Recultivation of abandoned agricultural lands in Ukraine: Patterns and drivers

The recent rise in agricultural commodity prices and the expectation that high price will persist have triggered a wave of farmland expansion in regions where land resources are still available. One such region is the former Soviet Union, where the collapse of socialism caused massive agricultural abandonment and where some of these lands are now being brought back into production. Yet, the extent and spatial patterns of recultivation, and what determines these patterns, remains unclear. We examined the extent of recultivation of abandoned agricultural land in Ukraine since 2007 using a new, satellite-based recultivation map and assessed the effect of biophysical and socioeconomic determinants on recultivation patterns using boosted regression trees. We found key predictors of recultivation to be related to the suitability of land for agriculture (i.e., soil quality, temperature). Accessibility to major cities was also important, with most recultivation happening closer to settlements, but this influence varied across Ukraine. Variables related to agricultural management (fertilizer input, mechanization) and demography were negligible in explaining recultivation in our analyses. These factors suggest that recultivation patterns were primarily driven by factors related to land productivity, with recultivation focusing on the most promising areas. Given the remaining large amount of unused agricultural land in Eastern Europe and the former Soviet Union, and considering that much abandonment occurred in areas only marginally suited to agriculture, our findings provide important insights into where recultivation can be expected to happen and thus for assessing the potential socioeconomic and environmental impacts of recultivation.     

A timescale decomposed threshold regression downscaling approach to forecasting South China early summer rainfall

A timescale decomposed threshold regression(TSDTR) downscaling approach to forecasting South China early summer rainfall(SCESR) is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR.The two models are developed based on the partial least squares(PLS) regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915–84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation(PDO). On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985–2006, compared to other simpler approaches. This study suggests that the TSDTR approach,considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.     

天山南麓雷暴日短期预报研究

利用阿克苏及邻近地区12个气象站1980—2013年雷暴资料,以及同期高空资料,统计了各站年均雷暴日数,对发生区域雷暴天气的环流形势进行分类,归纳出各型的入型指标。通过逐步回归法,建立阿克苏及邻近地区区域雷暴概率回归预报模型,并对2013年进行试预报。结果表明:(1)阿克苏及邻近地区区域雷暴的影响系统主要分为4类:巴湖低槽型、急流型、西北气流型和温度槽型。(2)对2002—2012年5—9月(共1683 d)历史资料进行判别,满足入型条件的样本数为876 d,消空率为48%;对2013年5—9月(共153 d)历史资料进行判别,入型样本数为80 d,消空率为48%。(3)对2002—2012年5—9月所有入型样本进行回代检验,平均准确率为72.0%(平均TS评分为30.1%);对2013年5—9月所有入型样本进行试预报,平均准确率为63.2%(平均TS评分为28.2%)。     

SAR图像海岸线检测的区域距离正则化几何主动轮廓模型

合成孔径雷达(SAR)卫星遥感图像可以极大地提高全国海岸线覆盖频率,然而受到海洋波浪所引起的随机海水表面粗糙度的影响,海岸目标与海水背景边界易混淆不清,因此本文提出了基于区域距离正则化几何主动轮廓模型(RDRGAC),引入距离正则项,解决重复初始化水平集函数为符号距离函数的问题,提高了算法收敛速度。此外,将区域面积项系数与SAR图像等效视数(ENL)建立非线性拟合关系,实现RDRGAC模型根据不同SAR遥感图像的自适应调整,改善海岸线自动提取精度。通过河北省北戴河和大连市金州湾SAR数据海岸线提取对比试验,验证了所提方法的有效性。     

回归-Elman网络在矿坝变形预测中的应用

金矿开展矿坝的变形监测工作,引入多层递阶回归分析模型,有较高的预测精度,但方法较繁琐,计算较复杂。由于变形数据可分离成趋势项与随机项,趋势项可用多元线性回归良好地拟合;随机项的预测,文中采用Elman网络建模计算,最后利用矿坝的实测高程位移数据进行验证,并与多层递阶回归进行比较。结果表明:回归-Elman网络模型比多层递阶回归的预测精度更高,效果更好,且方法简洁实用性强。     

