Forest Health Assessment Research Articles

Modeling forest canopy structure and developing a stand health index using satellite remote sensing

Biotic and abiotic disturbances modify tree structure and degrade stand health. Accurate geospatial data on stand structure is important for monitoring tree growth, forest health, progression and severity of diseases and pests, and estimating resilience to climate stress. The live crown ratio (LCR) of trees serves as a key health indicator but has been understudied at the landscape level using remote sensing data. This study generated the leaf area index (LAI) and a novel spatial layer of LCR at site and landscape scales using a combination of satellite data and ground observations. We conducted field surveys to collect plot-level (10 m × 10 m) data in four eastern white pine (EWP; Pinus strobus L.)-dominated sites in the state of Maine, USA. The plot-level data were used to develop regression models for LAI and LCR estimation using microwave (Sentinel-1) and optical (Sentinel-2) remote sensing data and applying the Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms. The RF model showed higher prediction accuracy than the SVM model at the site level. Moreover, the prediction accuracy at the site and landscape levels were comparable for LAI (R2 > 0.76) and LCR (R2 > 0.71) using the RF model. Furthermore, the predicted LAI and LCR were integrated with canopy height and stand density to develop a novel health index map for EWP. The resulting health index map successfully delineated patches representing various health categories. Forestry practitioners and decision-makers can use the derived health index map and intermediate spatial data layers (LAI and LCR) to guide stand management. The developed framework can potentially be applied to other coniferous and broadleaved species for remote sensing-based LCR estimation and forest health assessment upon further studies and verification.

Spatiotemporal forest health assessment for ecosystem management of Sariska National Park (India) under regional climatic inconstancies

Spatiotemporal forest health assessment for ecosystem management of Sariska National Park (India) under regional climatic inconstancies

Formulation of Quality Assurance Social Indicators in Community Forest Health Assessment

Community forests provide benefits to their functions and roles in meeting community needs and environmental sustainability based on economic, social, and ecological perspectives. Social indicators play this role in the sustainability of community forest health. This study aims to obtain a weighted value (quality assurance) of social indicators in assessing the health of community forests. The stages of research carried out included, among others, conducting interviews with questionnaire instruments with respondents, determining the priority scale with the Analytic Hierarchy Process (AHP) method, and determining eigenvalues with the Analytic Networking Process (ANP) method. Based on the results of the study, four social indicators can describe the health condition of community forests: education indicators, employment indicators, participation indicators, and institutional indicators. These indicators have the same level of importance, where the eigenvalues obtained do not have significant differences. It means that community forest farmers have realized that social indicators can support environmental sustainability in aspects of forest health. Thus, the weighted values obtained by social indicators from the highest to the lowest are indicator participation (0.29), education indicator (0.27), institutional indicator (0.23), and employment indicator (0.21). The formation of farmer groups needs to be done to improve local institutions. Therefore, they can support community forest management regulations

EVALUATION OF THE BARK BEETLE GREEN ATTACK DETECTABILITY IN SPRUCE FOREST FROM MULTITEMPORAL MULTISPECTRAL UAV IMAGERY

Abstract. Forests have always been a major concern for public authorities given their significant role as a resource for timber production, as well as for climate regulation. The infestation of bark beetles poses a challenging problem in forests, causing tree mortality, reduced timber quality, and ecosystem disruption. Multispectral imagery (MS) captured from unmanned aerial vehicles (UAVs) are increasingly employed for forest health assessment and have been extensively used to map the tree mortality caused by bark beetles. However, its utilisation for the identification of the initial stage of infestation known as "green attack" when infested trees do not display any visible symptoms yet, is still uncertain. This study is innovative in the ways it works with so far rare dense and comprehensive time series of MS UAV imagery starting in the initial month prior to the infestation and before the development of the Bark Beetle (BB) infestation visual symptoms, aiming to evaluate the detectability of bark beetle (Ips typographus L.) green attack. The study area predominantly covered by Norway spruce (Picea abies (L.) Karst.) is located in the Krkonoše Mountains National Park, in the Czech Republic. From May to August 2022, a total of nine MS UAV datasets were acquired with a DJI Phantom 4 MS sensor. The research question is whether infested trees exhibit significantly different changes in the spectra compared to healthy trees at an early stage. The detectability of green attacks was statistically investigated along with the underlying factors (flowering, growth of new shoots) that can affect the accuracy of detection. The results showed a distinct reduction in tree vitality of the infested trees in the late summer season and later stages of infestations. Based on our findings, we conclude that the proposed methods using UAV MS images can be employed to map local infestations and evaluate the tree vitality throughout the season, but the early detection of the green attack stage (in the absence of visible symptoms at the crown level) of Bark Beetle infestation from UAV MS data remains unfeasible. The precision of the detection using statistical methods can be further investigated using a time series of UAV hyperspectral images that offer a higher spectral resolution.

