How deep is your love – Of nature? A psychological and spatial analysis of the depth of feelings towards Dutch nature areas

The most binding nature protection policy in the Netherlands and the EU primarily designates areas based on ecological value, ensuring their sustained protection. Of less concern to these policies are the current and future needs of the average citizen, who is experiencing increases in education, income, and health. This paper argues that although existing policies protect biodiversity needs, the spatialemotional needs of the public are of similarly high importance. We analyze the spatial and emotional characteristics of favorite natural places in the Netherlands selected on the Hotspotsmonitor survey. Content analysis of the open question, "Why is this place important to you?" identified three categories: peace & quiet, explore, and peak. These were mapped using GIS to identify predominant hotspots and compared against the most binding nature protection policy - Natura 2000. The important societal benefits of augmenting ecologically based nature protection policies with emotional valuation are discussed.     

云南省怒江傈僳族自治州居住地景观审美感知研究

本地居民对居住地景观的审美感知水平对于区域大尺度景观的保护与持续利用具有重要影响,这在相对欠发达但具有较高审美价值景观资源的地区意义更为重大。本文以云南省怒江傈僳族自治州为例,基于620份针对当地居民的调查问卷与现场调研,通过构建Logistic回归模型,分析了怒江州本地居民对居住地景观的审美感知状况及相关影响因素。结果表明,怒江州本地居民对于居住地景观的审美感知水平较高,有75．81％的受访者认为居住地周边景观是美的。对这一审美感知具有影响的因素共有8项,在直接影响因素中,是否在保护区内、行政村周边高程标准差具有正向影响;行政村到最近县城距离、是否认为近年生态环境恶化、是否认为旅游开发存在不好的影响、是否希望进入城市生活具有负向影响。在间接影响因素中,包括住房类型和家中是否有电视都具有正向影响。本文据此尝试提出提高怒江州居民对居住地景观审美感知水平、促进当地景观保护与持续利用的相关政策建议。     

黔东南苗岭国家地质公园的旅游地质景观特征和类型

在对贵州黔东南苗岭国家地质公园区的旅游地质景观实地调查研究的基础上,指出其旅游地质景观类型具有观赏与休闲性、奇异与独特性、多样性、古老性、科学性、不可再生性和文化特性;其类型有地层剖面大类、地质构造大类、生物化石大类、地貌景观大类、水体景观大类、环境地质遗迹景观大类和其他旅游地质景观。其丰富多彩的旅游地质景观为旅游开发提供了物质基础,旅游开发潜力大。     

云南腾冲地热景观空间分布研究

选取腾冲典型的53处地热景观,采用地理集中指数、景观平均分布值与不平衡指数,揭示地热景观空间分布特征及其控制性因素。研究表明:腾冲地热景观不平衡指数值小,表明整体分布均衡,地热景观数量地域相差不大;地理集中指数值稍大,且大于景观平均分布值。研究区地热景观小范围聚集于清水乡热海景区;腾越镇、中和镇、芒棒镇、曲石镇、猴桥镇、五合乡是地热景观开发潜力区。研究结果揭示了腾冲地热景观的空间分布规律,可为地热景观保护及其全域旅游开发提供借鉴。     

Reducing landscape heterogeneity for improved land use and land cover (LULC) classification across the large and complex Ethiopian highlands

This paper presents a land use and land cover (LULC) classification approach that accounts landscape heterogeneity. We addressed this challenge by subdividing the study area into more homogeneous segments using several biophysical and socio-economic factors as well as spectral information. This was followed by unsupervised clustering within each homogeneous segment and supervised class assignment. Two classification schemes differing in their level of detail were successfully applied to four landscape types of distinct LULC composition. The resulting LULC map fulfills two major requirements: (1) differentiation and identification of several LULC classes that are of interest at the local, regional, and national scales, and (2) high accuracy of classification. The approach overcomes commonly encountered difficulties of classifying second-level classes in large and heterogeneous landscapes. The output of the study responds to the need for comprehensive LULC data to support ecosystem assessment, policy formulation, and decision-making towards sustainable land resources management.     

Mapping natural and urban environments using airborne multi-sensor ADS40–MIVIS–LiDAR synergies

