Exurbia from the bottom-up: Confronting empirical challenges to characterizing a complex system

We describe empirical results from a multi-disciplinary project that support modeling complex processes of land-use and land-cover change in exurban parts of Southeastern Michigan. Based on two different conceptual models, one describing the evolution of urban form as a consequence of residential preferences and the other describing land-cover changes in an exurban township as a consequence of residential preferences, local policies, and a diversity of development types, we describe a variety of empirical data collected to support the mechanisms that we encoded in computational agent-based models. We used multiple methods, including social surveys, remote sensing, and statistical analysis of spatial data, to collect data that could be used to validate the structure of our models, calibrate their specific parameters, and evaluate their output. The data were used to investigate this system in the context of several themes from complexity science, including have (a) macro-level patterns; (b) autonomous decision making entities (i.e., agents); (c) heterogeneity among those entities; (d) social and spatial interactions that operate across multiple scales and (e) nonlinear feedback mechanisms. The results point to the importance of collecting data on agents and their interactions when producing agent-based models, the general validity of our conceptual models, and some changes that we needed to make to these models following data analysis. The calibrated models have been and are being used to evaluate landscape dynamics and the effects of various policy interventions on urban land-cover patterns.     

Development of a remotely sensed,historical land-cover change database for rural Chihuahua,Mexico

The purpose of this article is to describe the development of a remotely sensed, historical land-cover change database for the northwestern quarter of Chihuahua, Mexico, The database consists of multi-temporal land-cover classifications and change detection images. The database is developed to facilitate future investigations that examine urban–rural linkages as possible drivers of rural land-use and land-cover changes. To develop the needed land-cover change database, this study uses the North American Landsat Characterization (NALC) MSS triplicates because of their temporal depth and spatial breadth. Challenges exist, however, to effective classification and change detection using the NALC triplicates, including illumination differences across multiple scenes and periods caused by topographic and solar variations and the lack of ground reference data for historic periods. Therefore, creation of the database is a four step process. First, extensive pre-processing is performed to enhance comparability of multi-date images. Pre-processing includes topographic correction, mosaic creation and multi-date radiance normalization. Second, ancillary sources of land-cover data are combined with visual interpretations of enhanced images to define reference pixels used to classify the images using the maximum likelihood algorithm. Third, classification accuracy is assessed. Fourth, post-classification change detection is performed. Results indicate significant image improvements after pre-processing that permit very good overall classification (86.26% classified correctly) and change detection. To conclude, findings are presented that indicate significant changes to arid grasslands/shrublands and forest resources in mountainous regions.     

Changes in ecosystem carbon stocks following grassland afforestation of semiarid sandy soil in the southeastern Keerqin Sandy Lands, China

The ecological consequences of grassland afforestation in arid/semiarid sandy regions are not well known with respect to tree species and stand age. The present study quantifies the changes in above- and belowground carbon (C) stocks following afforestation in the southeastern Keerqin Sandy Lands with species of Mongolian pine and poplar. We studied 15-, 24-, and 30-year-old Mongolian pine plantations, 7-, 11-, and 15-year-old poplar plantations, and adjacent grasslands. The results show that total ecosystem C stocks increased following grassland afforestation. Aboveground C stocks increased at a rate of 2.75 Mg C ha⁻¹ yr⁻¹ in the poplar plantations, and 1.06 Mg C ha⁻¹ yr⁻¹ in the Mongolian pine plantations. Mineral soil C stocks decreased during the early stage of forest establishment, but recovered with increasing stand age. Root C stock increased significantly in the Mongolian pine plantations, but the poplar plantations showed no such increase relative to the grassland. Our results indicate that afforestation of the grassland in the southeastern Keerqin Sandy Lands would sequester more C than would continuous grassland. Tree species selection and stand developmental age should be considered in planning future afforestation projects.     

