Spaceborne light detection and ranging (LiDAR), including ICESat-2 and GEDI, supports large-scale forest aboveground biomass (AGB) mapping, but accurate estimation across heterogeneous forest types remains challenging. This study developed forest-type-specific footprint-level AGB equations using ICESat-2 and GEDI observations and integrated the derived footprint AGB labels with Sentinel-1 and Sentinel-2 imagery in a U-Net framework to generate continuous AGB maps. After photon denoising and geolocation correction, GEDI RH95 showed stronger agreement with ALS-derived PH95 than ICESat-2 (R = 0.62–0.65 and 0.52–0.58, respectively), whereas ICESat-2 showed smaller bias magnitudes. At the footprint-level, the two sensors showed complementary performance. ICESat-2 performed better in broadleaf forests (R = 0.85, RMSE = 23.96 Mg/ha) and mixed forests (R = 0.89, RMSE = 20.39 Mg/ha), while GEDI performed better in coniferous forests (R = 0.85, RMSE = 22.50 Mg/ha). The U-Net model achieved improved regional AGB prediction with R = 0.91 and RMSE = 17.21 Mg/ha on the independent test set. These results demonstrate the value of combining ICESat-2, GEDI, Sentinel imagery, and deep learning for forest-type-specific AGB estimation and continuous biomass mapping.

Objective: The purpose of this article was to use the Random Forest method and nonlinear mixed-effects method to develop a model for determining tree height–diameter at breast height (DBH) for a natural coniferous and broad-leaved mixed forest in Jilin Province and to compare the advantages and disadvantages of the two methods to provide a basis for forest management practice. Method: Based on the Chinese national forest inventory data, the Random Forest method and nonlinear mixed-effects method were used to develop a tree height–DBH model for a natural coniferous and broad-leaved mixed forest in Jilin Province. Results: The Random Forest method performed well on both the fitting set and validation set, with an R2 of 0.970, MAE of 0.605, and RMSE of 0.796 for the fitting set and R2 of 0.801, MAE of 1.44 m, and RMSE of 1.881 m for the validation set. Compared with the nonlinear mixed-effects method, the Random Forest model improved R2 by 33.83%, while the MAE and RMSE decreased by 67.74% and 66.44%, respectively, in the fitting set; the Random Forest model improved R2 by 9.88%, while the MAE and RMSE decreased by 14.38% and 12.05%, respectively, in the validation set. Conclusions: The tree height–DBH model constructed based on the Random Forest method had higher prediction accuracy for a natural coniferous and broad-leaved mixed forest in Jilin Province and had stronger adaptability for higher-dimensional data, which can be used for tree height prediction in the study area.

Bamboo forests exhibit a unique efficient growth pattern that makes them invaluable in reducing atmospheric CO2 levels. Additionally, bamboo forests offer a diverse range of products, thus holding the potential to bolster local income. Despite these benefits, the comprehensive assessment of bamboo forests’ potential in both carbon abatement and improving local income enhancement has been hindered by the absence of a detailed bamboo biomass map. In this study, we address this gap by amalgamating a bamboo aboveground biomass (AGB) map covering three prominent producing provinces in southern China, utilizing multi-source remote sensing datasets. The results not only demonstrate a satisfactory consistency with China’s Ninth National Forest Inventory but also provide a more detailed spatial distribution. Based on this AGB estimation, we project an approximately threefold potential increase in annual bamboo culm harvest from existing bamboo forests. This represents a significant opportunity for expanding carbon abatement efforts, elevating local income levels, and facilitating the production of bamboo-derived biofuels. Furthermore, the adoption of an optimized management strategy has the potential to further enhance bamboo production. This study generates the first high-resolution bamboo AGB map and underscores the substantial potential of China’s bamboo forests in contributing to carbon sequestration and improving local income. The favorable income generated for local residents can serve as a compelling incentive for the implementation of sustainable forest management practices, offering a promising pathway toward achieving carbon-related objectives within the forestry sector and providing necessary support for forestry designation projects.

