INTRODUCTION
Forests play a vital role in our daily lives, influencing activities such as having breakfast, reading the newspaper, switching on lights, commuting, and making shopping lists Forest products are essential for countless everyday items, highlighting their indispensable importance Beyond their obvious uses, forests provide critical habitats for wildlife, support human livelihoods, and contribute to watershed protection and soil erosion prevention Additionally, forests are crucial in mitigating climate change by absorbing carbon dioxide Our reliance on forests is fundamental to our survival, yet many people remain unaware of their profound impact on our lives and the environment.
A forest is defined based on canopy cover, with the United Nations Food and Agriculture Organization (FAO) describing it as land of at least 0.5 hectares with over 10% potential canopy cover and trees reaching a height of at least five meters In 2006, forests encompassed approximately four billion hectares (16 million square miles), representing about 30% of the Earth’s land area.
Understanding forest data is essential for effective management and protection The canopy coverage indicates whether the forest is dense or sparse, providing insights into its current state and potential risks The canopy cover ratio measures the proportion of land covered by tree canopies, serving as a key indicator for assessing forest health, monitoring changes, and guiding conservation efforts.
Canopy coverage refers to the proportion of the forest floor shaded by the vertical projection of tree crowns, offering insight into forest density at ground level In contrast, canopy closure describes the percentage of the sky hemisphere obscured by vegetation when observed from a single point, reflecting the overall density of the canopy above Distinguishing between these two metrics is essential for accurate assessment of forest structure and ecosystem health.
Estimation of forest canopy cover has recently become an important part of forest inventories Throughout history there have been very many methods from handmade to
Measuring canopy cover ratio requires modern methods tailored to environmental, climate, and topographical conditions for accurate results Traditional techniques, such as point counting, profile diagrams, and ocular estimation, are simple but subjective, with results varying due to observer influence and external factors like weather, wind, or observer height Objectivity can be improved by dividing the plot into smaller sections and averaging estimates for each Advanced methods, such as fisheye photography and satellite imagery, offer higher accuracy but come with higher costs and logistical challenges; fisheye tools are expensive, scarce in Vietnam, bulky, and difficult to transport into forests Choosing the appropriate method depends on balancing accuracy, cost, and accessibility.
This research introduces three methods for determining canopy coverage: traditional handmade techniques, the modern GLAMA (Gap Light Analysis Mobile App) method, and the 100-point and profile diagram approaches, providing comprehensive tools for accurate canopy analysis.
The first method involves using 100 measurement points to determine canopy coverage on established sample plots By evenly distributing these points across each plot, we can accurately assess the canopy cover ratio This data helps evaluate the extent of a closed or broken forest canopy, serving as a valuable basis for making informed silvicultural management decisions While this manual method is safe and highly accurate, it is time-consuming and requires significant effort to complete the measurements.
A widely used simple method to identify and illustrate the second floor of a forest is the cross-sectional profile approach developed by David and Richards in 1952 This handmade technique relies on collecting data from trees and involves observing and simulating the canopy coverage on grid paper By applying this method, researchers can effectively determine the structure and layers of the forest's second canopy.
The GLAMA (Gap Light Analysis Mobile App) is a useful software tool for measuring canopy cover directly from a smartphone This Android application allows users to rapidly capture hemispherical photographs in the field and instantly calculate the Canopy Cover Index, providing a quick, accurate, and observer-bias-free estimate of canopy coverage The program, available for free on Google Play, supports various photograph types—including hemispherical, wide-angle, and standard images with known lens angles—making it versatile for field measurements Designed primarily for field use, GLAMA can analyze photographs saved on external storage, offering a robust and efficient method that closely aligns with visual canopy estimates but with increased precision and reduced subjectivity This mobile app streamlines the process of canopy cover assessment, enabling researchers and forestry professionals to obtain immediate, reliable data directly in the field.
In this paper, i will determine canopy coverage in Luot mountain, Xuan Mai, Chuong
This study in Hanoi involves establishing three sample plots to evaluate and compare three different methods for measuring forest canopy cover By analyzing the data collected from each method and examining the implementation process, the research aims to identify the most accurate, practical, and user-friendly method for canopy cover assessment The comparison will focus on data accuracy, ease of use, and overall efficiency to recommend the best approach for forest canopy measurement.
