Integrated approach for understanding spatio-temporal changes in forest resource distribution in the central Himalaya

Intense anthropogenic exploitation has altered distribution of forest resources. This change was analyzed using visual interpretation of satellite data of 1979, 1999 and 2009. Field and interactive social surveys were conducted to identify spatial trends in forest degradation and data were mapped on forest cover and land use maps. Perceptions of villagers were compiled in a pictorial representation to understand changes in forest resource distribution in central Himalaya from 1970 to 2010. For- ested areas were subject to degradation and isolation due to loss of con- necting forest stands. Species like Lantana camara and Eupatorium adenophorum invaded forest landscapes. Intensity of human pressure differed by forest type and elevation. An integrated approach is needed to monitor forest resource distribution and disturbance.

An integrated method for matching forest machinery and a weight-value adjustment

Proper matching of forestry machinery is important when raising mechanization levels for forestry production. In the matching process, forestry machinery needs not only expertise, but also improved methods for solving problems. I propose combination of case-based reasoning （CBR） and rule-based reasoning （RBR） by calculating the similarity of quantitative parameters of various forestry machines in an analytical and hierarchical process. I calculated the similarity of machin-ery used in forest industries to enable better selection and matching of equipment. I propose a weight-value adjusting method based on sums of squares of deviations in which the individual parameter weights were modified in the process of application. During the process of system design, I put forward a design method knowledge base and generated a dynamic web reasoning framework to integrate the processes of forest industry machinery selection and weight-value adjustment. This enables expansion of the scope of the complete system and enhancement of the reasoning efficiency. I demonstrate the validity and practicability of this method using a practical example.

Multi-modal Gesture Recognition using Integrated Model of Motion, Audio and Video

Gesture recognition is used in many practical applications such as human-robot interaction, medical rehabilitation and sign language. With increasing motion sensor development, multiple data sources have become available, which leads to the rise of multi-modal gesture recognition. Since our previous approach to gesture recognition depends on a unimodal system, it is difficult to classify similar motion patterns. In order to solve this problem, a novel approach which integrates motion, audio and video models is proposed by using dataset captured by Kinect. The proposed system can recognize observed gestures by using three models. Recognition results of three models are integrated by using the proposed framework and the output becomes the final result. The motion and audio models are learned by using Hidden Markov Model. Random Forest which is the video classifier is used to learn the video model. In the experiments to test the performances of the proposed system, the motion and audio models most suitable for gesture recognition are chosen by varying feature vectors and learning methods. Additionally, the unimodal and multi-modal models are compared with respect to recognition accuracy. All the experiments are conducted on dataset provided by the competition organizer of MMGRC, which is a workshop for Multi-Modal Gesture Recognition Challenge. The comparison results show that the multi-modal model composed of three models scores the highest recognition rate. This improvement of recognition accuracy means that the complementary relationship among three models improves the accuracy of gesture recognition. The proposed system provides the application technology to understand human actions of daily life more precisely.

Study on Integration of Suburban Forest Park and Eco-city into “Mega-landscape”

On the basis of analyzing the development of suburban forest parks, the paper proposed the combination of eco-city construction and suburban forest parks, and the construction of "mega-landscape" mode, which offers a new concept for the development of suburban forest parks.

Turkey Creek—A Case Study of Ecohydrology and Integrated Watershed Management in the Low-Gradient Atlantic Coastal Plain, USA

Water yield, water supply and quality, wildlife habitat, and ecosystem productivity and services are important societal concerns for natural resource management in the 21st century. Watershed-scale ecohydrologic studies can provide needed context for addressing complex spatial and temporal dynamics of these functions and services. This study was conducted on the 5240 ha Turkey Creek watershed (WS 78) draining a 3rd order stream on the Santee Experimental Forest within the South Carolina Atlantic Coastal Plain, USA. The study objectives were to present the hydrologic characteristics of this relatively undisturbed, except by a hurricane (Hugo, 1989), forested water-shed and to discuss key elements for watershed management, including water resource assessment (WRM), modeling integrated water resources management, environmental assessment, land use planning, social impact assessment, and information management. Runoff coefficients, flow duration curves, flood and low flow frequency curves, surface and ground water yields were assessed as elements of the WRM. Results from the last 10 years of interdisciplinary studies have also advanced the understanding of coastal ecohydrologic characteristics and processes, water balance, and their modeling including the need of high resolution LiDAR data. For example, surface water dynamics were shown to be regulated primarily by the water table, dependent upon pre- cipitation and evapotranspiration (ET). Analysis of pre- and post-Hugo streamflow data showed somewhat lower but insignificant (α = 0.05) mean annual flow but increased frequency of larger flows for the post-Hugo compared with the pre-Hugo level. However, there was no significant difference in mean annual ET, potentially indicating the resiliency of this coastal forest. Although the information from this study may be useful for comparison of coastal ecohydrologic issues, it is becoming increasingly clear that multi-site studies may be warranted to understand these complex systems in the face of climate change, sea level rise, and increasing development in coastal regions.

