1. Introduction
The vigorous development of rural leisure and comprehensive tourism, and emphasized
the important value of the tourism industry in multiple speeches, determining its
primary position in the five major happiness industries [1]. With the surge in demand for rural tourism, a large number of traditional agriculture
and rural areas are moving towards the development path of tourism transformation,
which has also promoted economic growth in rural areas, expanded employment opportunities
for villagers, and become an important way to achieve rural revitalization [2,
3]. However, some regions excessively pursue the economic benefits of the tourism industry,
completely disregarding the local natural background conditions, blindly developing
tourism, and even directly imitating successful cases from other regions. Not only
does it cause homogenization of rural scenery and tourism products, but it also disrupts
the normal operation of rural ecosystems, leading to a series of ecological problems
such as hindered ecological processes, decline in ecosystem service capabilities,
and destruction of species diversity [4,
5]. In 2017, the “National Tourism Work Report” clearly pointed out the misconceptions
of “blind development, excessive development, and predatory development” in tourism
development, and the phrase “wholeheartedly fishing and damaging the environment”
exposed the real situation of some rural tourism development [6].
The root cause of this phenomenon lies in a lack of understanding of the landscape
science, it is not only necessary to explore the specific methods of tourism oriented
rural landscape planning and design, spatial optimization, but also to grasp the laws
and influencing factors of landscape evolution from a temporal perspective, attach
importance to the scientific evaluation of rural landscape resources, and implement
rural landscape planning, design, and construction according to local conditions.
Rural planning design units in various provinces and cities have carried out a large
number of village planning and design practices, and have made certain progress. However,
due to a lack of profound understanding and practical experience in rural areas, the
formulation of village planning is also in the exploratory stage. From the current
results of village planning, there are still some problems [7,
8]. The rural planning process often relies heavily on government intentions and the
experience of design units, lacking scientific evaluation of natural and cultural
resources. This results in subjectivity and a focus on living and production spaces,
such as construction land development and permanent farmland protection, while neglecting
the ecological environment and optimization of natural spatial structures [9,
10]. A limited understanding of rural spatial dynamics leads to static planning approaches
that overlook the laws of spatial development [11]. Additionally, insufficient theoretical research contributes to the lack of systematic
methods in rural planning. Current academic studies often focus on landscape style,
regional culture, and industry-scenery integration, which are important for rural
protection and development but fail to address ecological and environmental issues
[12]. Research on rural ecological landscapes remains at the theoretical level, with limited
practical guidance for landscape planning. The interaction between landscape pattern
and process, central to landscape ecology, is crucial for understanding landscape
mechanisms, evaluating resources, and optimizing space, offering essential theoretical
support for landscape planning and ecological space management [13,
14].
However, there are still shortcomings in the research and application of this theory
in rural areas. It is necessary to conduct rural landscape research based on landscape
ecology, grasp the spatial evolution process and laws of rural areas, and explore
spatial planning and design approaches. The requirement to establish a village planning
database based on GIS platform for planning results indicates that China’s rural planning
and construction work is undergoing a transformation from traditional and extensive
to precise and scientific planning models [15,
16]. The digitization of rural planning is not only reflected in the digitization of
planning results, but also in the inevitable trend of rural spatial planning based
on data analysis and evaluation. GIS technology provides technical support for the
scientific evaluation, analysis, and spatial optimization of rural landscape resources
[17,
18]. Firstly, identify the “ecological source area” of the Mashan Peninsula, and establish
an “ecological resistance surface” in combination with the actual situation.
2. Theory and Model of Hierarchical Iterative Multimodal Interactive Learning Method
2.1. Teaching Interaction Model for Augmented Reality Integrated Learning Environment
There are two widely accepted definitions of augmented reality. They believe that
augmented reality is a state between “reality” and “virtuality”. As shown in Eqs.
