An Approach for Mapping Ecotourism Suitability

SVM displayed a notable performance gap compared with RF, AdaBoost, and XGBoost.

Key factors include soil and water conservation potential, NDVI, slope, and relief.

XGBoost had the highest accuracy at 0.9207.

Soil and water conservation potential and NDVI, which reflect the ecological environment's carrying capacity.

Land evaluation contributes to sustainable regional development by promoting scientific planning, rational use of land resources, and the healthy development of ecotourism, leading to coordinated environmental protection and economic growth.

Unsuitable zones in Cili are larger due to flat topography that has developed into residential areas and farmland, limiting ecotourism resources.

It delineates the spatial configuration of preexisting ecotourism destinations and identifies prospective ecotourism resources.

Robustness, adaptability, generalizability, and effective data fitting even with complex data distributions.

XGBoost had the best performance, followed by AdaBoost, RF, and SVM.

Temperature, precipitation, and soil conservation capacity, accounting for 15.44%, 13.69%, and 13.83% respectively.

XGBoost exhibited superior performance compared to RF, AdaBoost, and SVM.

The principle of sustainable development is central to ecotourism and essential for constructing an assessment framework for ecotourism suitability.

All the features exhibited a positive mutual information estimate, suggesting their relevance to the labels.

High vegetation cover, excellent soil and water conservation capacity, and unique flora and fauna make these zones well-suited for ecotourism.

The unsuitable zones in Sangzhi exhibit a northeast-southwest-oriented strip-like pattern influenced by the local geological environment.

Cili has larger unsuitable zones due to its flat topography, which has developed into residential areas and farmland with limited ecotourism resources.

Machine learning techniques derive feature importance based on inherent attributes of analyzed sample data without human intervention, while traditional methods often rely on expert scoring or AHP, which can introduce subjectivity.

To consolidate geospatial data from different sources and create feature matrices for modeling.

Tree-based models possess greater learning capacities, making them better equipped to capture intricate spatial nuances in the data.

The framework utilizes suitability probabilities applied to individual raster cells, offering a high-resolution approach to local ecotourism planning.

The RF algorithm benefits from the bagging technique, which improves prediction accuracy and increases model diversity using random sampling.

XGBoost focuses on reducing the prediction residuals of the previous tree to improve overall prediction accuracy.

Evaluation findings can identify areas suitable for ecotourism and those needing protection, guiding the formulation of precise land policies and improving land use planning.

Increased relief height contributes to the formation of unique geological features, supporting the development of ecotourism resources with exceptional scenic value.

A higher forest age indicates greater potential for ecotourism resources focused on tall vegetation, enhancing opportunities for visitors to explore plant diversity.

The northern part of Yongding is characterized by urban land use with a significant amount of farmland on the periphery, including permanent basic farmland essential for food security.

The unsuitable zones in Sangzhi exhibit a northeast-southwest-oriented strip-like pattern influenced by the unique topography created by the local geological environment.

To classify data by identifying an optimal hyperplane.

The confusion matrix visualizes the model’s classification performance.

Residential buildings, water conservation land, and permanent basic farmland dominate these areas, which do not support ecotourism development.

The spatial variability in model fit was consistent with the actual growth patterns of ecotourism in the region.

The variance threshold and mutual information methods were integrated for feature selection.

The variance threshold test revealed no redundant features with a variance of zero.

Permanent basic farmland is essential for food security in China and is located on the periphery of the urban area of Zhangjiajie.

The Third Survey and Mapping Institute of Hunan Province.

The urban area of Zhangjiajie was found to be unsuitable for ecotourism development due to the prevalence of human activity.

Land policies regulate ecotourism and can either hinder or support its development, influencing land use planning and the formulation of effective ecotourism policies.

The southern part of the urban area of Zhangjiajie exhibited high suitability for ecotourism development.

The study utilized learning curves, grid search, cross-validation, and regularization techniques to prevent overfitting and enhance model applicability.

Popular tourist destinations include Tianmen Cave, Ghost Valley Trail, and Glass Viewing Platform.

XGBoost exhibited superior performance compared to the remaining three types of models.

Adequate precipitation is essential for vegetation growth and the development of unique ecological landscapes, which are crucial for ecotourism initiatives.

Processing sample points and training the machine learning model using extracted raster layer values.

The confusion matrix and ROC curves were used to visualize the model's performance.

Soil conservation affects a region's environmental carrying capacity, minimizing the negative effects of tourism on the environment.

A higher forest age indicates greater potential for ecotourism resources focused on tall vegetation, allowing visitors to immerse themselves in nature and explore plant diversity.

