Changes In Vegetation Cover Research Articles

Interplay of Environmental Shifts and Anthropogenic Factors with Vegetation Dynamics in the Ulan Buh Desert over the Past Three Decades

In arid and semiarid regions, vegetation provides essential ecosystem services, especially retarding the desertification process. Vegetation assessment through remote sensing data is crucial in understanding ecosystem responses to climatic factors and large-scale human activities. This study analyzed vegetation cover changes in the Ulan Buh Desert from 1989 to 2019, focusing on the impacts of human activities and key meteorological factors. The results showed that both climatic and human activities contributed to an increasing trend in vegetation cover (normalized difference vegetation index (NDVI)) over the 30-year period. Temperature and precipitation significantly affected the NDVI in the desert, with temperature having a more substantial influence. The combined impact of average temperature and precipitation on the NDVI was notable. Human activities and meteorological factors caused the vegetation restoration area in the desert to be approximately 35% from 1989 to 2019. Human activities were the primary influencers, responsible for about 60% of vegetation restoration across the study area. Especially from 2004 to 2019, the conversion to farmland driven by human activities dominated the region’s NDVI increase. The research underscores the importance of considering both climatic and human factors in understanding and managing ecosystem dynamics in arid areas like the Ulan Buh Desert. By integrating these factors, policymakers and land managers can develop more effective strategies for sustainable ecosystem management and combating desertification.

Multitemporal Analysis of Land Cover Changes in Areas with Contrasted Forest Management and Conservation Policies in Northern Mexico

This study evaluates and contrasts changes in vegetation cover over three decades in two forest areas in the State of Chihuahua in northern Mexico with different management statuses: one with sustainable forest management and the other protected as a Flora and Fauna Protection Area. The hypothesis proposed that both areas would have maintained or increased their vegetation cover since 1995. Satellite images from the periods 1995–2008, 2008–2014, 2014–2022, and 1995–2022 were analyzed. The results showed that Ejido El Largo y Anexos significantly increased forest areas and reduced grasslands due to sustainable management practices, with a notable expansion of pine–oak and pine forests. In contrast, the Tutuaca Flora and Fauna Protection Area experienced a notable loss of oak and oak–pine forests, suggesting ineffectiveness in its conservation policies. However, there was less loss in Douglas Fir forests, indicating some effective protection efforts. The comparison reveals opposing dynamics: while Ejido El Largo y Anexos demonstrates success in sustainable management, the Tutuaca Flora and Fauna Protection Area faces conservation challenges. In conclusion, this study highlights the need for active management approaches to maintain ecosystem cover and functionality.

Farmland Inventory Delineation and Vegetation Cover Change Using Space-Based Technology in Federal Capital Territory (Fct), Nigeria

Farmland Inventory Delineation and Vegetation Cover Change Using Space-Based Technology in Federal Capital Territory (Fct), Nigeria

Spatial and Temporal Characteristics of Fractional Vegetation Cover and Its Response to Urbanization in Beijing

Exploration of the spatiotemporal changes in fractional vegetation cover （FVC） and its response characteristics to urbanization is of great significance for urban ecological protection and planning in Beijing. This study analyzed the spatiotemporal characteristics of vegetation cover changes in Beijing from 2000 to 2020 using the Theil-Sen Median and Mann-Kendall methods based on a long-term time series vegetation cover dataset. Then, this study used the urbanization index as a key indicator of spatial urbanization and utilized the transect line and global grid analysis methods to investigate the response characteristics of FVC to different urbanization gradients. The results indicated that： ① FVC changes showed spatial and temporal heterogeneity. From 2000 to 2020, Beijing was predominantly covered by high vegetation, accounting for 65.22% of the total area, which was mainly distributed in ecological conservation areas consistent with the Jundu, Xishan, and Yaji Mountain ranges. The FVC presented an overall positive development trend, with a decreasing trend of areas with low FVC. The increase in FVC was significant （by 28.68%）, mainly distributed in ecological conservation areas and within a range of 10-12 km in concentric circles centered around Tiananmen Square. The urbanization index and FVC change rate were relatively high in Haidian District, Chaoyang District, Fengtai District, Shijingshan District, and Changping District. ② The artificial land surface in 2000, 2010, and 2020 was 9.69%, 13.64%, and 21.19%, respectively, with significant spatial agglomeration and strong spatial heterogeneity. During the urbanization process in Beijing, the increase in artificial land surface reached 11.5%, with the conversion from arable land to artificial land surface accounting for 53.83% of the total land use conversion area. ③ There was a significant negative correlation between FVC and the urbanization index, indicating that urbanization had a negative impact on regional FVC. However, as the urbanization process stabilized, this negative correlation tended to gradually weaken. Although the central urban areas were mainly characterized by low FVC, there was a significant increasing trend in the FVC, indicating a positive development in the FVC and an improvement in regional ecological quality, which was closely related to the governance of the mountain-water-forest-field-lake-grass-sand system. The results of the study can provide a basis for the development of vegetation restoration programs and ecological management measures in Beijing.

