Forest Health Monitoring and Forest Inventory Analysis Programs Monitor Climate Change Effects in Forest Ecosystems

2001 ◽  
Vol 7 (5) ◽  
pp. 1297-1316 ◽  
Author(s):  
Kenneth W. Stolte
Keyword(s):  
Climate Change ◽  
Health Monitoring ◽  
Forest Inventory ◽  
Forest Ecosystems ◽  
Forest Health ◽  
Inventory Analysis ◽  
Climate Change Effects ◽  
Forest Health Monitoring

scholarly journals SIMPANAN KARBON SEBAGAI SALAH SATU INDIKATOR KESEHATAN HUTAN PADA HUTAN RAKYAT (Studi Kasus di Hutan Rakyat Kelurahan Pinang Jaya, Kecamatan Kemiling, Kota Bandar Lampung, Provinsi Lampung)

2021 ◽  
Vol 4 (2) ◽  
pp. 164
Author(s):  
Vita Arianasari ◽  
Rahmat Safe'i ◽  
Arief Darmawan ◽  
Hari Kaskoyo
Keyword(s):  
Climate Change ◽  
Carbon Storage ◽  
Health Monitoring ◽  
Global Climate Change ◽  
Measurement Method ◽  
Global Climate ◽  
Forest Health ◽  
Community Forests ◽  
Forest Health Monitoring ◽  
Non Destructive

Global climate change and forest health are currently two things that need to be studied more deeply. Forests store carbon, including in the community forests. A healthy forest can perform its function properly including as a carbon sinker as well as a carbon repository. This study aims to analyze carbon storage as an indicator of forest health in community forests in Pinang Jaya Village. The data were collected by using cluster plots based on the method of Forest Health Monitoring (FHM), totaling 15 units. The measurement method used is destructive and non-destructive. The result showed that the average carbon storage in community forests in Pinang Jaya Village is 54.59 tC/ha. The largest contribution to carbon storage was AGB with a percentage of 95.71%, followed by necromass at 4.23%, and 0.05% litter and understorey. Based on the results of the analysis, carbon storage can perform as an indicator of forest health in the community forests of Pinang Jaya Village with the bad, medium, and good categories. The plot clusters are in the Good category (70.61 tC/ha - 83.66 tC/ha), namely cluster plots 13, Moderate categories (57.55 tC/ha - 70.60 tC/ha), namely cluster plots 4 and 14, and the Bad category (44.49 tC/ha - 57.54 tC/ha), namely cluster plots 1-3, 5-12.15, with the percentage of each category of 7%, 13%, and 80%.

scholarly journals Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

2018 ◽  
Vol 10 (7) ◽  
pp. 1120 ◽  
Author(s):  
Angela Lausch ◽  
Erik Borg ◽  
Jan Bumberger ◽  
Peter Dietrich ◽  
Marco Heurich ◽  
...  
Keyword(s):  
Remote Sensing ◽  
21St Century ◽  
Health Monitoring ◽  
Data Science ◽  
Forest Ecosystems ◽  
Multiple Stressors ◽  
Forest Health ◽  
Monitoring Network ◽  
Ecosystem Functions ◽  
Forest Health Monitoring

Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.

Forest health monitoring in New England: 1990 annual report

1992 ◽  
Author(s):  
Robert T. Brooks ◽  
David R. Dickson ◽  
William B. Burkman ◽  
Imants Millers ◽  
Margaret Miller-Weeks ◽  
...  
Keyword(s):  
New England ◽  
Health Monitoring ◽  
Annual Report ◽  
Forest Health ◽  
Forest Health Monitoring

Seeing the forest for the trees: forest health monitoring

Forest Health ◽  
2012 ◽  
pp. 321-343 ◽  
Author(s):  
M. Fierke ◽  
D. Nowak ◽  
R. Hofstetter
Keyword(s):  
Health Monitoring ◽  
Forest Health ◽  
Forest Health Monitoring