The consequences of land-cover changes on soil erosion distribution in Slovakia

Soil erosion is a complex process determined by mutual interaction of numerous factors. The aim of erosion research at regional scales is a general evaluation of the landscape susceptibility to soil erosion by water, taking into account the main factors influencing this process. One of the key factors influencing the susceptibility of a region to soil erosion is land cover. Natural as well as human-induced changes of landscape may result in both the diminishment and acceleration of soil erosion. Recent studies of land-cover changes indicate that during the last decade more than 4.11% of Slovak territory has changed. The objective of this study is to assess the influence of land-cover and crop rotation changes over the 1990–2000 period on the intensity and spatial pattern of soil erosion in Slovakia. The assessment is based on principles defined in the Universal Soil Loss Equation (USLE) modified for application at regional scale and the use of the CORINE land cover (CLC) databases for 1990 and 2000. The C factor for arable land has been refined using statistical data on the mean crop rotation and the acreage of particular agricultural crops in the districts of Slovakia. The L factor has been calculated using sample areas with parcels identified by LANDSAT TM data. The results indicate that the land-cover and crop rotation changes had a significant influence on soil erosion pattern predominately in the hilly and mountainous parts of Slovakia. The pattern of soil erosion changes exhibits high spatial variation with overall slightly decreased soil erosion risks. These changes are associated with ongoing land ownership changes, changing structure of crops, deforestation and afforestation.     

Longitudinal analysis of the road network development and land-cover change in Lop Buri province, Thailand, 1989-2006

Land use and land-cover change (LUCC) is mainly a consequence of human activities such as road network development. In Thailand, the explicit objective of road network development has been to foster economic and social development. The extent of change in roads and land-cover change in Lop Buri between 1989-2006 is analyzed. We hypothesized that road development is a key driver of land-cover change and will cause, substantial changes in areas of forest cover, cultivated food and cash crops. We used land-cover classifications of Landsat imagery and resultant class trajectories to measure change. We then analyzed the relationships between roads, land-cover types, and land-cover trajectories. Overall, cash crops increased while forest and food crops declined between 1989-2006. The relationships between distance to roads and land- cover trajectories indicate that as the distance to roads increase, there are fewer changes in LUCC. The results suggest that in this case study, an increase in road network contributes to an increase in upland crops. In turn, the increase in upland crop production is one of the factors linked to economic development.     

Application of GIS and logistic regression to fossil pollen data in modelling present and past spatial distribution of the Colombian savanna

Climate changes affect the abundance, geographic extent, and floral composition of vegetation, which are reflected in the pollen rain. Sediment cores taken from lakes and peat bogs can be analysed for their pollen content. The fossil pollen records provide information on the temporal changes in climate and palaeo-environments. Although the complexity of the variables influencing vegetation distribution requires a multi-dimensional approach, only a few research projects have used GIS to analyse pollen data. This paper presents a new approach to palynological data analysis by combining GIS and spatial modelling. Eastern Colombia was chosen as a study area owing to the migration of the forest–savanna boundary since the last glacial maximum, and the availability of pollen records. Logistic regression has been used to identify the climatic variables that determine the distribution of savanna and forest in eastern Colombia. These variables were used to create a predictive land-cover model, which was subsequently implemented into a GIS to perform spatial analysis on the results. The palynological data from the study area were incorporated into the GIS. Reconstructed maps of past vegetation distribution by interpolation showed a new approach of regional multi-site data synthesis related to climatic parameters. The logistic regression model resulted in a map with 85.7% predictive accuracy, which is considered useful for the reconstruction of future and past land-cover distributions. The suitability of palynological GIS application depends on the number of pollen sites, the distribution of the pollen sites over the area of interest, and the degree of overlap of the age ranges of the pollen records.     

Monitoring the brazilian pasturelands: A new mapping approach based on the landsat 8 spectral and temporal domains

In a world marked by a rapid population expansion and an unprecedented increase in per capita income and consumption, sustainable food production is certainly the most pressing issue affecting mankind. Within this context, the brazilian pasturelands, the main land-use form in the country, constitute a particularly important asset as a land reserve, which, through improved land-use strategies and intensification, can meet food security goals and contribute to the mitigation of greenhouse gas emissions. In this study, we utilized the entire set of Landsat 8 images available for Brazil in 2015, from which dozens of seasonal metrics were derived, to produce, through objective criteria and automated classification strategies, a new pasture map for the country. Based on the Random Forest algorithm, individually modelled and applied to each one of the 380 Landsat scenes covering the Brazilian territory, our map showed an overall accuracy of 87%. Another result of this study was the thorough spatial and temporal assessment of Landsat 8 data availability in Brazil, which indicated that about 80% of the country had 12 or fewer observations free of clouds or cloud shadows in 2015.     