LITERATURE REVIEW
Forests cover approximately 30% of the Earth's mainland, with forest canopies serving as vital gateways that regulate the exchange of energy, carbon, and water vapor between terrestrial ecosystems and the atmosphere (FAO 2001; Law et al 2001; Parker et al 2004) The structural characteristics of a forest canopy significantly influence the amount, quality, and distribution of light within the stand, which affects ground vegetation, temperature, humidity, and the physiological activity of various tree organs and organisms (Jennings et al 1999; Kobayashi and Iwabuchi 2008) Understanding these interactions is crucial for comprehending forest ecosystem dynamics and their role in global environmental processes.
The forest canopy is crucial for understanding forest stand dynamics and wildlife habitat quality, yet its condition is often misunderstood even by professionals Sunlight reaching the forest floor significantly influences microhabitat conditions and promotes herbaceous plant growth There is a strong relationship between the overstory canopy and herbaceous production, which plays a vital role in supporting biodiversity and wildlife populations.
Vietnam's forest cover spans over 13 million hectares, with 10 million hectares of natural forest and 3 million hectares of plantation forest, representing approximately 40.2% of the country's land area as of 2011 The country’s forests are categorized into three main types: production forests covering 6.3 million hectares, protection forests totaling 4.8 million hectares, and nearly 2 million hectares of special-use forests, contributing to Vietnam's rich biodiversity and ecological stability.
"Canopy cover" and "canopy closure" are two important terms used to describe forest canopy conditions, though they are often misunderstood or used interchangeably Canopy closure refers to the proportion of the sky’s hemisphere obscured by vegetation when viewed from a single point, providing a measure of how much sky is blocked by the canopy (Jennings et al., 1999; Zhu et al., 2003) Conversely, canopy cover generally describes the extent of ground area shaded by the canopy, though its precise definition can vary depending on the context Accurate understanding of these terms is essential for forest management and ecological studies related to forest structure and health.
5 understood to be the vertical projection of the forest floor that is obscured by forest canopy (Jennings et al 1999; Zhu et al 2003)
Various methods exist for measuring forest canopy, with considerations of cost and practicality influencing their suitability Instruments like the Moosehorn, densiometers, photographic cameras, point quadrat equipment, and allometric formulas offer cost-effective options, with the lowest prices among these tools In contrast, litter traps and harvest approaches incur higher costs due to maintenance, inspection, and labor demands Advanced technologies such as the LAI-2000 are more expensive, with terrestrial laser scanning (TLS) representing a significant investment, often 50–80 times the cost of a hemispherical camera Accounting for operator qualifications and boundary conditions is essential, as instrument prices fluctuate, but using relative price classes provides a useful overview of the required investments (Seidel & Fleck 2011).
The choice of research methods often involves necessary trade-offs depending on the study's objectives, as each method offers distinct strengths and limitations The development of new methodologies is driven by ongoing research questions, emerging fields of investigation, and recent findings, highlighting the evolving nature of scientific inquiry (Dominik Seidel, Stefan Fleck, Christoph Leuschner, & Tom Hammett, 2011).
The 3D structure of forest canopies is crucial for current ecological research, increasing the demand for advanced instruments that provide detailed spatial data Studies by Lovell et al (2003), Parker et al (2004), Takeda and Oguma (2005), and Pretzsch and Schütze (2005) highlight the importance of developing tools capable of capturing precise three-dimensional representations of forest canopies to support ecological analysis and forest management.
Recent advances in research methods for canopy coverage emphasize balancing accuracy and cost-effectiveness Various techniques have been developed to minimize personal error and reduce operational costs, but some methods sacrifice accuracy, while others overemphasize it Since a method suitable for measuring certain vegetation characteristics may not be effective for others, it is essential to regularly modify and adapt these methods to achieve maximum accuracy at a reasonable cost.
