Mountain Vegetation Research Articles

Widespread plant species temporal variation along environmental and microclimate gradients in Norwegian mountains in a climate change context

The effect of climate change on mountain vegetation is influenced by environmental factors and site effects. To monitor the effect of climate change we therefore need to understand species' sensitivity to microclimate and environmental gradients. The objective of this study is to study widespread plant species' temporal and spatial variation along environmental and microclimate gradients in Norwegian mountains along a coast–inland gradient. Occurrence and abundance of plant species were surveyed in 110 study plots in four mountains at two points in time, seven years apart. Of the 222 plant species registered, Salix herbacea, Phyllodoce caerulea, Carex bigelowii, Juncus trifidus, Vaccinium myrtillus, Avenella flexuosa, and Empetrum nigrum were widespread across all mountains. These species responded differently to environmental and microclimate gradients, and abundance data were more sensitive than occurrence data. During the short time span we observed some indications of response which might support the assumption that boreal species outcompete alpine species in the forest transition zone, but our data do not indicate this effect at higher altitudes. Monitoring of climate change in mountains needs to include plots along environmental and microclimate gradients as well as an abundance of a set of widespread plant species that represent regional, local environmental, and climate gradients. However, when monitoring perennial plant species, the need for long‐term monitoring projects is high because such species develop slowly over several decades.

Surface soil phytolith assemblages across an altitudinal transect in the Qilian Mountains of Northwestern China, and their implications for palaeoecologic analysis in arid alpine regions

Reconstructing changes in altitudinal vegetation belts is crucial for comprehending vegetation response to past environmental changes in arid alpine regions, however, proxy data are limited. In this study, we report 51 surface soil phytolith samples from various vegetation types across an altitudinal transect in the arid alpine region of the Qilian Mountains, to elucidate the relationships between vegetation types and phytolith assemblages. Our results show that phytolith morphotypes in surface soil samples could be categorized into 16 classes, with Gobbet, Crenate, Rondel, Elongate entire, and Acutebulbosus being the predominant classes. The characteristics of surface soil phytolith assemblages vary across an altitudinal transect, reflecting differences in local vegetation. Discriminant analysis identifies Cubic, Elongate entire, Crenate, Gobbet, and Rondel as discriminant variables that correlated with five vegetation types (desert vegetation, mountain steppe vegetation, mountain coniferous forest vegetation, subalpine shrub meadow vegetation, and alpine meadow vegetation). We establish five discriminant functions based on the relationships between vegetation types and phytolith assemblages, demonstrating a high accuracy of our method for distinguishing between different vegetation types. These discriminant functions are applicable for distinguishing between diverse vegetation types in the mountains of arid inland regions of Asia. They are also effective in identifying coniferous forest vegetation in mid-latitude mountains. Therefore, these discriminant functions hold significant potential for reconstructing changes in altitudinal vegetation belts in mountainous regions and determining the vegetative types recorded by terminal lakes in arid regions. Our study introduces a new perspective on palaeoecologic analysis in arid alpine regions, advancing our understanding of ecosystem dynamics in response to environmental fluctuations.

The snow cover is more important than other climatic variables on the prediction of vegetation dynamics in the Pyrenees (1981–2014)

The dynamics of the mountain vegetation is governed by multiple climatic drivers including temperature, precipitation, radiation and snow cover variability. However, in the Mediterranean environment, little is known about the relative importance of each variable. In this study we assess how different snowpack indices (the maximum annual accumulation, the length of the snow season, and the melt-out date) and key climate variables (precipitation, temperature and shortwave solar radiation) control the interannual variability of the maximum Normalized Difference Vegetation Index (peak NDVI) in the Pyrenees. We use a 33 year long remote sensing dataset (1981–2014) to build a statistical model relating the annual peak NDVI with snow and climate variables. In elevated areas characterized by a well developed seasonal snowpack the melt-out date was the most important climatic variable for predicting the annual peak NDVI. However, at lower elevations where snow presence is ephemeral, shortwave solar radiation was the most important variable. This change in the relative importance of climatic variables occurs around 1300 m a.s.l. The results do not show a significant contribution of maximum snow accumulation, suggesting that indicators of snow presence (i.e. melt-out date or snow season duration), which are significantly easier to obtain than snow mass indicators from remote sensing, could be used to model the influence of the snowpack on peak NDVI at regional scale.