PENILAIAN DAMPAK KEBAKARAN PADA TEGAKAN AKASIA DI BKPH PARUNG PANJANG KPH BOGOR, PERUM PERHUTANI UNIT III JAWA BARAT DAN BANTEN Fire Severity Assessment on Akasia stand at BKPH Parung Panjang KPH Bogor, Perum Perhutani III West Java and Banten

2017 ◽  
Vol 8 (1) ◽  
pp. 55-62
Author(s):  
Lailan Syaufina ◽  
Vera Linda Purba
Keyword(s):  
Growth Performance ◽  
Forest Fire ◽  
Health Monitoring ◽  
Fire Severity ◽  
Forest Health ◽  
Chemical Properties ◽  
Fire Effects ◽  
Burned Areas ◽  
Forest Health Monitoring ◽  
Physical And Chemical

Forest fire is one of the problem in forest management. The objectives of the study was to measure the forest fire severity based on soil physical and chemical properties. The forest fire effects were assessed using fire severity method and forest health monitoring plot. The study indicated that the burned areas at BKPH Parung Panjang after two years included in low fire severity. The site properties and growth performance analysis showed that the fire has only affected on pH, Mg and tree diameter significantly, whereas the other parameters such as bulk density, P, N, Na, K, Ca and height were not significantly affected. In addition, both burned and unburned areas are classified as in health condition.Key words : fire severity, forest health monitoring, growth performance, site properties

Forest Health Monitoring in the U.S.

2002 ◽  
Author(s):  
William Smith ◽  
Iral Ragenovich ◽  
John Coulston ◽  
Barbara Conkling ◽  
Sally Campbell ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Forest Health ◽  
Forest Health Monitoring ◽  
The U.S

scholarly journals PENILAIAN KESEHATAN KEBUN BENIH SEMAI Pinus merkusii DENGAN METODE FHM (FOREST HEALTH MONITORING) DI KPH SUMEDANG Health Assessment for Seedling Seed Orchard of Pinus merkusii Using FHM (Forest Health Monitoring) Method in KPH Sumedang

2019 ◽  
Vol 9 (2) ◽  
pp. 99-108
Author(s):  
Supriyanto . ◽  
Taufik Iskandar
Keyword(s):  
Health Monitoring ◽  
Forest Health ◽  
Seed Orchard ◽  
Tree Damage ◽  
Damage Level ◽  
Seedling Seed Orchard ◽  
Forest Health Monitoring ◽  
Gum Rosin ◽  
Damage Condition ◽  
Pinus Merkusii

Pine (Pinus merkusii) is tree species that provides timber and gum rosin. To meet the needs of wood and non wood (gum rosin) products, planting by using superior or high quality seeds are needed. Seed procurements for planting are obtained from seedling seed orchard (SSO). However, Cijambu’s SSO was attacked by pine woolly aphid (Pineus boerneri). Therefore, assessment of Cijambu’s SSO needs to be done to evaluate the severity pest attacks that could affect to the quality and the quantity of seed production. Forest Health Monitoring (FHM) method is one of the methods to assess the health level of a stand. The number of trees found in all cluster plots in Cijambu’s SSO were 270 trees. Based on the value of the VCR (Visual Crown Rating), the trees located in all cluster plot have health level between low to high. Based on the value of the VCR showed 38.52% (104 trees) having VCR’s value was high, 49.26% (133 trees) having VCR’s value was middle; 12.22% (33 trees) having VCR’s value was low; and no tree having very low VCR’s value. The average of VCR’s value in all cluster plots were 3.25 and classified as middle health. Based on the value of TDLI (Tree Damage Level Index) from 270 trees in all cluster plot showed that 189 trees (70.00%) in healthy condition; 69 trees (25.56%) in slight damage condition; 11 trees (4.07%) in middle damage condition; and 1 tree (0.37%) in heavy damage condition. The value of damage in all cluster plots (ALI) was 261.22 and classified as in health condition. The trees located in all cluster plots were mostly suitable to be seed sources as 242 trees (89.63%), while 28 trees (10.37%) were not suitable for seed sources.Keywords: Forest Health Monitoring, Pinus merkusii, seedling seed orchard, Tree Damage Level Index, Visual Crown Rating

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