LAI) through integration of remote sensing imagery collected by multiple sensors.     

低空无人机遥感在草原监测评价中的应用进展

低空域无人机遥感技术具有高时效性、高分辨率、低成本、易操控等优势,作为地面与高空遥感（航天与航空遥感）间测量尺度空缺的有益补充,低空无人机遥感扩展了样地样方空间尺度,提高了中、小尺度遥感观测信息的精细化程度,实现了草原生境信息的快速采集、处理与分析应用,是草原"星-空-地"一体化监测的重要组成。针对草原监测评价,总结了国内外低空无人机遥感在草原基况调查（草原草层高度监测、草原植被覆盖度监测与草原地上生物量估算）、草原动态监测（草原植被长势监测、草原产草量估测与草畜平衡监测）和草原应急管理（草原火灾、雪灾与生物灾害监测）中的应用。结合大数据、人工智能、云计算与物联网等新型技术,分析了低空无人机遥感在草原生态监测领域存在的不足和未来的发展方向,以期为低空无人机遥感关于草原监测评价与智慧草原的后续研究提供参考。     

利用遥感技术实现作物模拟模型区域应用的研究进展

作物模拟模型从单点发展到区域应用时,模型中一些宏观资料的获取和参数的区域化方面出现困难,利用遥感技术将实现作物模拟模型的区域应用．文中综述了近年来遥感反演作物模型所需的地表生物物理参数的方法、利用遥感信息直接获取生物量的途径和遥感信息与作物模拟模型之间时空匹配问题等方面的研究概况,重点介绍了利用遥感技术实现作物模拟模型区域应用的3种解决方案(强迫型、调控型和验证型)及其研究进展,并讨论了目前存在的问题和今后研究的方向．     

Remote sensing imagery in vegetation mapping: a review

Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.Methods Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments.Important findings The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.     

复杂地形草地植被碳储量遥感估算研究进展

草地生态系统是我国最大的陆地生态系统，其植被碳储量的准确评估对维护国家生态安全和指导畜牧发展有重要作用。植被生物量和草地面积是草地植被碳储量估算的关键，随着遥感技术的发展，两者估算精度和效率显著提高，先后发展出多种草地生物量遥感估算模型和土地覆被产品，并已在平坦地区取的较好估算结果。然而，复杂地形区迥异于平地的几何形态和水热分布所产生的不均一的生态系统结构和功能，给草地生物量和草地面积的遥感估算带来诸多困难，影响对草地植被碳储量的准确判定。本文在回顾国内外草地植被碳储量遥感估算方法与所需关键参数的基础上，对遥感估算复杂地形草地植被碳储量过程中所面临“遥感影像地形效应的去除和尺度选择”、“植被指数与地形指标的选取”、“过程模型植被生长参数的率定”、“草地面积估算”以及“气象数据与复杂地形上微气候的匹配”等问题进行了总结并提出相应的解决思路，以期为草地植被碳储量遥感估算模型的合理构建以及估算精度的提高提供参考。     

草地植物物种多样性测度方法及发展趋势

植物物种多样性是维持陆地生态系统多功能性和稳定性的关键要素之一.本文梳理了传统草地植物物种多样性的测度方法,结合无人机技术介绍了目前应用于草地植物物种多样性测度的新理念和新方法.传统的草地植物物种多样性测度方法依赖地面观测,需要投入大量的人力、物力且耗时,仅适合小范围的观测;卫星遥感方法受制于分辨率,难以直接对群落结构...     

