Tree Age as Adjustment Factor to Ndvi

Berra, Elias Fernando; Fontana, Denise Cybis; Kuplich, Tatiana Mora

doi:10.1590/1806-90882017000300007

Cited by 9 publications

(9 citation statements)

References 27 publications

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“…Afterward, Maritime pine wood production (V) and aboveground biomass production (Wa) were fitted by simple linear regression using the plots' average NDVI as the explanatory variable. Due to the poor correlation observed between the plots' average NDVI and V and Wa, the transformed NDVI, set as NDVI_a = NDVI (t/R) with R = 40 year, was also essayed [12]. In the WEKA software, under "Classify," several modeling algorithms are available, namely simple linear regression.…”

Section: Ndvi and Maritime Pine Production (2007)mentioning

confidence: 99%

“…These indices use the most sensitive spectral bands that allow highlighting a particular target (e.g., land cover and/or its change and temporal trend). Nowadays, the most popular spectral indices in use are the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), respectively, to monitor vegetation productivity [11][12][13][14][15][16] and to detect burned areas and their severity [2][3][4]9,[17][18][19]. These indices have also been incorporated in time series analysis to systematically detect burned areas and monitor long-term vegetation recovery [9,[20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Vegetation Monitoring and Post-Fire Recovery: A Case Study in the Centre Inland of Portugal

Alegria

2022

Sustainability

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Wildfires are a major environmental issue that have an impact on land degradation. Remote sensing spectral indices provide valuable information for short-term mitigation and rehabilitation after wildfires. A study area in the Centre inland of Portugal occupied with Maritime pine and Eucalypts forests and affected by wildfires in 2003, 2017 and 2020 was used. The aims of the study were twofold: (1) to compute the Normalized Difference Vegetation Index (NDVI) and with forest inventory data derivate a Maritime pine production model, differentiate evergreen coniferous forests (e.g., Maritime pine), evergreen broadleaved forests (e.g., Eucalypts), and shrubland, and monitor vegetation and its post-fire recovery; and (2) to compute the Normalized Burn Ratio (NBR) difference between pre-fire and post-fire dates for burn severity levels assessment. The plots of a previous forest inventory were used to follow the NDVI values in 2007 and from 2020 to 2022. An aerial coverage in 2007 and the Sentinel-2 imagery in 2020–2022 were used. Linear models fitted maritime pine production with the transformed NDVI by age, showing a fitting efficiency of 60%. The stratification of cover types by stand development stage and fire occurrence was possible using the NDVI time curve, which also showed the impact of fire and of low precipitation. Cover types were ranked by decreasing NDVI values as follows: mature Eucalypts plantations, young Maritime pine regeneration, mature Maritime pine, young Eucalypts plantations, Strawberry tree shrubland, Eucalypts plantations post-fire, Maritime pine post-fire, tall shrubland, and short shrubland. Vegetation post-fire recovery was lower in higher burn severity level areas. Maritime pine areas have lost their natural regeneration capability due to the wildfires’ short cycles. Spectral indices were effective tools to differentiate cover types and assist in the evaluation of forest and shrubland conditions.

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Section: Ndvi and Maritime Pine Production (2007)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vegetation Monitoring and Post-Fire Recovery: A Case Study in the Centre Inland of Portugal

Alegria

2022

Sustainability

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“…The planting was identified from the coordinates of the central point of the plots, in the images of Landsat 5 satellite, TM sensor, orbit-point 224/81 of September 25th, 2009 and October 17th, 2011, closest to the data collection and free of clouds at the time of acquisition, considering its 16-day temporal resolution. These images were obtained free of charge from the National Institute for Space Research -INPE (2012) and were used to generate the spectral response attributes of the areas corresponding to the sampling points, after the geo-referencing and atmospheric correction operations.…”

Section: Methodsmentioning

confidence: 99%

“…For the atmospheric correction, each original image for the bands TM1 (0.45 to 0.52 µm -B: Blue), TM2 (0.52 µm to 0.60 µm -G: Green), TM3 (0.63 to 0.69 µm -R: Red), TM4 (0.76 to 0.90 µm -NIR: Near InfraRed), TM5 (1.55 to 1.75 µm -SWIR1: Short Wave InfraRed 1) and TM7 (2.09 to 2.35 µm -SWIR2: Short Wave InfraRed 2) of TM/ Landsat 5, was initially converted from digital number (ND) to radiance at the top of the atmosphere (ToA using the spectral radiance parameters of the Landsat 5 satellite (CHANDER et al, 2007). Then, the radiance images at the top of the atmosphere were converted to reflectance on the Earth's surface by means of the atmospheric correction algorithm called Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), available in application Environment for Visualizing Images ENVI 4.5 ® (ITT, 2009), also used by other authors (LI et al, 2020;ZHAO et al, 2019;BERRA et al, 2017). For each scene, information concerning the nominal altitude of the sensor, date and time of satellite pass, the center coordinates of the image, solar elevation angle, average altitude of the terrain, maximum spectral radiance values and minimum sensor and conditions atmosphere were inserted, at the time of the satellite passage (visibility, concentration of gases and aerosols).…”

Section: Methodsmentioning

confidence: 99%

Estimation of wood volume of <i>Eucalyptus dunnii</i> and <i>urograndis</i> of different ages using TM/Landsat 5i

Goergen

Narvaes

Adami³

2021

Ciênc. Florest.

