基于"3S"的梵净山自然保护区植被分布探讨

利用“3S”技术对梵净山国家自然保护区森林植被类型的空间分布进行分析,并为森林植被的监测与管理提供依据,更好地对各个区域(核心区、缓冲区、影响区)危害因子进行监控,从而有效地对其森林植被进行有效的保护。     

Significance of stemflow in groundwater recharge. 2: A cylindrical infiltration model for evaluating the stemflow contribution to groundwater recharge

A primary model for evaluating the effect of stemflow on groundwater recharge has been developed. The model, a cylindrical infiltration model (CI model), is based on the infiltration area of stemflow-induced water instead of canopy projected area for determining the stemflow inputs to the soil surface. The estimated ratio of recharge rate by stemflow to the total recharge rate determined with this model agrees closely with values obtained from the mass balance of chloride in subsurface waters. This primary model is considered to be useful for estimating the effect of stemflow on groundwater recharge.     

ESTIMATING INFILTRATION PARAMETERS FROM BASIC SOIL PROPERTIES

Infiltration data were collected on two rectangular grids with 25 sampling points each. Both experimental grids were located in tropical rain forest (Guyana), the first in an Arenosol area and the second in a Ferralsol field. Four different infiltration models were evaluated based on their performance in describing the infiltration data. The model parameters were estimated using non-linear optimization techniques. The infiltration behaviour in the Ferralsol was equally well described by the equations of Philip, Green–Ampt, Kostiakov and Horton. For the Arenosol, the equations of Philip, Green–Ampt and Horton were significantly better than the Kostiakov model. Basic soil properties such as textural composition (percentage sand, silt and clay), organic carbon content, dry bulk density, porosity, initial soil water content and root content were also determined for each sampling point of the two grids. The fitted infiltration parameters were then estimated based on other soil properties using multiple regression. Prior to the regression analysis, all predictor variables were transformed to normality. The regression analysis was performed using two information levels. The first information level contained only three texture fractions for the Ferralsol (sand, silt and clay) and four fractions for the Arenosol (coarse, medium and fine sand, and silt and clay). At the first information level the regression models explained up to 60% of the variability of some of the infiltration parameters for the Ferralsol field plot. At the second information level the complete textural analysis was used (nine fractions for the Ferralsol and six for the Arenosol). At the second information level a principal components analysis (PCA) was performed prior to the regression analysis to overcome the problem of multicollinearity among the predictor variables. Regression analysis was then carried out using the orthogonally transformed soil properties as the independent variables. Results for the Ferralsol data show that the parameters of the Green–Ampt and Kostiakov model were estimated relatively accurately (maximum R² = 0.76). For the Arenosol, use of the second information level together with PCA produced regression models with an R² value ranging from 0.38 to 0.68. For the Ferralsol, most of the variance was explained by the root content and organic matter content. In the Arenosol plot, the fractions medium and fine sand explained most of the observed variance.     

Teaching Spatial Thinking and Geospatial Technologies Through Citizen Mapping and Problem-Based Inquiry in Grades 7-12

Our project introduced students in grades 7 through 12 to spatial thinking and geospatial technologies in the context of challenges in their community. We used a mix of levels of inquiry to advance learning from teacher- to student-guided through a citizen mapping group activity. Student-suggested problem-based topics included parks and community gardens, crime, housing, and youth employment opportunities. Qualitative methods were used to evaluate students’ knowledge of spatial thinking and geospatial technologies, including map interpretation, a case study, daily exit slips, and interviews. Overall, the students’ awareness of their community, spatial thinking, and geospatial technologies increased as a result of participation.     

The Faxinal System: Forest fragmentation and soil degradation on the communal grazing land

The Brazilian faxinal is a traditional agrosilvopastoral system, which combines extensive grazing, subsistence cropping and low impact forest extraction, managed within a communal property system. Today it is at serious risk of disappearing due to increasing grazing pressure and logging activities that are leading to environmental problems such as forest fragmentation and soil degradation. The objectives of the study were to: a) evaluate soil physical degradation levels in different grazing areas within the faxinal system; and b) correlate soil degradation, fodder quantity and livestock dynamics in different pasture areas according to the household's location. Landscape mapping, vegetation surveys, and livestock grazing dynamics were assessed. In addition, soil physical parameters were evaluated for each land use (e.g., resistance, bulk density, porosity, moisture, and water infiltration rates). Results showed that livestock grazing and household locations are the main driving forces that lead to forest fragmentation. The household location creates three interrelated gradients in the communal grazing land: 1) soil physical degradation decreases from the pasture areas to araucaria forests; 2) fodder quantity decreases from pastures to araucaria forests; and 3) the livestock grazing intensity decreases from pastures to araucaria forests. In addition, some management practices to mitigate land degradation, including improvement of pastures, installation of removable troughs for drinking water in the upland areas and restriction of livestock from the riparian zone, have been proposed.     

