Broad-scale spatial pattern of forest landscape types in the Guiana Shield

Detecting broad scale spatial patterns across the South American rainforest biome is still a major challenge. Although several countries do possess their own, more or less detailed land-cover map, these are based on classifications that appear largely discordant from a country to another. Up to now, continental scale remote sensing studies failed to fill this gap. They mostly result in crude representations of the rainforest biome as a single, uniform vegetation class, in contrast with open vegetations. A few studies identified broad scale spatial patterns, but only when they managed to map a particular forest characteristic such as biomass. The main objective of this study is to identify, characterize and map distinct forest landscape types within the evergreen lowland rainforest at the sub-continental scale of the Guiana Shield (north-east tropical South-America 10° North-2° South; 66° West-50° West). This study is based on the analysis of a 1-year daily data set (from January 1st to December 31st, 2000) from the VEGETATION sensor onboard the SPOT-4 satellite (1-km spatial resolution). We interpreted remotely sensed landscape classes (RSLC) from field and high resolution remote sensing data of 21 sites in French Guiana. We cross-analyzed remote sensing data, field observations and environmental data using multivariate analysis. We obtained 33 remotely sensed landscape classes (RSLC) among which five forest-RSLC representing 78% of the forested area. The latter were classified as different broad forest landscape types according to a gradient of canopy openness. Their mapping revealed a new and meaningful broad-scale spatial pattern of forest landscape types. At the scale of the Guiana Shield, we observed a spatial patterns similarity between climatic and forest landscape types. The two most open forest-RSLCs were observed mainly within the north-west to south-east dry belt. The three other forest-RSLCs were observed in wetter and less anthropized areas, particularly in the newly recognized “Guianan dense forest arch”. Better management and conservation policies, as well as improvement of biological and ecological knowledge, require accurate and stable representations of the geographical components of ecosystems. Our results represent a decisive step in this way for the Guiana Shield area and contribute to fill one of the major shortfall in the knowledge of tropical forests.     

Analyzing the effects of scale and land use pattern metrics on land use database generalization indices

The generalization index system is one of the critical issues for computer-aided land use database generalization. This paper studies the scale and land use pattern effects on land use database generalization indices and estimates the thresholds of these indices based on a typical land use database sample. The index system of land use database generalization is discussed and constructed from macro and micro perspectives. Six land use pattern metrics, namely, land use diversity index, land use dominance index, land use homogeneity index, land use fragmentation index, the index of land use type dominance, and the index of land use type fragmentation, are designed to characterize land use patterns and are introduced into the analysis of land use pattern effect on land use database indices. The analysis framework of the scale and land use pattern effects on the land use database indices are proposed by employing statistical techniques. Based on the land use database samples at multiple spatial scales collected in various land use regions across China, the study generates rules for both scale and land use pattern effects on the indices, including map area proportion of land use types, total map load, parcel map load, and minimum parcel area. The thresholds of these indices in land use database generalization are produced at the scales of 1:50,000, 1:100,000, 1:250,000, and 1:500,000. An experimental generalization at county level demonstrates how to determine the generalization index values considering scale and land use pattern, and how to evaluate the generalization results using our macro indices.     

Forest inventories by LiDAR data: A comparison of single tree segmentation and metric-based methods for inventories of a heterogeneous temperate forest

Inventories of temperate forests of Central Europe mainly rely on terrestrial measurements. Rapid alterations of forests by disturbances and multilayer silvicultural systems increasingly challenge the use of conventional plot based inventories, particularly in protected areas. Airborne LiDAR offers an alternative or supplement to conventional inventories, but despite the possibility of obtaining such remote sensing data, its operational use for broader areas in Central Europe remains experimental. We evaluated two methods of forest inventory that use LiDAR data at the landscape level: the single tree segment-based method and an area-based method. We compared a set of structural forest attributes modeled by these methods with a conventional forest inventory of the highly heterogeneous forest of the Bavarian Forest National Park (Germany), which partially includes stands affected by severe natural disturbances. Area-based models were accurate for all structural attributes, with cross-validated average root mean squared error ranging from ∼3.4 to ∼13.4 in the best modeling case. The coefficients of variation for the mapped area-based estimations were mostly minor. The area-based estimations were varied but highly correlated (Pearson’s correlations between ∼ 0.56 and 0.85) with single tree segmentation estimations; undetected trees in the single tree segmentat-based method were the main sources of inconsistency. The single tree segment-based method was highly correlated (∼ 0.54 to 0.90) with data from ground-based forest inventories. The single tree-based algorithm delivered highly reliable estimates for a set of forest structural attributes that are of interest in forest inventories at the landscape scale. We recommend LiDAR forest inventories at the landscape scale in both heterogeneous commercial forests and large protected areas in the central European temperate sites.     

