A global review of the impact of forest property rights interventions on poverty

Secure property rights are widely understood as critical for socio-economic development and sustainable land management in forested areas. Policies and programs, ranging from devolution of specific resource rights to formal land titling, have therefore been implemented to strengthen forest tenure and property rights in countries around the world. Despite the prevalence and importance of these efforts, however, systematic understanding of their effects on poverty remains lacking. We address this gap by systematically reviewing evidence on the impact of forest property rights interventions on poverty worldwide. We drew from a systematic map of evidence on forest-poverty links (Cheng et al., 2019) and used a population-intervention-comparator-outcome (PICO) framework to identify relevant studies. Our final dataset included 61 articles published from 2002 to 2016 comprising 91 case studies across 24 countries. Of these, only 11 articles (22 cases) used quasi-experimental methods to control for confounders. We find that almost all studied interventions (n = 88; 97%) focused on rights to access a forest area or withdraw resources from it. Relatively few studied interventions supported the more extensive property rights of exclusion (32%) and alienation (10%). Overall, reported impacts on both income/consumption and capital/assets dimensions of poverty were generally positive or mixed. Results from more robust quasi-experimental assessments showed greater variation, with case studies as likely to report negative as positive impacts on both poverty dimensions. We find tentative support for the economic theory that more secure property rights yield positive welfare effects. However, the paucity of evidence from more robust impact assessments constrains our ability to draw generalizable conclusions about the poverty impacts of different kinds of forest property rights interventions.     

Classification of forest land attributes using multi-source remotely sensed data

The aim of the study was to (1) examine the classification of forest land using airborne laser scanning (ALS) data, satellite images and sample plots of the Finnish National Forest Inventory (NFI) as training data and to (2) identify best performing metrics for classifying forest land attributes. Six different schemes of forest land classification were studied: land use/land cover (LU/LC) classification using both national classes and FAO (Food and Agricultural Organization of the United Nations) classes, main type, site type, peat land type and drainage status. Special interest was to test different ALS-based surface metrics in classification of forest land attributes. Field data consisted of 828 NFI plots collected in 2008–2012 in southern Finland and remotely sensed data was from summer 2010. Multinomial logistic regression was used as the classification method. Classification of LU/LC classes were highly accurate (kappa-values 0.90 and 0.91) but also the classification of site type, peat land type and drainage status succeeded moderately well (kappa-values 0.51, 0.69 and 0.52). ALS-based surface metrics were found to be the most important predictor variables in classification of LU/LC class, main type and drainage status. In best classification models of forest site types both spectral metrics from satellite data and point cloud metrics from ALS were used. In turn, in the classification of peat land types ALS point cloud metrics played the most important role. Results indicated that the prediction of site type and forest land category could be incorporated into stand level forest management inventory system in Finland.     

Review on Montane Forest Eco-hydrology in Arid Area

Since the forest eco-hydrology of arid area shows a well sensitivity of the global climate change, the relationship between forest and water and the hydrological function has attracted the attention of academic communites and management departments. This paper expounds the research progress in arid mountain forest eco-hydrology, and analyses the formation and stable mechanism of forest patch pattern, the relationship between forest and water yield and the response of forest eco-hydrology to climate change from three aspects: Forest spatial pattern, hydrological process and its response to climate change. In addition, combined with the current research progress, the research emphases in the future are put forward: Strengthening the research on the coupling of vegetation pattern and hydrological process; solving the scale problem by using remote sensing technique and model; enhancing the understanding towards the hydrological function of forest; determining the suitable forest scale which can balance the relationship of ecology and hydrological effect.     

Stability and capsizing analysis of nonlinear ship rolling in wind and stochastic beam seas

Considering the actual seaway condition, stability and capsizing of nonlinear ship rolling system in stochastic beam seas is of significant importance for voyage safety. Safe zone are defined in the phase space plan of the unperturbed Hamilton system to qualitatively distinguish ship motions as capsize and noncapsize. Capsize events are defined by solutions passing out of the safe zone. The probability of such an occurrence is studied by virtue of the random Melnikov function and the concept of phase space flux. In this paper, besides conventional wave excitation, the effect of wind load is also taken into account. The introduction of wind load will lead to asymmetry, in other words, it transforms the symmetric heteroclinic orbits into asymmetric homoclinic orbits. For asymmetric dynamical system, the orbital analytic solutions and its power spectrum are not readily available, and the technique of discrete time Fourier transformation (DTFT) is used. In the end, as verification of theoretical critical significant wave height, capsizing probability contour diagram is generated by means of numerical simulation. The contour diagram shows that these analytical methods provide reliable and predictive results about the likelihood of a vessel capsizing in a given seaway condition.     