Turbidity is a water quality indicator that is essential for the sustainable development of aquatic ecosystems and the protection of biodiversity. The turbidity of different water surfaces and its response mechanisms to regional climatic factors and human activities in the Yangtze River Delta Region (YRDR), an important rapid economic development region in China, remain poorly understood. To enhance the knowledge of turbidity variations and dominant drivers of YRDR water surfaces, a complete long-term turbidity series was obtained using Landsat images from 1990 to 2020. The results show that the turbidity trend differed from -1.3 NTU/yr to 0.7 NTU/yr in different water surfaces. Turbidity decreased significantly in the mainstream of the Yangtze River (MYR), aquaculture ponds (AP) and other water bodies, whilst increasing significantly in the medium lakes (ML) and mainstream of the Qiantang River (MQR). Meanwhile, no significant changes in turbidity were observed in the great lakes (GL) and small lakes (SL). Rather than climatic factors, urbanisation and decreasing wastewater discharge were the dominant drivers of turbidity trends during the study period. In addition, ecological engineering in AP increased water transparency. The Three Gorges Dam also decreased turbidity in MYR. Increasing turbidity in the downstream of MQR was driven by increasing seasonal water surfaces and reclamation projects near Hangzhou Bay. GL faced no significant increase in turbidity due to the offset of afforestation to urbanisation-induced turbidity increase. These findings provide important information for government decision-making for subsequent aquatic environmental protection and restoration in the YRDR.

Forest biomass is an important indicator of forest ecosystem productivity, and it plays vital roles in the global carbon cycling, global climate change mitigating, and ecosystem researches. Multiscale, rapid, and accurate extraction of forest biomass information is always a research topic. In this study, comprehensive investigation of a larch (Larix olgensis) plantation was performed using remote sensing and field-based monitoring methods, in combination with LiDAR-based multisource data and machine learning methods. On this basis, a universal, multiscale (single tree, stand, management unit, and region), and unit-high-precision continuous monitoring method was proposed for forest biomass components. The results revealed the following. (1) Airborne LiDAR point cloud variables exhibited significant correlation with the aboveground components (except leaves) and the whole-plant biomass (Radj2 > 0.91), suitable for extraction or estimation of forest parameters such as biomass and stock volume. (2) In terms of biomass monitoring at forest stand and management unit scale, a random forest model performed well in fitting accuracy and generalization ability, whereas a multiple linear regression model produced clearer explanation regarding the biomass of each forest component. (3) Using seasonal phenological characteristics in the study area, larch distribution information was extracted effectively. The overall accuracy reached 90.0%, and the kappa coefficient reached 0.88. (4) A regional-scale forest biomass component estimation model was constructed using a long short-term memory model, which effectively reduced the probability of biomass underestimation while ensuring good estimation accuracy, with R2 exceeding 0.6 for the biomass of the aboveground and whole-plant components. This research provides theoretical support for rapid and accurate acquisition of large-scale forest biomass information.

以江西省抚州市受损山体为研究对象,通过遥感影像识别、现场调查与复核,研究了城市周边受损山体情况,基于统计分析、工程填图、模拟仿真等方法探讨了受损山体生态修复工程设计.调查共发现23处受损山体,其中18处需要进行人工修复.基于破损山体类型、面积、受损程度以及受损原因分析,应用边坡治理、槽穴、客土种植等工程技术设计了5种修复模式进行生态修复.

为摸清埃塞俄比亚本尚古勒—古马兹州低地竹资源的数量和分布情况,本文通过采用“3S”(GPS、RS、GIS)技术并结合地面调查的方法对该地区的竹资源进行了调查,共发现竹林总面积63.36万hm2,总立竹量325 663.06万株.其中:郁闭度0.7～1.0竹林面积8.12万hm2、占竹林总面积的12.82％,立竹量79626.28万株、占总立竹量的25.29％;郁闭度0.5～0.6竹林面积18.78万hm2、占竹林总面积的29.65％,立竹量132 604.39万株、占总立竹量的39.88％;郁闭度0.2～0.4竹林面积36.45万hm2、占竹林总面积的57.53％,立竹量113 432.39万株、占总立竹量的34.83％.

系统回顾了上一轮指标体系实施成效,在充分分析宏观背景和规划目标基础上,通过广泛查阅文献资料或调研访谈,收集分析六十余种生态文明有关考核评价指标体系,采用频度统计法和专家咨询法,初选形成指标备选库,再通过定性和定量相结合的方法进行筛选优化,最终分别得到3个约束性和3个预期性指标,构建了新一轮林地保护利用规划指标体系,为新一轮林地保护利用规划的编制和管理提供了有力的理论支撑.

针对当前在区域性的湿地保护上仍缺乏有效的理论探索和实践的情况,基于生态承载力的湿地评价结果,对平阳县湿地资源进行了分区分级保护,以有效实现区域湿地的系统性保护,为沿海区域性湿地保护提供了范例.