全球历史森林数据中国区域的可靠性评估

全球历史土地利用数据集对于深入理解全球或区域环境变化具有重要意义。历史森林数据作为其重要组成部分,在区域尺度上的可靠性至今鲜有评估。以中国区域为研究对象,依据中国学者基于历史文献资料重建的中国历史森林数据(CHFD),采用趋势、数量和空间格局等对比法,对全球数据集(SAGE、PJ和KK10)中国森林数据的可靠性进行评估。结果表明:①虽然全球数据集中国森林数据与CHFD在近300年的变化趋势上均呈减少态势,但数量上差异较大。其中,SAGE数据集对中国1700年以来的森林面积估算较CHFD高出约20%～40%;KK10数据集重建的1700-1850年森林数量则高出约32%～46%;而PJ数据集由于吸纳了区域性研究成果,其总量与CHFD较为接近,多数时点的数量差异低于20%。②在省区尺度上,从总量与CHFD较为接近的PJ数据集来看,其与CHFD数据集森林变化趋势差异较大省区占到84%,而数量差异较大的省区占比高达92%。③在网格尺度上,PJ与CHFD数据集相对差异率> 70%的网格占比高达60%～80%,二者的时空动态格局差异明显。④全球数据集中国历史森林数据未能客观反映该区域森林变化的过程与格局特征,造成这一现象的原因在于全球与区域性数据集重建历史数据所依据的资料源不同,以及基于不同空间尺度构建的重建方法的差异等。     

Characterizing Non-Industrial Private Forest Landowners' Forest Management Engagement and Advice Sources

Non-industrial private forestland (NIPF) owners have options for engagement by following management strategies that reduce wildfire risk on their forestlands. Forest management engagement is a broad term with underlying categories and management implications. To better understand these categories, we examine interview data on the engagement of forest landowners from a case study of private forestland owner perspectives in northeast Oregon, USA. NIPF landowners outline two types of forest management engagement, one for property and one for community-focused forestland management. NIPF owners describe actions for engagement in public forestland management and how these actions differ from engagement in private management. Additionally, NIPF owners establish barriers to engagement in both public and private forestland management. Our findings can be used to better identify unengaged private forestland owners in the U.S. West, informing the design and implementation of extension and outreach for NIPF owners.     

随机森林回归模型的悬浮泥沙浓度遥感估算

三峡工程蓄水以来,清水下泄,坝下游河段发生了长时间、长距离的沿程冲刷,河流悬浮泥沙浓度发生改变,给沿岸生态系统带来了不利影响。随机森林算法灵活、稳健,已被广泛应用于各类生态环境变量的回归预测分析,但其在水体悬浮泥沙浓度估算方面的能力尚未得到充分认识。基于泥沙站点监测数据和MODIS卫星遥感反射率数据,通过构建随机森林非参数回归预测模型,对三峡工程坝下游宜昌至城陵矶河段在建坝前后14年间(2002年—2015年)各月的悬浮泥沙浓度进行遥感估算。研究表明:(1)基于随机森林的悬浮泥沙浓度估算模型表现较好,模型预测值与实测值间相关性好、预测精度高,优于其他模型(线性回归、支持向量机、人工神经网络模型)。(2)在参与模型构建的MODIS波段变量中,红波段被认为是最重要的预测变量,但不能单独使用它进行预测,悬浮泥沙遥感预测需要多变量共同参与。(3)将悬浮泥沙数据按季节分类所构建的随机森林模型,其平均误差为0.46 mg/L,平均相对均方根误差为12.33%,估算效果最优,能够满足较高精度下悬浮泥沙浓度估算的需求。综上,可以考虑以季节为划分依据,用随机森林回归模型估算悬浮泥沙浓度,并用于后期坝下游河道悬浮泥沙浓度时空反演。     

多模型耦合下的植被冠层可燃物含水率遥感反演

植被冠层可燃物含水率FMC(Fuel Moisture Content)是评估野火风险及估算火灾蔓延速率的重要指标。以中国西部6个典型研究区为例,基于辐射传输模型,利用Landsat 5 TM及Landsat 8 OLI遥感数据,开展草原、森林冠层FMC定量反演研究。为克服基于物理模型的病态反演问题、FMC自身的弱敏感性问题及西南森林多具复杂的双层冠层结构问题,研究中考虑了模型参数之间的相关特征,使用多波段遥感数据及耦合辐射传输模型等方法。反演结果显示,总体植被冠层FMC反演精度R~2为0.64,RMSE为44.86%,其中草地冠层FMC的反演精度(R~2=0.64,RMSE=47.57%)略低于森林冠层FMC的反演精度(R~2=0.71,RMSE=30.82%)。为进一步论证该反演结果对野火风险评估的有效性,研究中选取并分析了2011年3月2日于云南大理白族自治州剑川县金华镇金和村森林火灾爆发前、爆发时及灾后该区域植被冠层FMC的变化特征。结果显示,火灾爆发时该地区植被冠层FMC明显低于火灾发生前后(约一月时间)植被冠层FMC,证明了本文FMC反演结果对野火风险评估的有效性。     

随机森林方法支持的复杂地形区土地利用/土地覆被分类研究

随机森林方法目前已经成为遥感分类机器学习中一种有效方法,探索基于中等分辨率的Landsat卫星数据与随机森林方法相结合对复杂地形区长时间序列数据的获取及土地利用/土地覆被变化及模拟研究是非常有意义的。本文基于Landsat8OLI卫星多光谱数据,采用随机森林分类方法对青海省湟水流域复杂地形区土地利用类型进行了分类研究。针对复杂地形区域的情况,将研究区进行地理分区,根据每个分区的特点,选择相应的地形特征参数,并通过提取Landsat 8数据的光谱信息与纹理信息构建最优特征集,探索随机森林方法在复杂地形区土地利用分类的适用性。结果表明：使用Landsat8OLI数据进行随机森林分类,能较好地得到湟水流域复杂地形区域的土地利用类型结果;光谱、地形及纹理信息的结合在不同分区的表现结果不同。在脑山区光谱与地形信息结合能使随机森林分类效果最佳,总体精度达到91.33%,Kappa系数为0.886;而在浅山区与川水区综合考虑光谱、地形、纹理信息进行随机森林分类效果最佳,浅山区与川水区总体精度分别达到92.09%和87.85%,Kappa系数分别为0.902和0.859;利用随机森林算法进行优化选择纹理特征组合可以在保证分类精度的同时能够快速地提取土地利用类型信息,为复杂地形区土地利用类型的区分提供了实际可行的方法。