STUDY GOAL, OBJECTIVES, SCOPE OF THE STUDY AND
STUDY GOAL, OBJECTIVES
Additional knowledge about forest cover and proficient use methods to measurement of canopy cover ratio
Understanding the condition of forests and forestry in dusty mountain regions is essential for effective management The review process involves analyzing the formation of sub-level forest conditions, which are influenced by natural canopy cover Canopy density significantly impacts species diversity, overall forest health, and regeneration quality Key factors include the ratio of healthy to unhealthy trees, regeneration rates, future prospects, and the composition of target tree species, all of which are vital for sustainable forest development.
Recommend some appropriate solutions to increase forest cover than before
Objective 1: Try to understand what is forest cover and additional knowledge about forest cover
Objective 2: proficient use three methods and pointed out the advantages and disadvantages of using three method to determine canopy coverage in Luot mountain
Objective 3: Propose the most appropriate method from 3 methods previously measured and explain why choose this method in study areas
Objective 4: propose appropriate solutions to increase the canopy cover ratio in plantations forest in Luot mountain through the results collected.
SCOPE OF THE STUDY
3.2.1 Geographical location, Topography in Luot mountain
Located in Xuan Mai town, Chuong My district, Hanoi, this plantation forest is strategically positioned at the intersection of Road No.6 and Road No.21A, with the West and North surroundings providing easy access and connectivity.
8 site near Luong Son district, Hoa Binh Province; The East and the South site near Thuy Xuan Tien town, Chuong My district
The total area of Luot mountain forest is about 133 hectares It is the experimental forest of Vietnam National University Forestry
Luot mountain includes 2 low hills with the highest peak is 133m, another peak is 76m above the sea
The average slope is 15o and the most slope area is about 27o and do not have any stream found in this area
The average precipitation in Luot mountain is 146mm The rainfall disposes unsteadily in one year
The average air temperature is 23.2oC and different in 4 seasons: Spring, Summer, Autumn and Winter
The humidity is different in each season, it‟s dry in Winter and wetter in Summer The average humidity is 84%
The main kind of soil in Luot mountain is Ferralsols soil with the pH This shows that this forest area possesses very large diameter trees
The mean height (Hvn) of trees in the standard plot is 21.5 meters, indicating a relatively tall forest stand The standard error (S) of 1.7 meters suggests that the variation in tree heights is not significant, reflecting consistent growth within the plot Overall, these results demonstrate that the trees have a high average height with minimal variability, providing valuable insights for forest assessment and management.
Observations and calculations reveal that Sample Plot 1 exhibits larger plant diameters and heights due to its convenient location, which enhances plant growth Additionally, most plants in this plot are perennial species, allowing them to grow larger compared to other plants.
Sample plot 2 hosts a diverse wood tree community with 60 species having a diameter at breast height (D1.3) of 3 cm or greater Key species include Erythrophleum fordii with four individuals, Acacia mangium with two, Knema corticosa Lour with four, Castanopsis boisii with three, Microcos paniculata with two, and Pinus kesiya, represented by nine species This rich diversity highlights the complexity of the forest ecosystem in the area.
- Trees with the largest diameter breast height is 67.5-83.5 cm include Acacia mangium and some other species
- Trees with the smallest diameter breast height is 11-14 cm include Microcos paniculata, Knema corticosa Lour, Pinus kesiya and some other species
- Trees with the height tallest is 24-26 m include Acacia mangium and some other species
- Trees with the height shortest is 9-13 m include Microcos paniculata and some other species
- With targets about D1.3, i have Mean of D13 = 27.2 =>This shows that this forest area possesses very large diameter trees
The analysis of Hvn targets reveals that the mean height (Mean of Hvn) is 19, indicating that the trees have an average height that is neither too tall nor too short Additionally, the standard error (S = 0.6) suggests that the variation in tree heights is relatively uniform across the sample, reflecting consistent growth levels among the trees.
The analysis reveals that the sample plot primarily consists of trees with small to average diameter at breast height, indicating a dominance of younger or less mature trees Additionally, the distribution of plants by height is relatively uniform, suggesting a balanced growth pattern across the plot.