(1) and (2), he distributes the real environment and virtual environment at both ends, while
augmented reality and augmented virtual are located in the middle and included in
the mixed reality scene.
He advocates that augmented reality technology should have three specific features:
the combination of virtual and real, three-dimensional registration, and real-time
interaction. Since the invention of a headshot display, people’s exploration of augmented
reality technology has not stopped. As shown in Eqs. (3) and (4).
As shown in Eqs. (5) and (6), This provides educators with a new teaching perspective, allowing learners to interact
in a more natural way, significantly enhancing their engagement. Especially in the
teaching of abstract content, teaching effects that are difficult to achieve solely
through image display can be achieved through augmented reality technology.
Fixed equipment, can be used in museums or public places. As shown in Eqs. (7) and (8), they can be composed of a screen, a binocular with a base, or a projector, which
can directly display images on physical objects. Mobile devices can be freely carried
by users, and the most. Head mounted displays are becoming increasingly popular.
In AR devices, there are usually two types of perspective methods used, namely optical
perspective based on optical principles and video perspective based on camera principles.
Optical perspective equipment consists of a semitransparent screen, as shown in Eqs.
(9) and (10), where information is covered on the screen and appears to be a part of the real
world. Fixed transparent screens typically use optical perspective. Video perspective
devices use one or more cameras to capture scenes, add virtual information to recorded
images, and display it to users. Most handheld devices use video perspective.
2.2. Hierarchical Iterative Multimodal Teaching Interaction Strategy
Three basic features of augmented reality technology were proposed, namely the combination
of virtual and real, 3D registration, and real-time interaction. Augmented reality
technology can achieve a combination of virtual and real effects through optical or
video perspective, allowing users to clearly see the real world within their field
of view while also seeing the virtual objects displayed by the device. As shown in
Eqs. (11) and (12), unlike the complete immersion in virtual reality, augmented reality brings a feeling
that is not detached from the real world.
The three-dimensional registration technology possessed by augmented reality technology
calculates the position information through the transmitter and perceptron of sensor
signals, thereby determining the position and adjusting the position of the displayed
virtual object in the real environment. Real time interaction technology stands out
as the interaction between devices and users, as shown in Eqs. (13) and (14). In addition to traditional interaction through input devices, it can also perform
interaction in areas such as voice and action. Its efficient computation can also
ensure real-time interaction.
Unlike virtual reality presents the virtual environment to users, as shown in Eqs.
(15) and (16), augmented reality technology can use virtual data augmentation to overlay virtual
objects on top of the real world on the basis of displaying the real world.
As shown in Eqs. (17) and (18), augmented reality technology has many advantages that other technologies cannot
match, so it has broad application space and bright development prospects in the field
of education. Visualizing both real and virtual content simultaneously is crucial
in the field of augmented reality.
Use a pair of glasses to display enhanced content overlaid on the real surrounding
environment. The difference between HoloLens released by Microsoft and most other
similar devices is that it is a complete AR system running the Windows 10 operating
system and includes a central processing unit, customized holographic processing units,
various types of sensors, as shown in Eqs. (19) and (20), and optical lenses viewed using a holographic projector. Wearers can see virtual
objects placed on top of the real environment while seeing the real environment, and
operate through eye contact, voice, and gestures, giving people a brand new visual
experience.
3. Response Of Ecosystem Service Value to Changes in Landscape Pattern of Tourism
Oriented Rural Areas Based on GIS Algorithms
3.1. Response Analysis Method for The Value of Ecosystem Services In Tourism Oriented
Rural Areas
The HoloLens device features a color camera that adjusts resolutions to capture screenshots
and videos aligned with the user’s line of sight, but it is not used for tracking
or positioning [19,
20]. It also includes four grayscale tracking cameras—-two in the front with high field
overlap and two on the sides with minimal overlap [21,
22]. Additionally, the device has a TOF depth-sensing camera for measuring pixel range
using “long pulse” (0.8-3.5m) and “short pulse” (0-0.8m) modes [23,
24]. The short pulse is primarily for hand gesture recognition, while the long pulse
is used for environmental mapping. The TOF camera’s view overlaps with the color camera
but only partially [25,
26]. The HoloLens SDK retrieves camera positions using two reference systems: TOrigin,
which describes the device’s position with translation components only, and TDevice,
which includes both translation and orientation relative to TOrigin. Fig. 1 illustrates the interactive GIS decision tree optimization algorithm, showing how
the system manages positional data in real-time applications [27,
28].