Analysis of Spatial Differentiation of NDVI and Climate Factors on the Upper Limit of Montane Deciduous Broad-Leaved Forests in the East Monsoon Region of China

The vertical transition zone of mountain vegetation is characterized by high species diversity, and the width of the transition zone may serve as an indirect indicator of climate change. However, research into the differential characteristics of vegetation response to climate changes at the boundary of vertical transition zones has been limited. This study employs MODIS and climate data spanning 2001 to 2018 to investigate spatiotemporal trends in precipitation (PRE), temperature (TMP), radiation (RAD), and Normalized Difference Vegetation Index (NDVI) across nine montane deciduous broad-leaved forests in the eastern monsoon region of China. It explores the time-lag and -accumulation effects of climatic variables on NDVI, quantifying their relative contributions to both its short-term and interannual variations. Results show that, notably, with the Qinling-Daba Mountains as a demarcation, northern regions exhibit significant increases in RAD (0.874–2.047 W m−2/a), whereas southern regions demonstrate notable rises in TMP (0.59–0.73 °C/10a). Areas of lower annual PRE correspond to the most rapid increases in annual average NDVI (5.045 × 10−3/a). NDVI’s lag time and cumulative duration responses to TMP are the shortest (0 and 2~4 periods), while its correlation with RAD is the strongest (0.815–0.975), generally decreasing from higher to lower latitudes. TMP significantly affects NDVI variations, impacting both short-term and interannual trends, with PRE driving short-term fluctuations and RAD dictating long-term shifts. This research provides critical data and a theoretical framework that enhances our understanding of how regional vegetation’s vertical zonation responds to climate change, thereby making a substantial contribution to the study of mountain vegetation’s diverse adaptability to climatic variations.

Biogeographic isolation and climate shape the evolutionary heritage of Neotropical inselbergs

AbstractAimQuaternary climatic shifts can explain the current distribution of ancient ecosystems as well as the current distributions of gradients that hold species richness and diversity of several lineages in old, climatically buffered, infertile landscapes (OCBILs) as inselbergs. Thus, the combination of phylogenetic approaches and temporal landscape connectivity allows disentangling the mechanisms involved in the origin of the disjunct distribution of plant species and the evolutionary heritage of Neotropical inselbergs.LocationBrazilian Atlantic Forest and Caatinga.Time PeriodPliocene until the current period.Major TaxaAngiosperms.MethodsWe used a compiled data set of 42 inselbergs across the Atlantic Forest and Caatinga biomes in eastern Brazil to describe their structure and phylogenetic diversity and map the landscape resistance distances and the effects of resistance on their phylogenetic beta diversity. We also aimed to identify the effectiveness of protected areas and gaps in the conservation of plant species in Brazilian inselbergs.ResultsWe found evidence of dispersal limitation in the inselberg species pool and a latitudinal gradient in plant species richness and phylogenetic diversity across the Neotropical inselberg landscape, with greater isolation between the northeastern and southeastern core areas. Our findings indicate that inselbergs can lead to a high turnover of phylogenetic diversity, thus imposing distinctiveness on the evolutionary lineages of the inselberg flora.Main ConclusionsOur results suggest that the current distribution of inselberg's flora in isolated ecosystems may result from a more connected distribution of this flora in the past, as postulated by the Pleistocene habitat fluctuations. However, the patterns of diversity have probably been influenced by events much older than Quaternary climate shifts, due to inselbergs climate stability areas (refugia) since ancient periods. Conservation of mountain vegetation is a crucial strategy for maintaining biodiversity and distinct phylogenetic lineages in the current rapid global climate and land use change scenario.