The Influence of Community Participation on Health Trends in the Mangrove Forest of Labuhan Maringgai District, East Lampung Regency

The health trend of mangrove forests is influenced by community participation around the forest. Communities have a role in managing mangrove forests. The purpose of this study was to determine the effect of community participation on the health trend of mangrove forests. The stages of this research are: Assessment of forest health based on ecological indicators and data analysis using the FHM method; collection of participation data with questionnaires to the public; and data analysis using the multiple linear regression method. Based on the results of the study, the final value of forest health at CL 1 was 4.11 (bad), CL 2 was 6.95 (good), CL 3 was 6.69 (good), and CL 4 was 7.4 (good). The average value for forest health is 6.29 in the moderate category. The significant values obtained are.023* (preparation),.321 (implementation), and.851 (evaluation). The planning aspect has an influence on the health condition of mangrove forests. Community participation affects the condition of forest health from an ecological perspective. So the role of the community is very important to support the preservation of mangrove forests.

Assessment of Forest Ecosystem Variations in the Lancang-Mekong Region by Remote Sensing from 2010 to 2020.

Five countries in the Lancang-Mekong region, including Myanmar, Laos, Thailand, Cambodia, and Vietnam, are facing the threat of deforestation, despite having a high level of forest coverage. Quantitatively assessing the forest ecosystem status and its variations based on remote sensing products for vegetation parameters is a crucial prerequisite for the ongoing phase of our future project. In this study, we analyzed forest health in the year 2020 using four vegetation indicators: forest coverage index (FCI), leaf area index (LAI), fraction of green vegetation cover (FVC), and gross primary productivity (GPP). Additionally, we introduced an ecosystem quality index (EQI) to assess the quality of forest health. To understand the long-term trends in the vegetation indicators and EQI, we also performed a linear regression analysis from 2010 to 2020. The results revealed that Laos ranked as the top-performing country for forest ecosystem status in the Lancang-Mekong region in 2020. However, the long-term trend analysis results showed that Cambodia experienced the most significant decline across all indicators, while Vietnam and Thailand demonstrated varying degrees of improvement. This study provides a quality assessment of forest health and its variations in the Lancang-Mekong region, which is crucial for implementing effective conservation strategies.

Health assessment of natural larch forest in arxan guided by forestry remote sensing integrated with canopy feature analysis

This work aims to propose a more accurate assessment method for forest health in natural larch pine forests of the Arxan by integrating remote sensing technology with tree crown feature analysis. Currently, forest health assessment of natural Larch pine forests relies mainly on ground surveys, and there is a gap in the application of remote sensing technology in this field. This work introduces deep learning technology and proposes a spectral-Gabor space discrimination and classification model to analyze multi-spectral remote sensing image features. Additionally, quantitative indicators, such as tree crown features, are incorporated into the forest health assessment system. The health status of natural Larch pine forests is evaluated using forest resource survey data. The results show that the health levels of natural Larch pine forests in different areas vary and are closely related to factors such as canopy density, community structure, age group, and slope. Both quantitative and qualitative indicators are used in the analysis. The introduction of this innovative method enhances the accuracy and efficiency of forest health assessment, providing significant support for forest protection and management. In addition, the classification accuracy of the health assessment model suggested that the maximum statistical values of average classification accuracy, average classification effectiveness, overall classification accuracy, and Kappa were 74.19%, 61.91%, 63.18%, and 57.63%, respectively. This demonstrates that the model can accurately identify the health status of natural larch forests. This work can effectively assess the health status of the natural larch forest in the Arxan and provide relevant suggestions based on the assessment results to offer a reference for the sustainable development of the forest system.