- At sample plot found is 80 species of wood trees having D1.3≥ 3 cm, in which has: 2
Erythrophleum fordii, 4 Acacia mangium, 4 Knema corticosa Lour, 2 Microcos paniculata and a lot of Castanopsis boisii and Pinus kesiya, with the number respectively are 16 and 12
- Trees with the largest diameter breast height is 65-84.5 cm include Acacia mangium and some other species
- Trees with the smallest diameter breast height is 9.5-11 cm include Microcos paniculata, Castanopsis boisii and some other species
- Trees with the height tallest is 24-28 m include Acacia mangium, Castanopsis boisii and some other species
- Trees with the height shortest is 7-10 m include Microcos paniculata, Knema corticosa Lour and some other species
- With targets about D1.3, i have Mean of D13 = 24.9 => This shows that this forest area possesses large diameter trees
The mean height of the trees (Hvn) is 17.6 units, indicating that the trees have an average height that is neither particularly tall nor short The standard error (S) of 0.6 suggests that the variation in tree heights is relatively consistent and uniform across the sample These findings demonstrate that the forest features a relatively uniform distribution of tree heights around the average, providing valuable insights for ecological assessments and forest management.
- In sample plot 3, uneven distribution of trees according to height, a lot of tree in this here but canopy cover ratio is low because cutting trees
4.1.2 Comparison of tree growth between sample plot 1, plot 2 and plot 3
To determine tree growth, I depend on the growing of trees in height and diameter So,
I use the data of height and DBH to compare the growing state between three sample plot
- We have Mean of each plot μ1= 29.7 > μ2= 27.1> μ3= 24.9 => Plot 1 has DBH(diameter breast height) largest than Plot 2 and Plot 3 And Plot 3 has DBH(diameter breast height) smallest (24.9cm)
- In plot 3 has DBH(diameter breast height) is relatively uniform than plot 2 and plot 1 ( base on Standard Error)
We have Mean of each plot μ1= 21.5 > μ2= 19> μ3= 17.5 => Plot 1 has height taller than Plot 2 and Plot 3 And Plot 3 has height shortest (17m)
- In plot 2 and plot 3 has height is relatively uniform than plot 1 ( base on Standard Error)
The diameter at breast height (DBH) of the three standard plots is consistently large and uniform, indicating well-developed and vigorous plant growth in the area However, uneven tree density influences forest conditions and impacts the overall production potential.
The height of three standard plot is average range, with distance from 12-25 m is the hardwoods group, the average timber
Excessive tree density in standard plots is not a reasonable practice, as overly thick forests can negatively impact individual tree development High forest density hampers growth processes by limiting access to essential nutrients and space, ultimately reducing the health and vitality of the trees Maintaining optimal forest density is crucial for promoting healthy growth and sustainable forest management.
THE RESULT AND DISCUSSION COMPARISON OF THREE METHODS TO
4.2.1 The result and discussion comparison of three methods
After calculation, The results are shown on table below
Mean Error Mean Error Mean
Table 4.2.1 List of the results of the Canopy Coverage and standard error of three methods in Luot mountain, Xuan Mai, Chuong My,Ha Noi
Because profile diagram is the handmade method, so there are no values error
4.2.1.1 Gap light analysis mobile app (GlAMA) method:
After determine by my cellphone has been installed the software gap analysis light mobile app (GlAMA), We Got the 7 data plot of three standard include:
Total No of Pixels in Circle (Frame) : 407150 px
The circle in the photograph represents the virtual boundary of the hemisphere (hemisphere horizon) The application calculates the total number of pixels within this circle to determine the size of the hemisphere When only a partial view of the hemispherical circle is visible, the number of visible pixels is indicated in brackets, ensuring accurate analysis even with incomplete images.
Note: The number of pixels in the circle (frame) is counted independently from the defined mask, which does not influence the results
Number of White (Black) Pixels - The average value of each sample plot in turn the following:
Plot 1 has the mean = 49840.13 px
Plot 2 has the mean = 51352.87 px
Plot 3 has the mean = 86880.43 px
=> plot 3 has the number of white (Black) Pixels larger than plot 1 & 2
It‟s mean that plot 3 has the real number of white (black) pixels in visible frame more than plot 1 & 2
Note: The number of „white‟ („black‟) pixels is limited by the defined mask All masked „white‟ pixels are counted as „black‟
Gap Fraction of Selected Area I have mean of each plot in turn the following: Plot 1 has the mean = 12.237%
So, plot 1 &3 has the ratio between „white‟ and all pixels counted within the visible part of the circle lager than plot 2
Part of Hemisph Taken by Camera = 97.14
It is the percentage ratio of the visible part of the hemisphere
Plot 1 shows a greater proportion of the sky hemisphere obscured by canopy elements when viewed from a single point compared to Plots 2 and 3 Canopy closure is quantified through hemispherical photographs by calculating the weighted sum of black pixels as fractions of all pixels within the image segments The weighting of these area fractions depends on the zenith angle and is influenced by the camera lens projection and geometric distortion.