Trends of forest cover and prospects of Great Ethiopian Renaissance Dam (GERD) to Ethiopia and the downstream countries

The need for forest products, agricultural expansion, and dependency on biomass for the household energy source has largely influenced Ethiopia’s forest resources. Consequently, the country lost its forest resources to less than 6% until the millennium. In this study, quantitative and qualitative historical data analysis was employed to understand the socioeconomic benefits of large dam construction to Ethiopia and downstream countries. Moreover, remotely sensed data was also used to analyze the trends of vegetation cover change in the Nile catchment since the commencement of the dam; focusing on areas where there are high settlement and urban areas. It was identified that Ethiopia has one of the lowest electricity consumption per capita in Africa; about 91% of the source of household energy supply depends on fuelwood today and more than 55.7% of the population does not have access to electricity. The normalized difference vegetation index result shows an increment of vegetation area in the Nile catchment and a reduction of no vegetation area from 2011–2021 by 37.1%; which is directly related to the protection of the dam catchment for its sustainability in the last decade. The hydroelectric dam construction has prospects of multi-benefit to Ethiopia and downstream countries either through the direct benefit of hydropower energy production, related socioeconomic values, and reducing risks of destructive flood from Ethiopian highlands. Generally, it explains the reason why to not say ‘No’ to the reservoir as it is an ever more vital tool for fulfilling growing energy demand and supporting ecological stability.

Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin

Abstract Ensuring the quality protection of geospatial elements entails environmental control, a task unattainable without precise measurement results. This article aims to conduct a spatio-temporal analysis of soil degradation and vegetation status within the influence zone of the Kolubara Mining Basin in Serbia. Remote sensing is employed to assess vulnerability to erosion using the erosion potential method. A geographic information system environment is utilized to generate an erosion map, illustrating erosive processes across different time periods, particularly comparing the present situation (2022) to 1983. Results indicate that observed areas are experiencing erosion due to changes in land use. Furthermore, this study investigates the use of the normalized difference vegetation index to monitor vegetation cover changes from 1992 to 2022. The objective is to demonstrate that these methods effectively depict degradation levels and vegetation status in the area. This comprehensive overview provides insights into the changes occurring across the analyzed years. Such insights are crucial for informing future efforts to restore the region to its natural state prior to lignite mining.

Improving evapotranspiration partitioning by integrating satellite vegetation parameters into a land surface model

Land evapotranspiration (ET) primarily involves vegetation transpiration, canopy interception loss, and soil evaporation. Previous studies have made significant progress in total ET estimation; however, substantial challenges remain in partitioning ET on a regional scale, largely due to the intricate water and energy balance that is disrupted by vegetation cover changes. The accuracy of land surface models in representing ET components may be constrained by their inadequate consideration of vegetation dynamics. In this study, we integrate satellite leaf area index (LAI) and fraction of vegetation coverage (FVC) into the Variable Infiltration Capacity model (VIC) to improve ET partitioning ability in the Loess Plateau of China, a region that has experienced substantial vegetation dynamics. The results showed that satellite dynamic vegetation parameters in modeling are effective in improving the estimation of ET components compared with the default/static vegetation parameters. Considering LAI dynamics in the model enhances the representation of the inter- and intra-annual variations in vegetation transpiration and canopy interception loss. Dynamic FVC reasonably allocates transpiration to soil evaporation, capturing evaporation in forest gaps effectively. This effect is particularly relevant in arid and semi-arid regions. Among the ET components, transpiration was the most sensitive to the two dynamic vegetation parameters, followed by canopy interception loss and soil evaporation. Through the VIC model with dynamic vegetation parameters, our study revealed that soil evaporation was twice that of transpiration in the Loess Plateau, which is consistent with its semi-arid region and relatively sparse vegetation coverage. Our study offers valuable insights regarding the use of vegetation coverage for partitioning ET and highlights the advantage of integrating satellite vegetation products into land surface models.