Accuracy, precision, recall, F1, and area under the curve (AUC) were selected as evaluation metrics.

The predictions of the four models exhibited a high degree of consistency in the Wulingyuan District.

The dual emphasis on conservation and sustainable development aims to balance ecological protection with the expansion of eco-tourism activities, preserving natural habitats while improving local economic wellbeing.

Future research should explore incorporating transfer learning to reuse models or samples from different domains and conduct a global-scale evaluation of ecotourism suitability.

By introducing a kernel function to map the data to a higher-dimensional space.

Precise geographic location data for ecotourism attractions.

Moderately suitable areas are primarily concentrated around the periphery of human-intensive areas and mountainous regions, offering protective functions and development potential.

Reliable training data is crucial for minimizing variations in models and improving their strength and applicability in ecotourism predictions.

The objective function consists of a loss function and a regularization term.

The average prediction across the four models was computed to verify the predictive results.

The government has instituted land use policies such as ecological protection red lines and restrictions on urban expansion.

Outstanding predictive performance, scalability, interpretability, support for parallel computing, and automatic handling of missing values.

The importance of a feature is determined by the frequency with which it is chosen as a split point or the degree of information gain resulting from the split.

Data acquisition and preprocessing, model training, and model evaluation and prediction.

The regularization term controls model complexity to prevent overfitting and improve model generalization ability.

It aligns with fundamental geographic principles, indicating a strong relationship.

The effectiveness of machine learning methods relies on having a sufficient number of labeled samples; limitations may arise if training samples are insufficient or lacking.

g i = ∂ ˆ y ( t − 1 ) i l ( y i , ˆ y ( t − 1 )) and h i = ∂ 2 ˆ y ( t − 1 ) i l ( y i , ˆ y ( t − 1 ))

Temperature influences the attractiveness of ecotourism destinations, especially in regions like Zhangjiajie, which offers a comfortable environment during summer.

The zones with high suitability are mostly located in the central region of Wulingyuan, southern and southeastern regions of Yongding, northwestern, western, and southwestern regions of Cili, and northern, northwestern, and eastern regions of Sangzhi.

The high suitability is related to the presence of Tianmen Mountain National Forest Park and the transitional zone between the basin topography of the urban area of Zhangjiajie and the southern Tianmen Mountain.

High suitability zones were located in central Wulingyuan, southern and southeastern Yongding, and other specified regions.

The AUC of the SVM model was 0.9567.

It represents the trained strong learner based on the weighted sum of weak learners.

The area is characterized by mountainous terrain, lush vegetation, rivers, and diverse natural scenery.

To mitigate the risk of relying on a single model and enhance prediction accuracy.

The overall error rate of AdaBoost was slightly higher than that of SVM, but the difference was not substantial.

XGBoost performed the best with an AUC of 0.9884.

CSPON provides real-time monitoring and early warning systems for national and regional spatial plans, enhancing the scientific rigor and effectiveness of spatial planning and management.

The Wulingyuan District is generally well suited for ecotourism due to its unique natural and cultural tourism resources.

The principle of sustainable development.

The variance threshold and mutual information methods were integrated.

The study demonstrated the effectiveness of using machine learning methods for regional ecotourism suitability evaluation, achieving objective results without human interference in determining factor weights.

To combine multiple weak classifiers into a single robust classifier by adjusting weights assigned to samples and classifiers.

The correlation value was 0.87, indicating a strong connection.

Moderate suitability zones exhibit a distribution pattern similar to highly suitable zones, primarily concentrated around their periphery.

∼ L ( t ) = I ∑ i = 1 ( g i f t ( x i ) + 1/2 h i f 2 t ( x i )) + Ω ( f k )

The Wulingyuan Scenic Spot is a renowned ecotourism destination in the Wulingyuan District.

The highly suitable zones in Cili are characterized by mountainous terrain, high vegetation cover, strong soil and water conservation capacity, and outstanding aesthetic landscape value.

19.34% of the total area was classified as highly suitable for ecotourism.

Transfer learning is a viable option for model transfer in such regions.

It adjusts the position of the hyperplane in the feature space.

Land use can be organized to allow tourism to coexist harmoniously with the natural environment by considering the needs of ecotourism activities and developing targeted land and soil remediation plans to address illegal land use.

Biodiversity and ALSV (Aboveground Live Stock Value).

It revealed no redundant features with a variance of zero.

Constraints include transportation infrastructure, topography, tourism-related amenities, and undeveloped policies.