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O objetivo deste estudo foi a geração de modelos de volume comercial com casca (Vcc) em povoamentos de Eucalyptus dunnii e urograndis em diferentes estágios de crescimento e condições de sítio, utilizando dados de reflectância das diferentes regiões do espectro eletromagnético e de índices de vegetação. As regressões lineares múltiplas geradas resultaram em três modelos preditivos, sendo que os mais significativos, em ordem decrescente foram para as espécies de Eucalyptus dunnii e Eucalyptus urograndis com 50 e 59 meses de idade, respectivamente, seguido pelo modelo para Eucalyptus urograndis de 25 meses, apesar da equação de regressão para Eucalyptus dunnii de 34 meses, não tenha sido significativa. O índice de vegetação ajustado ao solo (SAVI 0,5) mostrou-se relevante na construção dos três modelos, pelos diferentes graus de contribuição da componente solo, decorrente do não fechamento das copas em povoamentos jovens de diferentes idades. A região do infravermelho de ondas curtas (SWIR 1) também compôs os modelos de Eucalyptus dunnii e Eucalyptus urograndis, analisados conjuntamente e o modelo preditivo para Eucalyptus dunnii, demonstrando a importância da estrutura do dossel florestal por esta região do espectro eletromagnético estar relacionada ao aumento do conteúdo de lignina e em decorrência da evolução de sua capacidade produtiva.

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“…Furthermore, NDVI has saturation effects on perennial crops such as oil palm, which has a steadier leaf area index over age (Chong et al 2017). This is because NDVI is often saturated, particularly in crops with a dense canopy, and increasing biomass does not enhance reflectance in such instances (Berra et al 2018). Additional research utilising narrow-band indices to reduce this effect should be explored.…”

Section: Perspectivementioning

confidence: 99%

Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

Ang

Shafri

Lee³

et al. 2022

Preprint

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Predicting yields on a bigger scale in a timely and accurate manner is essential for preventing climate risk and ensuring food security, particularly in the light of climate change and the escalation of extreme climatic events. Furthermore, crop yield estimates are affected by various factors including weather, nutrients and management practices. In this study, integrating multi-source data (i.e. satellite-derived vegetation indices (VIs), satellite-derived climatic variables (i.e. land surface temperature (LST) and rainfall precipitation, weather station and field-surveys), we built one multiple linear regression (MLR), three machine learnings (XGBoost, support vector regression, and random forest) and one deep learning (deep neural network) model to predict oil palm yield at block-level within the oil palm plantation. Moreover, time-series moving average and backward elimination feature selection techniques were implemented at the pre-processing stage. The yield prediction models were developed and tested using MLR, XGBoost, support vector regression (SVR), random forest (RF) and deep neural network (DNN) algorithms. Their model performances were then compared using evaluation metrics and generated the final spatial prediction map based on the best performance. DNN achieved the best model performances for both selected (R2=0.91; RMSE= 2.92 tonnes per ha; MAE= 2.56 tonnes per ha and MAPE= 0.09) and full predictors (R2=0.76; RMSE of 3.03 tonnes per ha; MAE of 2.88 tonnes per ha; MAPE of 0.10 tonnes per ha). In addition, advanced ensemble ML techniques such as XGBoost may be utilised as a supplementary for oil palm yield prediction at the block level. Among them, MLR recorded the lowest performance. By using backward elimination to identify the most significant predictors, the performance of all models was improved by 5% - 26% for R2, and that decreased by 3% - 31% for RMSE, 7% - 34% for MAE, and 1% - 15% for MAPE, respectively. DNN generates the most accurate statistical metrics, with an increase of around 15% for R2, 11% for RMSE, 32% for MAE and 1% for MAPE. Our study successfully developed efficient, effective and accurate yield prediction models for timely predicting oil palm yield over a large area by integrating data from multiple sources. These can be potentially handled by plantation management to estimate oil palm yields to speed up the decision-making process for sustainable production.

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Tree Age as Adjustment Factor to Ndvi

Cited by 9 publications

References 27 publications

Vegetation Monitoring and Post-Fire Recovery: A Case Study in the Centre Inland of Portugal

Vegetation Monitoring and Post-Fire Recovery: A Case Study in the Centre Inland of Portugal

Estimation of wood volume of <i>Eucalyptus dunnii</i> and <i>urograndis</i> of different ages using TM/Landsat 5i

Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

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