Lacunarity analysis of spatial pattern: A comparison

Lacunarity analysis has been proposed as a general method for the analysis of spatial pattern, in particular for patterns of the dispersion of points. The method is clearly an improvement over the variance:mean ratio approach based on quadrat counts, because it examines dispersion at a range of spatial scales. This paper examines the properties of lacunarity analysis and compares it with other methods of pattern analysis. Lacunarity analysis gives different results for complementary patterns, which may be an advantage depending on circumstances. The method, however, is not precise in determining the scale or the patch size in pattern with known properties. A modification that improves the interpretability of the results of the analysis is introduced but a weakness of this approach is that it does provide clear indications of the characteristics of cases that exhibit more than one scale of pattern. Because different methods react to different features in data, it is recommended that data be analysed by more than one method and the results compared for greater insight into their characteristics.     

Long-term,landscape patterns of past fire events in a montane ponderosa pine forest of central Colorado

Parameters of fire regimes, including fire frequency, spatial extent of burned areas, fire severity, and season of fire occurrence, influence vegetation patterns over multiple scales. In this study, centuries-long patterns of fire events in a montane ponderosa pine – Douglas-fir forest landscape surrounding Cheesman Lake in central Colorado were reconstructed from fire-scarred trees and inferences from forest stand ages. We crossdated 153 fire-scarred trees from an approximately 4000 ha study area that recorded 77 total fire years from 1197 to the present. Spatial extent of burned areas during fire years varied from the scale of single trees or small clusters of trees to fires that burned across the entire landscape. Intervals between fire years varied from 1 to 29 years across the entire landscape to 3 to 58 years in one stand, to over 100 years in other stands. Large portions of the landscape did not record any fire for a 128 year-long period from 1723 to 1851. Fire severity varied from low-intensity surface fires to large-scale, stand-destroying fires, especially during the 1851 fire year but also possibly during other years. Fires occurred throughout tree growing seasons and both before and after growing seasons. These results suggest that the fire regime has varied considerably across the study area during the past several centuries. Since fires influence plant establishment and mortality on the landscape, these results further suggest that vegetation patterns changed at multiple scales during this period. The fire history from Cheesman Lake documents a greater range in fire behavior in ponderosa pine forests than generally has been found in previous studies.     

Landscape dynamics in crown fire ecosystems

Crown fires create broad-scale patterns in vegetation by producing a patch mosaic of stand age classes, but the spread and behavior of crown fires also may be constrained by spatial patterns in terrain and fuels across the landscape. In this review, we address the implications of landscape heterogeneity for crown fire behavior and the ecological effects of crown fires over large areas. We suggest that fine-scale mechanisms of fire spread can be extrapolated to make broad-scale predictions of landscape pattern by coupling the knowledge obtained from mechanistic and empirical fire behavior models with spatially-explicit probabilistic models of fire spread. Climatic conditions exert a dominant control over crown fire behavior and spread, but topographic and physiographic features in the landscape and the spatial arrangement and types of fuels have a strong influence on fire spread, especially when burning conditions (e.g., fuel moisture and wind) are not extreme. General trends in crown fire regimes and stand age class distributions can be observed across continental, latitudinal, and elevational gradients. Crown fires are more frequent in regions having more frequent and/or severe droughts, and younger stands tend to dominate these landscapes. Landscapes dominated by crown fires appear to be nonequilibrium systems. This nonequilibrium condition presents a significant challenge to land managers, particularly when the implications of potential changes in the global climate are considered. Potential changes in the global climate may alter not only the frequency of crown fires but also their severity. Crown fires rarely consume the entire forest, and the spatial heterogeneity of burn severity patterns creates a wide range of local effects and is likely to influence plant reestablishment as well as many other ecological processes. Increased knowledge of ecological processes at regional scales and the effects of landscape pattern on fire dynamics should provide insight into our understanding of the behavior and consequences of crown fires.     