Satellite remote sensing of wetlands

To conserve and manage wetland resources, it is important to inventoryand monitor wetlands and their adjacent uplands. Satellite remote sensing hasseveral advantages for monitoring wetland resources, especially for largegeographic areas. This review summarizes the literature on satellite remotesensing of wetlands, including what classification techniques were mostsuccessful in identifying wetlands and separating them from other land covertypes. All types of wetlands have been studied with satellite remote sensing.Landsat MSS, Landsat TM, and SPOT are the major satellite systems that have beenused to study wetlands; other systems are NOAA AVHRR, IRS-1B LISS-II and radarsystems, including JERS-1, ERS-1 and RADARSAT. Early work with satellite imageryused visual interpretation for classification. The most commonly used computerclassification method to map wetlands is unsupervised classification orclustering. Maximum likelihood is the most common supervised classificationmethod. Wetland classification is difficult because of spectral confusion withother landcover classes and among different types of wetlands. However,multi-temporal data usually improves the classification of wetlands, as doesancillary data such as soil data, elevation or topography data. Classifiedsatellite imagery and maps derived from aerial photography have been comparedwith the conclusion that they offer different but complimentary information.Change detection studies have taken advantage of the repeat coverage andarchival data available with satellite remote sensing. Detailed wetland maps canbe updated using satellite imagery. Given the spatial resolution of satelliteremote sensing systems, fuzzy classification, subpixel classification, spectralmixture analysis, and mixtures estimation may provide more detailed informationon wetlands. A layered, hybrid or rule-based approach may give better resultsthan more traditional methods. The combination of radar and optical data providethe most promise for improving wetland classification.     

Comparing and Combining Landsat Satellite Imagery and Participatory Data to Assess Land-Use and Land-Cover Changes in a Coastal Village in Papua New Guinea

In regions lacking socio-economic data, pairing satellite imagery with participatory information is essential for accurate land-use/cover (LULC) change assessments. At the village scale in Papua New Guinea we compare swidden LULC classifications using remote sensing analyses alone and analyses that combine participatory information and remotely sensed data. These participatory remote sensing (PRS) methods include participatory land-use mapping, household surveys, and validation of image analysis in combination with remotely sensed data. The classifications of the swidden area made using only remote sensing analysis show swidden areas are, on average, two and a half times larger than land managers reported for 1999 and 2011. Classifications made using only remote sensing analysis are homogeneous and lack discrimination among swidden plots, fallow land, and non-swidden vegetation. The information derived from PRS methods allows us to amend the remote sensing analysis and as a result swidden areas are more similar to actual swidden area found when ground-truthing. We conclude that PRS methods are needed to understand swidden system LULC complexities.     

Vegetation maps based on remote sensing are informative predictors of habitat selection of grassland birds across a wetness gradient

Vegetation is a major environmental factor influencing habitat selection in bird species. High resolution mapping of vegetation cover is essential to model the distribution of populations and improve the management of breeding habitats. However, the task is challenging for grassland birds because microhabitat variations relevant at the territory scale cannot be measured continuously over large areas to delineate areas of higher suitability. Remote sensing may help to circumvent this problem. We addressed this issue by using SPOT 5 imagery and phytosociological data. We mapped grassland vegetation in a floodplain using two methods. We (i) mapped the continuous Ellenberg index of moisture and (ii) identified 5 vegetation classes distributed across the wetness gradient. These two methods produced consistent output maps, but they also provided complementary results. Ellenberg index is a valuable proxy for soil moisture while the class approach provided more information about vegetation structure, and possibly trophic resources. In spite of the apparent uniformity of meadows, our data show that birds do not settle randomly along the moisture and vegetation gradients. Overall birds tend to avoid the driest vegetation classes, i.e. the highest grounds. Thus, vegetation maps based on remote sensing could be valuable tools to study habitat selection and niche partition in grassland bird communities. It is also a valuable tool for conservation and habitat management.     

Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing

1. This study evaluates the efficacy of remote sensing technology to monitor species composition, areal extent and density of aquatic plants (macrophytes and filamentous algae) in impoundments where their presence may violate water‐quality standards. 2. Multispectral satellite (IKONOS) images and more than 500 in situ hyperspectral samples were acquired to map aquatic plant distributions. By analyzing field measurements, we created a library of hyperspectral signatures for a variety of aquatic plant species, associations and densities. We also used three vegetation indices. Normalized Difference Vegetation Index (NDVI), near‐infrared (NIR)‐Green Angle Index (NGAI) and normalized water absorption depth (D_H)_, at wavelengths 554, 680, 820 and 977 nm to differentiate among aquatic plant species composition, areal density and thickness in cases where hyperspectral analysis yielded potentially ambiguous interpretations. 3. We compared the NDVI derived from IKONOS imagery with the in situ, hyperspectral‐derived NDVI. The IKONOS‐based images were also compared to data obtained through routine visual observations. Our results confirmed that aquatic species composition alters spectral signatures and affects the accuracy of remote sensing of aquatic plant density. The results also demonstrated that the NGAI has apparent advantages in estimating density over the NDVI and the D_H. 4. In the feature space of the three indices, 3D scatter plot analysis revealed that hyperspectral data can differentiate several aquatic plant associations. High‐resolution multispectral imagery provided useful information to distinguish among biophysical aquatic plant characteristics. Classification analysis indicated that using satellite imagery to assess Lemna coverage yielded an overall agreement of 79% with visual observations and >90% agreement for the densest aquatic plant coverages. 5. Interpretation of biophysical parameters derived from high‐resolution satellite or airborne imagery should prove to be a valuable approach for assessing the effectiveness of management practices for controlling aquatic plant growth in inland waters, as well as for routine monitoring of aquatic plants in lakes and suitable lentic environments.     

A comparison of cover calculation techniques for relating point-intercept vegetation sampling to remote sensing imagery

Accurate and timely spatial predictions of vegetation cover from remote imagery are an important data source for natural resource management. High-quality in situ data are needed to develop and validate these products. Point-intercept sampling techniques are a common method for obtaining quantitative information on vegetation cover that have been widely implemented in a number of local and national monitoring programs. The use of point-intercept data in remote sensing projects, however, is complicated due to differences in how vegetation cover indicators can be calculated. Decisions on whether to use plant intercepts from any canopy layer (i.e., any-hit cover) or only the first plant intercept at each point (i.e., top-hit cover) can result in discrepancies in cover estimates which are used to train remotely-sensed imagery. Our objective in this paper was to explore the theory of point-intercept sampling relative to training and testing remotely-sensed imagery, and to test the strength of relationships between top-hit and any-hit methods of calculating vegetation cover and high-resolution satellite imagery in two study areas managed by the Bureau of Land Management in northwestern Colorado and northeastern California. We modeled top-hit and any-hit percent cover for six vegetation indicators from 5m-resolution RapidEye imagery using beta regression. Model performance was judged using normalized root mean-squared error (RMSE) from a 5-fold cross validation. Any-hit cover estimates were significantly higher (α < 0.05) than top-hit cover estimates for forbs and grasses in the White River study area, but only marginally higher in Northern California. Pseudo-R² values for beta regression models of vegetation cover from RapidEye image information varied from 0.1525 to 0.7732 in White River and 0.2455 to 0.6085 in Northern California, with little pattern to whether any-hit or top-hit indicators produced better model fit. However, normalized RMSE was lower for any-hit cover (indicating better model performance) or minimally higher than top-hit cover for all indicators in each study area. Our results do not support the idea that top-hit cover estimates from point-intercept sampling are the most appropriate for remote sensing applications in arid and semi-arid shrub-steppe environments. In fact, having two sets of different indicators calculated from the same data may cause additional confusion in a situation where there is already considerable debate on how vegetation cover should be measured and used. Ultimately, selection of indicators to use for developing remote sensing classification or predictive models should be based first on the meaning or interpretation of the indicator in the ecosystem of interest, and second on how well the indicator performs in modeling applications.     

