Förnyelse av äldre arbetsområden i Göteborg

Törnqvist, Anders: Förnyelse av äldre arbetsområden i Göteborg. Geografisk Tidsskrift 83: 60–64. May 1., 1983.

A recently started research and development project at Chalmers Institute of Technology in cooperation with the City of Gothenburg is presented. Background research on the physical and economic status of old industrial districts in Sweden shows the need for renewal. Difficulties and possibilities are analysed. A two-phase negotiation strategy involving the landowners, the firms, the employees and local government branches is outlined.     

Farm Size and Land Use Pattern Changes in Postsocialist Poland∗

The postsocialist transition resulted in widespread structural change in Polish agriculture, but fine-scale assessments of how these changes affected farming and rural landscapes are scarce. This article examines changes in farm sizes and land use patterns after the breakdown of socialism in Poland using micro-census data and satellite images. Our study area is Pyrzyce County. The analysis indicates a polarization of the farm size distribution (e.g., medium-sized farms disappeared) as well as marked changes in land use patterns (e.g., increasing field sizes and field aggregation). Interviews with farmers and stakeholders suggest that this shift can be explained by decreasing competitiveness of medium-sized farms in the emerging new agricultural value chain, which forced farmers to increase and intensify production or to downsize their farms, especially when the preference was for other occupations.     

Rebuilding the Brazilian rainforest: Agroforestry strategies for secondary forest succession

Does the adoption of agroforestry by small farmers in the Brazilian Amazon promote secondary forest succession on the degraded pastures and crop fields? New results from a small-scale farm agroforestry demonstration project, the Rondônia Agroforestry Pilot Project (RAPP) that began in 1992 are presented in this paper. In 1992, 242 farmers were surveyed by a stratified random sampling protocol, 50 of whom were selected to participate in the RAPP, constituting its experimental group. The remaining 191 farmers served as a control group. Farmers from both groups were re-surveyed in 2002 (after 10 years) and again in 2010 (after 18 years). Annual site visits to the experimental group farms were conducted from 1993 through 2003 to monitor agroforest plot development and management, and changes in pertinent socio-economic and household demographic characteristics such as household capacity, production systems, and social participation. Differences in property size, number of people permanently residing on the property, and social participation were found between the experimental and control group, with the experimental group having larger properties, more residents, and more participation in mutual aid associations. Control group farmers were also more reliant on cattle production (based on 2009 sales) despite having similar amounts of pasture as farmers in the experimental group. Within the experimental group, very few differences were found between farmers based on the type of agroforestry plot: timber, mixed or non-timber. Remote sensing analyses reveal long-term (10 years+) spectral differences in terms of the similarity to primary forest of both the agroforestry plots and the entire properties of the farms in this study. Experimental group farmers with mixed or timber-based agroforestry plots allowed more secondary forest succession to occur in and around their plots than farmers with non-timber plots. Although, on average, farm properties have become less spectrally similar to primary forest since 1992, properties with agroforestry plots tend to have more secondary succession and/or primary forest on their land in 2011. Several example properties are shown to illustrate the tendency of farmers with agroforestry plots to allow more secondary forest succession to occur on their land.     

绿洲与其外围区域地表温度差异的遥感分析

以外流河绿洲卫宁平原为例,基于MODIS和ETM+资料研究了绿洲与其外围区域的LST差异。结果显示:1)绿洲与其外围区域的LST差异具有明显的年内变化,该差异在1月最小,在7月上旬最大;2)绿洲与外围LST差异有明显的年际变化,降水越多的年份绿洲与外围的LST差异越小;3)20世纪70年代以来研究区低温斑块的面积明显增大,将来低温斑块有再扩张的可能;4)土地开发利用或保护恢复措施会影响绿洲的大小和形状,从而影响冷岛结构,所以认为土地利用规划和管理中应考虑不同利用方式对冷岛结构的影响。     

Uncertainties in estimates of cropland area in China: a comparison between an AVHRR-derived dataset and a Landsat TM-derived dataset

The large uncertainties in estimates of cropland area in China may have significant implications for major cross-cutting themes of global environmental change—food production and trade, water resources, and the carbon and nitrogen cycles. Many earlier studies have indicated significant under-reporting of cropland area in China from official agricultural census statistics datasets. Space-borne remote sensing analyses provide an alternative and independent approach for estimating cropland area in China. In this study, we report estimates of cropland area from the National Land Cover Dataset (NLCD-96) at the 1:100,000 scale, which was generated by a multi-year National Land Cover Project in China through visual interpretation and digitization of Landsat TM images acquired mostly in 1995 and 1996. We compared the NLCD-96 dataset to another land cover dataset at 1-km spatial resolution (the IGBP DIScover dataset version 2.0), which was generated from monthly Advanced Very High Resolution Radiometer (AVHRR)-derived Normalized Difference Vegetation Index (NDVI) from April, 1992 to March, 1993. The data comparison highlighted the limitation and uncertainty of cropland area estimates from the DIScover dataset.     