Study on Forest Health Assessment of eastern Qilian Mountains in Qinghai Province

A total of 17 indexes including forest structure, productivity, stability and soil quality were established in the eastern Qilian Mountains. The weights of the indexes were determined by side analysis and entropy weight method. The health status of forest resources in eastern Qilian Mountains of Qinghai Province was evaluated by comprehensive weighting method. The results showed that the distribution and health level of forest resources in the eastern Qilian Mountains of Qinghai province were sub-health.

Forest Health Assessment in Four Jordanian Reserves Located in Semi-Arid Environments

Healthy forests are essential to human life because they provide food, energy, and other benefits including carbon sequestration. The objective of this study was to assess the forests health status in Mediterranean ecosystems, specifically, arid to semi-arid. Four forest reserves directed by Royal Society for the Conservation of Nature, Jordan were evaluated. Plant health indicators [(gas exchange (photosynthesis, stomatal conductance, transpiration), chlorophyll, middy stem water potential (Ψsmd), relative water content], regeneration, lichens, plant disease, as well as soil variables (respiration CO2-C, electrical conductivity (EC), pH, microorganisms’ abundance) were measured. The Ψsmd values in those semi-arid/arid ecosystems were within the normal ranges (−0.3 to −1.3 MPa) in spring but was under extreme water stress (−1.6 to −5.3 MPa) in summer in three reserves. Similarly, gas exchange variables reduced by 25%–90% in summer (compared to spring) across the studied forests. Although the regeneration (seedling per 1000 m2) was higher than 100 in two forest (Ajloun and Dibbeen), the number of seedlings in hiking sites was extremely low in both forests. Soil health indicators reveled that soil respiration CO2-C were higher than 25 mg kg−1 in two forests [Ajloun, Dibbeen, (except hiking zone)]. The mean soil saprophytes (number g−1) ranged from 86 to 377 across the forests reserves. In addition, the mean arbuscular mycorrhizal fungi (spores 100g−1 soil) was between 350 and 877. Soil EC was consistently optimal (less than 0.5 dS m−1) and pH was slightly basic (7.5–8.3) across the reserves. The results revealed that the fluctuation of rainfall and anthropogenic pressures (grazing, hiking) led to partial forest degradation. When forests (Dana Biosphere Reserve) received 81 mm annual precipitation, Ψsmd values in Juniperus phoenicea at summer ranged from −4.4 to −5.3 MPa, regeneration and lichens were less than 20 per 1000 m2, and several trees were dead after infected with soil and air borne pathogens including wilt diseases and die back. Intensive hiking activities (Dibbeen forests, tourism area) and heavy grazing (Yarmouk frosts) reduced regeneration, lichens and soil respiration. Interestingly, the native species had better water relations (RWC, Ψsmd) and gas exchange performance than the introduced species. Overall, it is better to grow native species, and exclude anthropogenic pressure on the territory of introduced species. The conservation programs must persist to sustain several native historical forest trees including Juniperus phoenicea (>600 year old), Quercus ithaburensis (>500 year old), and Pinus halepensis (>100 year old) at Mediterranean semi-arid forests.