Vegetation Cover Change in the Loess Plateau: Patterns, Driving Factors, and Impacts

This paper reviews the changes in vegetation cover in the Loess Plateau, focusing on patterns, driving factors, and impacts. Over recent decades, significant improvements in vegetation cover have been observed, primarily due to large-scale ecological restoration projects and natural climate variability. Key drivers include precipitation, temperature, land use changes, and human activities such as reforestation. These changes have important ecological, socio-economic, and hydrological implications, including reduced soil erosion, improved biodiversity, and potential trade-offs with water availability. Understanding these dynamics is crucial for sustainable land management and ecological conservation in the region.

A study on the response of waterbird diversity to habitat changes caused by ecological engineering construction

Wetland degradation is a major factor in the decline of biodiversity. Ecological engineering construction efforts, such as water diversion, have been the main method of wetland restoration. To reveal the effects of biodiversity conservation and ecological engineering construction in the semiarid regions of China, waterbird surveys were carried out 2 years before and 8 years after the construction of the ecological engineering project discussed in this study. The relationships among waterbirds and habitat landscape patterns, hydrology and vegetation conditions were analyzed. The impacts of ecological engineering on habitats and waterbird diversity were revealed. The results showed that (1) there were significant changes in land use, hydrological distributions and vegetation cover in the study area before and after the construction of the ecological project. The quality of waterbird habitats after the construction project was better than that before construction. However, there were fluctuating conditions. (2) The areas of water bodies and marshes were positively correlated with the number and diversity of waterbirds. However, increases in arable land, forestland, grassland and saline land can stress rare waterbirds. Changes in water levels, water body areas and vegetation cover in the conservation area all directly affect the availability of waterbird habitats, which can affect waterbird diversity. The wetland water level is the most important impact factor. (3) The annual water diversion volume in the study area is 5.00 × 107 m3. When the water level is maintained at approximately 1.5-2.5m, the number of waterbirds remains stable. This study can provide scientific guidance and a methodological basis for the construction of ecological projects with waterbirds as protected targets.

Hydrological simulation and evaluation of drought conditions in the ungauged watershed Parishan lake Iran, using the SWAT model

ABSTRACT ke Parishan has experienced significant water scarcity, leading to its complete disappearance. This study utilized the SWAT model to assess the impact of changes in vegetation, precipitation, temperature, and evaporation on runoff and the lake's surface. Two strategies were employed: analyzing runoff variations based on land use and land cover (LULC) changes and evaluating the effects of precipitation, temperature, and humidity on runoff during the simulation period. A key challenge was the lack of runoff statistics, which was addressed by using data from donor watershed stations and Copernicus satellite information to improving simulation processes and calibration. The findings reveal that environmental changes, particularly land use, have increased evaporation and temperature fluctuations which results in reducing runoff. While Copernicus satellite data proved useful, runoff statistics from the neighboring Chamchit station provided more accurate simulations. The study finding suggests that the LULC strategy is an effective management approach, as in this case Parishan Lake's historical conditions with less urbanization and agriculture correlated with higher runoff and lake levels. Finally, meteorological and hydrometric drought and its impact on lake area changes were measured to reach a more comprehensive conclusion.

