Estimation of forest aboveground biomass using combination of Landsat 8 and Sentinel-1A data with random forest regression algorithm in Himalayan Foothills

Purohit, Saurabh; Patel, N. R.

doi:10.1007/s42965-021-00140-x

Cited by 22 publications

(7 citation statements)

References 105 publications

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“…The distinctiveness of the proposed technique from the conventional techniques can be elucidated in two forms-(i) data used and (ii) methodology adopted. (i) Data used: It is seen that the works of literature [38][39][40][41][42][43][44][45][46][47] use multispectral, hyperspectral, radar, and optical images. The proposed technique uses pan-sharpened versions of the multispectral images, which are available in the portals [1][2][3][4] .…”

Section: Significance Of the Proposed Techniquementioning

confidence: 99%

“…This data uniqueness indicates that the conventional vegetation detection/indices depend on the band difference and fusion of the bands in the images. (ii) Methodology adopted: While seeing the contributions in the closely related literature works [38][39][40][41][42][43][44][45][46][47] to this work, either single or combination of metrics (max. up to eight metrics) are used to validate the technique's effectiveness for LULCC and vegetation detection applications.…”

Section: Significance Of the Proposed Techniquementioning

confidence: 99%

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Effectiveness of Super-Resolution Technique on Vegetation Indices

Rohith

Kumar

2021

IEEE Access

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The identification and interpretation of remote sensed (RS) objects in an image depend on how well the sensor captures the region. In rare cases, RS images may be vulnerable to a lack of interpretability issues in some parts of the image due to the sensor's limits and preprocessing techniques. Conventionally, the interpretation of the land cover pattern's shape and size is apparent when the distance between the sensor and object is closer to the visualization level of objects and viable with digital airborne imagery. In this paper, integration of the Super-Resolution (SR) technique in the high-resolution imagery to achieve the closer visualization level for mapping the vegetation is proposed. This approach enables the higher interpretive potential to define the land pattern's shape and size with very high spatial resolution, closer proximity, detailed and distinguishable patterns. This approach helps to precisely predict the total vegetated study area for land use and land cover changes (LULCC) and chlorophyll-rich vegetation applications. The proposed algorithm is carried out in two phases. In the first phase, the SR technique applied test images are tested for vegetation detection and mapping the vegetation information in a region with fourteen vegetation metrics. In the second phase, similar testing is done without applying SR for input test images. The experiment revealed that test images using the SR technique yielded higher average values of 5 percent and 1.1 percent for the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC), respectively, as compared to images tested using the non-SR technique.

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Section: Significance Of the Proposed Techniquementioning

confidence: 99%

Section: Significance Of the Proposed Techniquementioning

confidence: 99%

Effectiveness of Super-Resolution Technique on Vegetation Indices

Rohith

Kumar

2021

IEEE Access

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“…Karlson et al [22] showed that L8 data, combined with RF models and optimized variables, were able to accurately map AGB in Burkina Faso, offering a viable, data-accessible method for woodland analysis. Another study achieved superior accuracy in forest AGB estimation across diverse ecosystems using the RF model, with multispectral satellite data and advanced variable selection techniques used to effectively characterize the forest's spatial distribution and complexity [23]. Some studies have also suggested that RF models are typically more resilient to outliers and noise, as well as more robust [24].…”