Preface

International Conference on Geospatial Science for Digital Earth Observation (GSDEO 2021) The international conference on “Geospatial Science for Digital Earth Observation” (GSDEO) 2021 was successfully held on a virtual platform of Zoom on March 26th and 27th, 2021. The conference was jointly organized by the Indian Society of Remote Sensing (ISRS), Kolkata chapter, and the Department of Geography, School of Basic and Applied Sciences, Adamas University. Due to the non-predictable behaviour of the COVID-19 second wave, which imposed restrictions on organizing offline events, the GSDEO (2021) organizing committee decided to organize the conference online, instead of postponing the event.Remotely sensed data and geographic information systems have been increasingly used together for a vast range of applications, which include land use/land cover mapping, water resource management, weather forecasting, environmental monitoring, agriculture, disaster management, etc. Currently, intensive research is being carried out using remotely sensed data on the geoinformatics platform. New developments have led to dynamic advances in recent years. The objective of the international conference on Geospatial Science for Digital Earth Observation (GSDEO 2021) was to bring the scientists, academicians, and researchers, in the field of geo-environmental sciences on a common platform to exchange ideas and their recent findings related to the latest advances and applications of geospatial science. The call for papers received an enthusiastic response from the academic community, and over 100+ participants from 50+ colleges, universities, and institutions participated in the conference. In total 50+ research papers had been presented through the virtual Zoom conference platform in GSDEO 2021.The conference witnessed the presentation of research papers from diverse applied fields of geospatial sciences, which include the application of geoinformatics in geomorphology, hydrology, urban science, land use planning, climate, and environmental studies. There were four sessions namely, TS 1: Geomorphology and Hydrology, TS 2: Urban Science, TS 3: Social Sustainability and Land Use Planning, and TS 4: Climate and Environment. Each session was further subdivided, into two parts, namely Technical Session 1-A and 1-B. Each sub-session had been designed with one keynote speech and 5 oral presentations. Oral sessions were organized in two parts and offered through live and pre-recorded components based on the preference of the presenters. The presentation session was followed by a live Q&A session. The session chairs moderated the discussions. Similarly, poster sessions were organized in three parts and offered e-poster, live, and pre-recorded components. The best presenter of each sub-session received the best paper award.Dr. Prithvish Nag, Ex-Director of NATMO & Ex Surveyor General of India delivered the inaugural speech, and Dr. P. Chakrabarti, Former Chief Scientist of the DST&B, Govt. of West Bengal delivered a special lecture after the inaugural session. Eight eminent keynote speakers, Prof. S.P. Agarwal from the Indian Institute of Remote Sensing, Prof. Ashis Kumar Paul from Vidyasagar University, Prof. Soumya Kanti Ghosh from the Indian Institute of Technology, Kharagpur, Prof. L. N. Satpati from the University of Calcutta, Prof. R.B. Singh from the University of Delhi, Dr. A.K. Raha, IFS (Retd), Prof. Gerald Mills from the University College Dublin and Prof. Sugata Hazra from Jadavpur University enriched the knowledge of participants in the field of geoinformatics by their informative lectures. The presentations and discussions widely covered the various spectrums of geoinformatics and its application in monitoring natural resources like vegetation mapping, agricultural resource monitoring, forest health assessment, water, and ocean resource management, disaster management, land resource management, water and climate studies, drought vulnerability assessment, groundwater quality monitoring, accretion mapping and the use of geospatial science in studying morphological, hydrological, and other biophysical characteristics of a region etc. Application of geoinformatics in predicting urban expansion, urban climate, disaster management, healthcare accessibility, anthropogenic resource monitoring, spatial-interaction mapping, and, sustainable regional planning were well-discussed topics of the conference.List of Committees, photos are available in the pdf.

A dataset of ecological factors in the distribution area of Tianshan spruce in Yili region in 2014

Tianshan spruce (Picea schrenkiana var. tianschanica ) is an important tree for water conservation, windbreak and sand fixation in the mountains of Xinjiang, and plays an important role in the formation and maintenance of forest ecosystem in Xinjiang. Ecological factors such as temperature, precipitation, soil and topography are important data bases for studying forest ecosystem. Based on multi-source data such as remote sensing image, DEM, meteorological data and soil data in Yili region of Xinjiang, we used multi-scale segmentation, nearest neighbor classification and spatial analysis to generate a dataset of ecological factors in the distribution area of Tianshan spruce in Yili region in 2014, covering six ecological factors, namely temperature, precipitation, sunshine duration, slope, slope direction and soil type. In this dataset, strict procedures of data quality control were carried out by TTA Mask accuracy verification and other methods to ensure the accuracy and reliability of the data. The dataset can provide data support for forest ecosystem health assessment.

