Disturbance frequency, intensity and forest structure modulate cyclone‐induced changes in mangrove forest canopy cover

Despite the low tree diversity and scarcity of the understory vegetation, the high morphological plasticity of mangrove trees induces, at the stand level, a very large variability of forest structures that need to be mapped for assessing the functioning of such complex ecosystems. Fully constrained linear spectral unmixing (FCLSU) of very high spatial resolution (VHSR) multispectral images was tested to fine-scale map mangrove zonations in terms of horizontal variation of forest structure. The study was carried out on three Pleiades-1A satellite images covering French island territories located in the Atlantic, Indian, and Pacific Oceans, namely Guadeloupe, Mayotte, and New Caledonia archipelagos. In each image, FCLSU was trained from the delineation of areas exclusively related to four components including either pure vegetation, soil (ferns included), water, or shadows. It was then applied to the whole mangrove cover imaged for each island and yielded the respective contributions of those four components for each image pixel. On the forest stand scale, the results interestingly indicated a close correlation between FCLSU-derived vegetation fractions and canopy closure estimated from hemispherical photographs (R2 = 0.95) and a weak relation with the Normalized Difference Vegetation Index (R2 = 0.29). Classification of these fractions also offered the opportunity to detect and map horizontal patterns of mangrove structure in a given site. K-means classifications of fraction indeed showed a global view of mangrove structure organization in the three sites, complementary to the outputs obtained from spectral data analysis. Our findings suggest that the pixel intensity decomposition applied to VHSR multispectral satellite images can be a simple but valuable approach for (i) mangrove canopy monitoring and (ii) mangrove forest structure analysis in the perspective of assessing mangrove dynamics and productivity. As with Lidar-based surveys, these potential new mapping capabilities deserve further physically based interpretation of sunlight scattering mechanisms within forest canopy.

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Comparison of LiDAR and Digital Aerial Photogrammetry for Characterizing Canopy Openings in the Boreal Forest of Northern Alberta

Remote Sensing ◽

10.3390/rs11161919 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1919 ◽

Cited By ~ 4

Author(s):

Annette Dietmaier ◽

Gregory J. McDermid ◽

Mir Mustafizur Rahman ◽

Julia Linke ◽

Ralf Ludwig

Keyword(s):

Boreal Forest ◽

Forest Structure ◽

Forest Canopy ◽

Canopy Structure ◽

Three Dimensional ◽

Canopy Cover ◽

Aerial Photogrammetry ◽

Northern Alberta ◽

Height Model ◽

Economical Alternative

Forest canopy openings are a key element of forest structure, influencing a host of ecological dynamics. Light detection and ranging (LiDAR) is the de-facto standard for measuring three-dimensional forest structure, but digital aerial photogrammetry (DAP) has emerged as a viable and economical alternative. We compared the performance of LiDAR and DAP data for characterizing canopy openings and no-openings across a 1-km2 expanse of boreal forest in northern Alberta, Canada. Structural openings in canopy cover were delineated using three canopy height model (CHM) alternatives, from (i) LiDAR, (ii) DAP, and (iii) a LiDAR/DAP hybrid. From a point-based detectability perspective, the LiDAR CHM produced the best results (87% overall accuracy), followed by the hybrid and DAP models (47% and 46%, respectively). The hybrid and DAP CHMs experienced large errors of omission (9–53%), particularly with small openings up to 20m2, which are an important element of boreal forest structure. By missing these, DAP and hybrid datasets substantially under-reported the total area of openings across our site (152,470 m2 and 159,848 m2, respectively) compared to LiDAR (245,920 m2). Our results illustrate DAP’s sensitivity to occlusions, mismatched tie points, and other optical challenges inherent to using structure-from-motion workflows in complex forest scenes. These under-documented constraints currently limit the technology’s capacity to fully characterize canopy structure. For now, we recommend that operational use of DAP in forests be limited to mapping large canopy openings, and area-based attributes that are well-documented in the literature.

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Mangrove forest structure and composition along urban gradients in Puerto Rico

10.1101/504928 ◽

2018 ◽

Cited By ~ 2

Author(s):

Benjamin Branoff ◽

Sebastian Martinuzzi

Keyword(s):

Puerto Rico ◽

Surface Water ◽

Water Chemistry ◽

Forest Structure ◽

Vegetation Cover ◽

Mangrove Forest ◽

Canopy Cover ◽

Basal Area ◽

Surface Water Chemistry ◽

Nitrogen Concentrations

Urban forests are repeatedly characterized as distinct in composition and structure in comparison with their non-urban counterparts. This holds true for mangroves, although previous studies lack quantified representations of urbanness as well as any inclusion of hydrology or water chemistry, which are important influences on mangrove forest structure, composition, and function. This study uses LiDAR and ground-based measurements of mangroves within well quantified urban gradients in Puerto Rico to test for the relative importance of urbanization alongside flooding metrics and surface water chemistry in explaining observed patterns of forest structure and composition. In simple regression, urban metrics were the most powerful predictors of forest composition but not structure. Results show higher tree diversity but lower mangrove diversity in the most urban forests. Structural measurements, however, were best explained by flooding, surface water chemistry, and non-urban land cover metrics. Nitrogen concentrations best explained stem density and tree size, while flooding metrics best explained stand biomass and basal area, and surrounding vegetation cover best explained canopy cover and height metrics. In multiple regression, land cover and surface water chemistry were more important than flooding, with population density again being the most important variable in explaining mangrove forest diversity. Results show that urbanization is an important influence on mangrove composition and basal area, leading to higher tree diversity and lower basal area, consistent with patterns in terrestrial forests. But urban mangrove forests are also lower in mangrove diversity and tend to have representation only by Laguncularia racemosa. Nitrogen concentrations and surrounding vegetation cover, both of which are indirectly influenced by urbanization, were positively related to tree size and canopy cover and height, respectively. These tests suggest urbanization is an important influence on mangrove forest structure and composition, but that flooding and water chemistry must also be considered when managing these forests.

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Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

Land ◽

10.3390/land10040433 ◽

2021 ◽

Vol 10 (4) ◽

pp. 433

Author(s):

Xiaolan Huang ◽

Weicheng Wu ◽

Tingting Shen ◽

Lifeng Xie ◽

Yaozu Qin ◽

...

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Forest Canopy ◽

Canopy Cover ◽

Google Earth ◽

Tree Canopy ◽

Landsat Data ◽

High Resolution Data ◽

Modis Ndvi ◽

Tree Canopy Cover

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

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Forest canopy cover analysis using UAS lidar

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2017.8127596 ◽

2017 ◽

Author(s):

Qingwang Liu ◽

Shiming Li ◽

Kailong Hu ◽

Yong Pang ◽

Zengyuan Li

Keyword(s):

Forest Canopy ◽

Canopy Cover

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Synchronous retrieval of forest canopy cover by airborn LiDAR and optical remote sensing

2010 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2010.5649315 ◽

2010 ◽

Author(s):

Chunxiang Cao ◽

Min Xu ◽

Yunfei Bao ◽

Hao Zhang

Keyword(s):

Remote Sensing ◽

Forest Canopy ◽

Canopy Cover ◽

Optical Remote Sensing

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Understory biomass estimation based on Structure from Motion data by Multi-layered forest drone survey

10.5194/egusphere-egu21-14670 ◽

2021 ◽

Author(s):

Yupan Zhang ◽

Yuichi Onda ◽

Hiroaki Kato ◽

Xinchao Sun ◽

Takashi Gomi

Keyword(s):