Remote sensing of vegetation and soil moisture content in Atlantic humid mountains with Sentinel-1 and 2 satellite sensor data

The satellite monitoring of vegetation moisture content (VMC) and soil moisture content (SMC) in Southern European Atlantic mountains remains poorly understood but is a fundamental tool to better manage landscape moisture dynamics under climate change. In the Atlantic humid mountains of Portugal, we investigated an empirical model incorporating satellite (Sentinel-1 radar, S1; Sentinel-2 optical, S2) and ancillary predictors (topography and vegetation cover type) to monitor VMC (%) and SMC (%). Predictors derived from the S1 (VV, HH and VV/HH) and S2 (NDVI and NDMI) are compared to field measurements of VMC (n = 48) and SMC (n = 48) obtained during the early, mid and end of summer. Linear regression modelling was applied to uncover the feasibility of a landscape model for VMC and SMC, the role of vegetation type models (i.e. native forest, grasslands and shrubland) to enhance predictive capacity and the seasonal variation in the relationships between satellite predictors and VMC and SMC. Results revealed a significant but weak relationship between VMC and predictors at landscape level (R2 = 0.30, RMSEcv = 69.9 %) with S2_NDMI and vegetation cover type being the only significant predictors. The relationship improves in vegetation type models for grasslands (R2 = 0.35, RMSEcv = 95.0 % with S2_NDVI) and shrublands conditions (R2 = 0.52, RMSEcv = 45.3 %). A model incorporating S2_NDVI and S1_VV explained 52 % of the variation in VMC in shrublands. The relationship between SMC and satellite predictors at the landscape level was also weak, with only the S2_NDMI and vegetation cover type exhibiting a significant relationship (R2 = 0.28, RMSEcv = 18.9 %). Vegetation type models found significant associations with SMC only in shrublands (R2 = 0.31, RMSEcv = 9.03 %) based on the S2_NDMI and S1_VV/VH ratio. The seasonal analysis revealed however that predictors associated to VMC and SMC may vary over the summer. The relationships with VMC were stronger in the early summer (R2 = 0.31, RMSEcv = 90.1 %; based on S2_NDMI) and mid (R2 = 0.37, RMSEcv = 70.8 %; based on S2_NDVI), butnon-significant in the end of summer. Similar pattern was found for SMC, where the link with predictors decreases from the early summer (R2 = 0.33, RMSEcv = 16.0 %; based on S1_VH) and mid summer (R2 = 0.30, RMSEcv = 17.8 %; based on S2_NDMI) to the end of summer (non-significant). Overall, the hypothesis of a universal landscape model for VMC and SMC was not fully supported. Vegetation type models showed promise, particularly for VMC in shrubland conditions. Sentinel optical and radar data were the most significant predictors in all models, despite the inclusion of ancillary predictors. S2_NDVI, S2_NDMI, S1_VV and S1_VV/VH ratio were the most relevant predictors for VMC and, to a lesser extent, SMC. Future research should quantify misregistration effects using plot vs. moving window values for the satellite predictors, consider meteorological control factors, and enhance sampling to overcome a main limitation of our study, small sample size.

Severely degraded high mountain vegetation recovers under different levels of wild herbivore grazing pressure, 1991–2021

Context It is important to understand the way in which wild herbivore grazing affects decadal vegetation dynamics after cessation of unnatural disturbances, especially in a context of climate change. Aims We investigated the decadal effects of different grazing regimes on treeless subalpine vegetation recovery from stock grazing and burning, on sites of different environmental character and initial state. Methods At each of four sites, two fenced areas that excluded mammalian herbivores, two that allowed in only rabbits and two grazed control plots were monitored every 5 years between 1991 and 2021. General linear models were developed to explain variation in change over the 30 years in different cover types. The years in which peak and trough values occurred were also determined, as were the incidence and direction of differences between treatments in sites and years. Key results There was marked variation in change over 30 years between the sites and lifeforms. Exclusion of mammalian herbivores increased the slow rate of revegetation. There was little effect from rabbits by themselves. Unexpectedly, the cover of both short and tall herbs was not promoted by grazing exclusion. Short term climatic variation affected some cover types, with many peaks and troughs in the dry year of 2001, but it was not possible to disentangle decades scale climate change effects from the process of recovery after disturbance. Conclusions The slight increase in revegetation rates in the absence of native herbivores and rabbits does not justify culling. Restoration interventions appear to be unnecessary. The prospect of increasing fire incidence and deer numbers suggests that it is desirable to continue monitoring the plots.