Fig. 1. Interactive GIS decision tree optimization algorithm diagram.
To obtain absolute position through each camera, the principle of generating indoor
3D data involves using HoloLens' spatial mapping function. By switching HoloLens to
developer mode and utilizing Microsoft’s project resource package, users can perform
spatial scanning and reconstruct indoor 3D spaces. During this process, users must
wear HoloLens, scanning in all directions indoors and briefly pausing to ensure complete
data capture. The resulting spatial map allows users to see a triangular mesh that
closely fits real indoor objects. Immersion theory, also known as flow theory, describes
a state of deep focus on enjoyable activities, similar to how artists or athletes
immerse themselves in their work. In this state, individuals find the activity itself
rewarding, regardless of achieving further goals. Initially applied to online activities,
immersion theory describes this state as a coherent sequence of continuous machine
interactions, characterized by pleasure, selflessness, and self-motivation.
The theory comprises two core concepts: skills, representing the user’s abilities,
and challenges, representing the user’s control over the environment. An imbalance
between skills and challenges can lead to disinterest or frustration. To achieve immersion,
a balance between these elements is essential. The immersion theory model has evolved
over time, with the four-channel segmentation model advancing from a three-channel
model by adding an imperceptible state. The eight-channel model further refines this
by introducing additional states: relaxed (between boredom and disinterest), worried
(between disinterest and anxiety), aroused (between anxiety and immersion), and controlled
(between immersion and boredom). Fig. 2 illustrates the architecture of a multi-scale landscape evaluation model based on
immersion theory. To experience immersion, it is crucial to achieve concentrated attention,
interest, and enjoyment during experiential activities. Augmented reality (AR) technology,
such as HoloLens, enhances this by helping students quickly engage in learning, stimulating
their interest, and providing high-quality enjoyment through real-time interaction,
thereby fostering an immersive experience. In educational contexts, “teaching interaction”
is proposed to describe meaningful interactive phenomena in distance education, distinguishing
it from general “interaction.” This concept allows for a more precise definition within
specific teaching scenarios, emphasizing its targeted educational significance and
avoiding conceptual generalization.
Fig. 2. Architecture diagram of multi-scale landscape evaluation model.
3.2. Methods and Optimization Strategies for Constructing a Safe Landscape Pattern
in Tourism Oriented Rural Areas
In this model, the teaching interaction of distance learning is divided into two levels:
conceptual interaction and information interaction. In this model, the three types
of teaching interactions are closely related and interact with each other, jointly
promoting the construction of learner concepts. As a low-level interaction, operational
interaction ensures the progress of high-level interaction activities. With the advancement
of science and technology and the enrichment and improvement of media functions, proficient
use of media lays the foundation for accurately and efficiently finding learning resources;
Information exchange takes operational interaction as a prerequisite, and interacts
with teachers, students, and teaching resources through media links in remote learning.
During the process of information exchange, students, based on their own experience,
integrate the results of the interaction between new and old concepts through feedback
from information exchange, and ultimately regulate their own reactions. During teaching
interaction, conceptual interaction belongs to top-level interaction, which occurs
simultaneously during information exchange, promoting the generation and formation
of new concepts. The teaching interaction model of augmented reality integrated learning
environment is also divided into two levels: conceptual interaction and information
interaction, both of which occur simultaneously during the class process. Fig. 3 shows the GIS assisted path optimization algorithm diagram. The underlying information
interaction consists of the interaction between teachers and students, as well as
the interaction between students and learning resources. The operational interaction
occurs in the interaction between students and the media, namely HoloLens. These three
interactions are also divided into two levels, namely the online level and the offline
level.