Changing Forestry Policy by Integrating Water Aspects into Forest/Vegetation Restoration in Dryland Areas in China

Restoration forestry (forest rehabilitation) or re-vegetation is one effective measure to solve environmental problems, notably soil erosion. It may be further stimulated by the Clean Development Mechanism for carbon sequestration. However, there is an intensive and on-going debate about the adverse effects arising from afforestation in dryland areas, such as soil drying up which may cause further damage to the success of forest restoration, and the water yield reduction from watershed which may harm the regional development. On other hand, some preliminary studies showed a possibility that these adverse effects may be diminished more or less by properly designing the system structure and spatial distribution of forest/vegetation in a watershed. However, it is urgent to develop an evidence-based and sustainable new forestry policy for harmonizing forest-water interrelation. As a leading country in afforestation, China is beginning to develop a more trans-disciplinary and cross-sectoral forestry policy for harmonizing forestry development with water management. The main points of the changing new forestry policy should include: (1) Establishing a regional development strategy focusing on harmonized forest-water relations; (2) Taking forest-water interactions as an important part of evaluation; (3) Reducing the 'eco-water' quota of forests through technical advancement; (4) Developing and extending water-adaptive forest management practices; (5) Strengthening forest ecohydrological research and decision support ability.

Ecotourism Development in the Context of Cultural and Tourism Integration:A Case Study of Wanli Mopan Mountain Forest Park

The integration of culture and tourism is a new form of tourism and a trend of tourism industry development.Mopan Mountain Forest Park in Wanli District of Nanchang City is taken as research object,and the excavation of its cultural elements is analyzed.It is thought that cultural elements can revitalize the culture and tourism industry,promote the integration of culture and tourism,encourage the mutual penetration of cultural innovation,design services and tourism products,improve the development and service level of cultural tourism products,and accelerate the development of characteristic cultural tourism.

Intelligent Deep Learning Enabled Wild Forest Fire Detection System

The latest advancements in computer vision and deep learning(DL)techniques pave the way to design novel tools for the detection and monitoring of forestfires.In this view,this paper presents an intelligent wild forestfire detec-tion and alarming system using deep learning(IWFFDA-DL)model.The pro-posed IWFFDA-DL technique aims to identify forestfires at earlier stages through integrated sensors.The proposed IWFFDA-DL system includes an Inte-grated sensor system(ISS)combining an array of sensors that acts as the major input source that helps to forecast thefire.Then,the attention based convolution neural network with bidirectional long short term memory(ACNN-BLSTM)model is applied to examine and identify the existence of danger.For hyperpara-meter tuning of the ACNN-BLSTM model,the bacterial foraging optimization(BFO)algorithm is employed and thereby enhances the detection performance.Finally,when thefire is detected,the Global System for Mobiles(GSM)modem transmits messages to the authorities to take required actions.An extensive set of simulations were performed and the results are investigated interms of several aspects.The obtained results highlight the betterment of the IWFFDA-DL techni-que interms of various measures.