The Spatio-Temporal Variation of Vegetation and Its Driving Factors during the Recent 20 Years in Beijing

As the most important city in China, Beijing has experienced an economic soar, large-scale population growth and eco-environment changes in the last 20 years. Evaluating climate- and human-induced vegetation changes could reveal the relationship of vegetation-climate-human activities and provide important insights for the coordination of economic growth and environmental protection. Based on a long-term MODIS vegetation index dataset, meteorological data (temperature, precipitation) and impervious surface data, the Theil-Sen regression and the Mann-Kendall method are used to estimate vegetation change trends in this study and the residual analysis is utilized to distinguish the impacts of climate factors and human activities on vegetation restoration and degradation from 2000 to 2019 in Beijing. Our results show that the increasing vegetation areas account for 80.2% of Beijing. The restoration of vegetation is concentrated in the urban core area and mountainous area, while the degradation of vegetation is mainly concentrated in the suburbs. In recent years, the vegetation in most mountainous areas has changed from restoration to significant restoration, indicating that the growth of mountain vegetation has continued to restore. We also found that in the process of urban expansion, vegetation browning occurred in 53.1% of the urban built-up area, while vegetation greening occurred in the remaining area. We concluded that precipitation is the main climatic factor affecting the growth of vegetation in Beijing’s mountainous areas through correlation analysis. Human activities have significantly promoted the vegetation growth in the northern mountainous area thanks to the establishment of environmental protection areas. The negative correlation between vegetation and the impervious surface tends to gradually expand outwards, which is consistent with the trend of urban expansion. The positive correlation region remains stable, but the positive correlation is gradually enhanced. The response of vegetation to urbanization demonstrated a high degree of spatial heterogeneity. These findings indicated that human activities played an increasingly important role in influencing vegetation changes in Beijing.

Significant anthropogenic impact on the mountain vegetation of Southeast China commenced ∼1 kyr BP, lagged behind similar changes in the lower Yangtze River basin and coastal plains by 2000–4000 years

Human impact on the coastal plain of Southeast China has been well studied over late Holocene timescales; however, an understanding of anthropogenic impact in mountainous regions is still lacking. In this paper, we present records of vegetation, fire, and human impact, spanning the past 4000 years, with a resolution of ∼40 years, obtained from an upland peatland in Southeast China. The results reveal that climate change (dominated by the evolution of the East Asia Monsoon) was the most critical factor controlling vegetation before 1.0 cal kyr BP, while human impact gradually emerged as the primary driver after 1.0 cal kyr BP in the mountains of Southeast China. As such, the record of anthropic impact in mountainous regions lagged behind the signal from the coastal plains by some 2000–4000 years. As the population migrating from northern China dispersed into the mountainous regions of Southeast China, demand for agricultural land promoted slash-and-burn cultivation and the destruction of broad-leaf forest.

Altitude-dependent responses of dryland mountain ecosystems to drought under a warming climate in the Qilian Mountains, NW China

Climate warming has intensified drought severity in many areas of the globe. Dryland mountain ecosystems are sensitive to climate variability. However, the response of mountainous vegetation to drought has high uncertainties due to the rugged terrain and varied local climate and is still not well understood. In this study, the Qilian Mountains (QLMs) in northwestern China, which have a typical dryland mountain ecosystem and experienced intense climate change, are selected as a case study area. We utilized a downscaled normalized difference vegetation index (NDVI) product and climate data to explore the spatiotemporal dynamics in vegetation greenness and the climatic drivers for the period 1982 ∼ 2018. We studied the response of different vegetation types to drought at different altitude gradients Results suggest that most areas in the QLMs displayed an upward trend in greenness and the greening rate was greater in the southeast, while vegetation degradation occurred in the western part. The grass and shrub communities showed a decreased sensitivity to drought at the medium altitudes and increased sensitivity at the lower and upper limits of their distribution altitudes. The broadleaf forests showed intensifying drought sensitivity at all elevations. Coniferous forests and alpine meadows respectively displayed decreasing and increasing trends of sensitivity to drought along elevations. A combination of the fastest warming and mildest wetting trends over the lowest (<3200 m) and highest (>4200 m) elevations are likely exacerbating the vulnerability of vegetation to drought. By contrast, the medium elevations (3200 ∼ 4200 m) have experienced rapid wetting and relatively slower warming, and the vegetation is expected to be less affected by drought if the current climate change trends continue. These findings help to better understand how these dryland mountainous vegetation communities respond to drought under a changing climate and provide some insights into the protection of these vulnerable ecosystems for the local managers.

