A new methodology for the estimation of biomass of coniferdominated boreal forest using NOAA AVHRR data

Häme, Tuomas; Salli, Arto; Andersson, Kristin; Lohi, Anssi

doi:10.1080/014311697217053

Cited by 105 publications

(59 citation statements)

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“…Various researchers developed different empirical models to estimate global forest biomass using satellite-derived NDVI and geographical location [30,32,94,95]. Among them, we followed the model used by [32] because this model used NDVI max to predict the biomass C density, which is also suitable for Nepal, rather than using growing season and annual average NDVI.…”

Section: Discussionmentioning

confidence: 99%

Spatial and Temporal Variation of NDVI in Response to Climate Change and the Implication for Carbon Dynamics in Nepal

Baniya

Tang

Huang

et al. 2018

Forests

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Abstract:Nepal is a country of contrast, with varying altitude, climate and vegetation from the top of high mountains to the tropical forest in low lands. The terrestrial vegetation has rapidly been altered by climate change in Nepal. The spatial and temporal evolution of vegetation and its linkage to climatic variables were analyzed using the Normalized Difference Vegetation Index (NDVI) obtained from Advanced Very High Resolution Radiometer (AVHRR) sensors. A linear regression model and Sen's slope method were used to estimate NDVI trends and the Pearson correlation between NDVI and climatic variable, i.e., temperature and precipitation were calculated to identify the role of climate in vegetation changes. The carbon dynamics were also measured using a biomass carbon density estimation model. Results showed that NDVI experienced an overall increasing trend in Nepal from 1982-2015. The NDVI significantly increased at the rate of 0.0008 year −1 (p < 0.05) with seasonal variation of 0.0004 year −1 , p > 0.05; 0.0007 year −1 , p < 0.05; 0.0008 year −1 , p < 0.05 and 0.0007 year −1 , p > 0.05 in winter, pre-monsoon, monsoon and post-monsoon seasons, respectively. The NDVI relative change ratio (RCR) was 6.29% during last 34 years in Nepal. The correlation between NDVI and temperature was significantly positive (r = 0.36, p = 0.03), but there was a negative correlation with precipitation (r = −0.21, p = 0.28). Altogether, 82.20% of the study areas showed a positive correlation with temperature in which 34.97% was significant and 69.23% of the area had a negative correlation (16.35% significant, p < 0.05) with precipitation. In addition, NDVI-based carbon estimation showed that Nepal's forest total carbon stock is 685.45 × 10 6 t C (i.e., an average of 115.392 t C/ha) with an annual carbon sequestration rate of 0.10 t C/ha from 1982-2015. The results suggest that NDVI variation is more sensitive to temperature than precipitation and it is valuable to measure carbon dynamics in Nepal.

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Section: Discussionmentioning

confidence: 99%

Spatial and Temporal Variation of NDVI in Response to Climate Change and the Implication for Carbon Dynamics in Nepal

Baniya

Tang

Huang

et al. 2018

Forests

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“…Para a estimativa de volume de madeira florestal em escala local e regional, são necessários dados provenientes de instrumentos sensores. Nesse sentido, podem ser utilizados dados do sensor TM da série de satélites Landsat como já demonstrado por diversos autores (FAZAKAS et al, 1999;HÄME et al, 1997;KRANKINA et al, 2004;TOMPPO et al, 2002;TURNER et al, 2004).…”

Section: Introductionunclassified

Estimativa do volume total de madeira em espécies de eucalipto a partir de imagens de satélite Landsat

Berra¹,

Brandelero²,

Pereira³

et al. 2012

Ciênc. Florest.

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RESUMOModelos relacionando respostas espectrais com parâmetros biofísicos visam estimar variáveis, tais como volume de madeira, sem a necessidade de avaliações constantes em campo. Objetivou-se desenvolver modelos estimadores de volume de madeira a partir de imagens TM do Landsat 5, com base em dados de inventário florestal regional. A imagem foi georreferenciada e convertida para imagem reflectância espectral. Em seguida, foram geradas as imagens índice NDVI (índice de vegetação da diferença normalizada) e SR (razão simples). Os valores das reflectâncias das bandas (TM1, TM2, TM3 e TM4) e dos índices (NDVI e SR) foram relacionados com os volumes de madeira. As maiores correlações com volume foram com os índices NDVI e SR. A seleção de variáveis foi feita pelo método Stepwise, o qual retornou três modelos de regressão como significativos para explicar a variação em volume. Por fim, selecionou-se o modelo com melhor ajuste (volume = -830,95 + 46,05 × (SR) + 107,47 × (TM2)), o qual foi aplicado sobre a imagem Landsat onde os pixels passaram a representar o volume estimado em m³/ha nas unidades de produção de Eucalyptus sp. Este modelo, significativo ao nível de 95 % de confiança, explica 68 % da variação de volume de madeira. Palavras-chave: parâmetros biofísicos; reflectância; índices de vegetação. ABSTRACTModels relating spectral answers with biophysical parameters aim estimate variables, such as wood volume, without the necessity of frequent field measurements. The objective was to develop models to estimate wood volume by Landsat 5 TM images, supported by regional forest inventory data. The image was georeferenced and converted to spectral reflectance. After, the images-index NDVI (Normalized Difference Vegetation Index) and SR (Simple Ratio) was generated. The reflectance values of both spectral bands

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“…The difference in overall reflectance is attributed to variation in surface cover (Wang et al 2005). Hame et al (1997) stated that the difference in vegetation structure is attributed to variation in LAI. In general, AVHRR-LAI is generally described by higher values over the overlapping period as compared with MODIS-LAI.…”

Section: Spatial Discrepancy Between Original Avhrr-and Modis-derivedmentioning

confidence: 99%

Estimate of extended long-term LAI data set derived from AVHRR and MODIS based on the correlations between LAI and key variables of the climate system from 1982 to 2009

Peng

Dong

2013

International Journal of Remote Sensing

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The global long-term leaf area index (LAI) series is a critical variable to validate the terrestrial ecological processes of simulation by Earth system models (ESMs) and ESM input. However, the lack of long-term LAI data restricts studies on the interaction between atmosphere and biosphere. This study focuses on obtaining a robust long-term LAI data set through combining Advanced Very High Resolution Radiometer (AVHRR; available from August 1981 to May 2001) and Moderate Resolution Imaging Spectroradiometer (MODIS; available from January 2000 to December 2009) data sets, and on investigating the relationship between LAI data and the key variables of the climate system. Regional discrepancies in LAI exist between these two data sets. In high northern latitudes, there are significant differences (>1.7 m 2 m −2 ) between AVHRR-and MODIS-derived LAI during the overlapping period from January 2000 to May 2001 because of effects of vegetation structure, low saturation threshold of remote-sensing data and cloud contamination, and the effects of aerosols and atmospheric water vapour on the AVHRR sensor. Using the LAI data set derived from MODIS as the benchmark, AVHRR-derived LAI data from the same periods as those of MODIS were first calibrated through a region-based linear regression method. Then the regression relationship was employed to other periods of AVHRR-derived LAI, resulting in a complete long-term LAI data set. The results showed that the data set has a better convergence and continuity than the original data set, with the regional discrepancies in LAI significantly reduced. Further analyses of correlations between LAI and variables of the climate system demonstrate that the modified LAI is more suitable to describe the response of vegetation to variables in the climate system. This is probably attributed to temperature as the main driver affecting vegetation and to the persistent presence of frozen soil in this region. In comparison with results from previous studies, the response to temperature, precipitation, and soil moisture in the long-term modified LAI data is more reasonable than for unmodified LAI.

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A new methodology for the estimation of biomass of coniferdominated boreal forest using NOAA AVHRR data

Cited by 105 publications

References 0 publications

Spatial and Temporal Variation of NDVI in Response to Climate Change and the Implication for Carbon Dynamics in Nepal

Spatial and Temporal Variation of NDVI in Response to Climate Change and the Implication for Carbon Dynamics in Nepal

Estimativa do volume total de madeira em espécies de eucalipto a partir de imagens de satélite Landsat

Estimate of extended long-term LAI data set derived from AVHRR and MODIS based on the correlations between LAI and key variables of the climate system from 1982 to 2009

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