The effect of dynamic changes in vegetation coverage on the mechanism of organic carbon loss in inter-rill erosion

The intricate effects of dynamic variations in vegetation coverage, representing different vegetation growth states and erosion conditions, on properties (content, enrichment and loss) of sediment and soil organic carbon (SOC) fraction in inter-rill erosion have not yet been fully elucidated. By setting micro-plots with different vegetation coverage ranges (0 %, 15 ∼ 35 %, 25 ∼ 55 % and 35 ∼ 70 %), the interaction mechanisms between vegetation coverage and inter-rill erosion, particle size distribution and SOC fractions properties during the vegetation growing period were studied. The average sediment yield, runoff yield and sediment concentration were highest under the bare plot, and diminished progressively with increased vegetation coverage. The effective clay content decreased with the increasing coverage, while ultimate clay was highest in the vegetation coverage range of 25 ∼ 55 %. Clay particle was transported as aggregates, with enrichment for all treatments except for the vegetation coverage range of 35 ∼ 70 %, providing transportation potential for SOC fractions loss. The content of SOC and mineral-associated organic carbon, as well as their enrichment ratio, decreased and then increased with coverage, which is the opposite of particulate organic carbon, and the loss of all fractions decreased with increasing vegetation coverage. Mineral-associated organic carbon dominated change in SOC, with proportion in SOC content and loss decreasing first and then increasing with vegetation coverage. The structural equation model showed the impact of vegetation coverage on SOC fraction loss was mainly achieved through indirect regulation of inter-rill erosion and particle size distribution. Our results provide new insights into the complex relationship between natural slope vegetation coverage and SOC loss, particularly from a fractions perspective, as well as understanding the ultimate fate of SOC in such environments.

Post-wildfire boreal forest vegetation cover change mapping via information fusion for secondary disaster risk assessments

Post-wildfire vegetation cover damage and loss can escalate the risks of secondary disasters such as flood, landslide, and water contamination, particularly in a major wildfire affected region where human settlements are situated. In assessments of the secondary disaster risks, the post-wildfire vegetation cover change plays a key role in influencing the distribution and intensity of the risks. In this work, a processing framework for mapping post-wildfire vegetation cover changes through information fusion has been generated and tested using Landsat8 and WorldView imagery data. The test site was the boreal forest region surrounding Fort McMurray, Alberta, Canada, affected by a massive wildfire in May 2016. The derived map results indicate that the fusion process in the framework is effective for generation of post-wildfire vegetation cover change information. The use of WorldView data revealed more variation details in distribution of the vegetation cover burn damages than use of Landsat data. Moreover, the uncertainty in vegetation burn severity using Landsat-based Differenced Normalized Burn Ratio (dNBR) index exists in the areas with low dNBR reading values due to the sub-pixel effect. The forest burn severity measured with dNBR index can be underestimated due to the quick herbaceous cover recovery after wildfire. These uncertainties in the post-wildfire vegetation cover mapping should be taken into consideration when the derived information is being used for risk assessments.

Multi‐decadal changes in vegetation and land use inferred from different data sources: Two case studies at the regional scale in the Slovenian karst

AbstractFew studies have used aerial imagery or various statistical records to analyse temporal land use and vegetation dynamics over large areas. This study presents an innovative approach that merges national‐scale data to provide temporal and spatial digital information on land use and vegetation cover change at high resolution. The work includes 326 historical aerial photographs from 1957 and 1980, derived digital terrain models, digital orthophotos (from 1994 to 1997), current land use (available from 2006), and forestry databases. Using two case studies in Slovenia, a detailed procedure for data preparation, processing (including orthorectification, on‐screen digitisation, visual photointerpretation), and result generation is presented. The used multiple sources showed great potential for the acquisition, mapping and quantifying selected land use categories. The study provided very important material for land use change assessment and implementation of the national documentary heritage. Although the processing methods were time‐consuming, they proved to be very suitable for delineating polygons of change with a spatial resolution of at least 1 m. The results show that the forest in the Rižana catchment has increased by 30 percentage points in the last 60 years and that in the Unica catchment 2/3 of the forest was damaged by large‐scale disturbances in 2014–2018.

Integrated analysis of remote sensing with meteorological and health data for allergic rhinitis forecasting in Tianjin.