竹笋采挖下克隆整合对毛竹林发笋成竹规律的影响

【目的】为研发毛竹林高产高效生态经营技术提供一定参考。【方法】调查浙西南地区采挖毛竹林竹笋后的笋-幼竹生长过程,通过存活曲线分析毛竹林挖笋后的发笋成竹规律,探讨挖笋对竹笋中单宁、粗脂肪、粗蛋白等品质指标的影响。【结果】竹笋采挖提高了毛竹林地的发笋能力,挖笋样地的发笋数比未挖笋样地增加了12.8%(P<0.05),而挖笋后选留竹笋的存活率也显著高于未挖笋样地;与未挖笋样地相比,挖笋样地内毛竹新分株的平均胸径并未发生显著改变(P>0.05),挖笋不会降低新分株胸径而使林分衰退;竹笋间的非对称性竞争有利于竹笋地径的径级为Ⅰ-Ⅲ级竹笋的生长与存活,随着地径的提高竹笋死亡率显著降低,而径级Ⅳ级以上竹笋则有着较高的死亡率。盛笋早期以后,径级相对较小(Ⅳ级以上)的竹笋开始大量萌发,这类竹笋若不及时采挖,大部分会出现退笋,造成资源浪费;挖笋和未挖笋样地竹笋的含水率、蛋白质和粗脂肪含量在各笋期均未见显著差异(P>0.05),尽管挖笋样地竹笋的单宁含量在末笋期显著高于未挖笋样地,但两种样地其他笋期单宁含量均未见显著差异。【结论】毛竹林竹笋的采挖,可缓解竹笋间的非对称性的资源竞争,有效避免大量退笋导致的毛竹母株营养消耗,实现母株资源的克隆整合,增加竹笋产量。因此,合理的竹笋采挖有利于毛竹笋用林的高效生产及可持续性经营。

数字经济下福建森林旅游与乡村振兴的融合发展

基于新古典经济学、文化生态、社会资本和环境经济学等相关理论,结合福建5G+平安鼓岭项目、智能导览典型案例、旅游元宇宙城市等数字化实践,探讨森林旅游和乡村振兴在经济、文化、社会及生态系统等方面融合发展的理论逻辑。结果表明:数字技术的应用优化了市场资源配置,加强了文化和社会联系,提升了生态系统的可持续性;数字经济下森林旅游与乡村振兴融合发展存在数字化森林生态模式、个性化大数据森林导览模式、数字森林产品电商销售模式、虚拟森林生态体验模式和社交媒体森林旅游推广模式5种可行模式。因此,为了推动福建省森林旅游与乡村振兴智能、高效、可持续地融合发展,应加强建设数字基础设施,努力提升居民数字素养,不断完善数字法律法规。

中国造纸业绿色战略演进历程

中国造纸业绿色战略的实践从萌芽期算起已经走过了60年艰难而富有成效的发展历程。在当今国家生态文明建设和实现双碳目标的背景下,对中国造纸业绿色发展过程进行回顾和总结,挖掘绿色战略的演进规律,并预估未来的战略趋势是十分必要的。根据绿色经济和绿色战略理论,以及可再生资源型企业绿色战略演进的一般规律,本文对中国造纸业绿色战略实践的重要事件和举措进行系统梳理,发现中国造纸业绿色战略总体上经历了开环和闭环绿色战略两大阶段。其中,开环绿色战略阶段包含原料导向(林纸一体化)绿色战略和末端治理绿色战略;闭环绿色战略阶段包含循环绿色战略和“循环+”绿色战略。据此,运用超循环经济理论,可以预计中国造纸业绿色战略的未来发展趋势,即林纸超循环绿色战略。

整体性保护视野下文化景观遗产地产业发展策略研究--以景迈山为例

文化景观保护的理论和实践都强调将人和非物质要素纳入到遗产的整体性保护之中。遗产地的产业发展方式深刻影响着遗产保护效果。普洱景迈山古茶林文化景观遗产地的产业发展因申遗成功迎来新的机遇,但由于整体性保护理念在制度中并未得到充分明确且在实践中认识不足,遗产地产业发展重点偏向物质要素而忽略了非物质要素,导致茶产业发展思路和村寨利用方式与遗产保护目标契合度不够,文旅产业偏向观光而没有全面展示遗产内涵,非物质文化遗产传承面临断层风险,影响了遗产地可持续发展的文化根基。挖掘非物质要素的产业价值,以整体性保护视角重构产业发展思路,构建“践行传统生产生活赋能产业发展”的良性循环,并在制度上明确整体性保护理念、完善产业发展引导和扶持规范,或许是化解遗产地发展需求与遗产保护间矛盾的可行策略。

基于生态韧性的山水林田湖草沙一体化保护修复框架

基于生态韧性内涵的山水林田湖草沙一体化保护修复是提升区域生态系统承载力和风险适应性的创新路径。本文阐述了生态韧性内涵与山水林田湖草沙生命共同体的特征,明确了一体化生态修复同生态韧性内涵关联逻辑,构建了山水林田湖草沙一体化保护修复的韧性作用机制和“问题识别-要素分析-策略制定”研究框架。以石家庄市为例,识别了其水土流失、湿地萎缩等生态问题,明确流域单元关键生态要素,并基于石家庄市生态系统地理空间格局以及面临的生态风险,提出调田节水、生境修复、林草保育等保护策略。以期为推进山水林田湖草沙系统修复工作以及增强生态系统应对干扰的抵抗力和适应力提供支撑。

