Guofan Shao

On the accuracy of landscape pattern analysis using remote sensing data

Advances in remote sensing technologies have provided practical means for land use and land cover mapping which is critically important for landscape ecological studies. However, all classifications of remote sensing data are subject to different kinds of errors, and these errors can be carried over or propagated in subsequent landscape pattern analysis. When these uncertainties go unreported, as they do commonly in the literature, they become hidden errors. While this is apparently an important issue in the study of landscapes from either a biophysical or socioeconomic perspective, limited progress has been made in resolving this problem. Here we discuss how errors of mapped data can affect landscape metrics and possible strategies which can help improve the reliability of landscape pattern analysis.

Geomorphological controls on coastal vegetation at the Virginia Coast Reserve

Abstract The establishment and succession of vegetation on migrating, low-profile barrier islands is greatly affected by the physical hydrogeomorphological processes that regulate island topography, saline and fresh groundwater table surfaces. Apart from the physical destruction of plants by overwash processes, fluctuations in water table elevations and variations in groundwater salinity, both spatially and temporally, also appear to have significant impact on the nature and distribution of vegetation on these islands. Species composition, community structure and biodiversity on the Virginia barrier islands are controlled by the same processes that give rise to landforms and maintain their form. These processes include marine water inundations, groundwater salinity variations and changes in depth to the fresh-water table. Land surface elevation, landform morphology and position on the barrier island determine exposure to high tides, storm surges, sand burial, and the extent of the fresh-water reserves. In this article, the underpinnings of a Long-term Ecological Research Program in which 25 geologists, geomorphologists, climatologists, and ecologists have a common research plan is presented and several examples of the product of this research partnership dealing with geomorphological and hydrologic controls on vegetation dynamics are detailed. Among the aspects of ecological dynamics examined in terms of geomorphological processes are vegetation zonation, succession, disturbance, and ecosystem state change.

Dynamic simulations of mixed broadleaved-Pinus koraiensis forests in the Changbaishan biosphere reserve of China

The development of mixed broadleaved-Korean pine (Pinus koraiensis Sieb. et Zucc.) forests in the Changbaishan Biosphere Reserve, located on the border with North Korea, was simulated using the gap model KOPIDE. Forest succession was simulated under three initial conditions from: (1) bare ground after clearcutting; (2) secondary forest; (3) old-growth forest. The simulations from the different initial conditions converged and support earlier successional theory that Korean pine is the climax species on the highlands of northeast China even under disturbed conditions. In addition to clear-cutting, the resilience of the forest to different levels of other human impacts, pine seed harvesting and selective cutting, was examined. These results further demonstrate that these forests possess a relatively stable structure characterized by the dominance of Korean pine. However, the model showed successional processes of the forest to be susceptible to high levels of pine seed harvesting. To predict forest dynamics at landscape scales, KOPIDE was linked with a Geographical Information System containing site and stand data sets. Running this model to simulate a forested area initially comprising several successional stages suggests that, in the absence of disturbance, Korean pine is likely to become increasingly dominant on the area over the next century.

The planning, construction, and management toward sustainable cities in China needs the Environmental Internet of Things

Chinas rapid urbanization and its success in developing the Internet of Things (IoT) will decide its future development direction. The construction of sustainable cities is crucial to China because China has such a large population. The Xiamen Long-term Urban Ecosystem Observation and Research Station (Xiamen LUEORS) was started in 2006, together with the research related to the Environmental Internet of Things (EIoT) for Xiamen LUEORS. This paper explains the purpose, general framework, and main features of EIoT, and outlines the results of performing EIoT experiments in some areas, including a ‘town village’, a peculiar phenomenon of Chinas urbanization. It also discusses the development trends of IoT and proposes the concept of ZeroSpace Interconnection of Things (ZeroIoT, or ZeroSIT).

Landsenses ecology and ecological planning toward sustainable development

ABSTRACT This paper proposes the concepts and associated contents of landsenses ecology and mix-marching data, and explains the roles of the meliorization model and Internet of Things (IoT) in the landsenses ecology-based land-use planning, construction and management. It also analyses the importance and application approaches of mix-marching data. In the current situation of rapid social-economic, scientific, and technological development, there exists an urgent need for us to further study landsenses ecology and its applications.

Mapping of boreal vegetation of a temperate mountain in China by multitemporal Landsat TM imagery

The Changbai Mountain Natural Reserve (2000 km 2 ), north-east China, is a very important ecosystem representing the temperate biosphere. The cover types were derived by using multitemporal Landsat TM imagery, which was modified with DEM data on the relationship between vegetation distribution and elevation. It was classified into 20 groups by supervised classification. By comparing the results of the classification of different band combinations, bands 4 and 5 of an image from 18 July 1997 and band 3 of an image from 22 October 1997 were used to make a false colour image for the final output, a vegetation map, which showed the best in terms of classification accuracy. The overall accuracy by individual images was less than 70%, while that of the multitemporal classification was higher than 80%. Further, on the basis of the relationship of vegetation distribution and elevation, the accuracy of multitemporal classification was raised from 85.8 to 89.5% by using DEM. Bands 4 and 5 showed a high ability for discriminating cover types. Images acquired in late spring and mid-summer were recognized better than other seasons for cover type identification. NDVI and band ratio of B4/B3 proved useful for cover type discrimination, but were not superior to the original spectral bands. Other band ratios like B5/B4 and B7/B5 were less important for improving classification accuracy. The changes of spectral reflectance and NDVI with season were also analysed with 10 images ranging from 1984 to 1997. Seperability of images in terms of classification accuracy was high in late spring and summer, and decreased towards winter. There were five vegetation zones on the mountain, from the base to the peak: deciduous forest zone, mixed forest zone, conifer forest zone, birch forest zone and tundra zone. Spruce-fir conifer dominated forest was the most dominant vegetation (33%), followed by mixed forest (26%), Korean pine forest (8%) and mountain birch forest (5%).