Canopy Openness – Measure simply complementary to Canopy Closure
Sample Plot 2 exhibits the highest canopy cover ratio at 72.84%, while Sample Plot 1 has the lowest at 50.91%, based on hemispherical photograph analysis, highlighting variations in vegetation density Canopy cover, defined as the perpendicular projection of tree crowns onto a horizontal surface, is a fundamental metric in vegetation science and forest ecology for assessing forest structure The Canopy Cover Index effectively converts hemispherical photograph projections into perpendicular light gap estimates, enabling accurate and direct measurement of canopy cover from single-position photographs Notably, the index does not require full hemispherical fisheye images; focusing on a horizon mask between 45° and 70° for vegetation above the plot yields more precise results.
Selected Colour Cut Level = 200 ASCII value of the colour in a grey scale as the boundary between light and dark pixels (0-255)
The data indicates that Luot Mountain, Xuan Mai, Chuong My, Ha Noi has a canopy cover ratio ranging from 0.5 to 0.7 This suggests that the area features moderate to high canopy cover, with plot 1 and 3 exhibiting an average coverage level, while plot 2 demonstrates a higher percentage of canopy cover.
Base on the Standard Error, we can see in plot 3 (1.97) has the canopy cover is uneven distribution and in the sample plot 2, there is a nearly equal distribution (1.55)
This method involves sampling three plots, with each plot containing 100 equally spaced points Each plot is divided into 10 transects parallel to the contour lines, spaced 2 meters apart Along each transect, 10 points are marked, with each point 2.5 meters from the next, resulting in a total of 300 measured points across all three samples.
With 100 points method, i have the results is as follows:
- Sample plot 1 is the total points is 48.9 => canopy cover ratio is 48.9 / 100 = 0.49 According to the assessment criteria about the canopy coverage So, in sample plot 1 is low level
- And in sample plot 2, we have the total score by 74 => canopy cover ratio is 74 / 100
= 0.74 According to the assessment criteria about the canopy coverage So, in sample plot 1 has high canopy cover ratio
Sample Plot 3 has a total point score of 59, resulting in a canopy cover ratio of 0.59 (59/100) Based on the assessment criteria for canopy coverage, this ratio indicates that Sample Plot 3 has a normal to average canopy cover, reflecting a balanced vegetation density.
=> Compare 3 sample plots together, we can easily recognize sample plot 1 is lowest, sample plot 3 is medium and sample plot 2 is largest so we have Plot 2 > Plot 3 > Plot 1
After conducting fieldwork on Luot Mountain, we collected detailed data on tree species, including diameter at breast height, total height, under-crown height, and crown diameter All tree locations, along with their measurements, were mapped onto a vertical grid paper at a scale of 1/200, creating accurate visual simulations of the forest scene These comprehensive measurements and drawings provide valuable insights into the forest structure and species composition of Luot Mountain.
This picture below is shown the method "Cross sectional profile" of David and Richards (1952) in three sample plot as follow:
Picture 4.2.1.3.1: Vertical and Cross profile in sample plot 1
Looking the picture can be seen this sample plot 1 no trees no trees forming eergent layer The majority of wood floors (A2) is
Acacia mangium, Erythrophleum fordii has a height of about 25-27m and is the species create the crown- creating horizontal, relative regularly, no broken The tree downstairs (A3) is
Pinus kesiya and some other species
The forest features a closed canopy that is intermittently developed, with heights ranging from 7 to 20 meters Spanning approximately 20 meters in width and 25 meters in length, the profile diagram illustrates the projection of the canopy from the tall trees down to the ground This creates a medium canopy coverage throughout the forest, contributing to its diverse structure and ecological richness.
