VTOL UAVs for Forest Monitoring and Management via Remote Sensing: A Case Study in Khao Yai National Park, Thailand

The utilization of UAVs (Unmanned Aerial Vehicles) has experienced a remarkable upsurge in various industries, including forestry. Their capacity to expeditiously and effectively cover large tracts of land has resulted in their widespread adoption as a valuable forest management and monitoring tool. The versatility of UAVs extends to their capability to perform quick and efficient surveys of large areas, inventory of tree species, and monitoring of forest health. This research paper reports on the successful utilization of VTOL (Vertical Takeoff and Landing) UAV that was designed and built at the IESSD (Institute of Earth Science and Sustainable Development) located in the AAA (Asia Aviation Academy) at KMITL (King Mongkut’s Institute of Technology Ladkrabang) Prince of Chumphon Campus, Thailand. The VTOL UAV is employed for resource and environmental missions, as well as forest monitoring by using remote sensing technology. VTOL UAVs are used for aerial surveillance to conduct air photography, data collection, and processing for resource and environmental missions. This research paper presents a comprehensive analysis of the areas at risk of deforestation and forest encroachment in a particular region of Khao Yai National Park in Thailand, highlighting the potential for the resulting photographs to inform evidence-based decision-making and facilitate sustainable forest management practices. This study offers recommendations to develop VTOL UAVs remote sensing capabilities and mitigate deforestation and forest encroachment in Khao Yai National Park.

Forest Resources Management Information System for Forest Farms Based on Remote Sensing Images and Web GIS

This study was to estabIish the forest resources management information system for forest farms based on the B/S structural WebGIS with trial forest farm of Hunan Academy of Forestry as the research field, forest resources field survey da-ta, ETM＋ remote sensing data and basic geographical information data as research material through the extraction of forest resource data in the forest farm, require-ment analysis on the system function and the estabIishment of required software and hardware environment, with the alm to realize the management, query, editing, analysis, statistics and other functions of forest resources information to manage the forest resources.

Drone remote sensing for forestry research and practices

Drones of various shapes, sizes, and functionalities have emerged over the past few decades, and their civilian applications are becoming increasingly appealing. Flexible, low-cost, and high-resolution remote sensing systems that use drones as platforms are important for filling data gaps and supplementing the capabilities of crewed/manned aircraft and satellite remote sensing systems. Here, we refer to this growing remote sensing ini- tiative as drone remote sensing and explain its unique advantages in forestry research and practices. Furthermore, we summarize the various approaches of drone remote sensing to surveying forests, mapping canopy gaps, mea- suring forest canopy height, tracking forest wildfires, and supporting intensive forest management. The benefits of drone remote sensing include low material and operational costs, flexible control of spatial and temporal resolution, high-intensity data collection, and the absence of risk to crews. The current forestry applications of drone remote sensing are still at an experimental stage, but they are expected to expand rapidly. To better guide the development of drone remote sensing for sustainable forestry, it isimportant to systematically and continuously conduct comparative studies to determine the appropriate drone remote sensing technologies for various forest conditions and/or forestry applications.

Monitoring Forest Cover Change and Fragmentation Using Remote Sensing and Landscape Metrics in Nyungwe-Kibira Park

The objective of this study was to evaluate forest cover change and forest degradation in Nyungwe-Kibira Park, a natural reserve straddling Rwanda and Burundi from 1986 to 2015. Landsat TM, ETM+ and 8OLI images of 30 m spatial resolution were used as primary datasets. Geographic Information System (GIS) techniques were used for forest cover mapping and landscape metrics were calculated by using FRAGSTATS software. Classification and change analysis of forest cover type and landscape patterns analysis were carried out. In addition, to analyze the correlated external disturbances, the buffer zone of 5 Km was delineated outside the boundary of Nyungwe-Kibira Park. The results revealed that in among 5 land cover classes considered within the Park, the dominant one was dense forest class covering over 70% of the entire Park area while in the buffer zone cultivated and open land dominated at over 90% between the years 1986 and 2015. Change detection highlighted that within Nyungwe-Kibira forest, approximately 0.27% (4.97 Km2) of forest cover was cleared while 0.07% (1.22 Km2) was regenerated annually. In the buffer zone, the annual cleared forest cover was about 0.76% (13.02 Km2). The five landscape indices chosen at class level indicated a considerable fragmentation of forest inside the Park and the highest fragmentation in the buffer zone. Indeed, these results shed a bleak image over the future of the Nyungwe-Kibira forest that should be helpful for the policy-makers and managers of these natural parks to establish adequate policies to mitigate the forest loss and degradation by implementing quick and effective solutions.