Evaluation of forest cover change using remote sensing techniques and landscape metrics in Moncayo Natural Park (Spain)

Monitoring the change in land cover in natural places, such as ecotones, has become an important tool for forest management, especially in protected areas. The present work analyses the spatial and temporal changes in forest cover in Moncayo Natural Park (Spain) from 1987 to 2010 using remote sensing techniques, geographical information systems (GIS) and quantitative indices of landscape ecology. Four Landsat images were used to map nine representative land cover categories in this preserved area in both years. The overall classification accuracies in land cover cartographies in 1987 and 2010 were 87.65% and 84.56%, respectively. Landscape metrics obtained at the landscape level show an increase in fragmentation and, as a result, an increase in landscape spatial diversity. Focusing on the class level, the results show a forest expansion of sessile oaks (Quercus petraea) and beech forest (Fagus sylvatica), two important bioclimatic indicators in this natural park, because they are the southernmost locations for these species in Europe. The decrease of mainly introduced pine forest and the transformation of mixed shrub areas into natural forested areas explain the aforementioned increase in fragmentation. These results are in agreement with the strategies for nature conservation designed by forest managers during the period evaluated.     

A new supervised classifier exploiting spectral-spatial information in the Bayesian framework

Conventional machine learning methods are often unable to achieve high degrees of accuracy when only spectral data are involved in the classification process. The main reason of that inaccuracy can be brought back to the omission of the spatial information in the classification. The present paper suggests a way to combine effectively the spectral and the spatial information and improve the classification’s accuracy. In practice, a Bayesian two-stage methodology is proposed embodying two enhancements: i) a geostatistical non-parametric classification approach, the universal indicator kriging and ii) the smooth multivariate kernel method. The former provides an informative prior, while the latter overcomes the assumption (often not true) of independence of the spectral data. The case study reports an application to land-cover classification in a study area located in the Apulia region (Southern Italy). The methodology performance in terms of overall accuracy was compared with five state-of-the-art methods, i.e. naïve Bayes, Random Forest, artificial neural networks, support vector machines and decision trees. It is shown that the proposed methodology outperforms all the compared methods and that even a severe reduction of the training set does not affect seriously the average accuracy of the presented method.     

20世纪90年代中国东北地区荒漠化的发展与区域气候变化

文中利用“吉林省陆地资源卫星遥感信息处理与应用的‘3 S’系统”,分别解译了 2 0世纪80年代与 90年代后期的 LANDSAT TM遥感信息 ,经对比分析发现 :中国东北西部地区的生态环境在恶化 ,主要表征为荒漠化 (盐渍化和沙漠化 )区域发展 ,并以经向扩展为主 ,荒漠化从 3个方面逼近中国的商品粮基地—吉林省中部松辽平原产粮区 ,对其构成严重威胁 ;哲里木盟的新开河与乌尔吉木伦河间的撂荒地明显增加。文中还计算了东北区 1 981～ 1 988年和1 989～ 1 996年两个 8a间平均气温和年、季降水量 ,经对比分析发现 :东北区是北半球欧亚大陆的第 3个高增温区 ,该区全年平均气温增高 0 .7℃以上 ,其中东北区西部的北段 (48～5 4°N)增温最为明显 (1 .0℃ ) ,中段 (44～ 48°N)增温次之 (约为 0 .9℃ ) ;东北区西部的中段是东北区降水减少的敏感带 ,年降水量平均减少 3 0 mm,其年际相对变率为 2 0 %～ 3 0 % ;而东北区西部的南段为降水量显著增加地带 (42～ 44°N) ,年降水量平均增加 44mm;东北区西部的北段降水量稍有增加 ,年平均增加 1 5 mm。研究表明 ,2 0世纪 90年代中国东北区西部对全球变化、全球增暖的区域响应为 :土地覆盖类型上的荒漠化经向发展和区域气候变化的暖干倾向 ,即中国东北区西部干旱化的主要特征