PENILAIAN KESEHATAN TAHURA BANTEN PADA BLOK KOLEKSI TUMBUHAN DAN ATAU SATWA

Banten Forest Park is one of the conservation areas with an area of 1,595.9 ha whose management is divided into five large blocks, one of which is a collection block of plants and or animals. Tahura Banten has the potential to have a fairly high economic and ecological value, it needs an important role in knowing the current forest conditions that allow for changes and trends in it by conducting forest health assessments. Tahura health can be assessed through biodiversity in a Region of Tahura identified as the criteria for the sustainability of forest ecosystems. The purpose of this study is to conduct a health assessment of Tahura Banten on the Plant and or Animal Collection Block which is intended to assess and report the current health condition (status) of Tahura based on ecological indicators of forest health. This research was conducted in November to December 2021 at the Plant and or Animal Collection Block in Tahura Banten. Data collection is carried out by creating a tahura health measurement plot using the Forest Health Monitoring method. Analysis of ecological indicators used in this study in the form of biodiversity, vitality (header conditions) and productivity. The results of data processing resulted in the value of the diversity status of tree types (H'), header conditions (VCRs), and productivity (LBDS) in 10 (ten) plot clusters. Tahura's health category is obtained based on the final value. The results of the data obtained can be categorized as 30% good, 40% moderate and 30% bad health conditions. The results of this study showed that the value of Tahura Health is in a moderate condition with a threshold value of 4,187-5,692 of 40%, which is able to define Tahura Banten in the Plant and or Animal Collection Block has a fairly healthy condition.

Assessment of Pinus sylvestris L. forests state near Krasnoyarsk (case study of the Karaul'noe forestry)

The research was conducted in Pinus sylvestris L. stands near Krasnoyarsk (subtaiga zone). The aim of the study is to assess the current state of recreational pine forests. Research methods included detailed forest pathological examination on six research plots, assessment of recreational disturbance (digression stage) by the transect method, integral assessment of forest health, graphical and correlation analysis. Recreational disturbance of the studied pine forests corresponds to I-IV stages of digression. From 1 to 40% of ground on the research plots has been trampled down. The vitality spectra of forest stands are characterized by right-sided asymmetry; the proportion of weakened trees is high in severely disturbed pine stands. With an increase in the recreational stress, the state of pine forests worsens significantly, and the proportion of dead trees increases. The absence of a significant relationship between the average health index and the stock of dead wood indicates that trees mortality cannot be used as an indicator when assessing the current state of recreational pine forests, which is explained by wood debris removal. The relative growing stock of healthy trees (with no signs of weakening), however, is an informative indicator.

Trend Analysis of Mangrove Forest Health in East Lampung Regency as Community Preparedness for Natural Disasters

The condition of mangrove forests in East Lampung Regency is currently experiencing degradation, resulting in the function of mangrove forests being reduced, especially in preventing natural disasters. This research is the third-year measurement of the activities of monitoring the health of mangrove forests in East Lampung Regency. The purpose of this study was to determine the value and trend category of the health condition of the mangrove forest in East Lampung Regency. The stages of this research, namely: measuring the health trend of mangrove forests in the six FHM clusters that have been built and analysing the data using the Forest Health Assessment Information System software. The results of this study indicate that the trend value of the health condition of mangrove forests in each location is Kuala Penet (8.64-20.90) with a good category because CL-1 and CL-2 are constant, Margasari (2.52-5.57) in the bad category because CL-3 and CL-4 decreased, and Purworejo (5.58-8.63) in the moderate category because CL-5 and CL-6 increased. Thus, the average trend value of the health condition of the mangrove forest in East Lampung Regency (7.70) is in the moderate category. This can provide information to the community on preparedness in dealing with natural disasters.

A fuzzy multi-criteria decision-making approach for the assessment of forest health applying hyper spectral imageries: A case study from Ramsar forest, North of Iran

The hyperspectral images have so far been widely utilized in monitoring and detecting the changes in a broad range of environmentally related matters. The hyperspectral image analysis yields maps that show spatial dispersion of physical and ecological characteristics of the terrain. Within the scope of the current study, an integrated Fuzzy-MCDM in a Geographic Information Systems (GIS) platform was used to map the health condition of Ramsar forest. Spectral indices can provide different methods for identifying vegetation coverings. For forest health analysis, spectral indices such as NDWI, CRI1, PSRI, PRI, and NDVI were used to infer the causative factors of forest health. The findings highlight the suitability of the used methodology in identifying potential forest statuses, where forest health protection measures can be taken in advance. The results also suggest that the southern and the western aspects of the study area are of “very low” to “low” forest health. Furthermore, the results indicate a high potentiality for applying the spatial MCDM techniques as an effective tool for the forest health investigation.