The recent and forthcoming availability of high spatial resolution imagery from satellite and airborne sensors offers the possibility to generate an increasing number of remote sensing products and opens new promising opportunities for multi-sensor classification. Data fusion strategies, applied to modern airborne Earth observation systems, including hyperspectral MIVIS, color-infrared ADS40, and LiDAR sensors, are explored in this paper for fine-scale mapping of heterogeneous urban/rural landscapes. An over 1000-element array of supervised classification results is generated by varying the underlying classification algorithm (Maximum Likelihood/Spectral Angle Mapper/Spectral Information Divergence), the remote sensing data stack (different multi-sensor data combination), and the set of hyperspectral channels used for classification (feature selection). The analysis focuses on the identification of the best performing data fusion configuration and investigates sensor-derived marginal improvements. Numerical experiments, performed on a 20-km stretch of the Marecchia River (Italy), allow for a quantification of the synergies of multi-sensor airborne data. The use of Maximum Likelihood and of the feature space including ADS40, LiDAR derived normalized digital surface, texture layers, and 24 MIVIS bands represents the scheme that maximizes the classification accuracy on the test set. The best classification provides high accuracy (92.57% overall accuracy) and demonstrates the potential of the proposed approach to define the optimized data fusion and to capture the high spatial variability of natural and human-dominated environments. Significant inter-class differences in the identification schemes are also found by indicating possible sub-optimal solutions for landscape-driven mapping, such as mixed forest, floodplain, urban, and agricultural zones.     

传统聚落景观基因的识别与提取方法研究

近年来,由中国学者提出的传统聚落景观基因理论在传统聚落区划、特征识别和旅游规划等领域得到了广泛的应用。然而,该理论还缺乏有效的景观基因识别方法。针对前述问题,结合实践探讨了传统聚落景观基因的特征解构提取方法和识别模式。首先,分析了传统聚落景观基因的分类方法并结合面向对象的思想提出了面向对象的景观基因分类模式（OOCPLG）,这为构建特征解构提取方法奠定了理论基础。其次,通过分析景观识别的要求,结合现有的元素提取、图案提取、结构提取和含义提取的不足与优点,建立了特征解构的基因提取方法。最后,总结了景观基因的识别模式和基本操作流程。     

Capitalism, masculinity and whiteness in the dialectical landscape: The case of Tarzan and the Tycoon

The intent of this paper is to explore how ‘reel’ and ‘real’ landscapes can be rendered dialectic. The analysis focuses on the filming of Tarzan's Secret Treasure (1941) at the wildlife preserve of Wakulla Springs in Florida, which was administered by the financial tycoon Ed Ball. Using Mitchell's recent commentaries on ‘good’ and ‘bad’ dialectics as our entry point, we offer two readings of this cinematic process. The first renders the on and off screen Wakulla Springs as ideological fodder, serving to valorise capital, masculinity and whiteness. The second reading addresses how this place has been actively constructed from discourses pertaining to class, gender and race, such that Wakulla exists as a series of fully relational spaces within diverse contexts, both ‘real’ and ‘reel’. In structuring our analysis in this manner, we hope to make clear why it does indeed matter for film geographers to acknowledge the conceptual presumptions brought to bear in the framing of the real and the reel as objects of analysis. This revised version was published online in August 2006 with corrections to the Cover Date.     

A Review of “Breathing Space: How Allergies Shape Our Lives and Landscapes”

This paper expands the human capital model to compare the migration propensities of Cubans, Mexicans, and Puerto Ricans within the U.S. between 1985 and 1990. Using the 5% PUMS from the 1990 U.S. Census, both aggregate migration streams and micro-level migration propensities are estimated for Cubans, Mexicans, and Puerto Ricans. The effects of personal factors, the economic environment, and the presence of fellow nationals are examined in the context of larger geographic patterns, and discussed in terms of each nationality's immigration history and cultural context.

While many factors effect migration behavior similarly, there are notable differences in the way education, English fluency, and unemployment rate affect migration propensity of the native-born and foreign-born of each nationality. Differences are also apparent at the macro-level. Puerto Ricans show signs of dispersing out of New York; Mexicans are redistributing within the Southwest; and Cubans are re-concentrating in Florida. Both levels of analysis point to how immigration history, settlement patterns, and cultural context influence migration behavior. The results demonstrate the benefits of using an expanded human capital approach to explain migration differences, and highlight the diversity of population redistribution occurring within Hispanic nationalities.     

Modeling of multi-strata forest fire severity using Landsat TM Data

Most of fire severity studies use field measures of composite burn index (CBI) to represent forest fire severity and fit the relationships between CBI and Landsat imagery derived differenced normalized burn ratio (dNBR) to predict and map fire severity at unsampled locations. However, less attention has been paid on the multi-strata forest fire severity, which represents fire activities and ecological responses at different forest layers. In this study, using field measured fire severity across five forest strata of dominant tree, intermediate-sized tree, shrub, herb, substrate layers, and the aggregated measure of CBI as response variables, we fit statistical models with predictors of Landsat TM bands, Landsat derived NBR or dNBR, image differencing, and image ratioing data. We model multi-strata forest fire in the historical recorded largest wildfire in California, the Big Sur Basin Complex fire. We explore the potential contributions of the post-fire Landsat bands, image differencing, image ratioing to fire severity modeling and compare with the widely used NBR and dNBR. Models using combinations of post-fire Landsat bands perform much better than NBR, dNBR, image differencing, and image ratioing. We predict and map multi-strata forest fire severity across the whole Big Sur fire areas, and find that the overall measure CBI is not optimal to represent multi-strata forest fire severity.