The effectiveness of richness,diversity and dominance textural indices when applied on digital ground photographs using a GIS for landscape visual analysis purposes

This study aims to investigate the effectiveness of three main textural indices for the automatic evaluation and classification of landscape scenic quality using digital ground photographs. Based on the textural indices that are commonly used in landscape ecology, the richness, diversity and dominance indices were selected to be examined, and the results from the application of these indices to a sample of ground photographs are described in this paper. The application of the textural indices to the sample of photographs takes place in two different ways, using a geographic information system (GIS) that aims to describe the general and partial heterogeneity of the landscape appearing in each photograph. Furthermore, we investigate the effectiveness of the indices selected as to the classification of the landscape's scenic quality, with the results being compared to other results derived from a research programme of the National Technical University of Athens. Comments and suggestions are presented for further investigation. The main conclusion of the investigation is that the texture indices are sensitized in the landscape's scenic quality, a fact that is positive and encourages the pursuit of further research.     

GeoSurveillance: a GIS-based system for the detection and monitoring of spatial clusters

This article introduces a software package named GeoSurveillance that combines spatial statistical techniques and GIS routines to perform tests for the detection and monitoring of spatial clustering. GeoSurveillance provides both retrospective and prospective tests. While retrospective tests are applied to spatial data collected for a particular point in time, prospective tests attempt to incorporate the dynamic nature of spatial patterns via analyzing time-series data to detect emergent clusters as quickly as possible. This article will outline the structure of GeoSurveillance as well as describe the statistical cluster detection methods implemented in the software. It concludes with an illustration of the use of the software to analyze the spatial pattern of low birth weights in Los Angeles County, California.      

A method for analyzing the expansion and shrinkage of point pattern

This article proposes a new method for analyzing the spatial expansion and shrinkage of point patterns. Spatial expansions of epidemic diseases and market areas are represented as the expansions of point patterns when disease cases and store customers are represented as points. The spatial expansion and shrinkage have been studied in many scientific fields. Existing analytical methods, however, are not sufficient for treating complicated spatiotemporal patterns. To answer this demand, this article develops a new method for analyzing the expansion and shrinkage of points. Three vector measures evaluate the degree and direction of expansion and shrinkage as functions of location and time. They are visualized as vector maps, which are valid for capturing the global spatiotemporal pattern as well as for discussing the local variation. Summary measures of these vectors allow us to grasp the overall spatiotemporal pattern efficiently. To test the validity of the proposed method, this article applies it to the analysis of visitors to Shinjuku and Ginza in Tokyo. The proposed measures permitted us to evaluate the spatiotemporal pattern of the visitors in detail and to consider its underlying structure from various perspectives, which indicated the soundness of the technique.     

Optimizing the spatial resolution of WorldView-2 imagery for discriminating forest vegetation at subspecies level in KwaZulu-Natal,South Africa

The objective of this study was to identify an appropriate spatial resolution for discriminating forest vegetation at subspecies level. WorldView-2 imagery was progressively resampled to coarser spatial resolutions. At a compartment level, 30 × 30-m subsets were generated across forest compartments to represent the five forest subspecies investigated in this study. From the centre of each subset, the spatial resolution of the original WorldView-2 image was resampled from 6 to 34-m, with increments of 4-m. The variance was then calculated at every resampled spatial resolution using each of the eight WorldView-2 bands. Based on the sampling theorem, the 3-m spatial resolution provided an appropriate resolution for all subspecies investigated. The WorldView-2 image was subsequently classified using the partial least squares linear discriminant analysis algorithm and the appropriate spatial resolution. An overall classification accuracy of 90% was established with an allocation disagreement of 9 and a quantity disagreement of 1.     