基于机器学习算法的森林生物量多源遥感估测

为进一步探索不同空间分辨率影像在10 m×10 m样地尺度下森林生物量估测性能及协同机器学习算法(RF、SVM、DT、GBM、k-NN、Stacking)的估测效果,利用光学遥感GF2(高分二号卫星)、sentinel 2A、Landsat 8 OLI、SUM(整合3种遥感数据源)影像及辅助变量DEM高程数据、环境因子、林分因子(森林类型、优势树种),在Boruta算法变量选择下用机器学习算法对元谋地区乔木林森林生物量(地上+地下)进行遥感估测,并比较4种影像下的估测精度。研究表明:(1)基于Boruta算法分别对3种影像及整合3种影像条件下进行变量选择,单一影像中sentinel 2A的植被指数PEIP、Landsat 8 OLI的纹理因子b2_ME_9×9、GF2的GNDVI分别为3种影像下的最高得分变量,多源融合估测森林生物量中GF2的GNDVI为最佳得分变量;(2)基于Boruta算法选择的变量构建RF、SVM、DT、GBM、k-NN算法以及对5个模型的Stacking集成算法,SUM的Stacking集成算法的估测效果最优,模型决定系数(R2)为0.73,均方根误差(RMSE)为28.46 t·hm^(-2),集成算法下的SUM的估测性能优于sentinel 2A、Landsat 8 OLI,GF2优于sentinel 2A,sentinel 2A的估测性能优于Landsat 8 OLI。研究结果说明在生物量遥感估测中高分辨率影像具有较好的估测效果,同时多源遥感协同估测、集成算法均可提高森林生物量遥感估测精度,可为森林生物量遥感估测提供参考和借鉴。

基于异质集成的井漏预警模型

钻井井漏事故具有突发性和难以控制的特点。因此,迫切需要建立一种有效的井漏预测方法。将随机森林、支持向量机和反向传播神经网络模型相结合的异质积分器Stacking应用于青海省柴达木盆地英西地区。首先对目标区块的数据集进行处理,运用灰色关联对数据进行相关性分析,选择其中10个相关性高的参数,后设置两层堆叠集成,第一层选择随机森林、支持向量机和BP神经网络模型作为基础学习器,第二层选择逻辑回归模型作为元学习器。结果表明,异质集成模型提高了预测精度(0.981的准确率、0.970的精确率、0.963的召回率和0.960的F 1分数),克服了同质分类器的局限性。强调了综合井漏预警预报中考虑多种地质因素的重要性。

平原水网区田林水共生森林覆盖率提升设计研究——以浙江省嘉善县长秀村为例

平原水网区以农田、水系为主,森林覆盖率低,难以满足国家关于生态绿色发展国土空间规划的要求.随着生态文明建设和乡村振兴战略的推进,如何提升平原水网区的森林覆盖率成为政府部门关注的重点.本文以浙江省嘉善县长秀村为例,基于田林水共生理论,提出平原水网区森林覆盖率提升设计方案,通过道路、河流、农田、村庄的绿化优化设计,在确保基本农田面积不减少前提下,能够有效提升森林覆盖率.该方案对平原水网区绿化设计具有参考价值.