Picture 4.2.1.3.2: Vertical and Cross profile in sample plot 2
In sample plot 2, also as sample plot 1, we can be seen no species emergent layer formation
The majority of wood floors (A2) is
Pinus kesiya, Acacia mangium has a height of about 20-25m and they generate canopy closure relatively stable The tree downstairs (A3) is
Microcos paniculata, Pinus kesiya and some other species has relatively thick density of trees and
In this sample plot, the canopy height ranges from 10 to 20 meters, indicating a tall and dense forest structure The visual assessment of the plot reveals that canopy coverage is relatively extensive compared to other areas of similar size Specifically, within a 500-square-meter sample plot, the forest exhibits significant canopy coverage, highlighting its lush and thriving vegetation These characteristics suggest a healthy, mature forest ecosystem with substantial vertical and horizontal canopy development.
Picture 4.2.1.3.3: Vertical and Cross profile in sample plot 3
Analysis on cross sectional profile
This sample plot, similar to Sample Plots 1 and 2, lacks the formation of an emergent tree layer It features a canopy primarily composed of species such as Pinus kesiya and Acacia mangium, which reach heights of approximately 20-25 meters These species form a relatively continuous and uninterrupted horizontal canopy coverage at the wood floor level (A2) The understory layer (A3) consists of other tree species, contributing to a layered forest structure without the development of a distinct emergent layer.
Myristicaceae and some other species has relatively thick density of trees ( mostly
Castanopsis boisii) and High around from
Sample Plot 3, covering an area of 500 square meters, hosts a higher number of species (80 species) compared to Sample Plots 1 and 2, indicating greater species diversity However, despite the increased number of species, its canopy cover is lower than that of Plot 2, reflecting a less developed canopy The low canopy closure suggests poor growth and development of the vegetation, likely due to factors such as frequent tree cutting and reduced overall vitality in this area.
A: Acacia mangium P: Pinus kesiya K: Knema corticosa Lour
M: Microcos paniculata E: Erythrophleum fordii C: Castanopsis boisii
And some species not recognize names: Sp 1, Sp 2.1, Sp 2.2, Sp 3 ( Sp: Species)
To calculate canopy coverage based on picture above of the method "Cross sectional profile" of David and Richards (1952) It is calculated as follows:
To calculate the canopy cover ratio, begin by drawing on grid paper with a 1/200 scale, as demonstrated above Use the drawing to estimate the white space, where each white box represents 1 cm White cells with half or three-quarter sizes of the white box correspond to 0.5 cm or 0.75 cm, respectively, allowing precise measurement of canopy coverage for accurate analysis.
Sample plot 1 has the total cell count is 47.89 We have 100 - 47.82 = 52.11
=> Canopy cover ratio is 52% in sample plot 1
Sample plot 2 has the total cell count is 28,88 We have 100 - 28.88 = 71.12
=> Canopy cover ratio is 71% in sample plot 2
Sample plot 3 has the total cell count is 42.11 We have 100 - 42.11 = 57.89
=> Canopy cover ratio is 58% in sample plot 3
Compare between three sample plot, we can see that the canopy coverage of sample plot 2 > sample plot 3 > sample plot 1 This result is similarities with two method above
4.2.2 Compare, selectection and explain why choose method to determine canopy coverage in Luot Mountain, Xuan Mai, Chuong My, Ha Noi
Base on the result is show in table above of three method, i will compare the data from each sample plots on the same methods, i have:
+ In Glama method: plot 2 (0.73) > plot 3 (0.59) > plot 1 (0.52)
+ In 100 Point method: plot 2 (0.74) > plot 3 (0.61) > plot 1 (0.49)
+ In Profile diagram method: plot 2 (0.71) > plot 3 (0.58) > plot 1 (0.52)
- Look at table above, the average value of three different method on the same standard plot we can easily see:
The average value of plot 2 > The average value of plot 3 > The average value of plot 1 (2)
- Now come back to compare between the value of three method in the same standard plot with the average value from the result, i have:
Glama has the data similar to the average value most than 2 remaining method (3)
From (1), (2) We can easily see All three methods have similar results together (small error), and Glama is the best method to determine the canopy coverage that I choose, because:
From (3), after compare and conclude, Glama is method has the result most exactly ( similar results with other methods)
The Canopy Cover Index offers a quick and reliable method for accurate canopy cover estimation, matching the precision of visual assessments while eliminating observer bias This versatile tool can be applied to existing photographs or used in the field with smartphones through the GLAMA Android app, enabling rapid capture of hemispherical images and instant calculation of canopy cover index values on-site.
