Development of the best retrieval models of non-optically active parameters for an artificial shallow lake by random forest algorithm

Karimi, Behnaz; Hashemi, Seyed Hossein; Aghighi, Hossein

doi:10.1016/j.rsase.2023.100926

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…As shown in previous research, the implementation of machine learning algorithms does not limit the number of variables [25,26], and machine learning has many advantages over traditional models [26]: (I) there are no prerequisite requirements for data types and formats in machine learning and (II) machine learning can be used to effectively handle the complex relationships between independent and dependent variables and to delve deeper into the connections between data. Two types of machine learning algorithms, RF and MLP, have been widely used [27][28][29][30][31][32]. RF is a classification or regression model [33] built based on decision trees, and the MLP model is based on the construction of multilayer hidden layers and the mapping output of its results through the activation function.…”

Section: Introductionmentioning

confidence: 99%

Study on the Regeneration Probability of Understory Coniferous Saplings in the Liangshui Nature Reserve Based on Four Modeling Techniques

Zhao,

Sun,

Jia

et al. 2023

Remote Sensing

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Forests are one of the most important natural resources for humans, and understanding the regeneration probability of undergrowth in forests is very important for future forest spatial structure and forest management. In addition, the regeneration of understory saplings is a key process in the restoration of forest ecosystems. By studying the probability of sapling regeneration in forests, we can understand the impact of different stand factors and environmental factors on sapling regeneration. This could help provide a scientific basis for the restoration and protection of forest ecosystems. The Liangshui Nature Reserve of Yichun City, Heilongjiang Province, is a coniferous and broadleaved mixed forest. In this study, we assess the regeneration probability of coniferous saplings (CRP) in natural forests in 665 temporary plots in the Liangshui Nature Reserve. Using Sentinel-1 and Sentinel-2 images provided by the European Space Agency, as well as digital elevation model (DEM) data, we calculated the vegetation index, microwave vegetation index (RVI S1), VV, VH, texture features, slope, and DEM and combined them with field survey data to construct a logistic regression (LR) model, geographically weighted logistic regression (GWLR) model, random forest (RF) model, and multilayer perceptron (MLP) model to predict and analyze the CRP value of each pixel in the study area. The accuracy of the models was evaluated with the average values of the area under the ROC curve (AUC), kappa coefficient (KAPPA), root mean square error (RMSE), and mean absolute error (MAE) verified by five-fold cross-validation. The results showed that the RF model had the highest accuracy. The variable factor with the greatest impact on CRP was the DEM. The construction of the GWLR model considered more spatial factors and had a lower residual Moran index value. The four models had higher CRP prediction results in the low-latitude and low-longitude regions of the study area, and in the high-latitude and high-longitude regions of the study area, most pixels had a CRP value of 0 (i.e., no coniferous sapling regeneration occurred).

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