面向对象和集成学习算法在森林蓄积量遥感估测中的应用

以江西省兴国县为研究区,设置51个杉木(Cunninghamia lanceolata)林样地、22个马尾松(Pinus massoniana)林样地;以2017年江西省森林资源二类调查的样点调查数据、陆地卫星8号携带的陆地成像仪(Landsat8 OLI)遥感数据为基础,将陆地卫星8号携带的陆地成像仪影像进行多尺度的面向对象分割(分割尺度为50~500,步长为50),通过计算遥感各影响因素与蓄积量之间的皮尔逊(Pearson)相关系数,对比依据对象提取的遥感特征与依据像素提取的特征的相关性,确定最佳的分割尺度;依据布尔塔(Boruta)算法进行特征选择,构建多元线性回归(MLR)、随机森林(RF)、K-最近邻(KNN)、支持向量机(SVM)、堆叠(Stacking)集成算法5种蓄积量估测模型,以决定系数、均方根误差、相对均方根误差检验模型的估测精度;以森林蓄积量为评价指标,分析面向对象和集成学习算法在森林蓄积量遥感估测中的应用、构建的依据对象的堆叠算法对森林蓄积量遥感估测效果、依据对象的堆叠算法与常用的像素级特征提取方法的差异。结果表明:遥感的影响因素与蓄积量的相关性,随着分割尺度的增大出现先增加后降低的趋势,当分割尺度在150~250之间时,遥感的影响因素与蓄积量之间的相关性出现峰值;与依据像素的蓄积量估计模型相比,依据对象的估计结果的均方根误差从57.42~75.22 m^(3)·hm^(-2)降低到了50.77~68.26 m^(3)·hm^(-2)。与多元线性模型相比,机器学习模型具有更强的蓄积量预测性能,其均方根误差从66.05~75.22 m^(3)·hm^(-2)降低至50.77~71.97 m^(3)·hm^(-2);机器学习模型最佳的估计结果来自200分割尺度时构建的堆叠集成算法,决定系数为0.63、均方根误差为50.77 m^(3)·hm^(-2)、相对均方根误差为25.38%。与依据像素的特征提取方式相比,依据对象的遥感特征提取可以减小由于异常像素导致的不确定性;与基学习器相比,堆叠集成算法可以获得精度更高、更稳定的估计结果。依据对象的蓄积量遥感制图具有更强的可靠性,可以大大缓解依据像素制图导致的“椒盐现象”。

长汀生态恢复区不同混交比例针阔混交林内马尾松种群动态及其驱动机制

[目的]分析我国亚热带生态恢复典型先锋物种马尾松在不同混交比例针阔混交林内的种群动态及其驱动机制,为深入理解亚热带植被重建与恢复进程奠定基础。[方法]基于福建省长汀县生态恢复区4块不同混交比例针阔混交林内马尾松种群调查数据,构建积分投影模型并运用弹性分析、生命表响应试验等方法,探究混交林内马尾松种群动态及其驱动机制。[结果]针阔混交林内马尾松种群整体表现为持续衰退状态(种群增长率λ<1),随着阔叶树种在林分中相对多度增加,马尾松个体平均存活率和平均繁殖率以及λ均呈先上升后下降趋势。弹性分析表明,个体存活对混交林内马尾松种群增长最重要,小径级个体正生长利于种群发展,繁殖对种群增长的影响较为有限。随着阔叶树种相对多度增加,对马尾松种群增长最重要的个体逐渐由幼苗转变为成年树,种群维持风险逐渐增大。生命表响应试验显示,个体生长差异是不同混交林间马尾松种群增长差异的主要来源。[结论]随着阔叶树种混交比例上升,马尾松种群内幼苗和幼年树的比例和重要性持续降低,成年树的比例和重要性不断增加,不同生活史阶段个体生命率的差异化响应驱动马尾松种群动态变化。

随机森林优化的静动态耦合模型在滑坡位移预测中的应用

以重庆市奉节县生基包滑坡为例,首先采用静态的支持向量回归(SVR)机器学习算法和动态的长短期记忆神经网络(LSTM)机器学习算法对滑坡位移进行预测;其次引入随机森林(RF)算法,在输入因素筛选的基础上,对SVR模型和LSTM模型的预测结果进行更优解分类预测;最后通过RF模型输出概率值,对静动态耦合模型(SVR-LSTM)进行权重赋值,得到RF优化的SVR-LSTM滑坡位移预测模型。结果表明LSTM模型预测整体优于SVR模型,RF优化的SVR-LSTM滑坡位移预测模型集成了静态SVR与动态LSTM预测模型的优势,其预测性能与单一的SVR模型和LSTM模型相比更优。研究提供了一种滑坡位移预测模型集成的思路,为三峡库区的地质灾害预测预报提供借鉴和参考。

基于Stacking集成学习的枣树智能灌溉系统设计与试验

南疆降雨量少,气候干燥,农业用水紧张,水资源节约尤为重要,针对此问题设计一套智能灌溉系统。系统使用阿里云服务器作为上位机,树莓派作为下位机,并搭建相应的操作页面。根据Penman-Monteith公式中需要的气象数据、过去7天需水量以及前1天气象数据为输入向量,作物需水量为输出向量,构建基于随机森林、BP神经网络与岭回归的Stacking集成学习预测模型。结果表明Stacking集成学习预测模型拟合系数R 2为0.973,且MAE、RMSE、MAPE三类误差更小,Stacking集成学习预测模型预测效果更强。灌溉试验中自动灌溉决策正确,系统运行稳定,为新疆地区农业提高水资源利用问题提供思路。