利用归一化植被指数评价酿酒葡萄生长状况的研究

 以生长势不同的酿酒葡萄品种赤霞珠和梅鹿辄为试材, 采用基于地物光谱特性的遥感技术获得地块内不同生长势(包括新梢长度、新梢日生长速度、单株叶面积、N-Tester指数) 小区的归一化植被指数(NDVI) 间存在显著差异, 并且NDVI与葡萄新梢长度、新梢日生长速度、植株叶面积以及N-Tester指数间具有显著或极显著的相关性。因此, 采用NDVI评价地块内葡萄长势差异性具有应用前景。     

杂交枇杷果实无核的相关性研究

试验对40株杂交枇杷实生树的果实进行调查,并对果实的各项基本指标做了相关性研究。结果表明:杂交枇杷果实种子数和果实横径与可溶性固形物相关。种子数与可溶性固形物、果实横径二元回归显著,建立模型z=-2．7515 0．4839x-51．780 7y-0．001x2-2．224 7xy 456．075 7y2(R2=0．381 67)。同时通过主成分分析发现,霞钟作为杂交亲本的母本,其后代少核概率比较大。     

Response of an aridland ecosystem to interannual climate variability and prolonged drought

Water is a key driver of ecosystem processes in aridland ecosystems. Thus, changes in climate could have significant impacts on ecosystem structure and function. In the southwestern US, interactions among regional climate drivers (e.g., El Niño Southern Oscillation) and topographically controlled convective storms create a spatially and temporally variable precipitation regime that governs the rate and magnitude of ecosystem processes. We quantified the spatial and temporal distribution of reduced grassland greenness in response to seasonal and annual variation in precipitation at two scales at the Sevilleta Long Term Ecological Research site in central New Mexico, using Normalized Difference Vegetation Index (NDVI) values from bi-weekly AVHRR data and seasonal ETM data from 1989 to 2005. We used spatially explicit NDVI Z-scores to identify times and places of significantly reduced greenness and related those to interactions between plant functional type, seasonal climate variation, and topography. Seasonal greenness was bimodal with a small peak in spring and a stronger peak following the summer monsoon. Greenness was generally spatially homogeneous in spring and more spatially variable in summer. From 2001 through spring 2002, drought effects were evidenced by a 4-fold increase in the number of pixels showing significantly low greenness. Spatial distribution of low greenness was initially modulated by topographic position, but as the drought intensified spread throughout the study area. Vegetation green up occurred rapidly when drought conditions ceased. We conclude that drought effects vary spatially over time, pervasive drought reduces broad-scale spatial heterogeneity, and greenness patterns recover rapidly when drought conditions end.     

主动作物冠层传感器GreenSeeker在马铃薯氮素营养诊断中应用的可行性分析

长期以来氮肥的施用在马铃薯生产中起到了重要作用。但是,随着氮肥施用量的增加,氮肥利用效率势必会降低并造成一定的环境问题。因此,根据马铃薯的需肥规律进行氮肥的施用,提高氮肥利用率已成为业界共识。然而,传统的氮肥推荐方法如土壤测试的方法费时费力、实时性差。随着遥感测试技术的发展,一些传感器应运而生,使基于光谱的植株氮营养快速检测成为可能。本文介绍了主动作物冠层传感器GreenSeeker对作物营养诊断的原理及其在玉米、小麦等作物上应用的国内外研究进展,并讨论其在马铃薯氮素营养诊断和精确施肥的重要性、可行性及应用前景。     

Urban green spaces analysis for development planning in Colombo,Sri Lanka,utilizing THEOS satellite imagery – A remote sensing and GIS approach

Upon conclusion of the 30 years of civil conflict, Sri Lanka has entered into a new era of development. The beautification project of Colombo, Sri Lanka's commercial capital, can be considered as one of the most important projects among urban planning and restoration endeavours implemented throughout the island. In such urban development endeavour, green spaces planning play a crucial role in improving the environmental quality and standard of living of the residents. The objective of this study is to analyse the environmental quality in Colombo city based on green spaces and to provide appropriate recommendations to raise the environmental quality to international standards.In this study, green space areas were extracted from Thailand Earth Observation System (THEOS) satellite imagery using Normalized Difference Vegetation Index (NDVI). Extracted green space areas were further analysed quantitatively with air quality indicators and population density utilizing deductive indexing method at Grama Niladhari (GN) administrative division level to identify correlations of these factors. An environmental criticality map based on population density and percentage green spaces has been constructed to identify the areas with high criticality. The study revealed that 24% of the entire Colombo Municipal Council (CMC) area consists of green spaces. This study also revealed that 34 GN divisions out of 55 lack the minimum per capita green space recommended by World Health Organization for healthy living. Recommendations are presented regarding regions where new green spaces should be established to enhance the environmental standard of living up to international standards.     

