Predictive performance of random forest on the identification of mangrove species in arid environments

Aviña-Hernández, Judith; Ramírez-Vargas, Mariana; Roque-Sosa, Francisco; Martínez‐Rincón, Raúl O.

doi:10.1016/j.ecoinf.2023.102040

Cited by 6 publications

(6 citation statements)

References 69 publications

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“…Model 2 might have involved some adjustments or additional parameters that improved its accuracy compared with model 1. Similarly, model 3, while having a modest difference in accuracy from model 4, might have also involved more careful parameter configuration to achieve significant performance improvements 78 . The kappa value of models 1 to model 4 was 0.696, 0.778, 0.80, and 0.78, respectively (Fig.…”

Section: Resultsmentioning

confidence: 98%

“…This is common in the classification of other vegetation, bare land, or water bodies 88 . The decrease in accuracy can be caused by several factors, such as phenological similarities, incorrect input parameters in modeling, and the level of heterogeneity of the objects being described 78 , 89 …”

Section: Discussionmentioning

confidence: 99%

“…This test involves 70% training data and 30% testing data. Predictive performance of mangrove classification refers to the extent to which a classification model can make accurate predictions related to the category or type of mangrove based on the parameters used as input for modeling 54 , 78 , 79…”

Section: Methodsmentioning

confidence: 99%

“…Predictive performance measures the model's ability to produce predictive results in accordance with the actual data. 78 A good model has high predictive performance, i.e., the predictions made match the reality or original data. This test involves 70% training data and 30% testing data.…”

Section: Training Sampling and Predictive Performancementioning

confidence: 99%

“…88 The decrease in accuracy can be caused by several factors, such as phenological similarities, incorrect input parameters in modeling, and the level of heterogeneity of the objects being described. 78,89 4.5 Spatial Distribution of Mangrove Species Based on the findings of the image analysis and field reflection measurements, the mangrove species identified in South Lampung Beach were R. mucronata, S. alba, A. lanata, and A. marina. R. mucronata species in the best model (model 3) has an area of 109.72 ha with a percentage of 16.69%; S. alba has an area of 17.83 ha with a percentage of 2.71%; A. lanata has an area of 222.98 ha with a percentage of 33.91%; and A. marina has an area of 307.06 ha with a percentage of 46.69%.…”

Section: Best Model For Mangrove Species Classificationmentioning

confidence: 99%

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Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm

Simarmata,

Wikantika,

Darmawan

et al. 2024

J. Appl. Rem. Sens.

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Mangroves maintain coastal balance and have the greatest potential for carbon sequestration. Most mapping studies on mangroves have focused on their extent and distribution and rarely featured mangrove species. Therefore, the objective of our study is to investigate mangrove species mapping from integrated Sentinel-2 imagery and field spectral data using a random forest (RF) algorithm. Study areas are located in East and South Lampung, Indonesia. The field samples used represented 144 points of mangrove species. The classification method used an RF algorithm and four models with varying parameters: model 1 with Sentinel-2; model 2 with both Sentinel-2 and field spectral data; model 3 with Sentinel-2, field spectral data, and spectrally transformed data; and model 4 only with spectrally transformed data. The results showed that Rhizophora mucronata, Sonneratia alba, Avicennia lanata, and Avicennia marina were the most common mangrove species in these areas, with reflectance values in the range of 0.002 to 0.493, 0.006 to 0.833, 0.014 to 0.768, and 0.002 to 0.758. Permutation importance (PI) that affects the classification model is the red band, near-infrared, and green normalized difference vegetation index, where the most PI in model 3 is 0.283. The highest level of agreement for mangrove species is found in model 3. Model 3 is the best parameter for RF classification that showed the best mapping accuracy, with the overall accuracy, user accuracy, producer accuracy, and kappa value being 81.25%, 81.68%, 81.25%, and 0.80, respectively.

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Section: Resultsmentioning

confidence: 98%