In addition, very easy to use and convenient to go to the forest to measure, not to worry bulky, expensive, difficult to use and time consuming as other methods
So, Glama is the best method in my study research selection method to determine canopy coverage in Luot mountain, Xuan Mai, Chuong My, Hanoi
GENERAL CONCLUSION AND RECOMMENDATION
Conclusion
This course covered essential tools and techniques for canopy coverage research across three 500 m² sample plots Participants learned how to utilize topographic maps and perform data analysis to derive significant insights, including statistical summaries of tree species, relationships between tree height and diameter, and regression analysis between different plots Additionally, the course focused on calculating key ecological metrics such as carbon stocks, biomass, stand density, stand volume, and total basal area, enhancing understanding of forest structure and carbon sequestration potential.
This study aims to identify an effective, cost-efficient canopy cover estimation method for plantation forests in Luot Mountain, addressing the limitations of existing techniques for large-scale inventories An ideal method should enable quick, accurate assessments while balancing cost, speed, and precision The research evaluates three ground-based techniques to determine their performance in estimating canopy cover, ensuring the chosen method meets the practical requirements of large-scale forest management.
Tree height and the length of the live crown do not influence canopy cover estimates; however, canopy closure increases with taller trees and decreases in height to the live crown base.
Our assessment reveals that while three methods produce similar and accurate results in measuring forest canopy cover ratio, the Glama software stands out as the most suitable choice Glama offers high usability, ease of use, and efficiency, enabling quick and effortless data analysis without wasting time Therefore, selecting the Glama software is ideal for obtaining rapid and reliable canopy cover measurements in forest studies.
The errors in this report are minimized through the use of advanced analytical tools such as Excel and ArcGIS, which ensure accurate data processing Additionally, reliable instruments like GPS are employed for precise data measurement, enhancing the overall accuracy and credibility of the results.
Measuring results can be affected by various disadvantages, including inaccuracies caused by challenging terrain and climate conditions Traditional tools like Blume Leiss, tapes, and calipers often yield imprecise measurements Additionally, GPS technology can sometimes provide unreliable data, further impacting the overall accuracy of the results.
Recommendation 34 REFERENCES
From the results, we have some suggestion as follow:
Accurate estimation of canopy cover from hemispherical photographs requires a robust field sampling strategy that captures representative images and minimizes bias caused by forest canopy patchiness To avoid sampling bias, it is recommended to collect images from multiple points along transects or grids and average the data Proper pixel classification is critical for reliable canopy cover analysis; software like GLAMA helps differentiate between 'black' and 'white' pixels using histogram-based parameters and manual visual checks Regularly verifying the cutoff levels ensures light gaps are not mistaken for vegetation and vice versa, resulting in precise and unbiased results in hemispherical photograph analysis.
This method offers relatively high accuracy in results; however, it is labor-intensive and challenging to implement in the field Measuring 100 points within a single sample plot is time-consuming and requires significant effort, especially when conducting measurements across three plots, totaling 300 points As a manual method, it demands quick, sharp-eyed, and healthy personnel to ensure precise and reliable results.
Appling the method "Cross sectional profile" of David and Richards (1952)
This method offers high accuracy for estimating forest stand scale and overall forest conditions, making it highly reliable for forest inventory In forest ecology research, canopy is indicated by the "tenth" index, which helps assess forest structure The technique requires precise drawing skills and careful observations to accurately represent grid illustrations on paper, ensuring reliable parameter measurements By analyzing these values, foresters can determine canopy coverage levels, such as the extent of a closed canopy or a broken forest canopy, providing essential data for making informed silvicultural management decisions.