加权Logistic回归模型在火山岩型铜矿预测中的应用:以宁芜盆地中段为例

文中探讨了加权Logistic回归模型在宁芜盆地中段火山岩型铜矿预测中的应用。首先,结合研究区的成矿地质背景,提取地质体、构造、围岩蚀变三大类证据因子;其次,分析各证据因子与铜矿点之间的空间关系,认为姑山旋回、娘娘山旋回火山机构控制了本区火山岩型铜矿的空间分布,根据计算结果,选取与火山岩型铜矿密切相关的龙王山组、姑山组地层,姑山旋回粗面斑岩、娘娘山旋回二长斑岩、NW向构造1.5 km缓冲区、NE向构造1.3 km缓冲区、EW向构造4.5 km缓冲区、硅化、褐铁矿化、黄铜矿化等作为模型自变量;最后采用加权Logistic回归模型进行成矿概率计算,并结合成矿地质背景,圈定四个成矿远景区,分别为P1、P2、P3、P4,其中P1、P2、P3呈北东向展布,主要受娘娘山和姑山火山机构控制,P4为东西向分布,主要受龙王山火山机构控制,在这些预测区中,均存在已发现的铜矿体,说明预测可信度较高。     

长春地区粉质黏土导热系数与其物性参数相关性研究

为研究导热系数与影响因素之间的相关关系,建立导热系数的推算公式,以长春地区粉质黏土为研究对象,对原状土样的导热系数与其物理参数之间的相关性进行回归分析。制作9个重塑土样,测其相关的参数值,以验证回归方程的适用性。结果表明,回归分析建立导热系数与2个物理参数之间的关系式不成立;考虑天然密度、含水率和孔隙度为自变量,其分别对应的相关性系数T检验显著值(Sig)都0. 05,复决定系数为0. 886,建立的回归方程成立,自变量能准确解释因变量的变化,且含水率与导热系数呈负相关,天然密度和孔隙度呈正相关。重塑土样相关参数代入回归方程得到的导热系数值与实验实测值之间相对误差低于4%,验证了该回归方程的普遍性和适用性。     

逻辑回归模型在边坡稳定性分析中的应用

采用逻辑回归模型和确定性系数CF建立概率模型,使用收集到的22个边坡实例,将重度、内聚力、坡角、坡高、内摩擦角和孔隙压力比六个影响因子作为自变量,边坡状态作为因变量,应用SPSS取不同的滑坡概率P与不滑坡概率1-P的比值的自然对数Z进行回归分析。将获得的滑坡概率拟合模型反馈到样本参数上,对比其误差,选取Z=±10时的函数模型对另外8组边坡进行验证,证明预测模型的合理性。     

Application of Geochemistry and VNIR Spectroscopy in Mapping Heavy Metal Pollution of Stream Sediments in the Takab Mining Area, NW of Iran

This study considered the possibility of using visible and near infrared (VNIR) spectral absorption feature parameters (SAFPs) in predicting the concentration and mapping the distribution of heavy metals in sediments of the Takab area. In total, 60 sediment samples were collected along main streams draining from the mining districts and tailing sites, in order to measure the concentration of As, Co, V, Cu, Cr, Ni, Hg, Ti, Pb and Zn and the reflectance spectra (350–2500 nm). The quantitative relationship between SAFPs (Depth500nm, R610/500nm, R1344/778nm, Area500nm, Depth2200nm, Area2200nm, Asym2200nm) and geochemical data were assessed using stepwise multiple linear regression (SMLR) and enter multiple linear regression (EMLR) methods. The results showed a strong negative correlation between Ni and Cr with Area2200nm, a significant positive correlation between As and Asym2200nm, Ni and Co with Depth2200nm, as well as Co, V and total values with Depth500nm. The EMLR method eventuated in a significant prediction result for Ni, Cr, Co and As concentrations based on spectral parameters, whereas the prediction for Zn, V and total value was relatively weak. The spatial distribution pattern of geochemical data showed that mining activities, along with the natural weathering of base metal occurrences and rock units, has caused high concentrations of heavy metals in sediments of the Sarough River tributaries.     

Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules

The dynamic relationships between land use change and its driving forces vary spatially and can be identified by geographically weighted regression (GWR). We present a novel cellular automata (GWR-CA) model that incorporates GWR-derived spatially varying relationships to simulate land use change. Our GWR-CA model is characterized by spatially nonstationary transition rules that fully address local interactions in land use change. More importantly, each driving factor in our GWR model contains effects that both promote and resist land use change. We applied GWR-CA to simulate rapid land use change in Suzhou City on the Yangtze River Delta from 2000 to 2015. The GWR coefficients were visualized to highlight their spatial patterns and local variation, which are closely associated with their effects on land use change. The transition rules indicate low land conversion potential in the city’s center and outer suburbs, but higher land conversion potential in the inner near suburbs along the belt expressway. Residual statistics show that GWR fits the input data better than logistic regression (LR). Compared with an LR-based CA model, GWR-CA improves overall accuracy by 4.1% and captures 5.5% more urban growth, suggesting that GWR-CA may be superior in modeling land use change. Our results demonstrate that the GWR-CA model is effective in capturing spatially varying land transition rules to produce more realistic results, and is suitable for simulating land use change and urban expansion in rapidly urbanizing regions.