Mountain Vegetation Classification Method Based on Multi-Channel Semantic Segmentation Model

With the development of satellite remote sensing technology, a substantial quantity of remote sensing data can be obtained every day, but the ability to extract information from these data remains poor, especially regarding intelligent extraction models for vegetation information in mountainous areas. Because the features of remote sensing images (such as spectral, textural and geometric features) change with changes in illumination, viewing angle, scale and spectrum, it is difficult for a remote sensing intelligent interpretation model with a single data source as input to meet the requirements of engineering or large-scale vegetation information extraction and updating. The effective use multi-source, multi-resolution and multi-type data for remote sensing classification is still a challenge. The objective of this study is to develop a highly intelligent and generalizable classification model of mountain vegetation utilizing multi-source remote sensing data to achieve accurate vegetation extraction. Therefore, a multi-channel semantic segmentation model based on deep learning, FCN-ResNet, is proposed to integrate the features and textures of multi-source, multi-resolution and multi-temporal remote sensing data, thereby enhancing the differentiation of different mountain vegetation types by capturing their characteristics and dynamic changes. In addition, several sets of ablation experiments are designed to investigate the effectiveness of the model. The method is validated on Mt. Taibai (part of the Qinling-Daba Mountains), and the pixel accuracy (PA) of vegetation classification reaches 85.8%. The results show that the proposed multi-channel semantic segmentation model can effectively discriminate different vegetation types and has good intelligence and generalization ability in different mountainous areas with similar vegetation distributions. The multi-channel semantic segmentation model can be used for the rapid updating of vegetation type maps in mountainous areas.

The construction of a mountain vegetation knowledge graph incorporating with geographical principles, maps and remote sensing images

The construction of a mountain vegetation knowledge graph incorporating with geographical principles, maps and remote sensing images

Analysis of error sources in the multi-scale remote sensing estimation of mountain vegetation gross primary productivity

Analysis of error sources in the multi-scale remote sensing estimation of mountain vegetation gross primary productivity

Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images

The composition and structure of mountain vegetation are complex and changeable, and thus urgently require the integration of Object-Based Image Analysis (OBIA) and Deep Convolutional Neural Networks (DCNNs). However, while integration technology studies are continuing to increase, there have been few studies that have carried out the classification of mountain vegetation by combining OBIA and DCNNs, for it is difficult to obtain enough samples to trigger the potential of DCNNs for mountain vegetation type classification, especially using high-spatial-resolution remote sensing images. To address this issue, we propose a self-adaptive-filling method (SAF) to incorporate the OBIA method to improve the performance of DCNNs in mountain vegetation type classification using high-spatial-resolution aerial images. Using this method, SAF technology was employed to produce enough regular sample data for DCNNs by filling the irregular objects created by image segmenting using interior adaptive pixel blocks. Meanwhile, non-sample segmented image objects were shaped into different regular rectangular blocks via SAF. Then, the classification result was defined by voting combining the DCNN performance. Compared to traditional OBIA methods, SAF generates more samples for the DCNN and fully utilizes every single pixel of the DCNN input. We design experiments to compare them with traditional OBIA and semantic segmentation methods, such as U-net, MACU-net, and SegNeXt. The results show that our SAF-DCNN outperforms traditional OBIA in terms of accuracy and it is similar to the accuracy of the best performing method in semantic segmentation. However, it reduces the common pretzel phenomenon of semantic segmentation (black and white noise generated in classification). Overall, the SAF-based OBIA using DCNNs, which is proposed in this paper, is superior to other commonly used methods for vegetation classification in mountainous areas.

