Percentage of Mangrove Canopy Cover and Mollusks Abundance in Benoa Bay Mangrove Ecosystem

This research was conducted to determine the condition of the mangrove ecosystem based on its canopy cover and to determine the types of mollusks (Bivalves and Gastropods) associated with the Benoa Bay mangrove ecosystem. Hemispherical photography is a method for observing mangrove canopy cover and line transect method for mollusks. The composition of mangrove species found in the research location were five species, namely Rhizophora stylosa, Rhizophora mucronata, Rhizophora apiculata, Bruguiera gymnorrhiza, and Avicennia marina. The percentage of mangrove canopy cover in the Benoa Bay mangrove ecosystem is in a good category (average = 76.59%). There are eight types of mollusks found in the research location. There are two types of bivalves (Polymesoda bengalensis and Gafrarium pectinatum) and six types of gastropods (Nerita balteata, Nerita picea, Neritina turrita, Pila ampullacea, Cassidula aurisfelis, and Littoraria melanostoma). The relationship between the percentage of mangrove canopy cover and abundance of mollusks in the Benoa Bay mangrove ecosystem is very strong (r) of 0.920. The higher the percentage value of mangrove canopy cover, the higher the mollusks abundance.

Extracting Canopy Closure by the CHM-Based and SHP-Based Methods with a Hemispherical FOV from UAV-LiDAR Data in a Poplar Plantation

Canopy closure (CC), a useful biophysical parameter for forest structure, is an important indicator of forest resource and biodiversity. Light Detection and Ranging (LiDAR) data has been widely studied recently for forest ecosystems to obtain the three-dimensional (3D) structure of the forests. The components of the Unmanned Aerial Vehicle LiDAR (UAV-LiDAR) are similar to those of the airborne LiDAR, but with higher pulse density, which reveals more detailed vertical structures. Hemispherical photography (HP) had proven to be an effective method for estimating CC, but it was still time-consuming and limited in large forests. Thus, we used UAV-LiDAR data with a canopy-height-model-based (CHM-based) method and a synthetic-hemispherical-photography-based (SHP-based) method to extract CC from a pure poplar plantation in this study. The performance of the CC extraction methods based on an angular viewpoint was validated by the results of HP. The results showed that the CHM-based method had a high accuracy in a 45° zenith angle range with a 0.5 m pixel size and a larger radius (i.e., k = 2; R2 = 0.751, RMSE = 0.053), and the accuracy declined rapidly in zenith angles of 60° and 75° (R2 = 0.707, 0.490; RMSE = 0.053, 0.066). In addition, the CHM-based method showed an underestimate for leaf-off deciduous trees with low CC. The SHP-based method also had a high accuracy in a 45° zenith angle range, and its accuracy was stable in three zenith angle ranges (R2: 0.688, 0.674, 0.601 and RMSE = 0.059, 0.056, 0.058 for a 45°, 60° and 75° zenith angle range, respectively). There was a similar trend of CC change in HP and SHP results with the zenith angle range increase, but there was no significant change with the zenith angle range increase in the CHM-based method, which revealed that it was insensitive to the changes of angular CC compared to the SHP-based method. However, the accuracy of both methods showed differences in plantations with different ages, which had a slight underestimate for 8-year-old plantations and an overestimate for plantations with 17 and 20 years. Our research provided a reference for CC estimation from a point-based angular viewpoint and for monitoring the understory light conditions of plantations.

Comparative Evaluation of Algorithms for Leaf Area Index Estimation from Digital Hemispherical Photography through Virtual Forests