Fig. 3. GIS assisted planning path optimization algorithm diagram.
The offline level occurs in the operational interaction between students and the augmented
reality device HoloLens. Unlike traditional online interaction that can occur at any
time, the teaching interaction model based on augmented reality integrated learning
environment focuses more on HoloLens augmented reality devices as a medium for students
to interact with online resources, promoting conceptual interaction between teachers
and students in offline face-to-face teaching, and helping students utilize online
teaching resources. The hierarchical structure of this model mainly reflects two levels:
online and offline. The online level represents the information interaction that students
achieve with teaching resources through operational interaction with the media. This
online interaction is not separated from classroom operational interaction and information
interaction. It occurs during the teacher’s teaching process, utilizing the augmented
reality feature of HoloLens to display virtual objects on the real world. Learners
can interact with the rich online teaching resources at any time during the listening
process. The offline level represents the information exchange between students and
teachers, which can be observed through inquiry-based language communication and unobservable
conceptual interaction. During the listening process, when the interaction between
HoloLens and online resources is not sufficient to answer any questions, students
can ask the teacher multiple times. The iterative nature of this model is mainly reflected
in the multiple rounds of information interaction between students and teachers, as
well as in the process of concept interaction. Table 1 shows the threat factors and related attributes of the Mashan Peninsula. From the
model, we can know that the learning process of students is the formation process
of new concepts. So, every information exchange between students and teachers is not
a repetitive process of the same concept, but a process of gradually forming new concepts
based on specific concepts of students under the influence of teaching resources.
Each concept exchange is an iterative spiral upward.
Table 1. Threat factors and related attributes of the Mashan Peninsula.
|
Threat factors
|
Maximum impact distance/km
|
Weight
|
|
Land used for building
|
0.2
|
0.8
|
|
Cultivated land
|
0.03
|
0.6
|
|
Naked ground
|
0.02
|
0.2
|
|
Other land use
|
0.08
|
0.4
|
The multimodality of this model refers to multimodal interaction, mainly reflected
in the operational interaction between students and the media. As an augmented reality
device, HoloLens not only connects rich online teaching resources, but also provides
multi-sensory interaction functions that cannot be provided by mobile phones, tablets,
and other devices. Through various sensory channels such as vision, hearing, and action,
human-computer interaction can be carried out. The multimodal interaction process
can provide students with a tangible experience and achieve a more natural interaction
effect. The application of GIS technology in rural spatial analysis abroad is relatively
comprehensive, including spatial pattern changes, spatial planning, ecological environment
protection, biodiversity protection, agricultural production, cultural heritage protection,
and various social issues. However, the application of domestic GIS technology in
spatial analysis of rural areas is not comprehensive enough. The main research content
focuses on the distribution characteristics, evolution process, influencing factors
of settlements, and spatial optimization based on suitability evaluation. There are
relatively many studies on this type of topic, and clear research ideas and methods
have been formed, which have been widely applied.
Combined with natural geographic data such as altitude, slope, water source, and roads,
the distribution of rural settlements in typical low mountain. Based on Voronoi plot
coefficient of variation and landscape pattern index, the distribution characteristics
of rural settlements in the semi-arid agricultural pastoral transitional zone of Inner
Mongolia were analyzed, and the main influencing factors of rural settlement distribution
were explored by combining factors such as water system, terrain, and topography.