Characterizing the effect of scaling errors on the spatial downscaling of mountain vegetation gross primary productivity

ABSTRACT High spatial resolution Gross Primary Productivity (GPP) estimation makes it feasible to better understand the spatial heterogeneity of mountain vegetation photosynthesis. Spatial downscaling is a practical approach to obtaining high resolution GPP estimates, whereas there is almost no attention given to the downscaled biases resulted from the widely reported scaling errors in medium or coarse resolution GPP estimates. To fill the above gap, this study adopted an eco-hydrological model to obtain 960 m resolution distributed GPP estimates (not including scaling errors) and lumped GPP estimates (including scaling errors) over four mountainous watersheds. Then, the distributed and lumped estimates were downscaled from 960 m to 30 m, respectively. Finally, the contribution of reducing scaling errors was characterized by the agreement index (d), BIAS and Root-Mean-Square-Error (RMSE) values between downscaled GPP and referenced GPP (directly generated at the spatial resolution of 30 m). Results showed that a large difference existed between lumped and distributed GPP, with d, BIAS, and RMSE of 0.79, 212, and 334 gC m−2 year−1, demonstrating that the scaling errors should be given enough attention to current coarse resolution GPP estimates. Before considering the scaling errors, large uncertainties were observed in the GPP downscaled from lumped values, with d, BIAS, and RMSE of 0.68, 220, and 480 gC m−2 year−1. After considering the scaling errors, a significant improvement was achieved in the GPP downscaled from distributed values, with an increased d value of 0.81, a decreased BIAS value of 10 gC m−2 year−1, and a decreased RMSE value of 388 gC m−2 year−1, indicating that reducing the medium or coarse resolution scaling errors would effectively improve the spatial downscaling of mountain vegetation GPP. Our study highlights the effect of scaling errors on the spatial downscaling of mountain vegetation productivity, which should be given more attention in the future carbon modeling.

Palaeoecological multiproxy reconstruction captures long-term climatic and anthropogenic impacts on vegetation dynamics in the Rhaetian Alps

Alpine ecosystems are particularly affected by climate change due to their high sensitivity to temperature variations. Understanding the response of vegetation to rapid temperature increase and human impact is necessary to produce accurate and reliable predictions of future mountain vegetation and mitigate climate change impacts. Our multiproxy study reconstructs climate, fire, and vegetation dynamics over the last 14,200 cal. BP at Lai da Vons (1991 m a.s.l.), a subalpine lake situated in the treeline ecotone in Eastern Switzerland. Our results based on geochemical analysis (XRF), pollen and charcoal analysis, and high-resolution macrofossil analysis reveal that early afforestation started already at the onset of the Holocene, dominated by Pinus cembra and Larix decidua. Larch and stone pine formed closed forests at the site at 10,300 cal. BP. From 9500 cal. BP onwards, when summer temperatures were warmer than today, Picea abies expanded simultaneously with Abies alba. In the past 6500 years, increasing human impact, culminating during two main phases of landscape opening and intensification of fire regime, led to a progressive increase of species richness. Considering future climate change, an upward shift of treeline and timberline is expected in the absence of domesticated herbivores. We advocate that low-intensity pastoralism in such a cultural landscape is essential to preserve the plant diversity of species-rich mountain meadows.

Vegetation response to grazing and drought (13 yr) in a conservation area in the Succulent Karoo, South Africa

Rangelands of arid ecosystems are driven by internal and external environmental controls. Grazing pressure is an important anthropogenic driver, but stochastic environmental events such as rainfall variability and prolonged drought can have profound effects on arid vegetation. We investigated the effect of a prolonged drought and initial high grazing pressure on range condition, perennial vegetation cover, life form cover, vegetation composition and vegetation diversity at five monitoring sites in a conservation area in arid Succulent Karoo vegetation. The response of the rocky mountainous vegetation to drought and grazing differed notably from the response of the sandy plains vegetation. Range condition and perennial vegetation cover reduced drastically in all habitats, although the extent of deterioration was less in the mountainous habitats. Annual vegetation cover was associated with inter-annual rainfall variability. Species richness decreased at three of five sites, whereas species evenness increased at three of five sites. Shannon-Wiener index of diversity and Simpson's diversity index trends were inconsistent. Vegetation composition changed directionally from a quasi-stable state in all five monitoring sites as the drought persisted. The extent of the change was less in mountainous habitats indicating potentially higher resilience than in plains habitats, though grazing pressure was lower in the mountains than on the plains over the study period. The existence of quasi-stable states, together with the observed directional changes in vegetation composition in response to both drought and grazing, indicated that both equilibrium and non-equilibrium vegetation dynamics apply.

