Assessment of forest spatial differentiation in Murmansk Province using field surveys and remote sensing data

The results of study of the spatial differentiation of forest using field data, remote sensing, and map data are presented. Different classification approaches are used while analyzing the diversity of forest communities, i.e., ecological-dominant, ecological-topological, and dynamical. The interpolation of local chatacteristics of plant associations and syntaxonomic units at the upper levels using the data of spectral satellite imagery and quantitative methods of processing allow to use in mapping important information on the structure and properties of vegetation. The results of our studies include thematic maps of the specific parameters of forest and a 1: 100000-scale vegetation map of the central part of Murmansk Province. The role of natural and anthropogenic factors is reflected in the legend to the map.     

基于GIS的浙江省水稻遥感估产最佳时相选择

水稻遥感估产最佳时相选择应包括水稻种植面积估算最佳时相和水稻产量预报最佳时相两部分。在水稻遥感估产最佳时相选择中,由于首次引入GIS技术提取水稻可能种植区域,缩小了研究范围,植被种类也较简单一,因此仅用农作物物候历即可确定水稻种植面积估算最佳时相,而不需要考虑所有的植被类型。利用盆栽试验和小区试验研究水稻产量与不同时期的农学参数、农学参数与植被指数及水稻产量与植被指数的关系,结果表明,水稻产量与农业参数、农学参数与光谱变量的关系均以孕穗以抽穗期最好,水稻产量与光谱变量的关系则从分蘖盛期到抽穗期的极显著。因此,以孕穗期到抽穗期作为建立水稻遥感估产模型的最佳时期。再利用1998年各地的水稻发育期观测资料,确定各区水稻产量遥感最佳时相。     

Mapping migratory bird prevalence using remote sensing data fusion

Background

Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA.

Methodology and Principal Findings

A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species.

Conclusion and Significance

Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.     

Mapping an invasive bryophyte species using hyperspectral remote sensing data

Reliable distribution maps are crucial for the management of invasive plant species. An alternative to traditional field surveys is the use of remote sensing data, which allows coverage of large areas. However, most remote sensing studies on invasive plant species focus on mapping large stands of easily detectable study species. In this study, we used hyperspectral remote sensing data in combination with field data to derive a distribution map of an invasive bryophyte species, Campylopus introflexus, on the island of Sylt in Northern Germany. We collected plant cover data on 57 plots to calibrate the model and presence/absence data of C. introflexus on another 150 plots for independent validation. We simultaneously acquired airborne hyperspectral (APEX) images during summer 2014, providing 285 spectral bands. We used a Maxent modelling approach to map the distribution of C. introflexus. Although C. introflexus is a small and inconspicuous species, we were able to map its distribution with an overall accuracy of 75 %. Reducing the sampling effort from 57 to 7 plots, our models performed fairly well until sampling effort dropped below 12 plots. The model predicts that C. introflexus is present in about one quarter of the pixels in our study area. The highest percentage of C. introflexus is predicted in the dune grassland. Our findings suggest that hyperspectral remote sensing data have the potential to provide reliable information about the degree of bryophyte invasion, and thus provide an alternative to traditional field mapping approaches over large areas.     

基于遥感数据源的景观分析系统的设计与实现

在Ｖｉｓｕａｌ Ｃ＋＋环境下,设计并实现了一个景观分析系统。该系统采用提取斑块边界象素集的方法,提高了斑块搜索的速度,而且提供了斑块间距离计算、相邻斑块间边界长度计算等景观分析功能。该系统被应用到北京西北郊同分析中。通过分析表明,该系统不仅能计算面积、周长、中心坐标和最近同类型斑块的距离等单个斑块指数,而且能在此基础上计算景观类型和整个景观层次上的各种指数,从而弥补了目前多数地理信息系统在分析应用     

遥感在森林地上生物量估算中的应用

生物量是地表C循环研究的重要组成部分,生物量研究有助于深入认识区域乃至全球的C平衡。森林作为地球最重要的陆地生态系统,区域乃至全球尺度的森林地上生物量估算一直是生态学研究的难点之一。在大的空间尺度上,遥感技术是估算森林地上生物量的有效手段。TM、AVHRR、SAR等数据以及多源数据的融合在森林生物量估算方面广泛应用,并取得了显著效果。运用遥感技术进行森林生物量估算时,所采用的数据源不同,分析方法也不相同,主要分析方法有:相关分析、多元回归分析、神经网络和数学模型模拟等。随着测定不同空间、时间和波谱分辨率的各种传感器的广泛使用,以及生物量遥感估算模型的进一步发展和完善,大尺度森林生物量的遥感估算研究必将向前迈进一大步。     

Estimation of forest area and its dynamics in Russia based on synthesis of remote sensing products

We review up-to-date, open access remote sensing (RS) products related to forest. We created a hybrid forest/non-forest map using geographically weighted regression (GWR) based on a number of recent RS products and crowdsourcing. The hybrid map has spatial resolution of 230 m and shows the extent of forest in Russia in 2010. We estimate area of Russian forest as 711 million ha (in accordance with Russian national forest definition). Compared to official data of the State Forest Register (SFR), RS estimates the area of forest to be considerably larger in European part (+12.2 million ha or +8%) and smaller in Asian (–39.8 million ha or–7%) part of Russia. We report the changing forest area in 2001–2010 and discuss main drivers: wildfire and encroachment of abandoned arable land. The methodology used here can by applied for monitoring of forest cover and enhancing the forest accounting system in Russia.     

利用遥感信息研究西藏地区主要植被年内和年际变化规律

利用1982～2000年NOAA-AVHRR月合成NDVI遥感资料和相关气象台站数据对我国西藏地区的稀疏草地、浓密草地和Tebit森林等主要植被的变化进行了初探。利用月合成NDVI的多年平均值分析了植被指数年内季节性变化规律及其与气候因子的关系,利用多年月合成NDVI的标准差描述了NDVI年际间波动情况。结果表明,在西藏地区,浓密草地和Tebit森林的NDVI植被指数年内变化规律呈明显的季节性,而稀疏草地则不明显;在年际变化方面,浓密草地月合成NDVI值波动幅度最大,Tebit森林次之,稀疏草地最小,且波动幅度较大的月份集中在NDVI值较高的植被生长季节6～10月份。     

Scaling-up carbon and carbonate metabolism of coral reefs using in-situ data and remote sensing

Areal up-scaling at reef-scale of organic and inorganic metabolism is possible using in-situ measurements and remote sensing data providing the extent of each bottom type inside the reef. Using a SPOT image and published values of metabolism, the gross production (93,560᎒³ kg C year^-1), excess production (10,017᎒³ kg C year^-1) and calcification (165,348᎒³ kg CaCO₃ year^-1) over 35 km² of coral reef environment in Moorea Island (French Polynesia) are estimated. While the computations are straightforward, certain assumptions must be made in order to conduct the scaling exercise. The exercise is valid only if the metabolism of reef benthos is additive through increasing spatial scale. Despite the difficulty of quantitatively assessing our extrapolations, spatial additivity seems to represent the reality. The other limitation is that the reef must be considered as a closed system, in an equilibrium state supposedly accurately described by the few available in-situ measurements. To consider the reef an open system, long-term metabolic measurements coupled with knowledge of oceanic and land forcing processes are required. These theoretical considerations point to the necessity of integrated multi-scale studies based on both remote sensing and in-situ data in order to better understand the productivity and calcification of reefs in the current global change context.     

Monitoring the dynamics of an invasive emergent macrophyte community using operational remote sensing data

Potamogeton crispus L. (curly pondweed) is a cosmopolitan aquatic macrophyte considered invasive in North America and elsewhere. Its range is expanding and, on individual water bodies, its coverage can be dynamic both within and among years. In this study, we evaluate the use of free and low-cost satellite remote sensing data to monitor a problematic emergent macrophyte community dominated by P. crispus. Between 2000 and 2006, we acquired eight satellite images of 24,000-ha Lake Sharpe, South Dakota (USA). During one of the dates for which satellite imagery was acquired, we sampled the lake for P. crispus and other emergent macrophytes using GPS and photography for documentation. We used cluster analysis to assist in classification of the satellite imagery and independently validated results using the field data. Resulting estimates of emergent macrophyte coverage ranged from less than 20 ha in 2002 to 245 ha in 2004. Accuracy assessment indicated 82% of image pixels were correctly classified, with errors being primarily due to failure to identify emergent macrophytes. These results emphasize the dynamic nature of P. crispus-dominated macrophyte communities and show how they can be effectively monitored over large areas using low-cost remote sensing imagery. While results may vary in other systems depending on water quality and local flora, such an approach could be applied elsewhere and for a variety of macrophyte communities.     

Canopy-cover thematic-map generation for Military Map products using remote sensing data in inaccessible areas

Canopy cover is one of the most important elements in concealing military structures and enemy reconnaissance. In this study, we propose an algorithm for automatic generation of density measure of percent canopy cover, which is an attribute of the digital Military Map product, using high-resolution satellite images of inaccessible areas. The thematic mapping process of canopy cover can be divided into image classification, segmentation, and texture analysis. QuickBird and SPOT-5 high-resolution images are classified using Landsat images and military maps. Then, forested areas are extracted from the classified images, and closing and opening operations are executed through morphology filtering. The extracted region is divided into unit-zone objects using a segmentation technique, and the percentage of canopy cover of each object is categorized as one of four levels (0–25, 26–50, 51–75, 76–100%). Two methods were used to establish the percentage of canopy cover for each segment: the discriminant method, using statistical analysis, and the classified canopy ratio method, which calculates the percentage of forest in the segment. The discriminant method showed 48% (QuickBird) and 61% (SPOT-5) accuracy and classified canopy ratio method showed 71% (QuickBird) and 87% (SPOT-5) accuracy.     

美国玉米和小麦产量动态预测遥感模型

粮食安全问题一直倍受世界各国关注,及时、准确地了解其他国家或地区的粮食生产状况,对于中国粮食贸易和粮食宏观调控,具有十分重要的意义.本文以美国冬小麦和玉米为研究对象,在分析各作物空间分布及生长季节的基础上,利用土地利用数据剔除非耕地信息,使提取的归一化植被指数(NDVI)客观地反映各作物的生长状况.以1998-2007年的SPOT VEGETATION旬最大值合成NDVI资料为数据源,研究了美国玉米和小麦生长季的旬NDVI与产量的关系,确定了不同月份的建模因子,分别建立了美国玉米和冬小麦不同月份的产量动态预报模型.通过对各模型估算产量与实际产量进行比较,各模型预报结果的相对误差大部分在3%以内,精度较高,说明建立的作物产量动态预报模型实用可行,能够投入产量预报业务应用.     

Quantification of plant stress using remote sensing observations and crop models: the case of nitrogen management

Remote sensing techniques offer a unique solution for mapping stress and monitoring its time-course. This article reviews the main issues to be addressed for quantifying stress level from remote sensing observations, and to mitigate its impact on crop production by managing cultural practices. The case of nitrogen fertilization is used here as a paradigm. The derivation of canopy state variables such as the leaf area index (LAI) and chlorophyll content (C(ab)) is first addressed. It is demonstrated that the inversion of radiative transfer models leads to useful estimates of these variables. However, because of the ill-posed nature of the inverse problem, better accuracy is achieved when using prior information on the distribution of the variables and when multiplying LAI by C(ab) to get canopy level chlorophyll content. This variable, LAIxC(ab) is well suited for quantifying canopy level nitrogen content. It is used for nitrogen stress evaluation by comparison with a reference unstressed situation which is, however, not easy to get in practice. The combination of remote sensing observations with crop models provides an elegant solution for stress quantification through assimilation approaches. It fuses several sources of information within our knowledge of the processes involved and accounts for the environmental budget which can be integrated when making decisions about cultural practices. Conclusions are drawn on the issues related to the retrieval of canopy state variables from remote sensing data, to the link between these observables and crop models, and to the assimilation approaches. Avenues for further research are finally discussed along with the required observation system.     

Three decades of pastoralist settlement dynamics in the Ethiopian Omo Delta based on remote sensing data

Human Ecology - River deltas provide important livelihoods to local populations, but at the same time are under increasing anthropogenic pressure. The opening of the Gibe III dam on the Omo River...     

Mapping tropical forest fractional cover from coarse spatial resolution remote sensing imagery

At regional to global scales the only feasible approach to mapping and monitoring forests is through the use of coarse spatial resolution remotely sensed imagery. Significant errors in mapping may arise as such imagery may be dominated by pixels of mixed land cover composition which cannot be accommodated by conventional mapping approaches. This may lead to incorrect assessments of forest extent and thereby processes such as deforestation which may propagate into studies of environmental change. A method to unmix the class composition of image pixels is presented and used to map tropical forest cover in part of the Mato Grosso, Brazil. This method is based on an artificial neural network and has advantages over other techniques used in remote sensing. Fraction images depicting the proportional class coverage in each pixel were produced and shown to correspond closely to the actual land cover. The predicted and actual forest cover were, for instance, strongly correlated (up to r = 0.85, significant at the 99% level of confidence) and the predicted extent of forest over the test site much closer to the actual extent than that derived from a conventional approach to mapping from remotely sensed imagery.