Cross-Scale Patterns in Shrub Thicket Dynamics in the Virginia Barrier Complex

A bstractTo interpret broad-scale erosion and accretion patterns and the expansion and contraction of shrub thickets in response to sea level rise for a coastal barrier system, we examined the fine-scale processes of shrub recruitment and mortality within the context of the influence of ocean current and sediment transport processes on variations in island size and location. We focused on Myrica cerifera shrub thickets, the dominant woody community on most barrier islands along the coastline of the southeastern USA. Observations suggest that M. cerifera, a salt-intolerant species, is increasing in cover throughout the Virginia barrier islands, yet rising sea level in response to climate change is increasing erosion and reducing island area. Our objective was to explain this apparent paradox using pattern–process relationships across a range of scales with a focus on ocean currents and sediment transport interacting with island characteristics at intermediate scales. Multi-decadal comparisons across scales showed a complex pattern. At the scale of the entire Virginia barrier complex, modest decreases in upland area were accompanied by large increases in shrub area. Responses were more variable for individual islands, reflecting inter-island variations in erosion and accretion due to differences in sediment transport via ocean currents. Several islands underwent dramatic shrub expansion. Only for within-island responses were there similarities in the pattern of change, with a lag-phase after initial shrub colonization followed by development of linear, closed canopy thickets. Understanding the fine-scale processes of shrub seedling establishment and thicket development, in conjunction with the influence of ocean currents and sediment transport, provides a framework for interpreting island accretion and erosion patterns and subsequent effects on shrub thicket expansion or contraction across scales of time and space.

Decision Support Systems in Forest Management

Numerous decision support systems have been developed for forest management over the past 20 years or more. In this chapter, the authors briefly review some of the more important and recent developments, including examples from North America, Europe, and Asia. In addition to specific systems, we also review some of the more-significant methodological approaches such as artificial neural networks, knowledge-based systems, and multicriteria decision models. A basic conclusion that emerges from this review is that the availability of DSSs in forest management has enabled more-effective analysis of the options for and implications of alternative management approaches for all components of forest ecosystems. The variety of tools described herein, and the approaches taken by the different systems, provide a sample of the possible methods that can be used to help stakeholders and decision makers arrive at reasoned and reasonable decisions.

Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations

Urban cellular automata (CA) models are broadly used in quantitative analyses and predictions of urban land-use dynamics. However, most urban CA developed with neighborhood rules consider only a small neighborhood scope under a specific spatial resolution. Here, we quantify neighborhood effects in a relatively large cellular space and analyze their role in the performance of an urban land use model. The extracted neighborhood rules were integrated into a commonly used logistic regression urban CA model (Logistic-CA), resulting in a large neighborhood urban land use model (Logistic-LNCA). Land-use simulations with both models were evaluated with urban expansion data in Xiamen City, China. Simulations with the Logistic-LNCA model raised the accuracies of built-up land by 3.0%-3.9% in two simulation periods compared with the Logistic-CA model with a 3?×?3 kernel. Parameter sensitivity analysis indicated that there was an optimal large window size in cellular space and a corresponding optimal parameter configuration. Extended neighborhood effects and their influence on urban dynamics were addressed in this study.A logistic regression urban CA model incorporating the extracted neighborhood rules, Logistic-LNCA, was developed.The Logistic-LNCA model achieved higher simulation accuracy than the Logistic-CA model with a 3?×?3 kernel.The simulation accuracy and the Kappa coefficient varied with window sizes and radius intervals.There is an optimal window size in cellular space and corresponding optimal parameter configurations.

Optimal combinations of data, classifiers, and sampling methods for accurate characterizations of deforestation

There are increasingly more choices from a complex of data resources, classification algorithms, and methods of training sample selections. To increase the repeatability of digital classifications of remotely sensed data with consistently high accuracy, it is essential to use optimal classification options or factors. In this paper, two temporal sets of Landsat thematic mapper (TM) data, three classifiers and three approaches of training sample selections were tested for mapping deforestation. The use of these different factors can have significant effects on classification accuracy. The mixed effects of the three factors can also magnify the variations of classification accuracy. The use of bi-temporal data, a spatial‐spectral classifier, and hybrid training samples results in steadily higher classification accuracy than the combination of uni-temporal data, a spectral classifier, and image training samples. For the purpose of characterizing managed forest lands, even a small increase in overall accuracy of image classification is important because it may represent a large decrease in the variations of the producers and users accuracy, which in turn can reduce the uncertainties of area measurements for forest coverage.