Integration of remote sensing,county-level census,and machine learning for century-long regional cropland distribution data reconstruction

The Lower Mississippi Alluvial Valley (LMAV) was home to about ten million hectare bottomland hardwood (BLH) forests in the Southern U.S. It experienced over 80 % area loss of the BLH forests in the past centuries and large-scale afforestation in recent decades. Due to the lack of a high-resolution cropland dataset, impacts of land use change (LUC) on the LMAV ecosystem services have not been fully understood. In this study, we developed a novel framework by integrating the machine learning algorithm, county-level agricultural census, and satellite-based cropland products to reconstruct the LMAV cropland distribution during 1850–2018 at a 30-m resolution. Results showed that the LMAV cropland area increased from 0.78 × 10⁴ km² in 1850 to 6.64 × 10⁴ km² in 1980 and then decreased to 6.16 × 10⁴ km² in 2018. Cropland expansion rate was the largest in the 1960s (749 km² yr⁻¹) but decreased rapidly thereafter, whereas cropland abandonment rate increased substantially in recent decades with the largest rate of 514 km² yr⁻¹ in the 2010s. Our dataset has three notable features: (1) the depiction of fine spatial details, (2) the integration of the county-level census, and (3) the inclusion of a machine-learning algorithm trained by satellite-based land cover product. Most importantly, our dataset well captured the continuous increasing trend in cropland area from 1930–1960, which was misrepresented by other cropland datasets reconstructed from the state-level census. Our dataset would be important to accurately evaluate the impacts of historical deforestation and recent afforestation efforts on regional ecosystem services, attribute the observed hydrological changes to anthropogenic and natural driving factors, and investigate how the socioeconomic factors control regional LUC pattern. Our framework and dataset are crucial to developing managerial and policy strategies for conserving natural resources and enhancing ecosystem services in the LMAV.     

民间信仰载体的地域格局及其影响因素——以兰州市榆中县祠庙文化为例

以祠庙祭祀为主体且祠庙保存良好的民间信仰是甘肃陇中地区民间文化的重要组成。甘肃陇中地区的民间信仰具有很强的趋同性,本文以陇中地区的榆中县为例,以各村的祠庙为民间文化载体,通过核密度分析、Logistic回归等方法探讨榆中县民间文化载体的空间分布及其影响因素对于探讨陇中地区人地关系,保护和弘扬民俗文化有着重要意义。研究结果表明：（1）根据已有研究将榆中县民间信仰归纳为山神信仰、水神信仰、天地信仰、女性信仰、英雄崇拜和祖先崇拜,祖先崇拜的祠庙数量占有重要地位;（2）榆中县祠庙主要分布在西北黄河南岸、中部陇海铁路沿线及南部风景名胜集聚区,村级层面空间差异不显著、乡镇空间差异相对较大。通过核密度分析,不同类别民间信仰空间分布热点各有不同;（3）榆中县民间信仰空间分布受到区位条件限制,民间信仰的祠庙多位于海拔高度较低、人口密度较大、交通可达性较好的地区,坡度和到水源的距离成为山神信仰和水神信仰祠庙空间分布显著的影响因子,榆中县祠庙呈现出山神信仰类祠庙“依山”,水神信仰类祠庙“傍水”的空间特点。     

历史遗址类旅游地的生命周期研究——以美国14家历史遗址公园为例

实证研究中旅游地生命周期理论的运用潜力备受质疑。论文选取美国14家国家历史遗址公园为例,采用旅游研究中较少使用的四参数Logistic模型结合一次函数、二次函数、三次函数、高斯多峰分析法拟合旅游地的生命周期。研究发现：四参数Logistic模型结合一次函数、二次函数、三次函数能较好地拟合旅游地的生命周期;利用四参数Logistic曲线的上弯点、拐点、下弯点能定量的划分出旅游地的起步探索、发展、巩固、停滞阶段,一次函数、二次函数、三次函数能拟合旅游地的衰退或复兴阶段,这回答了学者们对生命周期阶段难以定量划分的质疑。根据旅游地停滞期之后的发展趋势还能归纳出该类旅游地的生命周期类型;高斯多峰分析法将旅游地的生命周期拟合成一个个波动的高斯峰,更大程度上保留了旅游地演进的波动特征;将高斯多峰分析法与四参数Logistic模型结合起来,不仅能定量划分旅游地的生命周期阶段,还能准确地描述在生命周期阶段内的波动情况,甚至分析各生命周期阶段对整个生命周期的影响和作用。     

Estimating sagebrush cover in semi-arid environments using Landsat Thematic Mapper data

Sagebrush ecosystems of the western US provide important habitat for several ungulate and vertebrate species. As a consequence of energy development, these ecosystems in Wyoming have been subjected to a variety of anthropogenic disturbances. Land managers require methodology that will allow them to consistently catalog sagebrush ecosystems and evaluate potential impact of proposed anthropogenic activities. This study addresses the utility of remotely sensed and ancillary geospatial data to estimate sagebrush cover using ordinal logistic regression. We demonstrate statistically significant prediction of ordinal sagebrush cover categories using spectral (χ² = 113; p < 0.0001) and transformed indices (χ² = 117; p < 0.0001). Both Landsat spectral bands (c-value = 0.88) and transformed indices (c-value = 0.89) can distinguish sites with closed, moderate and open cover sagebrush cover categories from no cover. The techniques described in this study can be used for estimating categories of sagebrush cover in arid ecosystems.     