Section: Introductionmentioning

confidence: 99%

Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna

Wu,

Ou,

et al. 2024

Remote Sensing

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Improving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, which include various vegetation types, such as Pinus kesiya var. langbianensis, oak, Hevea brasiliensis, and other broadleaf trees. In this study, 2016 forest management inventory data are integrated with remote sensing variables from Landsat 8 OLI (L8) and Sentinel 2A (S2) imagery to estimate forest AGB. The forest age and aspect were utilized as stratified variables to construct the random forest (RF) models, which may improve the AGB estimation accuracy. The key findings are as follows: (1) through variable screening, elevation was identified as the main factor correlated with the AGB,with texture measures derived from a pixel window size of 7 × 7 perform best for AGB sensitivity , followed by 5 × 5, with 3 × 3 being the least effective. (2) A comparative analysis of imagery groups for the AGB estimation revealed that combining L8 and S2 imagery achieved superior performance over S2 imagery alone, which, in turn, surpassed the accuracy of L8 imagery. (3) Stratified models, which integrated aspect and age variables, consistently outperformed the unstratified models, offering a more refined fit for lowland tropical forest AGB estimation. (4) Among the analyzed forest types, the AGB of P. kesiya var. langbianensis forests was estimated with the highest accuracy, followed by H. brasiliensis, oak, and other broadleaf forests within the RF models. These findings highlight the importance of selecting appropriate variables and sensor combinations in addition to the potential of stratified modeling approaches to improve the precision of forest biomass estimation. Overall, incorporating stratification theory and multi-source data can enhance the AGB estimation accuracy in lowland tropical forests, thus offering crucial insights for refining forest management strategies.

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“…Among optical remote sensing data, Landsat products have emerged as a primary source for forest AGB estimation due to their large coverage, short acquisition time, high measurement accuracy, non-destructiveness, and accessibility as a free remote sensing data source [19]. Vegetation indices, spectral bands, and texture features have been commonly used spectral feature information for biomass estimation [34,38,39]. Additionally, researchers have incorporated auxiliary environmental factors such as topography, bioclimate, and soils into biomass estimation models to limit predicted biomass spatial distributions, reduce spatial uncertainty, and improve estimation accuracy [34,40].…”

Section: Introductionmentioning

confidence: 99%

Application of MaxEnt Model in Biomass Estimation: An Example of Spruce Forest in the Tianshan Mountains of the Central-Western Part of Xinjiang, China

Xue

Wang

2023

Forests

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Accurately estimating the above-ground biomass (AGB) of spruce forests and analyzing their spatial patterns are critical for quantifying forest carbon stocks and assessing regional climate conditions in China’s drylands, with significant implications for the sustainable management and conservation of forest ecosystems in the Tianshan Mountains. The K-Means clustering algorithm was used to divide 144 measured AGB samples into four AGB classes, combined with remote sensing data from Landsat products, 19 bioclimatic variables, 3 topographical variables, and 3 soil variables to generate probability distributions of four AGB classes using the MaxEnt model. Finally, the spatial distribution of AGB was mapped using the mathematical formulae available in the GIS software. Results indicate that (1) the area under the receiver operating characteristic curve (AUC-ROC) of the AGB models for all classes exceeded 0.8, indicating satisfactory model accuracy; (2) the dominant factors affecting the distribution of different AGB classes varied. The primary dominant factors for the first–fourth AGB classes model were altitude (20.4%), precipitation of warmest quarter (Bio18, 15.7%), annual mean temperature (Bio1, 50.5%), and red band (Band4, 26.7%), respectively, and the response curves indicated that the third AGB model was more tolerant of elevation than the first and second AGB classes; (3) the AGB has a spatial distribution pattern of being higher in the west and low in the east, with a “single-peaked” pattern in terms of latitude, and the average AGB of pixels was 680.92 t·hm−2; (4) the correlation coefficient between measured and predicted AGB is 0.613 (p < 0.05), with the average uncertainty of AGB estimation at 39.32%. This study provides valuable insights into the spatial patterns and drivers of AGB in spruce forests in the Tianshan Mountains, which can inform effective forest management and conservation strategies.

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Estimation of forest aboveground biomass using combination of Landsat 8 and Sentinel-1A data with random forest regression algorithm in Himalayan Foothills

Cited by 22 publications

References 105 publications

Effectiveness of Super-Resolution Technique on Vegetation Indices

Effectiveness of Super-Resolution Technique on Vegetation Indices

Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna

Application of MaxEnt Model in Biomass Estimation: An Example of Spruce Forest in the Tianshan Mountains of the Central-Western Part of Xinjiang, China

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