The Use of Drones in Forestry

Recently, drones have found applicability in a variety of study fields, one of these being forestry, where an increasing interest is given to this segment of technology, especially due to the high-resolution data that can be collected flexibly in a short time and at a relatively low price. Also, drones have an important role in filling the gaps of common data collected using manned aircraft or satellite remote sensing, while having many advantages both in research and in various practical applications particularly in forestry as well as in land use in general. This paper aims to briefly describe the different approaches of applications of UAVs （Unmanned Aircraft Vehicles） in forestry, such as forest mapping, forest management planning, canopy height model creation or mapping forest gaps. These approaches have great potential in the near future applications and their quick implementation in a variety of situations is desirable for the sustainable management of forests.

Radiative transfer models(RTMs)for field phenotyping inversion of rice based on UAV hyperspectral remote sensing

The nondestructive and rapid acquisition of rice field phenotyping information is very important for the precision management of the rice growth process.In this research,the phenotyping information LAI(leaf area index),leaf chlorophyll content(C_(ab)),canopy water content(C_(w)),and dry matter content(C_(dm))of rice was inversed based on the hyperspectral remote sensing technology of an unmanned aerial vehicle(UAV).The improved Sobol global sensitivity analysis(GSA)method was used to analyze the input parameters of the PROSAIL model in the spectral band range of 400-1100 nm,which was obtained by hyperspectral remote sensing by the UAV.The results show that C_(ab) mainly affects the spectrum on 400-780 nm band,C_(dm) on 760-1000 nm band,C_(w) on 900-1100 nm band,and LAI on the entire band.The hyperspectral data of the 400-1100 nm band of the rice canopy were acquired by using the M600 UAV remote sensing platform,and the radiance calibration was converted to the canopy emission rate.In combination with the PROSAIL model,the particle swarm optimization algorithm was used to retrieve rice phenotyping information by constructing the cost function.The results showed the following:(1)an accuracy of R^(2)=0.833 and RMSE=0.0969,where RMSE denotes root-mean-square error,was obtained for C_(ab) retrieval;R^(2)=0.816 and RMSE=0.1012 for LAI inversion;R^(2)=0.793 and RMSE=0.1084 for C_(dm);and R^(2)=0.665 and RMSE=0.1325 for C_(w).The C_(w) inversion accuracy was not particularly high.(2)The same band will be affected by multiple parameters at the same time.(3)This study adopted the rice phenotyping information inversion method to expand the rice hyperspectral information acquisition field of a UAV based on the phenotypic information retrieval accuracy using a high level of field spectral radiometric accuracy.The inversion method featured a good mechanism,high universality,and easy implementation,which can provide a reference for nondestructive and rapid inversion of rice biochemical parameters using UAV hyperspectral remote sensing.

Development and evaluation of low-altitude remote sensing systems for crop production management

Precision agriculture accounts for within-field variability for targeted treatment rather than uniform treatment of an entire field.It is built on agricultural mechanization and state-of-the-art technologies of geographical information systems(GIS),global positioning systems(GPS)and remote sensing,and is used to monitor soil,crop growth,weed infestation,insects,diseases,and water status in farm fields to provide data and information to guide agricultural management practices.Precision agriculture began with mapping of crop fields at different scales to support agricultural planning and decision making.With the development of variable-rate technology,precision agriculture focuses more on tactical actions in controlling variable-rate seeding,fertilizer and pesticide application,and irrigation in real-time or within the crop season instead of mapping a field in one crop season to make decisions for the next crop season.With the development of aerial variable-rate systems,low-altitude airborne systems can provide high-resolution data for prescription variable-rate operations.Airborne systems for multispectral imaging using a number of imaging sensors(cameras)were developed.Unmanned aerial vehicles(UAVs)provide a unique platform for remote sensing of crop fields at slow speeds and low-altitudes,and they are efficient and more flexible than manned agricultural airplanes,which often cannot provide images at both low altitude and low speed for capture of high-quality images.UAVs are also more universal in their applicability than agricultural aircraft since the latter are used only in specific regions.This study presents the low-altitude remote sensing systems developed for detection of crop stress caused by multiple factors.UAVs,as a special platform,were discussed for crop sensing based on the researchers'studies.

