Area-Based Approach for Mapping and Monitoring Riverine Vegetation Using Mobile Laser Scanning

Saarinen, Ninni; Vastaranta, Mikko; Vaaja, Matti; Lotsari, Eliisa; Jaakkola, Anttoni; Kukko, Antero; Kaartinen, Harri; Holopainen, Markus; Hyyppä, Hannu; Alho, Petteri

doi:10.3390/rs5105285

Cited by 27 publications

(25 citation statements)

References 62 publications

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“…On a larger scale, in a 2 × 2 m grid, MLS point clouds can be classified as bare, field, shrubs, and canopy layer using point cloud statistics (mean height, 95th percentile, [46]). Laser pulse intensity values are used to classify buildings and trees [47], and tree species [48], or the stem (wood material) and foliage of woody vegetation [45].…”

Section: Summarizing the Process Of Characterizing Mixed Floodplain Vmentioning

confidence: 99%

Determining Characteristic Vegetation Areas by Terrestrial Laser Scanning for Floodplain Flow Modeling

Jalonen

Järvelä

Virtanen

et al. 2015

Water

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Detailed modeling of floodplain flows and associated processes requires data on mixed, heterogeneous vegetation at river reach scale, though the collection of vegetation data is typically limited in resolution or lack spatial information. This study investigates physically-based characterization of mixed floodplain vegetation by means of terrestrial laser scanning (TLS). The work aimed at developing an approach for deriving the characteristic reference areas of herbaceous and foliated woody vegetation, and estimating the vertical distribution of woody vegetation. Detailed experimental data on vegetation properties were gathered both in a floodplain site for herbaceous vegetation, and under laboratory conditions for 2-3 m tall trees. The total plant area (Atot) of woody vegetation correlated linearly with the TLS-based voxel count, whereas the Atot of herbaceous vegetation showed a linear correlation with TLS-based vegetation mean height. For woody vegetation, 1 cm voxel size was found suitable for estimating both the Atot and its vertical distribution. A new concept was proposed for deriving Atot for larger areas from the point cloud attributes of small sub-areas. The results indicated that the relationships between the TLS attributes and Atot of the sub-areas can be derived either by mm resolution TLS or by manual vegetation sampling. OPEN ACCESSWater 2015, 7 421

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Section: Summarizing the Process Of Characterizing Mixed Floodplain Vmentioning

confidence: 99%

Determining Characteristic Vegetation Areas by Terrestrial Laser Scanning for Floodplain Flow Modeling

Jalonen

Järvelä

Virtanen

et al. 2015

Water

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“…In MLS, global navigation satellite system (GNSS) and inertia measurement units (IMUs) linked to the scanning system, allow a collection of point clouds during movement. MLS platforms can vary from backpack to logging machine [15,16]. The drawbacks of laser scanning systems are related to the price of the required equipment (20,000 € or more for TLS) and the limited number of commercial programs for data processing of the point cloud data.…”

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Evaluation of a Smartphone App for Forest Sample Plot Measurements

Vastaranta

Latorre

Luoma

et al. 2015

Forests

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Abstract:We evaluated a smartphone app (TRESTIMA TM ) for forest sample plot measurements. The app interprets imagery collected from the sample plots using the camera in the smartphone and then estimates forest inventory attributes, including species-specific basal areas (G) as well as the diameter (DgM) and height (HgM) of basal area median trees. The estimates from the smartphone app were compared to forest inventory attributes derived from tree-wise measurements using calipers and a Vertex height measurement device. The data consist of 2169 measured trees from 25 sample plots (32 m × 32 m), dominated by Scots pine and Norway spruce from southern Finland. The root-mean-square errors (RMSEs) in the basal area varied from 19.7% to 29.3% and the biases from 11.4% to 18.4% depending on the number of images per sample plot and image shooting location. DgM measurement bias varied from −1.4% to 3.1% and RMSE from 5.2% to 11.6% depending on the tree species. Respectively, HgM bias varied from 5.0% to 8.3% and RMSE 10.0% to 13.6%. In general, four images captured toward the center of the plot provided more accurate results OPEN ACCESSForests 2015, 6 1180 than four images captured away from the plot center. Increasing the number of captured images per plot to the analyses yielded only marginal improvement to the results.

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“…Tais efeitos causam alterações locais e globais, sendo o seu entendimento ainda limitado (Jia et al, 2014). Para entender as alterações em escala local dos diversos componentes da paisagem, são necessários dados com resolução espacial métrica ou, se disponível, submétrica (Flener et al, 2013;Saarinen et al, 2013).…”

Section: Introductionunclassified

Árvore De Decisão E Análise Baseada Em Objetos Na Classificação De Imagens Com Resolução Espacial Submétrica Adquiridas Por Vant

2017

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Resumo:As imagens com resolução espacial submétrica, como as adquiridas por Veículo Aéreo Não Tripulado (VANT), apresentam alta variabilidade espectral. A análise baseada em objetos possibilita a redução dessa variabilidade e a geração de atributos, aumentando a dimensionalidade do conjunto de dados. Funções de seleção de atributos relevantes e de reforço, disponíveis no algoritmo C5.0, e a análise baseada em objetos facilitam a classificação dessas imagens. Este trabalho teve como objetivos: (i) avaliar as classificações dos objetos em relação aos parâmetros de seleção de atributos (winnow), de reforço (trial) e do Número Mínimo de Amostras (NMA), (ii) determinar os atributos preditivos mais importantes e (iii) comparar a classificação por Árvore de Decisão com Máquina de Vetores Suporte. Para segmentar a imagem foi utilizado o método crescimento de regiões e para a classificação o algoritmo C5.0. Os valores dos parâmetros trial (10) e NMA (5) resultaram acurácias superiores a 0,8. Com esses parâmetros o valor do kappa foi superior a SVM. Ao habilitar o parâmetro winnow foi observado uma redução da dimensionalidade do conjunto de dados de aproximadamente 30%. Os dois atributos mais importantes na discriminação das classes foram a razão entre as bandas verde e azul e a média dos valores das elevações. Palavras-chave: mineração de dados, mapeamento da cobertura da terra, Veículo Aéreo Não Tripulado (VANT). Abstract:Remotely sensed images acquired with submetrical spatial resolution, such as those captured by Unmanned Aerial Vehicle (UAVs), usually present a high spectral variability. An object based

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Area-Based Approach for Mapping and Monitoring Riverine Vegetation Using Mobile Laser Scanning

Cited by 27 publications

References 62 publications

Determining Characteristic Vegetation Areas by Terrestrial Laser Scanning for Floodplain Flow Modeling

Determining Characteristic Vegetation Areas by Terrestrial Laser Scanning for Floodplain Flow Modeling

Evaluation of a Smartphone App for Forest Sample Plot Measurements

Árvore De Decisão E Análise Baseada Em Objetos Na Classificação De Imagens Com Resolução Espacial Submétrica Adquiridas Por Vant

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