Effect of 2D spatial variability on slope reliability: A simplified FORM analysis

To meet the high demand for reliability based design of slopes, we present in this paper a simplified HLRF(Hasofere Linde Rackwitze Fiessler) iterative algorithm for first-order reliability method(FORM). It is simply formulated in x-space and requires neither transformation of correlated random variables nor optimization tools. The solution can be easily improved by iteratively adjusting the step length. The algorithm is particularly useful to practicing engineers for geotechnical reliability analysis where standalone(deterministic) numerical packages are used. Based on the proposed algorithm and through direct perturbation analysis of random variables, we conducted a case study of earth slope reliability with complete consideration of soil uncertainty and spatial variability.     

Probabilistic stability analyses of undrained slopes by 3D random fields and finite element methods

A long slope consisting of spatially random soils is a common geographical feature. This paper examined the necessity of three-dimensional(3 D) analysis when dealing with slope with full randomness in soil properties. Although 3 D random finite element analysis can well reflect the spatial variability of soil properties, it is often time-consuming for probabilistic stability analysis. For this reason, we also examined the least advantageous(or most pessimistic) cross-section of the studied slope. The concept of"most pessimistic" refers to the minimal cross-sectional average of undrained shear strength. The selection of the most pessimistic section is achievable by simulating the undrained shear strength as a 3 D random field. Random finite element analysis results suggest that two-dimensional(2 D) plane strain analysis based the most pessimistic cross-section generally provides a more conservative result than the corresponding full 3 D analysis. The level of conservativeness is around 15% on average. This result may have engineering implications for slope design where computationally tractable 2 D analyses based on the procedure proposed in this study could ensure conservative results.     

基于Hyperion高光谱数据和随机森林方法的岩性分类与分析

探索利用高光谱数据的岩性填图新方法是遥感地质应用领域的重要需求之一。本文运用随机森林方法和EO-1Hyperion高光谱数据,对新疆塔里木西北部柯坪地区的局部区域进行岩性分类,并对相关问题进行分析。分别利用光谱特征以及加入光谱一阶导数特征进行岩性分类,并对不同特征对岩性分类的重要性进行分析,同时与现有的基于光谱角制图方法(SAM)进行比较。结果表明,与SAM方法相比,随机森林方法得到了更高精度的岩性分类结果,是一种有效可行的岩性分类方法。根据特征重要性的排序,蓝绿光波段、短波红外波段以及相应的一阶导数特征对研究区Hyperion数据的沉积岩岩性分类贡献更大。     

Late Pleistocene and Holocene Afromontane vegetation and headwater wetland dynamics within the Eastern Mau Forest,Kenya

The Mau Forest Complex is Kenya's largest fragment of Afromontane forest, providing critical ecosystem services, and has been subject to intense land use changes since colonial times. It forms the upper catchment of rivers that drain into major drainage networks, thus supporting the livelihoods of millions of Kenyans and providing important wildlife areas. We present the results of a sedimentological and palynological analysis of a Late Pleistocene–Holocene sediment record of Afromontane forest change from Nyabuiyabui wetland in the Eastern Mau Forest, a highland region that has received limited geological characterization and palaeoecological study. Sedimentology, pollen, charcoal, X-ray fluorescence and radiocarbon data record environmental and ecosystem change over the last ~16 000 cal a bp. The pollen record suggests Afromontane forests characterized the end of the Late Pleistocene to the Holocene with dominant taxa changing from Apodytes, Celtis, Dracaena, Hagenia and Podocarpus to Cordia, Croton, Ficus, Juniperus and Olea. The Late Holocene is characterized by a more open Afromontane forest with increased grass and herbaceous cover. Continuous Poaceae, Cyperaceae and Juncaceae vegetation currently cover the wetland and the water level has been decreasing over the recent past. Intensive agroforestry since the 1920s has reduced Afromontane forest cover as introduced taxa have increased (Pinus, Cupressus and Eucalyptus).     

随机森林模型预测岩溶区酸性煤矿井水锰污染

酸性煤矿井水严重威胁地下水的水质。如何更有效对受影响区域的地下水源进行动态监测是当前的一个关键问题。采用随机森林中的回归模型,利用自变量(采空区水位、岩溶水位、pH值、泉水流量、电导率)和因变量(污染离子浓度)的相关性,建立回归模型;使用测试数据进行误差分析,结果证明模型准度较高,所得预测值具有参考价值;得出各自变量对因变量影响的重要程度,分析结果与实际情况相符合。试验表明,随机森林回归模型在酸性煤矿井水污染预测方面具有适用性,可作为辅助手段监测水质污染情况,对今后工作有一定的指导意义和经济价值。     

Wall-to-wall spatial prediction of growing stock volume based on Italian National Forest Inventory plots and remotely sensed data

Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy.For a study area of more than 48,657 km² in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model.Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R² = 0.69 and RMSE_% = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI.