LiDAR remote sensing of forest structure

Light detection and ranging (LiDAR) technology provides horizontal and vertical information at high spatial resolutions and vertical accuracies. Forest attributes such as canopy height can be directly retrieved from LiDAR data. Direct retrieval of canopy height provides opportunities to model above-ground biomass and canopy volume. Access to the vertical nature of forest ecosystems also offers new opportunities for enhanced forest monitoring, management and planning.

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USO DE DADOS LiDAR NA ESTIMATIVA DE VARIÁVEIS BIOFÍSICA NA AMAZÔNIA, SOB DIFERENTES RESOLUÇÕES ESPACIAIS

Nativa ◽

10.31413/nativa.v6i0.6312 ◽

2018 ◽

Vol 6 ◽

pp. 841

Author(s):

Franciel Eduardo Rex ◽

Ana Paula Dalla Corte ◽

Aline Bernarda Debastiani ◽

Verônica Satomi Kazama ◽

Carlos Roberto Sanquetta

Keyword(s):

Laser Scanner ◽

Light Detection And Ranging ◽

Lidar Data ◽

Above Ground Biomass ◽

Light Detection ◽

Ground Biomass ◽

Carbon Biomass ◽

Significant Difference ◽

De Se ◽

Low Costs

A Floresta Amazônica é conhecida pela sua diversidade e quantidade de carbono estocado na biomassa acima do solo (do inglês, Above-Ground Biomass-AGB), o que atrai grande interesse em quantificar estes recursos naturais. Devido às dificuldades de mensuração desses dados em campo, o sensoriamento remoto oferece oportunidade na quantificação destes parâmetros (biomassa e carbono), de forma rápida e com custos relativamente baixos. Porém, a resolução espacial desses dados pode afetar essa estimativa, como é o caso dos resultantes tamanhos de pixels possíveis de se obter com o processamento de dados LiDAR (Light Detection and Ranging). No presente estudo, foram utilizados dados de laser scanner aerotransportado e de inventário florestal realizado na Floresta Nacional do Jamari, localizado em Rondônia. A partir destes dados, foram obtidos a AGB e Above-Ground Carbon (AGC) para sete diferentes tamanhos de pixel (10, 20, 30, 40, 50, 75 e 100 m) e avaliado seus efeitos nas estimativas de AGB e AGC. Não houve diferença significativas em nível de 95% de probabilidade entre as estimativas de AGB e AGC. Dados LiDAR apresentam grande potencial na obtenção de parâmetros como a AGB e AGC em floresta tropical, mesmo em diferentes resoluções espaciais.Palavras-chave: Floresta tropical, laser scanner, carbono, biomassa. USE OF LiDAR DATA IN THE ESTIMATE OF BIOPHYSICAL VARIABLES IN THE AMAZON, UNDER DIFFERENT SPATIAL RESOLUTIONS ABSTRACT:The Amazon Rainforest is known for its diversity and quantity of carbon stored in above-ground biomass (from English, Above-Ground Biomass-AGB), that attracts great interest in quantifying these natural resources. Due to the difficulties of measuring these data in the field, remote sensing offers the opportunity to quantify these parameters (biomass and carbon), quickly and with relatively low costs. However, the spatial resolution of these data can affect this estimate, as is the case with the resulting possible pixel sizes to be obtained with Light Detection and Ranging (LiDAR) data processing. In the present study, were used data from airborne scanner laser and forest inventory realized in the Jamari National Forest, located in Rondônia. From these data, AGB and Above-Ground Carbon (AGC) were obtained for seven different pixel sizes (10, 20, 30, 40, 50, 75 and 100 m) and evaluated for their effects on AGB and AGC estimates. There was no significant difference at the 95% probability level between AGB and AGC estimates. LiDAR data present great potential in obtaining parameters such as AGB and AGC in tropical forest, even in different spatial resolutions.Keywords: Rain forest, laser scanner, carbon, biomass.