Dampak Pengembangan Desa Wisata Terhadap Aspek Ekonomi, Sosial Budaya dan Lingkungan di Alamendah Kabupaten Bandung

The purpose of this study was to determine the impact of the development of a tourist village towards the aspect of economics, social culture, and physical environment in Alamendah. This study used descriptive qualitative approach. The data were collected by a researcher with direct observation, distribution of questionnaires, and interviews with the management of the Alamendah tourism village and the local community. The results showed that the development of the Alamendah tourism village had been carried out well, but in the attraction aspect there was still a lack of human resources, in terms of amenities, there was still a lack of hygiene &amp; sanitation facilities, there were no supporting facilities for tourist safety and lack of cleaning facilities. In the Accessibility aspect, there is still a lack of directions to Alamendah Tourism Village. And there is still no control on ??the Ancillary aspect. The impact on the economic aspect has a significant positive impact but the income and standard of living of the community have a slight impact and the distribution of Public Infrastructure and Facilities has an impact but not significant. The impact on socio-cultural aspect has a significant positive impact, it's just that the erosion of the local language has already been felt by the community. The impact on environmental aspects has a significant negative impact. Such as polluted water sources due to the discharge of liquid and solid waste, decreased air quality due to air pollution and also noise pollution in Alamendah Tourism Village due to exhaust emissions from each vehicle and also noise generated from busy vehicles, damage to mountain vegetation and wild areas, as well starting from the change of residential land that turned into commercial land. However, Wildlife in Alamendah Tourism Village is still well preserved.

Spatial and temporal analysis of drought resistance of different vegetation in the Ta-pieh Mountains based on multi-source data

Abstract This study focuses on the ecological and environmental safety of Ta-pieh Mountain. Drought episodes can lead to ecological problems such as vegetation damage. Therefore, quantifying the response of vegetation to drought is essential for ecological management. The study utilized normalized difference vegetation index (NDVI) and precipitation datasets from 1999 to 2019 to derive seasonal NDVI and standardized precipitation index (SPI) data. Using Theil-Sen median trend analysis and Mann-Kendall significance test analysis, we initially examined the characteristics of vegetation and drought for the 21-year time series. SPI is used to investigate and assess the occurrence and severity of drought in the research region. Then, the strength and variability of cropland, woodland, and grassland drought resistance in the Ta-pieh Mountains were discussed using the ratio of coefficient of variation (RCV). Finally, the cross-spectrum was used to calculate the vegetation lag time to drought. The study found that NDVI increased across all seasons, while SPI increased in spring and autumn and decreased in summer and winter. The spring drought had the most significant impact on vegetation. Cropland showed the highest improvement in drought tolerance and woodland showed the highest drought tolerance. The lagged response periods of cropland, woodlands, and grassland to drought were 1.62 months, 8.94 months, and 2.49 months, respectively. These findings provide a scientific basis for the management and preservation of the ecology of the Ta-pieh Mountains.

Vertical Characteristics of Vegetation Distribution in Wuyishan National Park Based on Multi-Source High-Resolution Remotely Sensed Data

Identifying vertical characteristics of mountainous vegetation distribution is necessary for studying the ecological environment quality and biodiversity and for evaluating its responses to climate change. However, producing fine vegetation distribution in a complex mountainous area remains a huge challenge. This study developed a framework based on multi-source high-resolution satellite images to strengthen the understanding of vertical features of vegetation distribution. We fused GaoFen-6 and Sentinel-2 data to produce 2 m multispectral data, combined with ALOS PALSAR digital elevation model (DEM) data, and used an object-based method to extract variables for establishing a classification model. The spatial distribution of vegetation types in Wuyishan National Park (WNP) was then obtained using a hierarchical random forest classifier. The characteristics of different vegetation types along the elevation gradient and their distribution patterns under different human protection levels were finally examined. The results show that (1) An overall accuracy of 87.11% and a Kappa coefficient of 0.85 for vegetation classification was achieved. (2) WNP exhibits obviously vertical differentiation of vegetation types, showing four compound dominant zone groups and five dominant belts. (3) The composition of vegetation types in the scenic area differs significantly from other regions. The proportions of Masson pine and Chinese fir exhibit a noticeably decreasing trend as the distance increases away from roads, while the changes in broadleaf forest and bamboo forest are less pronounced.