Fig. 4 shows the evaluation map of rural landscape types, analysis functions of remote sensing
data using GIS technology. And discuss its influencing factors and driving mechanisms
from the perspectives of nature, humanities, and society; Use the nearest neighbor
statistics, Ripley’s K function, Based on kernel density analysis, explore the spatial
distribution characteristics and influencing factors of rural settlements in Weixian
from the Shang and Zhou dynasties to the Qing dynasty. By utilizing GIS technology
for spatial analysis and data processing, combined with the multi factor comprehensive
evaluation method, the suitability evaluation of rural land use can be carried out,
which is conducive to promoting the rationality and scientific of rural spatial development
and utilization.
Fig. 4. Rural landscape type evaluation map.
4. Research on Rural Landscape Design Based on Comparative Learning Interactive GIS
Algorithm
The landscape pattern index is a key method in landscape ecology for quantitative
analysis, reflecting characteristics from various angles through simple calculations.
Landscape pattern changes are crucial to understanding the interaction between landscape
processes and the ecological environment. Although many studies have examined large-scale
landscapes and their relationship to ecological issues like natural disasters and
biodiversity, such research offers limited guidance for spatial planning in small
and medium-sized rural areas. In particular, tourism-oriented rural regions lack in-depth
analysis of landscape patterns. This study focuses on the Mashan Peninsula, a tourism-oriented
rural area in Wuxi City. By comparing the landscape patterns before and after the
development of tourism, the study explores the changes in landscape structure over
time. The coupling of tourism development and rural landscape changes provides insights
into the impact of tourism activities, offering valuable guidance for rural landscape
planning, ecological protection, and sustainable development. Table 2 highlights the carbon density parameters of various landscape types, illustrating
the environmental impacts.
Table 2. Carbon density parameters of different landscape types.
|
Landscape type
|
Aboveground carbon
|
Underground carbon
|
Soil carbon
|
Total
|
|
Land used for building
|
1.20
|
4.35
|
4.35
|
9.90
|
|
Broad leaved forest land
|
9.03
|
24.69
|
27.42
|
61.14
|
|
Shuitiandi
|
4.77
|
17.19
|
16.04
|
38.00
|
|
Shrubs and grasslands
|
5.67
|
14.38
|
16.78
|
36.83
|
|
Naked ground
|
0.01
|
4.29
|
7.46
|
11.76
|
|
Water system
|
0.06
|
7.90
|
8.11
|
16.07
|
|
Other land use
|
1.20
|
4.35
|
4.35
|
9.90
|
The dynamic degree of land use assesses the intensity of landscape changes over time,
using two indicators: “single land use dynamic degree” and “comprehensive land use
dynamic degree.” This study employs the “single land use dynamic degree” to analyze
changes in specific landscape types on the Mashan Peninsula. A lower absolute value
indicates stability, while higher values reflect intense changes. The landscape pattern
index, typically calculated using Fragstats, quantifies landscape characteristics
at the landscape and type levels, excluding patch-level data due to its low relevance
for regional analysis. This study focuses on key indices like landscape area, morphology,
and distribution. The Mashan Peninsula comprises seven landscape types: construction
land, broad-leaved forest, cultivated land, shrubs and grasslands, bare land, water
bodies, and others. Broad-leaved forests dominate the island with the largest area
and best connectivity but are decreasing and fragmenting over time. Meanwhile, construction
land expands significantly, transitioning from a complete state to fragmentation and
back toward completion.
From 1984 to 2020, construction land and shrubs in the Mashan Peninsula increased,
while broad-leaved forests, water bodies, bare land, and other land types decreased.
Cultivated land remained stable. Construction land saw the largest increase (+192.1%),
particularly from 1991 to 2010. Shrubs and grasslands grew by 62.5%, with the most
significant growth from 1991 to 2001. Broad-leaved forests experienced the sharpest
decline, followed by a partial recovery, while water bodies also shrank. Bare land,
despite its small base, saw a sharp reduction (-84.9%). By calculating land use dynamics
across four periods (1984-2020), the study reveals that from 1984 to 1991, the overall
landscape change was minimal, with a comprehensive dynamic degree of 1.64%, indicating
relative stability. Bare land had the highest dynamic increase (+30.92), while cultivated
land (-3.17%) and shrubs (-2.93%) showed significant reductions. Fig. 5 illustrates soil quality and vegetation cover evaluation. The dynamics of other landscape
types remained stable during this period.