The in situ leaf area index (LAI) measurement plays a vital role in calibrating and validating satellite LAI products. Digital hemispherical photography (DHP) is a widely used in situ forest LAI measurement method. There have been many software programs encompassing a variety of algorithms to estimate LAI from DHP. However, there is no conclusive study for an accuracy comparison among them, due to the difficulty in acquiring forest LAI reference values. In this study, we aim to use virtual (i.e., computer-simulated) broadleaf forests for the accuracy assessment of LAI algorithms in commonly used LAI software programs. Three commonly used DHP programs, including Can_Eye, CIMES, and Hemisfer, were selected since they provide estimates of both effective LAI and true LAI. Individual tree models with and without leaves were first reconstructed based on terrestrial LiDAR point clouds. Various stands were then created from these models. A ray-tracing technique was combined with the virtual forests to model synthetic DHP, for both leaf-on and leaf-off conditions. Afterward, three programs were applied to estimate PAI from leaf-on DHP and the woody area index (WAI) from leaf-off DHP. Finally, by subtracting WAI from PAI, true LAI estimates from 37 different algorithms were achieved for evaluation. The performance of these algorithms was compared with pre-defined LAI and PAI values in the virtual forests. The results demonstrated that without correcting for the vegetation clumping effect, Can_Eye, CIMES, and Hemisfer could estimate effective PAI and effective LAI consistent with each other (R2 > 0.8, RMSD < 0.2). After correcting for the vegetation clumping effect, there was a large inconsistency. In general, Can_Eye more accurately estimated true LAI than CIMES and Hemisfer (with R2 = 0.88 > 0.72, 0.49; RMSE = 0.45 < 0.7, 0.94; nRMSE = 15.7% < 24.21%, 32.81%). There was a systematic underestimation of PAI and LAI using Hemisfer. The most accurate algorithm for estimating LAI was identified as the P57 algorithm in Can_Eye which used the 57.5° gap fraction inversion combined with the finite-length averaging clumping correction. These results demonstrated the inconsistency of LAI estimates from DHP using different algorithms. It highlights the importance and provides a reference for standardizing the algorithm protocol for in situ forest LAI measurement using DHP.

Characterizing Canopy Openness Across Large Forested Landscapes Using Spherical Densiometer and Smartphone Hemispherical Photography

Canopy openness is an important forest characteristic related to understory light environment and productivity. Although many methods exist to estimate canopy openness, comparisons of their performance tend to focus on relatively narrow ranges of canopy conditions and forest types. To address this gap, we compared two popular approaches for estimating canopy openness, traditional spherical densiometer and modern smartphone hemispherical photography, across a large range of canopy conditions (from closed canopy to large gaps) and forest types (from low-elevation broadleaf to high-elevation conifer forests) across four states in the northeastern United States. We took 988 field canopy openness measurements (494 per instrument) and compared them across canopy conditions using linear regression and t-tests. The extensive replication allowed us to quantify differences between the methods that may otherwise go unnoticed. Relative to the densiometer, smartphone photography overestimated low canopy openness (&lt;10%) but it underestimated higher canopy openness (&gt;10%), regardless of forest type. Study Implications We compared two popular ways of measuring canopy openness (smartphone hemispherical photography and spherical densiometer) across a large range of forest structures encountered in the northeastern United States. We found that, when carefully applied, the traditional spherical densiometer can characterize canopy openness across diverse canopy conditions (including closed canopies) as effectively as modern smartphone canopy photography. Although smartphone photography reduced field measurement time and complexity, it was more susceptible to weather than the densiometer. Although selection of the right method depends on study objectives, we provide a calibration for these two popular methods across diverse canopies.

Hemispherical Photography Vegetasi Pantai di Perairan Pulau Sintok, Taman Nasional Karimunjawa

Negara kepulauan merupakan negara yang terdiri atas satu atau lebih gugusan pulau, diantara nya adalah pulau - pulau kecil. Pulau kecil terdiri dari komponen lautan dan daratan, komponen daratan terdiri dari pasir, batuan, vegetasi pantai dan lain sebagainya. Keberadaan vegetasi pantai memiliki manfaat dalam merendam gelombang tsunami, mencegah abrasi, erosi serta habitat bagi flora dan fauna untuk berkembangbiak. Penelitian ini bertujuan untuk mengetahui kualitas pesisir vegetasi pantai berdasarkan struktur komposisi vegetasi pantai dan persentase tutupan kanopi vegetasi pantai di Perairan Pulau Sintok, Taman Nasional Karimunjawa dengan metode Hemispherical Photography. Penelitian ini dilakukan dengan menggunakan metode deskriptif, data yang dikumpulkan dilakukan dengan mengambil sebagian data dari wilayah penelitian, sehingga diharapkan data mewakili kondisi lingkungan dari objek yang diteliti. Setiap stasiun penelitian dilakukan tiga kali pengulangan. Pengambilan data tutupan kanopi pohon menggunakan kamera HP yang telah diolah menggunakan Software ImageJ. Hasil penelitian menunjukan bahwa ditemukan 6 spesies vegetasi pantai di Perairan Pulau Sintok, Taman Nasional Karimunjawa, yaitu Terminalia catappa, Ficus septica, Premna odorata, Scaevola taccada, Wrightia pubescens, dan Casuarina equisetifolia. Spesies Ficus septica mendominasi di lokasi penelitian. Nilai Kerapatan vegetasi pantai berkisar 532–1165 ind/ha. Nilai Indeks Keanekaragaman (H’) dan Keseragaman (J’) vegetasi pantai di lokasi penelitian termasuk dalam kategori rendah. Hasil persentase tutupan kanopi Vegetasi Pantai berkisar 63,01±1,42% – 80,80±1,41%, sehingga termasuk kategori sedang.An archipelago state is a country consisting of one or more island groups, including them which are small islands. Small islands consist of ocean and land components, land components consist of sand, rocks, coastal vegetation, etc. The existence of coastal vegetation has benefits in reducing tsunami waves, preventing abrasion, erosion and habitat for flora and fauna to reproduce. This study aims to knowing the quality of coastal vegetation based on the structure of coastal vegetation composition and the percentage of coastal vegetation canopy cover in Sintok Island Waters, Karimunjawa National Park using the Hemispherical Photography method. This research was conducted using descriptive methods, the data collected was done by taking some of the data from the research area, so that it is expected that the data will represent the environmental conditions of the object under study. Each research station had three repetitions. Taking the data of tree canopy cover using an HP camera that has been processed using ImageJ Software. The results showed that 6 species of coastal vegetation were found in Sintok Island waters, Karimunjawa National Park, namely Terminalia catappa, Ficus septica, Premna odorata, Scaevola taccada, Wrightia pubescens, and Casuarina equisetifolia. Species of Ficus septica dominate the study site. The value of coastal vegetation density ranges from 532-1165 ind/ha. The value of the Diversity Index (H ') and Uniformity (J') of the coastal vegetation at the research location is in the low category. The results of the percentage of coastal vegetation canopy cover range from 63.01±1.42% - 80.80±1.41%, we can conclude that it is in the medium category.