RS,GPS和GIS集成系统在新疆北部天然草地估产技术中的应用进展

利用１９９１～１９９６年在新疆天山北坡不同草地类型上观测的草地可食产量,环境与遥感资料等,使用ＲＳ技术、ＧＰＳ和ＧＩＳ集成系统进行了多重相关分析和遥感估产技术的深入研究,并在图象处理、信息提取、信息应用和ＲＳ－ＧＰＳ－ＧＩＳ一体化估产方法及遥感知识与草原生态专业知识结合方面获得一定研究进展。研究结果表明,４个草地类型的可食鲜干草产量与两种遥感绿度值间存在着极显著相关性（Ｐ＜０．０１）,ｒ值均在０．６７９以上,且通过Ｆ检查和精度分析。一般在类型Ⅱ、Ⅲ和Ⅳ,是鲜草产量与ＲＶＩ相关性好于ＮＤＶＩ,而在类型Ⅰ则相反。进而从６种数学方程式中选优,建立了地学、光学和非线性遥感估产模型,并在实际估产中加以应用、检验和给出了生态学解释及机理分析,使大面积草地可食牧草遥感估产精度达到７５．８％以上,实现了遥感大面积估产目标和草地生态学意义及ＲＳ－ＧＰＳ－ＧＩＳ与草地专家系统一体化集成的应用。     

青藏高原草地地上生物量估算

及时准确评估草地产草量对草地资源的科学管理和可持续发展具有重要意义。青藏高原自然环境特殊,气候差异显著,地形复杂,仅依靠遥感信息准确监测草地地上生物量(Aboveground Biomass,AGB)变化有较大限制。基于青藏高原草地AGB野外实测数据与Landsat遥感影像,探索了植被指数表征草地AGB信息的有效性,评估了气象和地形信息对准确估算草地AGB的影响,综合利用气象、地形和遥感信息,在新一代地球科学数据和分析应用平台(Google Earth Engine)上构建了梯度增强回归树草地AGB估算模型,绘制了青藏高原多年草地AGB空间分布图。结果表明:(1)基于单因素遥感因子的线性回归模型仅能解释8%-40%的草地AGB变化情况,其中绿色归一化植被指数(Green Normalized Difference Vegetation Index, GNDVI)对草地AGB解释能力较强(40%)。(2)基于遥感因子构建的梯度增强回归树模型测试集R²为0.57。分别添加气象、地形信息,模型对草地AGB的估测准确性有所提升,测试R²为0.62和0.63。(3)基于气象、地形和遥感因子的多因素估测模型能够提高草地AGB估测精度,经递归特征消除法优选后,基于13个特征变量的梯度增强回归树模型拟合效果最好(训练数据集R²=0.79,RMSE=43.42 g/m²,P<0.01;测试数据集R²=0.66,RMSE=53.64 g/m²,P<0.01),可以解释66%草地AGB变化情况。(4)2010年青藏高原平均AGB为94.58 g/m²,2015年93.63 g/m²,2020年100.78 g/m²。青藏高原西北部草地AGB较低,东南部草地AGB较高,整体呈现自西北向东南逐渐增加的分布格局。研究结果为准确估算青藏高原草地产草量和碳储量等研究提供重要参考。     

Pine pest detection using remote sensing satellite images combined with a multi-scale attention-UNet model

Pest monitoring of forest areas is essential to pest control. The existing remote sensing satellite image methods have been widely used in detecting pine wilt disease due to their low cost and large detection range. However, most existing methods for pine wilt disease detection are based on multi-phase remote sensing satellite imagery and use manually designed features or machine learning-based algorithms. This makes these methods time-consuming and does not allow early detection of pest-infested forests and can also lead to further spread of the disease. In addition, machine learning-based algorithms can have poor detection performance and generalization ability. To address these shortcomings, this paper uses the pine forest in the Qingyuan area of Liaoning Province in China as a study area to analyze the physiological characteristics of pine pests based on the aerial photography data collected by a Quadrotor-type unmanned aerial vehicle (UAV). By combining these data with the artificial field survey data, the pest-infested areas of forest are marked in the Landsat 8 satellite remote sensing (SRS) images. Further, an end-to-end automatic pest detection framework is designed based on a multi-scale attention-UNet (MA-UNet) model and monophasic images. In addition, the detection performance of the developed model is further optimized using the data augmentation technique to extend the labeled dataset. Compared with the traditional model, the proposed model achieves a much better recall rate of 57.38% in detecting pest-infested forest areas, while the recall rates of the Support Vector Machine (SVM), UNet, attention-UNet, and MedT models are 14.38%, 49.33%, 48.02%, and 33.64%, respectively. According to the results, the proposed model can achieve timely detection and screening of pest-infested forest areas, improving forest management efficiency.     

