Forest stand age classification using time series of photogrammetrically derived digital surface models

Vastaranta, Mikko; Niemi, Mikko; Wulder, Michael A.; White, Joanne C.; Nurminen, Kimmo; Litkey, Paula; Honkavaara, Eija; Holopainen, Markus; Hyyppä, Juha

doi:10.1080/02827581.2015.1060256

Cited by 27 publications

(20 citation statements)

References 30 publications

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“…Pearson's r values of PPC and ground measures are comparable to those found by Vastaranta et al [60] for mean height between both LiDAR and PPCs, and field estimates. Our R 2 values from OLS regressions (see Figure 4) are also comparable to results in the literature, although we recognize that they are on the lower end of the fit seen in other studies.…”

Section: Vegetation Heightsupporting

confidence: 71%

“…This leads to higher RMSE % values, because the value by which the RMSEs are standardized, is lower. An initial review of the literature reveals that the majority of mean field vegetation or tree heights listed in the UAV and forest structure literature are between 15 and 20 m (e.g., [16,22,43,50,60]), while our mean field vegetation height was 12.44 m (see Table 2). Nevertheless, it is also probable that these large RMSE % values are again a result of the different measurement strategies between field crews and PPC calculations.…”

Section: Vegetation Heightmentioning

confidence: 99%

“…The latter is simply RMSE normalized by the mean of the observed (e.g., field-measured) values [50], and provides a relative measure of error that is more intuitive than the more traditional RMSE. It, too, is used by numerous examinations of PPC data sets within the context of vegetation studies (e.g., [13,[42][43][44]60]). It should be noted that tree DBH measurements were not derivable from our PPCs, and direct comparisons of these estimates through correlation or RMSE calculations were therefore not part of this analysis.…”

Section: Uav Data Collection and Processingmentioning

confidence: 99%

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Use of Unmanned Aerial Vehicles for Monitoring Recovery of Forest Vegetation on Petroleum Well Sites

et al. 2017

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Photogrammetric point clouds (PPCs) provide a source of three-dimensional (3-D) remote sensing data that is well-suited to use over small areas that are within the scope of observation by unmanned aerial vehicles (UAVs). We compared PPC-based structural metrics to traditional ground surveys conducted by field personnel in order to assess the capacity of PPC data to contribute to vegetation-reclamation surveys. We found good statistical agreement between key structural vegetation parameters, such as mean and maximum vegetation height, with PPC metrics successfully predicting most height and tree-diameter metrics using multivariate linear regression. However, PPC metrics were not as useful for estimating ground-measured vegetation cover. We believe that part of the issue lies in the mismatch between PPC-and ground-based measurement approaches, including subjective judgement on behalf of ground crews: a topic that requires more investigation. Our work highlights the emerging value of UAV-based PPCs to complement, and in some cases supplement, traditional ground-based sources of measured vegetation structure.

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Section: Vegetation Heightsupporting

confidence: 71%

Section: Vegetation Heightmentioning

confidence: 99%

Section: Uav Data Collection and Processingmentioning

confidence: 99%

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Use of Unmanned Aerial Vehicles for Monitoring Recovery of Forest Vegetation on Petroleum Well Sites

et al. 2017

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“…Vários estudos usando séries longas de imagens aéreas históricas para a geração de modelos digitais de superfície (MDS) em florestas têm sido realizados (Korpela, 2006;Korpela et al, 2007;Véga & St-Onge, 2008;Nurminen et al, 2015;Vastaranta et al, 2016) para extrair diversos tipos de atributos associados à geometria da floresta. Estruturas florestais podem ser caracterizadas pelas posições das árvores, dimensão vertical e mistura entre tipos de espécies arbóreas.…”

Section: Introductionunclassified

“…Seus estudos indicaram que ambas as técnicas foram capazes de fornecer informações para descrever a superfície do terreno e as propriedades do dossel florestal. As alturas das árvores foram obtidas com um erro aproximado de 1,30 m. Vastaranta et al (2016) propuseram uma abordagem para estimar idades em florestas usando uma série temporal de 68 anos, em que MDSs foram gerados para extrair modelos de alturas de árvores a partir de um modelo digital de terreno obtido por varredura a LASER.…”

Section: Introductionunclassified

Canopy Height Model Generation from Historical Images for Monitoring of Regeneration in Atlantic Rain Forest

Berveglieri¹,

Imai²,

Tommaselli³

et al. 2019

Anuário IGEO UFRJ

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ResumoA reconstrução 3D do dossel de florestas para estudos retrospectivos em longas datas é possível a partir de fotografias aéreas históricas, usando modernos algoritmos fotogramétricos. Isto permite a análise de mudanças estruturais em florestas e o monitoramento do status de conservação ou de regeneração em áreas degradadas ao longo do tempo. Nesse contexto, este artigo realiza a aplicação de técnicas fotogramétricas, combinadas com um modelo de terreno, obtido pelo processamento de uma nuvem LASER, para a geração de modelos de alturas de árvores. Dois modelos, um datado de 1978 e outro de 2017, foram produzidos para avaliar a regeneração de uma floresta tropical de Mata Atlântica. Em seguida, uma segmentação em superpixels foi feita em ambos os modelos de alturas para formar sub-regiões homogêneas no dossel, coincidentes entre as duas datas, e permitir análises estatísticas locais, viabilizando uma classificação em três níveis de altura, a qual foi validada com dados de campo. Como resultado, verificou-se que as áreas desmatadas estão se regenerando, porém apresentam dificuldades para atingir o dossel mais alto. O erro estimado em altura no modelo de superfície histórico foi de 0,55 m, o que é aceitável para os estudos em floresta e é comparável aos resultados obtidos em floresta boreal. Palavras-chave: Fotogrametria histórica; Reconstrução 3D; Modelo de altura absoluta Abstract 3D reconstruction of forest canopy for retrospective studies over time is possible from historical aerial photographs using modern photogrammetric algorithms. This enables the structural change analysis in forested areas and the conservation or regeneration monitoring in degraded areas. In this context, the article presents an application of photogrammetric techniques (combined with a terrain model derived from a LASER point cloud) to generate tree height models. Two height models, one dating from 1978 and another from 2017, were produced to evaluate the regeneration of an Atlantic Rainforest forest. A segmentation based on superpixels was made in both heights models to subdivide the canopy into homogeneous regions (coincident between the two dates). A local statistical analysis was conducted based on a classification in three levels of heights, which was validated with data of field. As a result, it was found that deforested areas are regenerating; however, such areas present difficulties to grow to the highest canopy. The error in height, estimated in the historical surface model, was 0.55 m, which is acceptable for forest studies and is comparable to results obtained in boreal forests.

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Categorisation of mango orchard age groups using Object-Based Image Analysis

Stephen,

Haldar

2024

Arab J Geosci

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Forest stand age classification using time series of photogrammetrically derived digital surface models

Cited by 27 publications

References 30 publications

Use of Unmanned Aerial Vehicles for Monitoring Recovery of Forest Vegetation on Petroleum Well Sites

Use of Unmanned Aerial Vehicles for Monitoring Recovery of Forest Vegetation on Petroleum Well Sites

Canopy Height Model Generation from Historical Images for Monitoring of Regeneration in Atlantic Rain Forest

Categorisation of mango orchard age groups using Object-Based Image Analysis

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