Fig. 5. Soil quality and vegetation coverage assessment map.
From 1991 to 2001, the comprehensive land use dynamic degree was relatively similar
to the previous period, at 1.63%. The three types of landscapes, namely shrubbery,
construction land, and cultivated land, exhibit positive dynamics. The attitude towards
shrubbery is the highest (10.94%), followed by construction land (5.54%) and cultivated
land (5.04%), indicating that the growth dynamics of shrubbery scale are more intense
at this time. The broad-leaved forest and bare land showed negative dynamics, with
results of -2.6% and -2.7%, respectively, indicating a trend of reduction in the area
of these two types of landscapes. The lowest dynamic calculation result of water landscape
indicates that its state is relatively stable. The comprehensive land use dynamics
showed a rapid increase, increasing to 1.99%, indicating that the regional landscape
dynamics were more intense and the landscape pattern had undergone significant changes.
Among them, the three types of landscapes, namely construction land, cultivated land,
and shrubs and grasslands, show positive dynamics. The dynamic value of construction
land is the highest (4.6%), and the increase is the largest. Cultivated land (1.85%)
and shrubs and grasslands (1.57%) are second to construction land, indicating that
the expansion trend of construction land in the Mashan Peninsula is the most obvious
at this time, and cultivated land and shrubs and grasslands are also increasing. The
dynamic value of bare ground is negative (-2.55%), indicating its decreasing trend.
From 2010 to 2020, the comprehensive land use dynamics in the Mashan Peninsula increased
slightly to 2.04%. Broad-leaved forests and construction land both showed positive
dynamics, with broad-leaved forests growing at a slightly higher rate (2.5%) than
construction land (2.0%). Farmland, shrub, and grassland landscapes exhibited negative
dynamics, with the largest decrease in bare land (-9.2%), followed by farmland (-2.7%)
and shrubs/grassland (-2.4%). Water landscapes remained stable with the lowest dynamic
value. The landscape type transfer matrix highlights key transitions, such as water
bodies transforming into cultivated land and bare land transitioning into construction
land. Broad-leaved forests, farmland, and shrubs also showed notable conversions into
bare land.
The development of ecosystem service research has evolved from global assessments
of ecosystem functions–such as supply, regulation, support, and cultural services–into
a focus on the interaction between human activities and ecosystem services. However,
studies have primarily focused on macro scales, neglecting rural areas and the role
of landscape structure in ecosystem service efficiency. There is a need for research
on ecosystem services at a rural scale to better inform landscape planning and improve
ecosystem management practices in these regions.
5. Experimental Analysis
In economics, sensitivity coefficient analysis is the degree to which changes in certain
key factors lead to changes in the net present value of investment plans, while ensuring
other conditions remain unchanged. Fig. 7 shows the evaluation of transportation network density and accessibility, reflecting
the sensitivity of net present value to changes in key factors. Similarly, in the
study of ecosystem services, the unit area ecosystem service value of various landscapes
represents the key factor, while the total value of ecosystem services represents
the net present value of investment plans.
Fig. 6. Water distribution and water quality assessment chart.
Fig. 8 shows the population density and evaluation of public service facilities. The sensitivity
coefficient can verify the accuracy of the “ecosystem service value per unit area”
of the landscape. If the sensitivity coefficient is less than 1, it indicates that
the ecosystem service value has no elasticity, and the calculation results have high
credibility. Anti regularization indicates that the ecosystem service value has elasticity,
but the accuracy of the calculation results is insufficient.