地表水热要素在青藏高原草地退化中的作用

在全球气候变暖和频繁的人类活动影响下,青藏高原草地生态系统发生了生产力下降、生物多样性减少及生态功能退化等一系列现象。与传统观测技术相比,遥感技术具有大范围、快速和连续监测等优点,因此被广泛用于区域尺度的草地植被长时间序列监测。以往对青藏高原草原植被影响因子的研究多集中在气温与降水,而相比较于气温和降水,地表温度和土壤湿度直接作用于植物的根部,对植物种子的萌芽和植株的生长也都有着重要影响,所以地表温度和土壤湿度与植被生长的关系更加紧密。基于遥感技术,利用青藏高原草地区域的MODIS和AVHRR数据,选择草地植被覆盖度作为草地退化的遥感监测指标,建立了青藏高原草地退化遥感监测和评价指标体系,并对青藏高原2001-2017年的草地退化状况进行了遥感监测和评价。同时,利用遥感数据获取青藏高原区域尺度的地表温度和温度植被干旱指数数据,用于指示地表水热状况,最后基于回归方法分析了地表水热要素在青藏高原草地退化中的作用。结果表明：从2001-2017年,青藏高原植被退化程度空间差异明显,柴达木盆地和青海湖附近退化较为严重,喜马拉雅山脉北部、昆仑山脉南部、冈底斯山脉北部交汇的地区退化也较严重。在2001-2017年间,青藏高原草地未退化面积从50.60%上升到59.00%,说明青藏高原草地整体上在朝着改善的方向发展。2001-2017年内,青藏高原草地整体上大部分时间处于轻度退化状态,但是2001年和2015年这两个年份青藏高原草地退化整体上达到中等退化水平。通过回归分析发现,土壤湿度主导的对青藏高原草地的影响面积达到14.04%。地表温度主导的影响面积达到草地总面积的约36.61%。但地表温度与植被之间相互影响,且主要呈现负相关关系。其中,在温性草甸地区,当植被覆盖度较低时,地表温度正向影响植被生长。     

High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail

While high resolution satellite remote sensing has been hailed as a very useful source of data for biodiversity assessment and monitoring, applications have been more developed in temperate areas. The biodiverse tropics offer a challenge of an altogether different magnitude for hyperspatial and hyperspectral remote sensing. This paper examines issues related to hyperspatial and hyperspectral remotely sensed imagery, which constitutes one of the most potentially powerful yet underutilized sources of for tropical research on biodiversity. Hyperspatial data with their increased pixel resolution are possibly best suited at facilitating the accurate location of features such as tree canopies, but less suited to the identification of aspects such as species identity, particularly when spatial resolution becomes too fine and pixels are smaller than the size of the object (e.g., tree canopy) being identified. Hyperspectral data on the other hand, with their high spectral resolution, can be used to record information pertaining to a range of critical plant properties related to species identity, and can be very effective used for discriminating tree species in tropical forests, despite the greater complexity of such environments. There remains a glaring gap in the easy availability of hyperspectral and hyperspatial satellite data in the tropics due to reasons of cost, data coverage, and security restrictions. Stimulating discussion on the applications of this powerful, but underutilized tool by ecologists, is the first step in promoting a more extensive use of such data for ecological studies in tropical biodiversity rich areas.     

遥感技术在昆虫生态学中的应用途径与进展

结合雷达、航空和卫星遥感本身的特点 ,从害虫本身、害虫所造成的危害、影响害虫发展的环境因子三方面介绍了遥感技术在区域性害虫的早期监测及预测中的应用途径与最新进展。针对害虫本身的个体大小、可动性和种群所在空间尺度的影响 ,作者强调应发挥不同遥感系统各自独特的优势 ,同时 ,综合应用“3S”技术和时空模型方法 ,才能够实现害虫动态的可视化、立体化、实时化和精确化监测及预测。