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A Validated and Accurate Method for Quantifying and Extrapolating Mangrove Above-Ground Biomass Using LiDAR Data

Remote Sensing ◽

10.3390/rs13142763 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2763

Author(s):

Rafaela B. Salum ◽

Sharon A. Robinson ◽

Kerrylee Rogers

Keyword(s):

Tree Height ◽

Canopy Height ◽

Lidar Data ◽

Mangrove Forests ◽

Above Ground Biomass ◽

Rhizophora Stylosa ◽

Ground Biomass ◽

Mangrove Tree ◽

Canopy Height Model ◽

Height Model

LiDAR data and derived canopy height models can provide useful information about mangrove tree heights that assist with quantifying mangrove above-ground biomass. This study presents a validated method for quantifying mangrove heights using LiDAR data and calibrating this against plot-based estimates of above-ground biomass. This approach was initially validated for the mangroves of Darwin Harbour, in Northern Australia, which are structurally complex and have high species diversity. Established relationships were then extrapolated to the nearby West Alligator River, which provided the opportunity to quantify biomass at a remote location where intensive fieldwork was limited. Relationships between LiDAR-derived mangrove heights and mean tree height per plot were highly robust for Ceriops tagal, Rhizophora stylosa and Sonneratia alba (r2 = 0.84–0.94, RMSE = 0.03–0.91 m; RMSE% = 0.07%–11.27%), and validated well against an independent dataset. Additionally, relationships between the derived canopy height model and field-based estimates of above-ground biomass were also robust and validated (r2 = 0.73–0.90, RMSE = 141.4 kg–1098.58 kg, RMSE% of 22.94–39.31%). Species-specific estimates of tree density per plot were applied in order to align biomass of individual trees with the resolution of the canopy height model. The total above-ground biomass at Darwin Harbour was estimated at 120 t ha−1 and comparisons with prior estimates of mangrove above-ground biomass confirmed the accuracy of this assessment. To establish whether accurate and validated relationships could be extrapolated elsewhere, the established relationships were applied to a LiDAR-derived canopy height model at nearby West Alligator River. Above-ground biomass derived from extrapolated relationships was estimated at 206 t ha−1, which compared well with prior biomass estimates, confirming that this approach can be extrapolated to remote locations, providing the mangrove forests are biogeographically similar. The validated method presented in this study can be used for reporting mangrove carbon storage under national obligations, and is useful for quantifying carbon within various markets.

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Estimating boreal forest canopy height and above ground biomass using multi-modal remote sensing; a database driven approach

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2016.7729645 ◽

2016 ◽

Cited By ~ 1

Author(s):

Michael Benson ◽

Leland Pierce ◽

Kamal Sarabandi

Keyword(s):

Remote Sensing ◽

Boreal Forest ◽

Forest Canopy ◽

Canopy Height ◽

Above Ground Biomass ◽

Ground Biomass

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Canopy Height and Above-Ground Biomass Retrieval in Tropical Forests Using Multi-Pass X- and C-Band Pol-InSAR Data

Remote Sensing ◽

10.3390/rs11182105 ◽

2019 ◽

Vol 11 (18) ◽

pp. 2105 ◽

Cited By ~ 1

Author(s):

Berninger ◽

Lohberger ◽

Zhang ◽

Siegert

Keyword(s):

High Resolution ◽

Tropical Forests ◽

Forest Degradation ◽

Canopy Height ◽

Carbon Accounting ◽

Lidar Data ◽

Above Ground Biomass ◽

High Resolution Data ◽

Ground Biomass ◽

Resolution Data

Globally available high-resolution information about canopy height and AGB is important for carbon accounting. The present study showed that Pol-InSAR data from TS-X and RS-2 could be used together with field inventories and high-resolution data such as drone or LiDAR data to support the carbon accounting in the context of REDD+ (Reducing Emissions from Deforestation and Forest Degradation) projects.

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Above ground biomass estimation across forest types at different degradation levels in Central Kalimantan using LiDAR data

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2012.01.010 ◽

2012 ◽

Vol 18 ◽

pp. 37-48 ◽

Cited By ~ 72

Author(s):

Karin Kronseder ◽

Uwe Ballhorn ◽

Viktor Böhm ◽

Florian Siegert

Keyword(s):

Biomass Estimation ◽

Lidar Data ◽

Above Ground Biomass ◽

Forest Types ◽

Central Kalimantan ◽

Ground Biomass

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Integrated radar and lidar analysis reveals extensive loss of remaining intact forest on Sumatra 2007–2010