联合运用多光谱和激光雷达技术构建的林分生物量估算模型

以广东省广州市从化石门国家森林公园为研究区域,选择4种不同林分类型(针阔混交林、阔叶林、针叶林、竹林),各林分类型选择3个20 m×20 m地块作为样方;结合激光雷达、多光谱图像、实测数据,构建多元非线性反演模型和多元线性回归模型,估算森林地上生物量,并选择最佳模型进行精度评价。结果表明:(1)依据多源数据建立的4种不同林分类型的多元非线性地上生物量反演模型的精度最高,针阔混交林样地地上生物量预测值为42.79 t·hm^(-2)、阔叶林样地地上生物量预测值为60.46 t·hm^(-2)、针叶林样地地上生物量预测值为32.99 t·hm^(-2)、竹林样地地上生物量预测值为1.92 t·hm^(-2)。(2)研究区中4种不同林分类型的多元非线性地上生物量反演模型的拟合精度,由大到小依次为竹林(决定系数为0.919)、阔叶林(决定系数为0.813)、针叶林(决定系数为0.786)、针阔混交林(决定系数为0.713),均符合精度要求。采用多光谱和激光雷达数据结合的方式,能够较精准地提取林分地上生物量信息,可准确估算针阔混交林、阔叶林、针叶林、竹林的地上生物量。

基于改进CNN的猕猴桃根区土壤含水率反演方法

为解决无人机遥感领域根据冠层光谱信息对猕猴桃果树根系土壤含水率(root soil water content, RSWC)进行反演时,现有算法对冠层图像信息分析不足的问题,该研究对传统卷积神经网络模型进行改进,提出一种复合视觉卷积回归神经网络(compound visual convolutional regression network, CVCRNet),该网络复合两种不同尺寸卷积层对图像数据进行卷积特征提取,并使用全连接层对卷积特征值进行降维,从而直接以多光谱图像为分析对象对RSWC进行反演,充分利用多光谱图像内所有数据,提升反演精度。研究采集徐香猕猴桃果树果实膨大期(5-9月)冠层多光谱信息和深度40 cm处的RSWC,把基于图像的CVCRNet网络反演方法与基于植被指数的传统反演方法进行对比,CVCRNet训练结果在验证集R^(2)为0.827,RMSE为0.787%,相较于传统方法在验证集R^(2)为0.759,RMSE为0.983%,反演结果相关性有了明显提升,准确率也有得到一定提高。结果表明,改进后的CNN网络能够作为冠层信息反演的重要工具,在冠层复杂的场景下达成良好的土壤数据反演效果。

无人机遥感技术在巡河场景中的应用研究

该文首先详细介绍无人机遥感技术在巡河场景应用的意义和重要性。在无人机技术方面,该文介绍其原理和基本应用,包括遥感数据的获取、处理和应用。在无人机技术方面,该文详细讲述其优势和应用,如无人机的机动性、高效性和低成本性等。研究结果表明,无人机遥感技术的深入研究和应用,可以更好地保护和管理河湖资源,为人类的生存和发展提供更好的保障,应该继续加强该领域的相关研究和实践。该研究对该领域的相关研究和实践具有重要的参考价值。

基于UNet-ResNet14^(*)半监督学习的无人机影像森林树种分类

无人机遥感在森林树种精细和高效分类制图中具有巨大的潜力。为了快速准确获取森林的优势树种分布信息,该研究探讨了半监督学习方法在树种分类方面的有效性。以福建省福州市、龙岩市和三明市的4个试验区为例,构建精简的ResNet18为主干的UNet树种分类模型(UNet-ResNet14^(*)),使用交叉熵和Dice系数的联合损失函数来优化模型参数,对比分析Self-training和Mean Teacher两种不同的半监督学习方法在无人机影像森林树种分类模型的泛化能力。结果表明,以ResNet14^(*)作为主干的分类模型与其他模型相比精度更高且预测速度更快,当联合损失函数权重值为0.5的情况下模型预测效果最好,总体精度达到了91.15%。经过Self-training的模型在木荷、马尾松、杉木3个样本充足的类别中精度均有所提升,总精度为91.08%,比原始模型略低,但在独立验证区的精度为88.50%,比原始模型高;Mean Teacher方法的总精度为88.56%,在独立验证区的精度为73.56%。因此,研究认为可以采用Self-trainin半监督方法结合UNet-ResNet14^(*)的方案快速得到试验区的树种组成信息。