Optimizing smartphone-based canopy hemispherical photography

1) Hemispherical photography (HP) is a long-standing tool for forest canopy characterization. Currently, there are low-cost fisheye lenses to convert smartphones into high-portable HP equipment; however, they cannot be used whenever since HP is sensitive to illumination conditions. To obtain sound results outside diffuse light conditions, a deep-learning-based system needs to be developed. A ready-to-use alternative is the multiscale color-based binarization algorithm, but it can provide moderate-quality results only for open forests. To overcome this limitation, I propose coupling it with the model-based local thresholding algorithm. I call this coupling the MBCB approach. 2) Methods presented here are part of the R package CAnopy IMage ANalysis (caiman), which I am developing. The accuracy assessment of the new MBCB approach was done with data from a pine plantation and a broadleaf native forest. 3) The coefficient of determination (R^2) was greater than 0.7, and the root mean square error (RMSE) lower than 20 %, both for plant area index calculation. 4) Results suggest that the new MBCB approach allows the calculation of unbiased canopy metrics from smartphone-based HP acquired in sunlight conditions, even for closed canopies. This facilitates large-scale and opportunistic sampling with hemispherical photography.

Modelling and Comparing Shading Effects of 3D Tree Structures with Virtual Leaves

Reduced solar radiation brought about by trees on agricultural land can both positively and negatively affect crop growth. For a better understanding of this issue, we aim for an improved simulation of the shade cast by trees in agroforestry systems and a precise estimation of insolation reduction. We present a leaf creation algorithm to generate realistic leaves to be placed upon quantitative structure models (QSMs) of real trees. Further, we couple it with an enhanced approach of a 3D model capable of quantifying shading effects of a tree, at a high temporal and spatial resolution. Hence, 3D data derived from wild cherry trees (Prunus avium L.) generated by terrestrial laser scanner technology formed a basis for the tree reconstruction, and served as leaf-off mode. Two leaf-on modes were simulated: realistic leaves, fed with leaf data from wild cherry trees; and ellipsoidal leaves, having ellipsoids as leaf-replacement. For comparison, we assessed the shading effects using hemispherical photography as an alternative method. Results showed that insolation reduction was higher using realistic leaves, and that the shaded area was greater in size than with the ellipsoidal leaves or leaf-off conditions. All shading effects were similarly distributed on the ground, with the exception of those derived through hemispherical photography, which were greater in size, but with less insolation reduction than realistic leaves. The main achievements of this study are: the enhancement of the leaf-on mode for QSMs with realistic leaves, the updates of the shadow model, and the comparison of shading effects. We provide evidence that the inclusion of realistic leaves with precise 3D data might be fundamental to accurately model the shading effects of trees.

Comparing canopy density measurement from UAV and hemispherical photography: an evaluation for medium resolution of remote sensing-based mapping

UAV and hemispherical photography are common methods used in canopy density measurement. These two methods have opposite viewing angles where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots.