Response of rural landscape disturbance changes to human activities in karst mountainous areas

Resposta das alterações da paisagem rural às atividades humanas em áreas montanhosas cársticas

ABSTRACT:

Landscape patterns in mountainous areas with rapid economic development are strongly affected by multiple human activities. However, the changes in landscape disturbances across karst mountainous areas attributed to complex human activities remain unclear. Research on the degree of landscape disturbance in mountainous areas under the influence of human activities is of great significance to the conservation and scientific management of mountainous landscapes. We selected rural zones with four typical small-scale landform types in karst mountainous areas as the research objects and analyzed the response of changes in rural landscape disturbance to human activity in these areas. Our results revealed that the landscape disturbance degree and human activity influence index increase in the majority of karst mountainous areas. The changes in human activity and landscape disturbances are generally observed in the low terrain relief region. High-intensity land use associated with rapid economic development has a serious negative impact on the degree of landscape disturbance, while ecological restoration projects exert a positive impact. Accelerating the implementation of the grain for green project and regulating the unregulated expansion of production and living land are crucial measures for the ecological management in karst mountainous areas. Future ecological management strategies should focus on relatively flat terrain areas. Our findings contributed to the landscape management and human activity regulation of different small-scale landform types in karst mountainous areas.

Key words:
Landscape disturbance degree; human activities; different landforms; terrain gradient; geographic information system; Guizhou Province

RESUMO:

Os padrões paisagísticos em zonas montanhosas com rápido desenvolvimento económico são fortemente afetados por múltiplas atividades humanas. No entanto, as mudanças nas alterações da paisagem nas áreas montanhosas cársticas atribuídas a atividades humanas complexas permanecem obscuras. A investigação sobre o grau de alteração da paisagem em zonas montanhosas sob a influência das atividades humanas é de grande importância para a conservação e gestão científica das paisagens montanhosas. Selecionamos zonas rurais com quatro tipos típicos de acidentes geográficos de pequena escala em áreas montanhosas como objetos de pesquisa e analisamos a resposta das mudanças na perturbação da paisagem rural à atividade humana nessas áreas. Nossos resultados revelam que o grau de alteração da paisagem e a atividade humana influenciam o aumento do índice na maioria das áreas montanhosas. As mudanças na atividade humana e nas perturbações paisagísticas são geralmente observadas na região de baixo relevo do terreno. A utilização intensiva do solo associada ao rápido desenvolvimento econômico tem um sério impacto negativo no grau de perturbação da paisagem, enquanto os projetos de restauração ecológica exercem um impacto positivo. Acelerar a implementação dos projetos para área “verde” e regular a expansão não regulamentada da produção e da terra viva são medidas cruciais para a gestão ecológica em áreas montanhosas cársticas. As futuras estratégias de gestão ecológica deverão centrar-se em áreas de terreno relativamente planas. Nossas descobertas contribuem para a gestão da paisagem e a regulação da atividade humana de diferentes tipos de relevo de pequena escala em áreas montanhosas cársticas.

Palavras-chave:
grau de alteração da paisagem; atividades humanas; diferentes formas de relevo; gradiente do terreno; sistema de informação geográfica; província de Guizhou

INTRODUCTION

Land use change is an important factor affecting the pattern and anthropogenic processes in landscapes (ADHIKARI et al., 2022). In recent decades, under the influence of intense human activity, rural land use in many countries has undergone dramatic changes, which has consequently altered the landscape structure and function, thereby affecting landscape stability and security (PROKOPOVA et al., 2019; VAN VLIET, 2019; LOPEZ et al., 2020; LIANG et al., 2022). Therefore, the strong interference of human activity on the landscape through land use has attracted much attention from global scholars (HOWISON et al., 2018; AROZENA et al., 2019; MA et al., 2022). Research on the response of landscape disturbance changes to human activities can clarify the formation and evolution mechanisms of landscape patterns, and is of great significance to regional landscape protection, scientific management and human activity control (ZHANG et al., 2017b; HUANG et al., 2022).

Research on landscape disturbance focuses on landscape pattern change under various disturbance factors, quantitative evaluations of landscape disturbance degree, and the relationship between human disturbance and landscape evolution (GLINA et al., 2019; XIE et al., 2022). Previous studies have found that disturbance factors mainly include natural factors such as climate change and natural disasters (ALLEN et al., 2014; KULKARNI et al., 2016) and various human factors such as urbanization, infrastructure construction, tourism development and deforestation (ZHANG et al., 2017a; LIU et al., 2020b; MELITO et al., 2021). In addition, research areas mainly focus on coastal areas (YANG et al., 2021), metropolis (WU et al., 2021), natural reserves (WU et al., 2019), and tourism areas (ZHANG et al., 2017a). The corresponding research methods mainly involve the establishment of disturbance indices based on landscape indicators (WALZ & STEIN 2014; TOOSI et al., 2022). With the progress of spatial technology, geographic information systems (GIS) have been gradually applied to the spatial analysis of landscape disturbances (MJACHINA et al., 2018). For example, ZHOU et al. (2018) employed a GIS grid analysis tool to establish a landscape disturbance index for the analysis of landscape pattern responses in the Jiangsu coastal area to human disturbances. However, these studies rarely focus on the correlation between the landscape disturbance degree and the human activity intensity in economically underdeveloped rural mountainous areas.

Karst mountainous areas are typical ecological fragile regions located across the globe (LAN et al., 2022). Such areas have a low environmental capacity, high variability sensitivity of ecological systems, a weak disaster bearing capacity and poor stability (CHEN et al., 2020; CHEN et al., 2021). In recent decades, under the action of various human activities, such as poverty alleviation and development, the conversion of farmland to natural vegetation such as forests, shrubbery, and grassland, and forest protection planning, the landscape of karst mountainous areas has been strongly disturbed, putting pressure on the ecological restoration, management, and sustainable development of these regions (LIU et al., 2014; PANG et al., 2018; PENG et al., 2020). Although scholars have investigated the landscape pattern and landscape risk in karst mountainous areas (XI et al., 2019; XU et al., 2020), there is a lack of studies on the landscape disturbance degree under the influence of human activities. In addition, due to the diversity of landform types in karst mountainous areas, the impact of human activities on different landforms on the landscape disturbance is heterogenous (HAN et al., 2020a). However, the impact of human activities on landscape disturbance changes in different landforms is unclear.

Therefore, four rural towns (Longchang, Liuguan, Xianchang, and Minxiao) in Guizhou Province of southwestern China, each characterized by different typical karst landforms (karst mid-mountain, karst basin, karst trough valley, and low hilly), were selected as the research objects to explore the response of landscape disturbance degree changes to human activities. The following hypotheses are proposed in this study: (1) the response of landscape disturbance changes to human activities in karst mountainous areas varies with different landform types and terrain gradients; and (2) there is a correlation between human activities and landscape disturbance in karst mountainous areas, and the correlation varies with landform type and terrain gradient.

MATERIALS AND METHODS

Study area

Guizhou Province in southwest China is a typical karst area, accounting for more than 70% of the total land area (HAN et al., 2020b). Based on the main landform types in karst mountainous areas, four typical towns: Longchang, Liuguan, Xianchang and Minxiao Town, in Guizhou Province, China, were selected as the research area (Figure 1). Longchang town is in western Guizhou Province and covers a total area of 240 km². It is a typical karst mid-mountain landform with high terrain relief and an average elevation of 1900 m. This town belongs to the important ecological environment monitoring reserve of the upper Yangtze River. Due to deforestation over the past few decades, vegetation has been greatly damaged, and soil erosion has become a serious issue. Liuguan town is located in the central part of Guizhou Province and covers an area of 41 km². It is a karst basin landform with an average elevation of 1300 m. Most of this town is flat, with low terrain relief hills surrounding the basin. The town is an important grain production area (rice, corn, potato, etc.). Xianchang town is situated in the central part of Guizhou Province and covers an area of 105 km². It is a karst trough valley landform with an average elevation of 870 m. The terrain at the bottom of the trough is flat, and the relatively small terrain relief has contributed to the region becoming a grain-producing area. Mountains dominate both sides of the karst trough valley landform and the forest coverage rate is high. Minxiao town is in the east part of Guizhou Province and covers an area of 268 km². It is a karst low hilly landform with an average elevation of 687 m. The Fanjing Mountain Nature Reserve is situated within its territory. The forest coverage rate of the town is high, and the ecological environment quality is superior. All four towns have a subtropical humid monsoon climate. Summer is hot and rainy; winter is mild and rainy. The annual average temperature ranges from 13 to 19 ℃, with precipitation ranging between 950 and 1,200 mm. This climate is conducive to the growth of a variety of subtropical plants, resulting in a region rich in biodiversity. Prior to 2020, the four towns were considered typical poor areas in southwest China. With the implementation of China’s targeted poverty alleviation policy, these towns have successfully overcome poverty. Living standards have improved and the economy level has developed rapidly. Furthermore, the implementation of the grain for green and rocky desertification control projects over the past two decades has played a crucial role in restoring the ecological function of karst mountainous areas (CHONG et al., 2021; HAN et al., 2022).

Figure 1
Location of the study area.

Data sources and research methods

Data sources and processing

Land use data of the four towns from 2010 were obtained from remote sensing images of the SPOT-5 satellite sensor with a spatial resolution of 5 m. Land use data from 2020 were obtained from remote sensing images from the Pleiades satellite with a spatial resolution of 2 m. ENVI 5.3 (Exelis Visual Information Solutions, Boulder, Colorado) software was used for the remote sensing analysis by artificial visual interpretation. Based on the characteristics of the study area, the landscape types of the study area were categorized into seven groups: farmland, forest, shrub-grassland, water body, built-up land, unused land and road (Figure 2). A total of 200 calibration points were selected for each town to verify the accuracy of the remote sensing interpretation. After field calibration, the accuracy of the remote sensing interpretation was over 90%. Elevation data with a spatial resolution of 3.5 m was downloaded from Google Earth (www.google.cn/intl/zh-CN/earth/). Using the elevation data, the ArcGIS 10.1 3D Analyst Tool was employed to generate slope data. Following MA et al. (2021), the terrain relief degree of the four study areas was calculated based on elevation and slope data, and divided into five gradients (I to V) using the natural breakpoint method based on the terrain relief degree values (from lowest to highest) (Table 1).

Figure 2
Spatial pattern of each landscape type in karst mountainous areas as figure 1.

Table 1
Grading of the terrain relief degree in karst mountainous areas.

Study area	Gradient Ⅰ	Gradient Ⅱ	Gradient Ⅲ	Gradient Ⅳ	Gradient Ⅴ
Longchang (karst mid-mountain)	0-87	87-141	141-207	207-295	295-504
Liuguan (karst basin)	0-15	15-30	30-45	45-63	63-103
Xianchang (karst trough valley)	0-52	52-87	87-125	125-173	173-347
Minxiao (low hilly)	0-75	75-129	129-182	182-252	252-526

• Note: The terrain relief degree was divided into five gradients (from gradient I to V) according to the value from lowest to highest.

Methods

(1) Calculation and spatial expression of landscape disturbance degree

The landscape disturbance index is used to reflect the degree of disturbance in the ecosystem represented by different landscapes (ZHOU et al., 2018; WU et al., 2022). According to the landscape ecology principle, landscape disturbance degree is generally determined by the degree of landscape fragmentation, landscape separation, and landscape dominance (DI et al., 2021). Based on this principle, the landscape disturbance degree is calculated as:

$C_i=n_i/A_i$(1),

$S_i=0.5\sqrt{\frac{n_i}{A}/\frac{A_i}{A}}$(2),

$D_i=0.25\times \left(\frac{n_i}{N}+\frac{m_i}{M}\right)+0.5\times A_i/A$(3),

$U_i=a\times C_i+b\times S_i+c\times D_i$(4),

where C _i is the landscape fragmentation; S _i is the landscape separation degree; D _i is the landscape dominance index; U _i is the landscape disturbance degree; C _i, S _i, D _i and U _i are described in detail in LIU et al. (2020a). n _i is the number of patches of landscape type I; A _i is the patch area of landscape type I; A is the total area of all landscape types; N is the number of patches of all landscape types; m _i is the number of grids in which landscape type i appears; M is the total number of grids; and a, b and c are the weights of landscape fragmentation degree, landscape separation degree and landscape dominance degree, and are assigned with values of 0.5, 0.3 and 0.2, respectively (GAO et al., 2020). n _i and A _i are calculated using Fragstats 4.2 software.

As the four towns are in different areas, the ArcGIS Grid tool was used to generate 140, 152, 145, and 133 grids in Longchang, Liuguan, Xianchang and Minxiao, respectively. The landscape disturbance degree calculated in each grid was imported into ArcGIS, and the spatial pattern of the landscape disturbance degree was obtained using the Kriging interpolation method. Using the ArcGIS Spatial Analysis Tool, the spatial change in the landscape disturbance degree from 2010 to 2020 was then calculated.

(2) Human activity impact index calculation

At present, indicators capable of representing the impact of human activity on the land surface include economic activity, population, land use and so on (LIU et al., 2022). A single index cannot fully capture the motivation and processes of human activities. Therefore, we referred to SANDERSON et al. (2002), CHENG et al. (2022) and SUN et al. (2022) to establish an evaluation index system for the human activity impact index, comprehensively considering land use, population, economics, infrastructure, and natural conditions. The human activity impact index is calculated as follows:

$H=P_i\times W_p+L_i\times W_l+R_i\times W_r+D_i\times W_d+S_i\times W_s$(5),

where H is the human activity impact index; P _i is population density; L _i is land use type; R _i is the distance the road factor; D _i is the distance to the administrative center factor; S _i is the slope factor; and W _p , W _l , W _r , W _d and W _s are the weights assigned to population density, land use type, distance to road, distance to administrative center, and slope factor, respectively.

The analytic hierarchy process method (AHP) was used to assign weights to the population density, land use type, distance to road, distance to administrative center, and slope factor as 0.27, 0.24, 0.21, 0.11, and 0.17, respectively. The AHP application process is as follows: First, five experts in landscape ecology, human geography and physical geography were selected according to the evaluation system of the human activity impact index. Second, based on the knowledge and experience of the experts, an evaluation scale of 1-9 was adopted to judge the relative advantages and disadvantages of the multiple factors. Each expert established the judgment matrix separately. The consistency test was satisfied when the consistency index of the judgment matrix fell below 0.01. Finally, the weights of each indicator were calculated according to the judgment matrix. The impact of each factor on the landscape was divided into five levels, with each level assigned a value of 5, 4, 3, 2, 1, according to the intensity of the impact based on the following grading criteria: i) population density- based on the population density of each village; ii) roads and administrative centers- based on distance; and iii) slope- based on the size of the slope. The greater (lower) the population density, the closer (farther) to the road and administrative center, and the greater (lower) the slope, the closer the grading value is to 5 (1). Land use type factors are graded according to the impact intensity of different land use types on the ecosystem. According to XU et al. (2015), the impact intensity of built-up land and road is assigned a value of 5, farmland is assigned a value of 4, water body is assigned a value of 3, forest and shrub-grassland are assigned a value of 2, and unused land is assigned a value of 1.

(3) Spatial correlation analysis between landscape disturbance degree and human activity impact index

The bivariate Moran’s I is introduced to quantitatively analyze the spatial clustering (positive spatial correlation) and spatial dispersion (negative spatial correlation) between the human activity impact index and landscape disturbance degree. This method tests whether there is spatial correlation between human activities and landscape disturbance (ANSELIN, 1995).

$I_{eu}=\frac{N{\sum }_i^N{\sum }_{j\ne i}^N{W}_{ij}{z}_i^e{z}_j^u}{(N-1){\sum }_i^N{\sum }_{j\ne i}^N{W}_{ij}}$(6)

where I _eu is the bivariate Moran’s I between the human activity impact index and landscape disturbance degree; W _ij is the spatial weight matrix; $z_i^e$ is the standardized value of the human activity impact index of the i _th spatial unit; and $z_j^u$is the standardized value of the landscape disturbance degree of the j _th spatial unit.

RESULTS

Changes in each landscape type

The areas of farmland and unused land decreased in four towns with different landforms, while the areas of forest, built-up land, and roads increased. The shrub-grassland area decreased in Longchang and Minxiao, with karst mid-mountain and low hilly landforms, respectively, while it increased in Liuguan and Xianchang, with karst basin karst trough valley landforms, respectively. In contrast, the water area exhibited an increase in Longchang and Minxiao, and reduced in Liuguan and Xianchang (Table 2).

Table 2
Overall change of each landscape type (hm²).

Landscape types	-Longchang (karst mid-mountain)-			-----Liuguan (karst basin)----			Xianchang (karst trough valley)			--------Minxiao (low hilly)-------
	2010	2020	2010-2020	2010	2020	2010-2020	2010	2020	2010-2020	2010	2020	2010-2020
Farmland	12198	11414	-784	2454	2186	-268	3465	2527	-937	4250	3172	-1078
Forest	6184	7153	969	784	909	125	4452	5085	633	19091	20078	987
Shrub-grassland	6753	6150	-603	454	541	87	2061	2346	285	1614	1392	-221
Water body	85	95	10	64	53	-11	43	34	-9	152	205	53
Built-up land	429	808	379	149	200	51	244	280	37	282	440	158
Unused land	25	23	-2	28	1	-26	48	21	-27	55	26	-29
Road	322	326	4	29	71	42	75	99	24	133	266	133

Changes in landscape disturbance degree

The landscape disturbance degree of Longchang (with a karst mid-mountain landform) declined from 2010 to 2020, while that of Liuguan (with a karst basin landform), Xianchang (with a karst trough valley landform), and Minxiao (with a low hilly landform) increased. The increase in the landscape disturbance degree of Liuguan and Xianchang over the past decade was higher than that of Minxiao. In terms of the five gradients (from I to V) based on the lowest to highest terrain relief degree, except for gradient Ⅰ, the landscape disturbance degree of Longchang was observed to decrease, with the magnitude of decrease increasing from gradient Ⅱ to Ⅴ. The increase in landscape disturbance degree decreased gradually as the terrain relief gradient increased (from Ⅰ to Ⅴ). The change in landscape disturbance degree of Xianchang and Minxiao in gradient Ⅰ was higher than that in the other gradients (Table 3).

Table 3
Overall change of landscape disturbance degree in karst mountainous areas.

Study area	Year	Gradient Ⅰ	Gradient Ⅱ	Gradient Ⅲ	Gradient Ⅳ	Gradient Ⅴ	Total area
Longchang (karst mid-mountain)	2010	40.01	36.71	29.59	25.63	19.84	33.83
	2020	42.01	33.48	26.15	20.07	10.39	29.59
	2010-2020	1.99	-3.23	-3.45	-5.56	-9.45	-4.24
Liuguan (karst basin)	2010	35.39	34.25	29.76	30.99	36.98	32.44
	2020	52.67	45.60	40.30	39.03	44.78	44.44
	2010-2020	17.28	11.35	10.54	8.04	7.80	12.00
Xianchang (karst trough valley)	2010	19.40	15.25	12.85	12.46	10.11	14.83
	2020	48.99	34.19	32.64	31.17	27.23	36.57
	2010-2020	29.60	18.94	19.80	18.70	17.12	21.73
Minxiao (low hilly)	2010	29.17	28.42	27.86	26.78	25.09	27.73
	2020	36.69	31.13	30.43	29.14	27.60	31.86
	2010-2020	7.52	2.72	2.57	2.35	2.52	4.13

• Note: The terrain relief degree was divided into five gradients (from gradient I to V) according to the value from lowest to highest.

The landscape disturbance degree in Longchang decreased, except in the south and southwest regions. The landscape disturbance degree of Liuguan increased in the northwest, west and south parts, while it decreased in the northeast, central and south. Except for sporadic areas in the western and southern parts, the landscape disturbance degree increased in most areas of Xianchang. The landscape disturbance degree of Minxiao increased in the northern and central regions, while it decreased in other areas (Figure 3).

Figure 3
Spatial pattern of landscape disturbance degree change in karst mountainous areas as figure1.

Changes in human activities impact index

Unlike Longchang (with a karst mid-mountain landform), the impact index of human activities in the other three towns increased from 2010 to 2020. The increases in Liuguan (with a karst basin) and Minxiao (with a low hilly) were higher than those of Xianchang (with a karst trough valley). The human activity impact index of Longchang increased in gradient Ⅰ between 2010 and 2020, while it decreased in gradients Ⅱ to Ⅴ. The human activity impact index of Liuguan, Xianchang, and Minxiao increased in each gradient from 2010 to 2020, with the increases for Liuguan and Xianchang in gradients Ⅰ and Ⅱ significantly higher than those in gradients Ⅲ, Ⅳ and Ⅴ. The increase in the human activity impact index for Minxiao declined as the terrain relief degree increased (from gradient Ⅰ to Ⅴ) (Table 4).

Table 4
Overall change of human activity impact index in karst mountainous areas.

Study area	Year	Gradient Ⅰ	Gradient Ⅱ	Gradient Ⅲ	Gradient Ⅳ	Gradient Ⅴ	Total area
Longchang (karst mid-mountain)	2010	3.7715	3.5651	3.3495	3.0557	2.4327	3.4559
	2020	3.8593	3.5477	3.2585	2.9217	2.2526	3.4334
	2010-2020	0.0878	-0.0174	-0.091	-0.134	-0.1801	-0.0225
Liuguan (karst basin)	2010	3.7847	3.5190	3.0818	3.0253	2.8511	3.3199
	2020	4.1747	3.8999	3.3393	3.2565	3.0662	3.6444
	2010-2020	0.3900	0.3809	0.2575	0.2312	0.2151	0.3245
Xianchang (karst trough valley)	2010	3.5615	2.9234	2.4602	2.3644	1.9649	2.8098
	2020	3.8016	3.1783	2.5672	2.4674	2.0589	2.9603
	2010-2020	0.2401	0.2549	0.1070	0.1030	0.094	0.1505
Minxiao (low hilly)	2010	3.5261	2.7977	2.4146	2.0083	1.6427	2.5179
	2020	3.9119	3.1546	2.7361	2.2692	1.7939	2.8317
	2010-2020	0.3858	0.3569	0.3215	0.2609	0.1512	0.3138

• Note: The terrain relief degree was divided into five gradients (from gradient I to V) according to the value from lowest to highest.

The human activity impact index increased in most areas of Longchang from 2010 to 2020. Longchang Town exhibited a decrease in the human activity impact index with a dispersed distribution. Increases in the human activity impact index in Liuguan were concentrated in the south and north, while decreases were mainly distributed in the western and central parts. Xianchang generally exhibited increases in the west and east regions and decreases in the central and southern parts. Increases in Minxiao were mainly situated in the south and north, while decreases were observed in the east (Figure 4).

Figure 4
Spatial pattern of human activity impact index changes in karst mountainous areas as figure 1.

Correlation analysis between landscape disturbance degree and human activity impact index

The correlation coefficient between the landscape disturbance degree and human activity impact index in gradients Ⅰ and Ⅴ of Longchang was significantly higher than those in gradients Ⅱ, Ⅲ and Ⅳ in 2010 and 2020. The correlation coefficient in gradient Ⅴ of Liuguan was lower than those in gradients from Ⅰ to Ⅳ in 2010 and 2020, while the correlation coefficient of Xianchang and Minxiao in gradient Ⅰ was significantly higher than those in gradients from Ⅱ to Ⅴ. The correlation degree between changes in landscape disturbance degree and changes in human activity impact index in Longchang from 2010 to 2020 was generally lower than those in Liuguan, Xianchang, and Minxiao. The correlation coefficient between landscape disturbance degree changes and human activity impact index changes in gradients Ⅰ, Ⅳ and Ⅴ was significantly higher than those of the other gradients from 2010 to 2020. The correlation coefficients in gradient Ⅰ of Liuguan, Xianchang and Minxiao were higher than those in the other four gradients (Table 5).

Table 5
Correlation coefficient between landscape disturbance degree and human activity impact index in karst mountainous areas

Study area	Year	Gradient Ⅰ	Gradient Ⅱ	Gradient Ⅲ	Gradient Ⅳ	Gradient Ⅴ	Total area
Longchang (karst mid-mountain)	2010	0.2130	0.1170	0.1229	0.1972	0.3374	0.1135
	2020	0.2495	0.1303	0.1016	0.1595	0.2579	0.1990
	2010-2020	0.1190	0.0212	0.0375	0.1285	0.1338	0.1400
Liuguan (karst basin)	2010	0.2767	0.2144	0.2589	0.2720	0.0364	0.2014
	2020	0.2628	0.1756	0.1251	0.1893	0.0416	0.1760
	2010-2020	0.2089	0.0765	0.0300	0.0780	0.0122	0.2922
Xianchang (karst trough valley)	2010	0.4280	0.1930	0.1992	0.2401	0.2055	0.3091
	2020	0.3813	0.1874	0.1474	0.1618	0.1876	0.2730
	2010-2020	0.2845	0.0560	0.0572	0.0306	0.0689	0.2699
Minxiao (low hilly)	2010	0.4296	0.0335	0.0068	0.1150	0.1326	0.0333
	2020	0.3859	0.0322	0.0521	0.0359	0.0794	0.0483
	2010-2020	0.3592	0.0362	0.1159	0.0763	0.0000	0.2938

• Note: The terrain relief degree was divided into five gradients (from gradient I to V) according to the value from lowest to highest.

DISCUSSION

Driving mechanisms of rural landscape disturbance by human activities

This study found that the landscape disturbance increased in all towns, except for Longchang town. This is attributed to the complex human activities prevalent in karst mountainous areas. Under the influence of China’s “grain for green” policy, production land (e.g., farmland) on sloping terrain in karst mountainous areas has been replaced by shrub, grassland, and other ecological land types (SHU et al., 2022), thereby increasing the proportion of natural vegetation (Table 2). This has reduced the landscape fragmentation and the degree of landscape separation. However, karst mountain villages are impoverished regions in western China. To reduce rural poverty, living land (such as roads and residential areas) in rural karst mountainous areas have increased substantially in the past decade under the influence of natural resource development and large-scale infrastructure construction (HAN et al., 2020a). This has strongly changed the landscape structure, thus causing landscape disturbances.

Furthermore, our results revealed heterogeneity in the impact of human activities on landscape disturbance across the four study areas with different landforms. For instance, karst mid-mountains have higher elevations and significant terrain relief, limiting the intensity of human activities. Over the past decade, ecological restoration policies, such as the grain for green project, have played a crucial role in natural landscape restoration, counteracting the negative impact of human activities on the landscape resulting from economic development. As a result, the landscape disturbance degree has been observed to decline. In contrast, the karst basin, karst trough valley, and low hilly landforms impose fewer restrictions on human activities. The rapid economic development has significantly increased the intensity of human activities, strongly disturbing the landscape in this region. While ecological restoration policies have a positive effect on the landscape’s ecological security in these three landform areas, the negative impact of the increasing intensity of human activities on the landscape disturbance degree outweighs the positive impact of ecological restoration projects. Thus, the landscape disturbance degree has been observed to increase. In terms of the differences in landscape disturbance in karst basins, compared with Liuguan (karst basin) and Xianchang (karst trough valley), Minxiao (low hilly) is situated in the FanJing Mountain Nature Reserve, and its ecological protection policy reduces the degree of landscape disturbance from human activities. This consequently results in a considerably lower landscape disturbance degree for Minxiao compared to Liuguan and Xianchang. In contrast, the relatively flat topography of Liuguan and Xianchang is favorable for numerous human economic activities and thus greatly increases the landscape disturbance in these two townships compared to Minxiao. Unlike Liuguan town, a valley runs through the middle of Xianchang, with mountains on both sides. The favorable conditions for human activities provided by the flat topography of the middle region induce a considerable increase in anthropogenic landscapes. Simultaneously, the grain for green project on both sides of the mountains has significantly altered its ecological landscape, resulting in substantial changes in the landscape disturbance degree of Xianchang.

In addition, we identified that human activities have a prominent influence on landscape disturbance in the gradient area with flat terrain relief (i.e., gradient I), which is consistent with the research results of LIU et al. (2017). This can be attributed to the impact of terrain relief on the ability to carry out human activities, consequently affecting the degree of landscape interference. Areas with low terrain relief are conducive to human activities and have thus become concentrated areas of multiple human activities. This makes the landscape in areas with flat terrain relief strongly disturbed by human activities. Thus, the degree of landscape disturbance in the low relief gradient area is greatly increased.

Ecological Management

To reduce the rural landscape disturbance degree in karst mountainous areas, we can accelerate the conversion of slope farmland to ecological land (such as shrub, forest, grassland), reduce the proportion of production land (such as farmland), and increase the proportion of ecological land by strengthening the implementation of the grain for green project. Additionally, it is also possible to increase the protection of natural landscapes on sloping land, improve the dominance of forest and shrub-grassland, and enhance the degree of landscape connection. Regions with low terrain relief degrees in karst mountainous areas are mainly farmland and built-up land. Rapid economic development has resulted in the occupation of large amounts of farmland being converted into construction land. Therefore, it is necessary to balance the relationship between production land and living land.

Conversely, remote village relocation can be implemented, and the degree of intensive use of residential land in karst rural areas can also be improved, thereby reducing the landscape fragmentation degree. On the other hand, fragmented farmland can achieve centralized contiguity and improve production efficiency through renovation. Karst mountain landforms have high elevations and terrain relief. Farming in areas with high altitudes and high degrees of relief will exacerbate soil erosion and lead to the fragmentation of the ecological landscape. Although, Guizhou’s grain for green project has resulted in the conversion of large amounts of sloping arable land into forests, shrubs, and grasslands, it has not fully eradicated disturbances in sloping arable land located in high-elevation and high-relief areas. Therefore, future management strategies should focus on reducing the proportion of farmland landscapes and accelerating the restoration of natural landscapes in these areas. The terrain relief in karst basin, karst trough valley, and low hilly landforms is relatively small, and the intensity of human activities is relatively high. Ecological management strategies must control the amounts of production and living land and realize the intensive utilization of built-up land. At the same time, attention should be focused on the protection of the natural landscape on the steep slopes of these three landforms to reduce the interference degree of human activities.

Limitations

The precision of landscape data is crucial for analyzing the human activity impact index and landscape disturbance degree. In this study, landscape data were extracted from SPOT-5 and Pleiades imagery with high accuracy. However, due to challenges in obtaining long-term historical remote sensing images (karst mountainous areas are often cloudy, reducing image quality), only images from 2010 and 2020 could be selected to analyze changes in the human activity impact index and landscape disturbance degree over the past 10 years. While the use of low-precision historical remote sensing images (e.g., Landsat) allows for a longer study period, data accuracy is significantly reduced, especially given the small area of these four towns. Therefore, this study is constrained by the short research period (2010 to 2020) resulting from difficulties in data acquisition.

CONCLUSION

By integrating remote sensing imagery with socio-economic and topographic data, GIS technology was employed to analyze the impact of human activities on rural landscape disturbance in karst mountainous areas. Based on the results, we derived the following key conclusions: Under the influence of human activities, the degree of landscape disturbance in karst geomorphological areas with higher elevations and larger topographic relief (karst mid-mountains) is reduced. Furthermore, the increase in landscape disturbance degree in karst geomorphological areas with relatively smaller topographic relief (karst basins, karst trough valleys, and low hilly areas) ranges from 4.1329 to 21.733. The most significant increase in landscape disturbance was observed in the karst trough valley. The correlation between the anthropogenic impact index and the landscape disturbance degree in karst mountainous areas varied with the topographic gradient zone. The differences in the landscape disturbance degree under the influence of human activities in different karst landforms are influenced by multiple factors, including micro-differences in karst landforms (e.g., degree of terrain relief), grain-for-green projects, ecological protection policies, and terrain restrictions on human activities. The results of this study hold significant scientific value for the ecological management and regulation of human activities in various small-scale landforms in karst mountainous areas. Future research will focus on the coupled impacts of natural changes (e.g., climate change) and human activities on landscape disturbance.

ACKNOWLEDGEMENTS

This research supported by the Natural Science Research Project of Education Department of Guizhou Province (KY[2021]075).

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• CR-2023-0107.R2

Edited by

• Editors: Leandro Souza da Silva (0000-0002-1636-6643) Ignacio Javier Diaz-Maroto (0000-0003-0552-480X)

Publication Dates

• Publication in this collection
  25 Oct 2024
• Date of issue
  2025

History

• Received
  23 Jan 2023
• Accepted
  03 June 2024
• Reviewed
  06 Sept 2024