基于无人机遥感的植被覆盖与管理因子计算

[目的]研究传统遥感的混合像元分解法在无人机遥感中的应用,提出小区域尺度的植被覆盖与管理因子(C因子)快速估算方法。[方法]借助无人机航拍获取南京江宁谷里地区土地利用情况的遥感影像,采用面向对象分类法提取各类型用地的地物盖度,基于混合像元分解的C因子模型计算了研究区C值,通过与已有研究成果的分析比较,评价该方法的准确性。[结果]对象分类结果(植被、裸地和非光合地物)总正确率都在95%以上,基于混合像元分解的C因子模型估算南京江宁谷里地区的林地、耕地和草地的C值分别为0.057,0.176,0.043,结果与已有研究成果接近。[结论]利用无人机遥感进行C因子估算是可行的,且相较于传统的径流小区实测法,更为高效便捷。

无人机影像湿地典型要素分类方法对比研究

针对厘米级的无人机影像,选取无锡某湿地公园作为研究对象,首先对湿地影像进行多尺度分割并利用ESP工具获取最佳分割参数,再进行特征选择,选取决策树(DT)、贝叶斯(Bayes)、随机森林(RF)等3种分类方法对湿地典型要素进行分类,并对比分析不同方法的分类结果及精度。对比结果表明,随机森林算法在湿地典型要素分类中精度最高,决策树和贝叶斯分类算法精度逊色于随机森林。从分类效率来看,随机森林算法耗时最长且涉及参数设置调整,而贝叶斯算法效率大幅领先决策树和随机森林,且该算法操作简单、无参数设置,易于在生产中应用。

基于无人机遥感的云南松林冠参数提取研究

以滇中地区典型天然云南松纯林为对象,结合无人机遥感影像获取和外业标准地调查,利用传统目视解译、多尺度分割和分水岭分割方法进行单木冠幅和郁闭度2个林冠参数的提取研究。结果表明,以地面实测数据为参考,3种单木分割方法的冠幅提取精度分别达91.48%、87.33%、84.04%;以目视解译结果为参考,多尺度分割方法提取郁闭度的精度达90.24%,且决定系数(R2)达0.8845;分水岭分割方法提取郁闭度的精度达87.40%,R2达0.7437。研究结果表明,基于无人机遥感影像的多尺度分割方法能很好地提取云南松纯林的林冠参数信息,且提取精度满足森林资源调查的要求,可有效提高森林资源调查的效率。

江苏省河湖动态遥感监测探索与实践

经过多年实践,卫星遥感监测技术已成为江苏省河湖监管的重要手段。2023年起,江苏省以“两河两湖”为试点开展每日动态遥感监测,利用高分卫星和无人机智能巡检技术,对存量问题进行全面清查建库,对增量问题建立“即时发现、前置处置、动态清零”的河湖违法占用动态监测处置体系,实现了河湖违法行为“早发现、早制止、早处置”,有效遏制了大型河湖违法事件的发生,为河湖管理和执法提供了即时解决方案,对其他河湖空间管控具有参考意义。

森林资源调查监测的关键技术探究

森林资源是重要的自然资源之一,对森林资源进行调查和监测对保护生态环境、维护生态平衡及促进可持续发展具有重要意义。随着科技的进步,越来越多的先进技术被应用于森林资源调查监测中,提高了调查监测的精度和效率。文章对森林资源调查监测的关键技术进行探究,为相关领域的研究和实践提供参考。

水利工程管理保护范围遥感划定方法研究与应用

针对小型水利工程管理保护范围划定工作无现行规范标准指导,也无既定可适用的流程问题,提出一种基于无人机高分影像的水利工程管理范围划定流程与方法。对无人机高分影像进行分类和信息提取,结合GPS勘测及空间数据输入,按照提出的判别方法、划定流程进行水利工程管理保护范围的划定,可明确具体的划定范围值。将划定方法在北京市通州区水利工程管理保护范围划定工作中予以应用验证,验证结果表明:基于无人机高分影像的水利工程管理范围的划定方法不仅可用于小型水利工程管理保护范围的划定,也可为大中型水利工程的具体划定提供参考,对加强水利工程的实施与管理具有重要意义。

Land cover change and carbon stores in a tropical montane cloud forest in the Sierra Madre Oriental,Mexico

Tropical montane cloud forest is one of the ecosystems with the highest biomass worldwide, representing an important carbon store. Globally its deforestation index is –1.1%, but in Mexico it is higher than –3%. Carbon estimates are scarce globally, particularly in Mexico. The objective of this study was to simulate future land-cover scenarios for the Sierra Madre Oriental in Mexico, by analyzing past forest cover changes. Another objective was to estimate stored carbon in the two study areas. These objectives involve the generation of information that could be useful inputs to anti-deforestation public policy such as the REDD+ strategy. Remote sensing was used to measure land cover change and estimate carbon stocks. Satellite images from 2015, 2000 and 1986 were used, and Dinamica EGO freeware generatedmodels of future projections. Between 1986 and 2015, 5171 ha of forest were converted to pasture. The annual deforestation rates were –1.5% for Tlanchinol and –1.3% for the San Bartolo Tutotepec sites. Distance to roads and marginalization were highly correlated with deforestation. By 2030, an estimated 3608 ha of forest in these sites will have been converted to pasture. Stored carbon was estimated at 16.35 Mg C ha-1 for the Tlanchinol site and 12.7 Mg C ha-1 for the San Bartolo site. In the Sierra Madre Oriental deforestation due to land cover change(–1.4%) is higher than levels reported worldwide. Besides having high values of stored carbon(14.5 Mg C ha-1), these forests have high biodiversity. The models' outputs show that the deforestation process will continue if action is not taken to avoid the expansion of livestock pasturing. This can be done by paying incentives for forest conservation to the owners of the land. The results suggest that REDD+ is currently the most viable strategy for reducing deforestation rates in tropical montane cloud forests in Sierra Madre Oriental.

Drone Applications for Supporting Disaster Management

Introduction: Besides the military and commercial applications of drones, there is no doubt in their efficiency in case of supporting emergency management. This paper evaluates some experiences and describes some initiatives using drones to support disaster management. Method: This paper focuses mainly on operational and tactical drone application in disaster management using a time-scaled separation of the application, like pre-disaster activity, activity immediately after the occurrence of a disaster and the activity after the primary disaster elimination. Paper faces to 5 disasters, like nuclear accidents, dangerous material releases, floods, earthquakes and forest fires. Author gathered international examples and used own experiences in this field. Results and discussion: An earthquake is a rapid escalating disaster, where, many times, there is no other way for a rapid damage assessment than aerial reconnaissance. For special rescue teams, the drone application can help much in a rapid location selection, where enough place remained to survive for victims. Floods are typical for a slow onset disaster. In contrast, managing floods is a very complex and difficult task. It requires continuous monitoring of dykes, flooded and threatened areas. Drone can help managers largely keeping an area under observation. Forest fires are disasters, where the tactical application of drone is already well developed. Drone can be used for fire detection, intervention monitoring and also for post-fire monitoring. In case of nuclear accident or hazardous material leakage drone is also a very effective or can be the only one tool for supporting disaster management.

Spectroscopic detection of forest diseases:a review(1970–2020)

Sustainable forest management is essential to confront the detrimental impacts of diseases on forest ecosystems.This review highlights the potential of vegetation spectroscopy in improving the feasibility of assessing forest disturbances induced by diseases in a timely and cost-effective manner.The basic concepts of vegetation spectroscopy and its application in phytopathology are first outlined then the literature on the topic is discussed.Using several optical sensors from leaf to landscape-level,a number of forest diseases characterized by variable pathogenic processes have been detected,identified and quantified in many country sites worldwide.Overall,these reviewed studies have pointed out the green and red regions of the visible spectrum,the red-edge and the early near-infrared as the spectral regions most sensitive to the disease development as they are mostly related to chlorophyll changes and symptom development.Late disease conditions particularly affect the shortwave-infrared region,mostly related to water content.This review also highlights some major issues to be addressed such as the need to explore other major forest diseases and geographic areas,to further develop hyperspectral sensors for early detection and discrimination of forest disturbances,to improve devices for remote sensing,to implement longterm monitoring,and to advance algorithms for exploitation of spectral data.Achieving of these goals will enhance the capability of vegetation spectroscopy in early detection of forest stress and in managing forest diseases.