[目的]构建基于机载LiDAR的落叶松组分生物量反演模型,讨论不同方法对模型构建的影响.[方法]以地面实测样地数据和同步获取的机载LiDAR点云数据为数据源,分别采用多元线性回归(MLR)和随机森林(RF)方法,估测了长白落叶松的组分生物量,利用“刀切法”评价了模型的泛化能力.[结果]表明:(1)MLR筛选得到的Hinternal、H80、D10、D20与各组分生物量普遍表现为显著(P＜0.05)或极显著水平(P＜0.01).(2)MLR模型的R2高于0.82(枝、叶除外);RF模型的R2均高于0.91,且均拥有较小rRMSE、TRE值.(3)MDI和MDA方法的变量相对重要值排序均能较好地体现LiDAR变量与生物量之间的关系,MDI在趋势性判断和阈值设定方面更具优势.[结论] LiDAR变量与组分生物量具有显著的相关性.RF拥有更好的拟合效果和泛化能力,MLR则对LiDAR和组分生物量的关系有更明确的解释能力.反演模型能较好地反映林分的现势特征,生物量被低估的现象会随着林龄的增加而逐步增多.

为提高森林资源监测工作效率、降低森林资源监测平台建设成本,提出了以网络地图为底图,以林业专题图为现势性较强的参考数据,结合网络地图开发技术、C/S和B/S混合部署模式、AES和MD5混合加密等相关技术和方法,开发森林资源监测平台.该平台以基础年数据为基准,以小班为最小单位,对森林资源数据进行管理,高效、快捷、准确地更新森林资源数据,监测森林资源的数量、质量和林木生长情况.它主要解决如下几个问题:一是采取分布式部署,充分利用硬件资源,提高平台性能;二是采取网络数据加密技术,提高数据的安全性;三是调用网络地图,降低数据建设成本.经过两年的试运行,该平台运行稳定、基本满足用户需求,具有一定的推广性.

依据黄土高原油松人工林试验样地林分情况,确定神经网络的输入变量和输出变量,利用2012—2015年的567组数据对网络模型进行训练和检验,构建了5:q:1的土壤水分变动的BP神经网络模型.结果表明:最适宜的网络结构为5:6:1,均方误差mse=0.002645,总体拟合精度为96.78％,模拟检验拟合精度为94.44％.

[目的]构建落叶松人工林单木和林分水平的相容性生物量模型,使之既在数据采集区域内能够表征不同水平下的差异程度,又具有较强的通用性.[方法]基于64株长白落叶松人工林样木生物量实测数据和40个每木检尺样地数据,在考虑和未考虑林龄2种情形下,利用哑变量和非线性似然无关回归方法相结合,构建单木和林分水平的一元相容性生物量模型.[结果]表明:(1)地上及全株生物量模型单木水平下的R2adj均大于0.95,林分水平下的R2adj均大于0.78,(2)利用哑变量考虑林龄因素后,单木水平下各评价指标总体稳定,参数b值范围从0.9055～2.5125减小为1.0470～2.2028.林分水平下R2提升0.2019,参数b值范围从0.0711～1.5607减小为0.7811～1.0551;且具有更小的TRE、MPE和MSE.(3)利用对数转换的线性回归模型,全株及各组分生物量模型残差的分布趋势均平行于横轴.[结论]非线性似然无关回归和哑变量相结合的方法灵活、建模过程简单、模型稳定性好,适用于不同因素下落叶松人工林相容性生物量模型构建.林龄因素对林分模型拟合效果的改善更显著,在建模过程中,单木模型可以不考虑林龄的影响,而林分模型需要考虑林龄的影响.

根据日本落叶松〔Larix kaempferi(Lamb.)Carr.〕99个分布记录数据和19个气候变量,利用MaxEnt模型预测了日本落叶松在中国当前时期以及未来2个时期(2041年至2060年以及2061年至2080年)RCP2.6、RCP4.5、RCP6.0和RCP8.5情景下的潜在分布区.结果表明:当前时期日本落叶松的适宜区和高适宜区面积分别为35.59×104和6.99×104 km2,分别占研究区总面积的3.71％和0.73％.其中,高适宜区主要集中在"秦岭-大巴山区"和"辽东地区",二者的面积占高适宜区总面积的85％以上.与当前时期相比,2041年至2060年以及2061年至2080年4种气候情景下日本落叶松的适宜区面积持续增加;RCP4.5、RCP6.0和RCP8.5情景下高适宜区面积均增加,而RCP2.6情景下高适宜区面积则减少."秦岭-大巴山区"高适宜区总体表现为缩小趋势,且破碎化明显,而"辽东地区"高适宜区则有向东北方向移动的趋势,纬度向北移动了0.8°～4.5°,经度向东移动了0.9°～5.5°,吉林为高适宜区面积增加最大的省份.影响日本落叶松分布的主要气候变量为最热季降水量、温度季节变化、降水量季节变化和年平均温度,累计贡献率达90％以上.上述研究结果可为未来气候变化背景下日本落叶松的经营管理提供参考.

This paper presents a system for updating forest compartments and sub-compartments, which is based on QR code technology and forest resources planning and design survey database. The system is divided into the PC-based sub-system and the mobile sub-system. On the PC, the data for the compartments and sub-compartments can be viewed, analyzed, compared and counted, and the changes of forest resources can be informed with the aid of these operations. Based on QR code technology, these operations such as query, comparison and online or offline update can be realized easily in the mobile sub-system. Compared with the traditional survey methods of forestry resources, the proposed system can greatly improve the efficiency of data update and management for compartments & sub-compartments and it can provide an innovative exploration mode for the construction of smart forestry.

介绍了杭州市森林资源、生态状况及动态监测体系,对森林资源动态监测技术的研建、优化及问题进行了深入的分析总结,并提出改进建议.

统计分析1960~2010年间影响、登陆浙东的热带气旋时、空特征，采用层次分析法和加权综合评价法相结合，建立了指标体系，评估了浙东热带气旋灾害的综合风险。结果表明：①热带气旋的年、月、日变化均具明显特征，7~9月发生概率高于81%，中午~傍晚发生概率达54.2%；②浙东的中、南部遭台风袭击的概率更高，达85.4%，苍南、玉环两地为最；台风以上级别的热带气旋路径特征最为典型，西、西北走向的概率为100%；③综合风险评估结果表明，苍南、玉环、乐清、温岭、象山等地风险值较高，且与台风以上级别热带气旋的路径特征有明显的相关性。

As the important ecological barriers, Coastal shelter forest plays a key role in the aspect of soil and water conservation, wind prevention, sand-fixation and regional ecological security. Based on the 2005 Landsat TM images, by using the method of analytic hierarchy process and weighted comprehensive evaluation, five indices including frequency of tropical cyclone, population density, and density of arable land, real GDP and the per capita output value of the tertiary industry were selected to carry out the tropical cyclone risk assessment. Meanwhile, gradual easy constraint method was adopted to build a multi-objective programming model to make the spatial optimum allocation of coastal shelterbelt forest in Eastern Zhejiang province. The main conclusions are as follows: ① The area of coastal shelterbelt forest increased significantly with an increase of 5.4%, and the forest coverage rate increased form 48.70%to 51.30%; ②The forest structure would be more reasonable, and the proportion of coniferous, broadleaf forest, mixed forest and shrub changed from 64.77%、19.84%、8.36%、7.03%to 40.30%、37.34%、18.06%、4.30%, which solve the problem of large-proportion coniferous forest effectively; ③ The whole index of land-use service values increased substantially, the ecological, social and economic benefits increased by3.7%, 2.4% and 5.5%, respectively; ④ Optimization was realized according to different Risk area, different grade and different objective. For tropical cyclone risk, economic benefit is positive related apparently, social and economic benefits are just the opposite.

Land use is one of the most important factors to regional ecosystem function. The study was carried out at Honghu County, Hubei Province, P. R. China. Based on the TM images and visual interactive interpretation, land use status of Honghu County(1989-2010) were acquired for quantitatively analyzing the responses of the ecosystem service values to land use change with the association of land use structure and urbanization indices. The results showed that ecosystem service values of Honghu County(1989-2010) were mainly supported by wetlands, waters and farmlands, which increased from 3.677 billion Yuan in 1989 to 3.697 billion Yuan in 2010. The dominant ecosystem service functions of Honghu county were hydrological regulation, waste management, climate regulation and biodiversity maintenance with a contribution rate from 73.78% to 74.30%. Land use structure indices of Honghu County were all linearly associated with ecosystem service values, whose diversity, evenness and strength increase while decline in dominance. Urbanization has a positive effect on ecosystem service values, which shows the increase of ecosystem service values was related with urbanization. We could conclude that it is an effective way to maintain Honghu County ecosystem service functions by making rational ecological planning according to the status of ecosystem service functions.

In order to study the influence of hyperspectral image on the monitoring of water quality parameters,the author dealed with inversion research of water quality parameters in East Dongting Lake,based on Hyperion image.Through atmosphere correction and spectral feature analysis,then estimation model for chlorophyll a concentration and suspended solids were built respectively.Spatial distribution of target water quality parameters were acquired by software ENVI4.7 and ArcGIS.Results showed that the effect of Hyperion atmosphere correction was ideal,which was suitable for the inversion of chlorophyll a concentration and suspended solids.The spatial variation of chlorophyll a in the study area was not obvious,which tended to be an upward trend with the distance increase from lakeshore.While the spatial differentiation of suspended solids concentration was obvious,the overall presentation tended to be a gradual increase then a slow decline with the distance increase from lakeshore.The suspended solids content in the mouth and center of East Dongting Lake was higher than other places,and in total the south was higher than the north.The research revealed the potential of Hyperion image on the inland water monitoring,which contributed to the accurate estimation of water parameter monitoring.