苹果园种植牧草对幼树生长及15N–尿素吸收、利用的影响

以两年生红富士/平邑甜茶为试材,采用田间小区试验及15N同位素标记示踪法,研究了低氮（50 kg ? hm-2）和中氮（100 kg ? hm-2）条件下分别种植3种牧草（白三叶、黑麦草和鼠茅草）对苹果幼树生长及15N–尿素吸收、利用的影响。结果表明,春梢停长期,与单作苹果相比,种植牧草后苹果植株生物量、吸氮量及15N肥料利用率显著降低,这在低氮处理下尤为明显;而到秋梢停长期,与单作苹果相比,种植牧草后苹果植株生物量、吸氮量、15N肥料利用率及根系活力显著提高。至秋梢停长期,0 ~ 20 cm土层15N丰度及总氮含量为种植白三叶 > 种植鼠茅草 > 种植黑麦草 > 单作苹果,而在20 ~ 40 cm及40 ~ 60 cm土层15N丰度及总氮含量为单作苹果 > 种植黑麦草 > 种植鼠茅草 > 种植白三叶,表明种植白三叶、黑麦草和鼠茅草降低了氮素的淋溶损失,有利于氮肥的保持,从而提高氮肥利用率,促进苹果幼树的生长。     

Greenness around Brazilian schools may improve students’ math performance but not science performance

Green spaces play a vital role in the social, economic, and physical well-being of people. To further research on this topic, in this paper, we estimated the association of greenness and academic performance at the school-level in Brazil. We analyzed this association using mixed-effects regression models, adjusted for air pollution, SES, and spatiotemporal terms. We used the Normalized Difference Vegetation Index (NDVI) as the exposure variable. Data from the high school national exam in Brazil (at the school level, measured with a score varying from 0 to 1000) was used to represent the academic performance. The primary analysis results indicate that green areas surrounding schools are positively associated with school-level academic performance in math, with an estimated coefficient of 17.18 (95%CI: 10.46; 23.90). The results were statistically insignificant for science, with a coefficient of − 2.39 (95%CI: −7.49; 2.71). Our findings are relevant for policymakers and urban planners to improve the environment surrounding schools to promote public health by making schools healthier.     

Statistical treatment and comparative analysis of scale-dependent aquatic transect data in estuarine landscapes

Estuarine ecosystem dynamics have evolved around and respond to landscape-level influences that are dynamic in space and time. The estuarine water column is effectively the physical and biologial integrator of these landscape inputs. In this paper, we present a floating window Analysis of Covariance (ANCOVA) technique to statistically compare and contrast aquatic transect data that were taken at different times and under different tidal conditions, yet were geographically parallel and spatially articulate. The floating window ANCOVA compared two transects by testing whether the means of the dependent variable were significantly different while also testing whether the slopes of patterns in the dependent variable were significantly different. By varying the size of the floating window where the ANCOVA was run, we were able to examine how scale affected the magnitude and spatial pattern of that variable. The percentages of total models run, at a given window size, that generated significantly different magnitudes (means) and patterns (slopes) in the dependent variable were referred to as the degree of dissimilarity. Plots of window size versus degree of dissimilarity elucidated temporal and spatial variability in water column parameters at a range of scales. The advantages of this new statistical method in relation to traditional spatial statistics are discussed.We demonstrated the efficacy of the floating window ANCOVA method by comparing chlorophyll and salinity transect data taken at the North Inlet, SC estuary during flooding and ebbing tides in Winter, Spring, and Summer 1991. Chlorophyll concentrations represented the biological characteristics of the estuarine water column and salinity represented the physical processes affecting that water column. We found total dissimilarity in the magnitude of salinity data from one season to the next at all scales, but inter-seasonal similarity in spatial patterns over both short (hourly) and long (monthly) time scales. We also found a large seasonal dissimilarity in the magnitude of chlorophyll levels, as expected. Spatial patterns in phytoplankton biomass (as chlorophyll concentrations) appeared to be largely controlled by the physical processes represented with the salinity data. Often, we observed greater dissimilarity in biological and physical parameters from one tide to the next [on a given day] than from one season to the next. In these cases, the greatest flood-ebb differences were associated with landscape-level influences - from rivers and the coastal ocean - that varied greatly with direction of tidal flow. We are currently using spatially articulate aquatic transect data and the floating window ANCOVA technique to validate spatial simulation models at different scales. By using this variable-scale statistical technique to determine coherence between the actual transect data and model output from simulations run at different scales, we will test hypotheses about the scale-dependent relationships between data resolution and model predictability in landscape analysis.