Structure From Motion ◽

Forest Structure ◽

Large Scale ◽

Forest Canopy ◽

Point Clouds ◽

Understory Vegetation ◽

Biomass Estimation ◽

Canopy Openness ◽

Cubic Model ◽

Overlap Analysis

Understory vegetation is an important part of evapotranspiration from forest floor. Forest management changes the forest structure and then affects the understory vegetation biomass (UVB). Quantitative measurement and estimation of&#160; UVB is a step cannot be ignored in the study of forest ecology and forest evapotranspiration. However, large-scale biomass measurement and estimation is challenging. In this study, Structure from Motion (SfM) was adopted simultaneously at two different layers in a plantation forest made by Japanese cedar and Japanese cypress to reconstruct forest structure from understory to above canopy: i) understory drone survey in a 1.1h sub-catchment to generate canopy height model (CHM) based on dense point clouds data derived from a manual low-flying drone under the canopy; ii) Above-canopy drone survey in whole catchment (33.2 ha) to compute canopy openness data based on point clouds of canopy derived from an autonomous flying drone above the canopy. Combined with actual biomass data from field harvesting to develop regression models between the CHM and UVB, which was then used to map spatial distribution of&#160; UVB in sub-catchment. The relationship between UVB and canopy openness data was then developed by overlap analysis. This approach yielded high resolution understory over catchment scale with a point cloud density of more than 20 points/cm2. Strong coefficients of determination (R-squared = 0.75) of the cubic model supported prediction of UVB from CHM, the average UVB was 0.82kg/m2 and dominated by low ferns. The corresponding forest canopy openness in this area was 42.48% on average. Overlap analysis show no significant interactions between them in a cubic model with weak predictive power (R-squared < 0.46). Overall, we reconstructed the multi-layered structure of the forest and provided models of UVB. Understory survey has high accuracy for biomass measurement, but it&#8217;s inherently difficult to estimate UVB only based on canopy openness result.

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ASSESSMENT OF MANGROVE FOREST DEGRADATION THROUGH CANOPY FRACTIONAL COVER IN KARIMUNJAWA ISLAND, CENTRAL JAVA, INDONESIA

Geoplanning Journal of Geomatics and Planning ◽

10.14710/geoplanning.3.2.107-116 ◽

2016 ◽

Vol 3 (2) ◽

pp. 107 ◽

Cited By ~ 6

Author(s):

Muhammad Kamal ◽

Hartono Hartono ◽

Pramaditya Wicaksono ◽

Novi Susetyo Adi ◽

Sanjiwana Arjasakusuma

Keyword(s):

Mangrove Forest ◽

Forest Canopy ◽

Vegetation Index ◽

Spectral Response ◽

Empirical Relationship ◽

Forest Degradation ◽

Empirical Method ◽

Estimation Accuracy ◽

Fractional Cover ◽

Semi Empirical

The Karimunjawa Islands mangrove forest has been subjected to various direct and indirect human disturbances in the recent years. If not properly managed, this disturbance will lead to the degradation of mangrove habitat health. Assessing forest canopy fractional cover (fc) using remote sensing data is one way of measuring mangrove forest degradation. This study aims to (1) estimate the forest canopy fc using a semi-empirical method, (2) assess the accuracy of the fc estimation and (3) create mangrove forest degradation from the canopy fc results. A sample set of in-situ fc was collected using the hemispherical camera for model development and accuracy assessment purposes. We developed semi-empirical relationship models between pixel values of ALOS AVNIR-2 image (10m pixel size) and field fc, using Enhanced Vegetation Index (EVI) as a proxy of the image spectral response. The results show that the EVI provides reasonable estimation accuracy of mangrove canopy fc in Karimunjawa Island with the values ranged from 0.17 to 0.96 (n = 69). The low fc values correspond to vegetation opening and gaps caused by human activities or mangrove dieback. The high fc values correspond to the healthy and dense mangrove stands, especially the Rhizophora sp formation at the seafront. The results of this research justify the use of simple canopy fractional cover model for assessing the mangrove forest degradation status in the study area. Further research is needed to test the applicability of this approach at different sites.

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