A data mining approach for evaluation of optimal time-series of MODIS data for land cover mapping at a regional level

Optical Earth Observation data with moderate spatial resolutions, typically MODIS (Moderate Resolution Imaging Spectroradiometer), are of particular value to environmental applications due to their high temporal and spectral resolutions. Time-series of MODIS data capture dynamic phenomena of vegetation and its environment, and are considered as one of the most effective data sources for land cover mapping at a regional and national level. However, the time-series, multiple bands and their derivations such as NDVI constitute a large volume of data that poses a significant challenge for automated mapping of land cover while optimally utilizing the information it contains. In this study, time-series of 10-day cloud-free MODIS composites and its derivatives – NDVI and vegetation phenology information, are fully assessed to determine the optimal data sets for deriving land cover. Three groups of variable combinations of MODIS spectral information and its derived metrics are thoroughly explored to identify the optimal combinations for land cover identification using a data mining tool.The results, based on the assessment using time-series of MODIS data, show that in general using a longer time period of the time-series data and more spectral bands could lead to more accurate land cover identification than that of a shorter period of the time-series and fewer bands. However, we reveal that, with some optimal variable combinations of few bands and a shorter period of time-series data, the highest possible accuracy of land cover classification can be achieved.     

一种基于空间网格的地物要素点云提取方法

LiDAR点云的分类提取是点云数据处理中的首要步骤。为了提高复杂场景中点云数据分类提取方法的适用性,文中根据三维数学形态学思想,提出一种基于地物空间形状特征的点云提取方法。方法首先建立网格索引,划分网格空间,进行点云数据组织,然后根据地物在网格空间中的形状特征设计出四种参数可控的空间网格算子,最后结合点云反射强度信息自动提取特定地物点云。通过对复杂场景中的铁路地物要素LiDAR点云中建筑、电力杆线、铁路轨道的提取和郊区机载LiDAR点云中的地面与建筑屋顶的提取,验证提取算法的适用性,为点云分类提取功能模块的程序设计提供便捷方法。     

Development of a spatial decision support system for monitoring earthquake-induced landslides based on aerial photographs and the finite element method

The evaluation of slope stability is essential for the management of landslide hazards. The integration of spatial information and geomechanical modeling facilitates the understanding and evaluation of landslide hazards. In this study, we use a spatial decision support system (SDSS)—incorporating aerial photographic data, GIS techniques, field investigations, and finite element geomechanical modeling—to analyze the mechanisms of the Hungtaiping landslide, which was induced by the 1999 Chi-Chi earthquake. The analysis clarifies the slide mechanisms that cannot be revealed either by examining aerial photographic or underground exploration data alone. The finite element modeling calibrated using digital aerial photographic data shows that the landslide results from the deformation and slides of the thick colluvium. Surficial displacements in the twenties of meters are attributed to the slide between the colluvium and the bedrock as well as the shear deformation and slides within the colluvium. The landslide SDSS can help determine model parameters, evaluate slide mechanisms and remediation measures, and predict slope behavior for a subsequent earthquake event.     

多源数据融合的高时空分辨率植被指数生成

高时空分辨率的植被指数VI(Vegetation Index)数据是农业和生态研究的重要基础数据集,目前常用的VI数据的时空分辨率存在不可调和矛盾。考虑VI时序变化对数据融合的影响,提出一种新的VI数据时空融合模型VISTFM(Vegetation Index Spatial and Temporal Fusion Model),VISTFM采用模糊C聚类算法,对存量时序VI数据按土地利用类型划分为若干子类,从高低分辨率影像中随土地覆被类的变化规律提取子类,结合低分辨率影像提取的土地覆被类变化规律融合生成高时空分辨率的VI数据。用常用的Landsat和MODIS数据验证该算法,测试表明,VISTFM能够较好的捕获VI的中间变化过程,与常用的基于线性混合模型的模型和时空自适应反射率融合模型及其改进模型相比,利用VISTFM获得的植被指数数据集具有更高的时空分辨率。