Snow accumulation in forests from ground and remote‐sensing data

Winter‐forest processes affect global and local climates. The interception‐sublimation fraction (F) of snowfall in forests is a substantial part of the winter water budget (up to 40%). Climate, weather‐forecast and hydrological modellers incorporate increasingly realistic surface schemes into their models, and algorithms describing snow accumulation and snow‐interception sublimation are now finding their way into these schemes. Spatially variable data for calibration and verification of wintertime dynamics therefore are needed for such modelling schemes. The value of F was determined from snow courses in open and forested areas in Hokkaido, Japan. The value of F was related to species and canopy‐structure measures such as closure, sky‐view fraction (SVF) and leaf‐area index (LAI). Forest structure was deduced from fish‐eye photographs. The value of F showed a strong linear correlation to structure: F = 0·44 ? 0·6 × SVF for SVF < 0·72 and F = 0 for SVF > 0·72, and F = 0·11 LAI. These relationships seemed valid for evergreen conifers, larch trees, alder, birch and mixed deciduous stands. Forest snow accumulation (S_F) could be estimated from snowfall in open fields (S_o) and to LAI according to S_F = S_o (1 ? 0·11 LAI) as well as from SVF according to S_F = S_o (0·56 + 0·6 SVF) for SVF < 0·72. The value of S_F was equal to S_o for SVF values above 0·72. The value of sky‐view fraction was correlated to the normalized difference snow index (NDSI) using a Landsat‐TM image for observation plots exceeding 1 ha. Variables F and S_F were related to NDSI for these plots according to: F = ?0·37NDSI + 0·29 and S_F = S_o (0·81 + 0·37NDSI). These relationships are somewhat hypothetical because plot‐size limitation only allowed one sparse‐forest observation of NDSI to be used. There is, therefore, a need to confirm these relationships with further studies. Copyright © 2004 John Wiley & Sons, Ltd.     

基于Landsat8卫星数据的城市相关指数变化分析

基于Landsat8 OLI遥感影像数据,提取了与城市环境密切相关的NDVI、NDBI、NDWI和LST,分析了不同地物类型遥感指数的分布特征和不同时间序列的变化特征;进一步探讨了NDVI、NDBI与LST的相关性,可为研究城市热岛效应提供依据。结果表明,山地的NDVI最大,建筑用地密集区域的NDBI、LST最大,水体的NDWI最大,且建筑用地的NDVI、NDBI随时间变化较稳定。     

基于GF-1与Landsat-8影像的山区苹果园地信息提取

GF-1卫星影像具有空间和时间分辨率高、纹理信息丰富等优势,而Landsat-8卫星影像具有多波段、光谱信息充足等优势。针对两种影像的特点,本文分别用面向对象分类方法进行苹果园地信息提取研究,结果表明:两种影像的分类精度都比较高,但由于研究区域属于山区,地块分布不均匀,GF-1影像发挥其空间分辨率较高的优势,苹果园地面积提取精度比Landsat-8高1.19%。     

A radiative transfer model-based method for the estimation of grassland aboveground biomass

This paper presents a novel method to derive grassland aboveground biomass (AGB) based on the PROSAILH (PROSPECT + SAILH) radiative transfer model (RTM). Two variables, leaf area index (LAI, m²m⁻², defined as a one-side leaf area per unit of horizontal ground area) and dry matter content (DMC, gcm⁻², defined as the dry matter per leaf area), were retrieved using PROSAILH and reflectance data from Landsat 8 OLI product. The result of LAI × DMC was regarded as the estimated grassland AGB according to their definitions. The well-known ill-posed inversion problem when inverting PROSAILH was alleviated using ecological criteria to constrain the simulation scenario and therefore the number of simulated spectra. A case study of the presented method was applied to a plateau grassland in China to estimate its AGB. The results were compared to those obtained using an exponential regression, a partial least squares regression (PLSR) and an artificial neural networks (ANN). The RTM-based method offered higher accuracy (R² = 0.64 and RMSE = 42.67 gm⁻²) than the exponential regression (R² = 0.48 and RMSE = 41.65 gm⁻²) and the ANN (R² = 0.43 and RMSE = 46.26 gm⁻²). However, the proposed method offered similar performance than PLSR as presented better determination coefficient than PLSR (R² = 0.55) but higher RMSE (RMSE = 37.79 gm⁻²). Although it is still necessary to test these methodologies in other areas, the RTM-based method offers greater robustness and reproducibility to estimate grassland AGB at large scale without the need to collect field measurements and therefore is considered the most promising methodology.     

Upscaling plot-scale soil respiration in winter wheat and summer maize rotation croplands in Julu County,North China

Soil respiration (R_s) data from 45 plots were used to estimate the spatial patterns of R_s during the peak growing seasons of winter wheat and summer maize in Julu County, North China, by combining satellite remote sensing data, field-measured data, and a support vector regression (SVR) model. The observed R_s values were well reproduced by the model at the plot scale, with a root-mean-square error (RMSE) of 0.31 μmol CO₂ m⁻² s⁻¹ and a coefficient of determination (R²) of 0.73. No significant difference was detected between the prediction accuracy of the SVR model for winter wheat and summer maize. With forcing from satellite remote sensing data and gridded soil property data, we used the SVR model to predict the spatial distributions of R_s during the peak growing seasons of winter wheat and summer maize rotation croplands in Julu County. The SVR model captured the spatial variations of R_s at the county scale. The satellite-derived enhanced vegetation index was found to be the most important input used to predict R_s. Removal of this variable caused an RMSE increase from 0.31 μmol CO₂ m⁻² s⁻¹ to 0.42 μmol CO₂ m⁻² s⁻¹. Soil properties such as soil organic carbon (SOC) content and soil bulk density (SBD) were the second most important factors. Their removal led to an RMSE increase from 0.31 μmol CO₂ m⁻² s⁻¹ to 0.37 μmol CO₂ m⁻² s⁻¹. The SVR model performed better than multiple regression in predicting spatial variations of R_s in winter wheat and summer maize rotation croplands, as shown by the comparison of the R² and RMSE values of the two algorithms. The spatial patterns of R_s are better captured using the SVR model than performing multiple regression, particularly for the relatively high and relatively low R_s values at the center and northeast study areas. Therefore, SVR shows promise for predicting spatial variations of R_s values on the basis of remotely sensed data and gridded soil property data at the county scale.