Biogeosciences ◽

10.5194/bg-12-6637-2015 ◽

2015 ◽

Vol 12 (22) ◽

pp. 6637-6653 ◽

Cited By ~ 4

Author(s):

M. B. Collins ◽

E. T. A. Mitchard

Keyword(s):

Average Rate ◽

Forest Biomass ◽

Forest Monitoring ◽

Synthetic Aperture ◽

Lidar Data ◽

Above Ground Biomass ◽

Swamp Forest ◽

L Band ◽

Field Plots ◽

Change Map

Abstract. Forests with high above-ground biomass (AGB), including those growing on peat swamps, have historically not been thought suitable for biomass mapping and change detection using synthetic aperture radar (SAR). However, by integrating L-band (λ = 0.23 m) SAR from the ALOS and lidar from the ICESat Earth-Observing satellites with 56 field plots, we were able to create a forest biomass and change map for a 10.7 Mha section of eastern Sumatra that still contains high AGB peat swamp forest. Using a time series of SAR data we estimated changes in both forest area and AGB. We estimate that there was 274 ± 68 Tg AGB remaining in natural forest (&geq; 20 m height) in the study area in 2007, with this stock reducing by approximately 11.4 % over the subsequent 3 years. A total of 137.4 kha of the study area was deforested between 2007 and 2010, an average rate of 3.8 % yr−1. The ability to attribute forest loss to different initial biomass values allows for far more effective monitoring and baseline modelling for avoided deforestation projects than traditional, optical-based remote sensing. Furthermore, given SAR's ability to penetrate the smoke and cloud which normally obscure land cover change in this region, SAR-based forest monitoring can be relied on to provide frequent imagery. This study demonstrates that, even at L-band, which typically saturates at medium biomass levels (ca. 150 Mg ha−1), in conjunction with lidar data, it is possible to make reliable estimates of not just the area but also the carbon emissions resulting from land use change.

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Forest Structure, Composition and Above Ground Biomass of Tree Community in Tropical Dry Forests of Eastern Ghats, India

Notulae Scientia Biologicae ◽

10.15835/nsb819746 ◽

2016 ◽

Vol 8 (1) ◽

pp. 125-133 ◽

Cited By ~ 8

Author(s):

Sudam Charan SAHU ◽

H.S. SURESH ◽

N.H. RAVINDRANATH

Keyword(s):

Forest Structure ◽

Tree Species ◽

Basal Area ◽

Eastern Ghats ◽

Tropical Dry Forests ◽

Above Ground Biomass ◽

Importance Value ◽

Shorea Robusta ◽

Ground Biomass ◽

C Stocks

The study of biomass, structure and composition of tropical forests implies also the investigation of forest productivity, protection of biodiversity and removal of CO2 from the atmosphere via C-stocks. The hereby study aimed at understanding the forest structure, composition and above ground biomass (AGB) of tropical dry deciduous forests of Eastern Ghats, India, where as a total of 128 sample plots (20 x 20 meters) were laid. The study showed the presence of 71 tree species belonging to 57 genera and 30 families. Dominant tree species was Shorea robusta with an importance value index (IVI) of 40.72, while Combretaceae had the highest family importance value (FIV) of 39.01. Mean stand density was 479 trees ha-1 and a basal area of 15.20 m2 ha-1. Shannon’s diversity index was 2.01 ± 0.22 and Simpson’s index was 0.85 ± 0.03. About 54% individuals were in the size between 10 and 20 cm DBH, indicating growing forests. Mean above ground biomass value was 98.87 ± 68.8 Mg ha-1. Some of the dominant species that contributed to above ground biomass were Shorea robusta (17.2%), Madhuca indica (7.9%), Mangifera indica (6.9%), Terminalia alata (6.9%) and Diospyros melanoxylon (4.4%), warranting extra efforts for their conservation. The results suggested that C-stocks of tropical dry forests can be enhanced by in-situ conserving the high C-density species and also by selecting these species for afforestation and stand improvement programs. Correlations were computed to understand the relationship between above ground biomass, diversity indices, density and basal area, which may be helpful for implementation of REDD+ (reduce emissions from deforestation and forest degradation, and foster conservation, sustainable management of forests and enhancement of forest carbon stocks) scheme.

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