Forestry Value for Health Status: An Ecological Review

Community forest (HKm) is a social forestry scheme located in the country’s forests by empowering people around forest areas. Management carried out by communities around forests must enhance ecological aspects with sustainable forest management Forest health can be an option to support sustainable forest management. Through forest health assessment, one can find out the status, changes, and tendencies experienced by a forest. This research aims to find out the value of forest health status in community forest management Gapoktan Margo Rukun and Harapan Sentosa, KPHL Batu Tegi, Tanggamus Regency, Lampung. The stages of the implementation of this study are the determination of plot clusters, the collection of data on forest health indicators (productivity, biodiversity, vitality, and quality of the site), and the final assessment of forest health. This study obtained the value of forest health with a range of values of 3.30 – 8.75. The average health status value of the community is 5.95, which is in the moderate category. Thus, the value of health status community orangutans managed in the condition of medium forest health.

What Is the Effect of Quantitative Inversion of Photosynthetic Pigment Content in Populus euphratica Oliv. Individual Tree Canopy Based on Multispectral UAV Images?

It is highly necessary to apply unmanned aerial vehicle (UAV) remote sensing technology to forest health assessment. To prove the feasibility of quantitative inversion of photosynthetic pigment content (PPC) in Populus euphratica Oliv. individual tree canopy (PeITC) by using multispectral UAV images, in this study, Parrot Sequoia+ multispectral UAV system was manipulated to collect the images of Populus euphratica (Populus euphratica Oliv.) sample plots in Daliyabuyi Oasis from 2019 to 2020, and the canopy PPCs of five Populus euphratica sample trees per plot were determined in six plots. The Populus euphratica crown regions were extracted by grey wolf optimizer-OTSU (GWO-OTSU) multithreshold segmentation algorithm from the normalized difference vegetation index (NDVI) images of Populus euphratica sample plots obtained after preprocessing, and the PeITCs were segmented by multiresolution segmentation algorithm. The mean values of 27 spectral indices in the PeITCs were calculated in each plot, and the optimal model was constructed for quantitative estimation of the PPCs in the PeITCs, then the inversion results were compared and verified based on GF-6 and ZY1-02D satellite imageries respectively. The results were as follows. (1) The average value of canopy chlorophyll content (Chl) was 2.007 mg/g, the mean value of canopy carotenoid content (Car) was 0.703 mg/g. The coefficient of variation (C.V) of both were basically the same and they were both of strong variability. The measured PPCs of the PeITCs in Daliyabuyi Oasis was generally low. The average contents of chlorophyll and carotenoid in PeITC in June were more than twice those in August, while the mean ratio between them was significantly lower in June than in August. The measured PPCs had no obvious spatial distribution law. However, that could prove the rationality of sample selection in this study. (2) NDVI had the best effect of highlighting vegetation among all quadrats in the study area. Based on the GWO-OTSU multithreshold segmentation method, the canopy area of Populus euphratica could be quickly and effectively extracted from the quadrat NDVI map. The best segmentation effect of PeITCs was obtained based on a multiresolution segmentation method when the segmentation scale was 120, the shape index was 0.7, and the compactness index was 0.5. Compared with manual vectorization method of visual interpretation, the root mean square error (RMSE) and Pearson correlation coefficient (R) values of the mean NDVI values in PeITCs obtained by these two methods were 0.038 and 0.951. (3) Only 12 of the 27 spectral indices were significantly correlated with Chl and Car at the significance level of 0.02. Characteristics of the calibration set and validation set were basically consistent with those of the entire set. The classification and regression tree-decision tree (CART-DT) model performed best in the estimation of the PPCs in the PeITCs, in which, when estimating the Car, the calibration coefficient of determination (R2C) was 0.843, the calibration root mean square error (RMSEC) was 0.084, the calibration residual prediction deviation (RPDC) was 2.525, the validation coefficient of determination (R2V) was 0.670, the validation root mean square error (RMSEV) was 0.251, the validation residual prediction deviation (RPDV) was 1.741. (4) Qualitative comparison of spectral reflectance and NDVI values between GF-6 multispectral imagery and Parrot Sequoia+ multispectral image on the 172 PeITCs can show the reliability of Parrot Sequoia+ multispectral image. The comparison results of five PeITCs relative health degree judged by field vision judgment, measured SPAD value, predicted value of Chl (Chlpre), the red edge value calculated by ZY1-02D (ZY1-02Dred edge) and the Carotenoid Reflection Index 2 (CRI2) value calculated by ZY1-02D (ZY1-02DCRI2) can further prove the scientificity of inversion results to a certain extent. These results indicate that multispectral UAV images can be applied for quantitative inversion of PPC in PeITC, which could provide an indicator for the construction of a Populus euphratica individual tree health evaluation indicator system based on UAV remote sensing technology in the next step.

Assessment of Canopy Health with Drone-Based Orthoimagery in a Southern Appalachian Red Spruce Forest

Consumer-grade drone-produced digital orthoimagery is a valuable tool for conservation management and enables the low-cost monitoring of remote ecosystems. This study demonstrates the applicability of RGB orthoimagery for the assessment of forest health at the scale of individual trees in a 46-hectare plot of rare southern Appalachian red spruce forest on Whitetop Mountain, Virginia. We used photogrammetric Structure from Motion software Pix4Dmapper with drone-collected imagery to generate a mosaic for point cloud reconstruction and orthoimagery of the plot. Using 3-band RBG digital orthoimagery, we visually classified 9402 red spruce individuals, finding 8700 healthy (92.5%), 251 declining/dying (2.6%), and 451 dead (4.8%). We mapped individual spruce trees in each class and produced kernel density maps of health classes (live, dead, and dying). Our approach provided a nearly gap-free assessment of the red spruce canopy in our study site, versus a much more time-intensive field survey. Our maps provided useful information on stand mortality patterns and canopy gaps that could be used by managers to identify optimal locations for selective thinning to facilitate understory sapling regeneration. This approach, dependent mainly on an off-the-shelf drone system and visual interpretation of orthoimagery, could be applied by land managers to measure forest health in other spruce, or possibly spruce-fir, communities in the Appalachians. Our study highlights the usefulness of drone-produced orthoimagery for conservation monitoring, presenting a valid and accessible protocol for the monitoring and assessment of forest health in remote spruce, and possibly other conifer, populations. Adoption of drone-based monitoring may be especially useful in light of climate change and the possible displacement of southern Appalachian red spruce (and spruce-fir) ecosystems by the upslope migration of deciduous trees.

Health Assessment Of Conservation Forest-Based On Biodiversity Indicator Tree Types (Case Study at Elephant Respont Unit Margahayu Way Kambas National Park)

Conservation forest health assessment based on indicators of tree diversity is needed because it is sensitive to changes, indicators of ecological systems, spatial, temporal, and trophic heterogeneity. This study aims to determine the variety of tree species in Margahayu Elephant Response Unit Way Kambas National Park as an indicator of forest health assessment. The research was conducted using the Forest Health Monitoring method. The area of Margahayu Elephant Response Unit in Way Kambas National Park is 35.2 ha. One cluster plot has an area of 0.4 ha, so the number of research clusters created in this study is 5 clusters with a total area of 2 ha of observational collections. The results showed that the final value of forest health status with indicators of biodiversity (tree species diversity) in Margahayu Elephant Response Unit Way Kambas National Park was in the excellent category of 20% in cluster plot 2, while 60% in cluster plots (3, 4, and 5), and 20% poor in cluster plot 1, thus indicating that the forest in the Margahayu Elephant Response Unit Way Kambas National Park is in a reasonably healthy (stable) condition with a moderate category.

TINGKAT KESEHATAN HUTAN MANGROVE DALAM HUBUNGANNYA DENGAN PERUBAHAN IKLIM (Studi Kasus Mangrove Pesisir Timur Kabupaten Lampung Timur)

The condition of mangrove forests on the east coast of East Lampung Regency is currently experiencing degradation and has suffered a lot of damage due to various causes and problems it faces. This condition causes the role and benefits and functions of mangrove forests to decrease, especially in climate change. Therefore, to determine the condition of the mangrove forest, periodic monitoring and assessment of mangrove forest health is required. The purpose of this study was to determine the importance of mangrove forest health in climate change. To achieve this, the stages include: determining the number of plot clusters to be made, making cluster plots, measuring the health of mangrove forests based on ecological indicators of mangrove forest health, processing and analyzing mangrove forest health data using SIPUT (Assessment Information System) software. Forest Health), and categorization and health assessment of mangrove forests. The results of this study indicate that the condition of mangrove forests on the East coast of East Lampung Regency in general has a forest health condition in the medium category. So, knowing the health condition of mangrove forests will affect climate change. Forest health makes mangrove plants grow with good physiological processes so that they can absorb carbon dioxide in the atmosphere optimally. These conditions will make the environment better.