This study adjusted the “unit area ecosystem service value” of different types of
landscapes in the Mashan Peninsula by 50%, evaluating the sensitivity of the total
regional. Fig. 9 shows the evaluation of economic activities and employment opportunities. To further
reveal the interaction mechanism between landscape pattern changes and ecosystem services
in the Mashan Peninsula, a quantitative model.
Fig. 7. Traffic network density and accessibility evaluation chart.
Fig. 8. Population density and evaluation of public service facilities.
Fig. 9. Economic activity and employment opportunity assessment chart.
In the regression model, the absolute value of the standardized regression coefficients
for each landscape index represents the explanatory power of the index for ecosystem
service value. The higher the value, the greater the impact of the landscape index
on ecosystem service value, indicating that the corresponding landscape pattern characteristics
have a strong impact on ecosystem services. Therefore, based on this method, identify
the main landscape pattern characteristics that affect the ecosystem service value
of the Mashan Peninsula, and use this as a basis to grasp the landscape pattern optimization
approach aimed at enhancing the efficiency of ecosystem services. The support service
function is most significantly influenced by the overall diversity of the landscape
in the Mashan Peninsula, the complexity of shrub and grassland shapes, and the scale
of broad-leaved forests. Fig. 10 shows the evaluation of cultural heritage sites and tourism resources.
Secondly, as shrubs and grasslands expand and spread towards broad-leaved forests,
the continuity of the broad-leaved forest landscape is also disrupted, the overall
connectivity of the landscape decreases, and the ecosystem service capacity declines.
The cultural service function is most significantly affected by the overall diversity
of the landscape in the Mashan Peninsula, as well as the morphology of construction
land and shrubland patches. Fig. 11 shows the energy consumption and carbon emissions assessment chart. The complexity
of construction land and shrub and grassland forms within the region indicates that
it is spreading towards broad-leaved forests, causing a serious decrease in the aesthetic
value of broad-leaved forests.
Fig. 10. Cultural heritage sites and tourism resource evaluation map.
Fig. 11. Energy consumption and carbon emission assessment chart.
6. Conclusions
The Mashan Peninsula’s landscape pattern has continuously changed with the development
of tourist resorts, showing an upward trend in dynamics. Construction land has expanded,
encroaching on surrounding landscapes, particularly broad-leaved forests, leading
to a fragmented, heterogeneous landscape with reduced connectivity and dominance.
Factors driving these changes include tourism construction, agricultural demand, and
policy promotion. As tourism develops, the value of ecosystem services in the region–covering
supply, regulation, support, and cultural services–has declined, with regulation services
most affected. There is a significant correlation between the ecosystem service value
and landscape pattern indices, though different indices impact ecosystem services
differently. Correlation and regression analysis help identify key grid characteristics
affecting ecosystem services, allowing for pattern optimization strategies to improve
efficiency. The study includes five typical rural areas, each covering 50 square kilometers,
with seven primary land use types analyzed.
Detailed GIS data collection and analysis yielded 350 data points across five regions.
Agricultural land averages 40% of the total area, followed by forest land at 30%,
construction land at 15%, water areas at 10%, and other land types at 5%. The average
vegetation coverage is 65%, ranging from a high of 78% to a low of 52%. The average
slope is 15 degrees, with flat terrain (less than 5 degrees) accounting for 20%, medium
slope (5-20 degrees) for 50%, and steep terrain (greater than 20 degrees) for 30%.
Water bodies average 12% distribution, with the largest covering 1.5 square kilometers
and the smallest just 0.2 square kilometers. To promote tourism-oriented rural landscapes,
it’s essential to balance ecological functions with the protection of local cultural
heritage. Constructing distinct “ecological security” and “cultural landscape security”
patterns can ensure ecological stability and safeguard cultural resources. By overlaying
these patterns for comprehensive zoning and strategic layout, we can support the coordinated
development of ecological protection and cultural initiatives, offering valuable insights
for sustainable landscape planning in tourism-focused rural areas.