共享社会经济路径下中国各省城市化水平预测

城市化水平预测与减缓及适应气候变化研究息息相关。基于国家统计局2005—2015年全国各省区城镇和乡村人口,以各省区2015年人均地区生产总值为指标进行分组,结合IPCC 5种共享社会经济路径（SSPs）的发展特征设置模型参数,运用Logistic模型预测了我国各省区2016—2050年城市化水平。结果表明,到2050年,各省区（除天津、北京、上海、西藏外）在5种典型SSPs下城市化水平收敛于75%左右。其中,SSP1、SSP3、SSP4、SSP5路径下,各省城市化水平比较趋同。而在SSP2路径下,全国总体上从东部到西部城市化程度逐渐降低,空间分布具有明显梯次递减性。5种SSPs路径下城市化速度方面,基本上呈现出中西部快而东部慢、西南快而东北慢的空间分布格局。同时,高收入省份不同路径下的城市化水平差别小,而中低收入省份的差别较大。     

Indicator Kriging without Order Relation Violations

Indicator kriging (IK) is a spatial interpolation technique aimed at estimating the conditional cumulative distribution function (ccdf) of a variable at an unsampled location. Obtained results form a discrete approximation to this ccdf, and its corresponding discrete probability density function (cpdf) should be a vector, where each component gives the probability of an occurrence of a class. Therefore, this vector must have positive components summing up to one, like in a composition in the simplex. This suggests a simplicial approach to IK, based on the algebraic-geometric structure of this sample space: simplicial IK actually works with log-odds. Interpolated log-odds can afterwards be easily re-expressed as the desired cpdf or ccdf. An alternative but equivalent approach may also be based on log-likelihoods. Both versions of the method avoid by construction all conventional IK standard drawbacks: estimates are always within the (0,1) interval and present no order-relation problems (either with kriging or co-kriging). Even the modeling of indicator structural functions is clarified.     

Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping

Data-driven prospectivity mapping can be undermined by dissimilarity in multivariate spatial data signatures of deposit-type locations. Most cases of data-driven prospectivity mapping, however, make use of training sets of randomly selected deposit-type locations with the implicit assumption that they are coherent (i.e., with similar multivariate spatial data signatures). This study shows that the quality of data-driven prospectivity mapping can be improved by using a training set of coherent deposit-type locations. Analysis and selection of coherent deposit-type locations was performed via logistic regression, by using multiple sets of deposit occurrence favourability scores of univariate geoscience spatial data as independent variables and binary deposit occurrence scores as dependent variable. The set of coherent deposit-type locations and three sets of randomly selected deposit-type locations were each used in data-driven prospectivity mapping via application of evidential belief functions. The prospectivity map based on the training set of coherent deposit-type locations resulted in lower uncertainty, better goodness-of-fit to the training set, and better predictive capacity against a cross-validation set of economic deposits of the type sought. This study shows that explicit selection of training set of coherent deposit-type locations should be applied in data-driven prospectivity mapping.     

一种矢量线数据零水印算法

为了满足矢量地理空间数据高精度和零扰动的实用性需要,该文提出一种零水印算法。首先利用Logistic混沌映射置乱原始水印图像;然后对原始矢量线数据进行离散傅里叶变换,获得离散傅里叶变换后的相位值,并将相位值由弧度值转换为角度值;接着随机选取均匀分布的角度值,向下取整后转换为相应的二进制形式,得到二值矩阵;最后将置乱后的水印图像与由相位值构成的二值矩阵进行异或操作,从而构造出零水印图像。实验结果表明,该算法能够抵抗常见的平移、缩放等几何攻击,并且对数据格式转换攻击、投影变换攻击具有较好的鲁棒性。     

Construction and assessment of a logistic regression model applied to short-term forecasting of thunderstorms in León (Spain)

Forecasting thunderstorms is one of the most difficult tasks in weather prediction due to the scarce knowledge on how to characterise the mechanisms taking part in the formation of thunderstorms. New tools based upon the objective recognition of appropriate conceptual models have been developed in the last years. A good example of this are the statistical models, based on variables that characterise the preconvective atmospheric conditions.This paper presents the results obtained by applying a short-term forecast model to thunderstorms during the summer periods in León (Spain). This model makes use of the logistic function as a binary forecasting technique determining storm/no storm. The logistic function used was a combination of 15 previously selected meteorological variables. The model has been constructed with the data collected on 152 occasions, and it has been verified on 77 other occasions.The skill scores obtained show that the use of statistical models combining the data, provided by variables characterizing the preconvective conditions and the triggering mechanisms, represent an important step in the difficult task of short-term thunderstorm forecasting.     

Comparison of spatial and aspatial logistic regression models for landmine risk mapping

Landmines continue to affect the lives of millions of people living in war-torn countries. One major challenge in humanitarian mine action (HMA) is finding new and integrated approaches to land release, which remains a slow and costly process. The use of geographic information systems (GIS) in HMA can improve the land release process by efficient mapping and prioritizing of landmine risk areas. This study explores the usage of aspatial and spatial regression techniques to construct a predictive geo-statistical model for landmine risk mapping in a small 160 km² municipality in Bosnia and Herzegovina (BiH) and a large 4500 km² region in Colombia. The first application of logistic geographically weighted regression to landmine risk mapping is presented. The results show that in the BiH study area, the effect of local parameters that influence the distribution of landmine risk varies significantly across the study area. Conversely, in the Colombia case study the effect of explanatory variables remains more homogeneous over the study area. We produced two landmine risk maps for each study area, based on aspatial and spatial regression models. Risk maps are classified into five classes, i.e. very low, low, medium, high, and very high risk. The landmine risk maps created through the usage of these innovative methodologies improve the assessment of risk and prioritization of the land release process in mine-contaminated areas, compared to existing approaches.