Studying forest conservation and management highlights the vital roles of both natural and plantation forests for human well-being It is essential to regularly survey species composition, enhance plant diversity in plantation forests, and implement sustainable methods for forest exploitation Monitoring canopy coverage provides critical data on the current forest status, enabling targeted recommendations for improvement Additionally, expanding the area and increasing canopy coverage through reforestation, environmental protection, and effective policies are key strategies to strengthen forest preservation and ensure sustainable use.
Emerging methods for canopy cover estimation are essential to meet the increasing demand for accurate canopy-related information Geographically representative regression models offer a cost-effective and practical approach for estimating canopy cover over large areas, but developing nation-wide models demands significant resources and research In the long term, remote sensing is poised to become the preferred method for canopy cover estimation due to its scalability and efficiency However, reliable ground truth measurements and validated models of canopy cover are crucial for the development and calibration of remote sensing techniques.
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Plants forest of Viet Nam Vietnam Science & Engineering Publishing House
20 Simon Fraser University, Cary Institute of Ecosystem Studies All rights reserved 1999;
21 Welles JM (1990) Some indirect methods of estimation canopy stucture In: Goel NS,
Norman JM (eds) Instrumentation for studying vegetation canopies for remote sensing in optical and thermal infrared regions Hardwood Academic, UK, pp 31–43
I Appendix A : Table Table Tree data collection
No of data collection plot: ……
Forest type:……… Inventory date:……… Surveyor:……… Forest status:………
Table 01: Descriptive statistics for DBH frequency distributions of three sample plot in
Luot mountain, Xuan Mai, Chuong My,Ha Noi
DBH(cm) PLOT 1 PLOT 2 PLOT 3
Table 02: Descriptive statistics for Heigh of three sample plot in Luot mountain, Xuan
Mai, Chuong My,Ha Noi
Table 03 Descriptive statistics for canopy cover of three sample plot of using glama method in Luot mountain, Xuan Mai, Chuong My,Ha Noi
CANOPY COVER PLOT 1 PLOT 2 PLOT 3
Table 04 Descriptive statistics for canopy cover ratio of three sample plot of using 100 points method in Luot mountain, Xuan Mai, Chuong My,Ha Noi
CANOPY COVER PLOT 1 PLOT 2 PLOT 3
Some picture indata collection process
2 Binoculars are made by way curled paper A4 with diameter is 3 centimeters and looked up at the trees to determine canopy cover ratio
3 Picture of location sample plot 1
4 The result of glama method on cellphone
1 Some tools: linear tape, Paper, Knife,
12 bamboo pipes, GPS, Calipers, Blume Leiss
2 Processing set up sample plot
3 Using Calipers to determine DBH
4 Blume Leiss to determine Height
5 Using GPS to identify coordinates of sample plot
Some picture in process determine canopy coverage of three methods
1 Using 100 point method to determine canopy coverage
2 Profile diagram method to determine canopy coverage
3 Using Glama in cellphone to determine canopy coverage
Table 01 List of tree data collection of sample plot 1 in Luot mountain, Xuan Mai, Chuong
No Name E-W N-S Aver H(tota)(m) H(under)(m) E-W N-S Aver Quality
Table 02 List of tree data collection of sample plot 2 in Luot mountain, Xuan Mai, Chuong My,Ha Noi
No Name E-W N-S Aver H(tota)(m) H(under)(m) E-W N-S Aver Quality
Table 03 List of tree data collection of sample plot 3 in Luot mountain, Xuan Mai, Chuong
No Name E-W N-S Aver H(total)(m) H(under)(m) E-W N-S Aver Quality
Table 04 List of the results of Gap Light Analysis mobile app method of sample plot 1 in
Luot mountain, Xuan Mai, Chuong My,Ha Noi
Table 05 List of the results of Gap Light Analysis mobile app method of sample plot 2 in
Luot mountain, Xuan Mai, Chuong My,Ha Noi
Table 06 List of the results of Gap Light Analysis mobile app method of sample plot 3 in
Luot mountain, Xuan Mai, Chuong My,Ha Noi
Table 07 List of the results of 100 point method of sample plot 1 in Luot mountain, Xuan
Mai, Chuong My,Ha Noi
Table 08 List of the results of 100 point method of sample plot 2 in Luot mountain, Xuan
Mai, Chuong My,Ha Noi