Long time series of vegetation monitoring can be carried out by remote sensing data, the level of urban greening is objectively described, and the spatial characteristics of plant pollen are indirectly understood. Pollen is the main allergen in patients with seasonal allergic rhinitis. Meteorological factors affect the release and diffusion of pollen. Therefore, studying of the complex relationship between meteorological factors and allergic rhinitis is essential for effective prevention and treatment of the disease. In this study, we leverage remote sensing data for a comprehensive decade-long analysis of urban greening in Tianjin, which exhibits an annual increase in vegetative cover of 0.51 per annum, focusing on its impact on allergic rhinitis through changes in pollen distribution. Utilizing high-resolution imagery, we quantify changes in urban Fractional Vegetation Coverage (FVC) and its correlation with pollen types and allergic rhinitis cases. Our analysis reveals a significant correlation between FVC trends and pollen concentrations, with a surprising value of 0.71, highlighting the influence of urban greenery on allergenic pollen levels. We establish a robust connection between the seasonal patterns of pollen outbreaks and allergic rhinitis consultations, with a noticeable increase in consultations during high pollen seasons. our findings indicate a higher allergenic potential of herbaceous compared to woody vegetation. This nuanced understanding underscores the importance of pollen sensitivity, alongside concentration, in driving allergic rhinitis incidents. Utilizing a Generalized Linear Model, significant features influencing the number of visits for allergic rhinitis (P < 0.05) were identified. Both GLM and LSTM models were employed to forecast the visitation volumes for rhinitis during the spring and summer-autumn of 2022. Upon validation, it was found that the R² values between the simulated and actual values for both GLM and LSTM models surpassed the 95% confidence threshold. Moreover, the R² values for the summer-autumn seasons (GLM: 0.56, LSTM: 0.72) were higher than those for spring (GLM: 0.22, LSTM: 0.47). Comparing the errors between the simulated and actual values of GLM and LSTM models, LSTM exhibited higher simulation precision in both spring and summer-autumn seasons, demonstrating superior simulation performance. Overall, our study pioneers the integration of remote sensing with meteorological and health data for allergic rhinitis forecasting. This integrative approach provides valuable insights for public health planning, particularly in urban settings, and lays the groundwork for advanced, location-specific allergenic pollen forecasting and mitigation strategies.

Spatial and temporal variations of vegetation cover on the central and eastern Tibetan Plateau since the Last glacial period

Long-term changes in vegetation cover of the Tibetan Plateau (TP) are essential for understanding vegetation change under future climate. Previous studies have mainly concentrated on the Holocene and the eastern region of the TP, but here, we establish a relationship between modern pollen data (including both pollen percentage and concentration) and vegetation cover using a random forest (RF) model based on 362 soil-surface samples from the TP, as well as using it to quantitatively reconstruct the vegetation cover history of the Dagze Co (central TP, covering the last 19.5 cal. ka BP) and Koucha Lake (eastern TP, covering the last 33.8 cal. ka BP) regions. The RF results indicate that both the models based on pollen percentages or concentrations perform similarly (former: R2 = 0.538, RMSEP = 19.772%; latter: R2 = 0.540, RMSEP = 19.723%). However, when considering the reconstructed vegetation cover of Dagze Co and Koucha Lake, the results based on pollen concentrations appear to be more reliable. Before 13.4 and 16.8 cal. ka BP, Dagze Co and Koucha Lake has low vegetation cover of 25% and 30%, respectively, dominated by alpine desert or desert steppe. After that, changes in vegetation cover show different trends. At Dagze Co, the vegetation cover reaches a high level (54%) between 13.4 and 5.3 cal. ka BP, followed by a decrease until it starts increasing again at 2 cal. ka BP, in response to the change in the Indian Summer Monsoon (ISM). At Koucha Lake, the vegetation cover fluctuates at around 60%, indicating less sensitivity to climate change. Our research highlights the importance of pollen concentrations in quantitatively reconstructing past vegetation cover and the sparse vegetation status during the LGM on the TP.

The Impact of Meteorological Drought at Different Time Scales from 1986 to 2020 on Vegetation Changes in the Shendong Mining Area

The Shendong Mining Area, being the largest coal base in the world, has significant challenges in the intensive development and utilization of coal resources, as well as the impact of a dry climate, which can have serious negative effects on the growth of flora in the region. Investigating the spatial and temporal patterns of how meteorological drought affects vegetation in the Shendong Mining Area at various time scales can offer a scientific foundation for promoting sustainable development and ecological restoration in the region. This study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) data from 1986 to 2020 in the Shendong Mining Area. It employs Slope trend analysis, a Mann–Kendall test, a Geographic Detector, and other methods to examine the spatiotemporal distribution characteristics of meteorological drought at various time scales. Additionally, the study investigates the influence of these drought patterns on vegetation growth in the Shendong Mining Area. Across the mining area, there was a general decrease in the monthly average SPEI on an annual basis. However, on a seasonal, semi-annual, and annual basis, there was a gradual increase in the annual average SPEI, with a higher rate of increase in the southern region compared to the northern region. When considering the spatial variation trend in different seasons, both positive and negative trends were observed in winter and summer. The negative trend was mainly observed in the western part of the mining area, while the positive trend was observed in the eastern part. In spring, the mining area generally experienced drought, while in autumn, it generally experienced more precipitation. The mining area exhibits a prevailing distribution of vegetation, with a greater extent in the southeast and a lesser extent in the northwest. The vegetation coverage near the mine is insufficient, resulting in a low NDVI value, which makes the area prone to drought. Over the past few years, the mining area has experienced a significant increase in vegetation coverage, indicating successful ecological restoration efforts. Various forms of land use exhibit distinct responses to drought, with forests displaying the most positive correlation and barren land displaying the strongest negative correlation. Various types of landforms exhibit varying responses to drought. Loess ridge and hill landforms demonstrate the most pronounced positive association with monthly-scale SPEI values, whereas alluvial and floodplain landforms display the poorest positive correlation with yearly scale SPEI values. The general findings of this research can be summarized as follows: (1) The mining area exhibits a general pattern of increased humidity, with the pace of humidity increase having intensified in recent times. Seasonal variations exhibit consistent cyclic patterns. (2) There are distinct regional disparities in NDVI values, with a notable peak in the southeast and a decline in the northwest. The majority of the mining area exhibits a positive trend in vegetation recovery. (3) Regional meteorological drought is a significant element that influences changes in vegetation coverage in the Shendong Mining Area. Nevertheless, it displays complexity and is more obviously impacted by other factors at a small scale. (4) It should be noted that forests and barren land exert a more significant influence on SPEI values, despite their relatively lesser spatial coverage. The predominant land use type in most locations is grasslands; however, they have a relatively minor influence on SPEI. (5) A shorter time period, higher elevation, and steeper slope gradient all contribute to a larger correlation with drought.

Artificial intelligence and remote sensing for spatiotemporal analysis and prediction of vegetation changes: a case study of El Oued, northeastern Algeria

This study investigates vegetation ecology and rangeland degradation in the arid and desert regions of northeastern Algeria from 2015 to 2023, with predictions extending to 2031. Utilizing remote sensing techniques, specifically Landsat 8 and Landsat 9 imagery, and the Modified Soil Adjusted Vegetation Index (MSAVI), we mapped and analyzed changes in vegetation cover. The QGIS MOLUSCE plugin facilitated spatiotemporal analysis, revealing significant natural and anthropogenic impacts on a study area. Less degraded rangelands have also seen an increase, from 44,110,116 hectares in 2015 to 48,481,851 hectares in 2023, due to drought, overgrazing, and socioeconomic pressures. With a slight decrease to 48,450,495 hectares projected for 2031. This suggests efforts in land rehabilitation and sustainable rangeland management are yielding positive results, though there remains room for improvement. The study recorded that Agricultural croplands have experienced a slight decrease from 1,404,153 hectares in 2015 to 1,350,693 hectares in 2023. However, a modest increase to 1,587,242 hectares is projected by 2031. The findings indicate that Oases date palm areas have increased from 1,287,342 hectares in 2015 to 1,630,197 hectares in 2023, with further growth to 1,816,296 hectares anticipated by 2031. Our research highlights the critical role of remote sensing in monitoring vegetation dynamics within arid and desert ecosystems. The methodology provides a robust framework for similar studies, emphasizing the necessity for ongoing monitoring using advanced télédétection technologies. To mitigate further degradation and preserve these fragile ecosystems, the implementation of sustainable land management practices is essential.

Monitoring of vegetation changes based on RS monitoring in Changbai Mountain Nature Reserve

High vegetation cover helps maintain the ecosystem's stability and reduces the risk of soil erosion and natural disasters. Vegetation restoration is essential for the ecological value of Changbai Mountain Nature Reserve. In this study, the interannual changes in vegetation cover in Changbai Mountain Nature Reserve, China, from 2013 to 2022 were studied in depth based on Landsat remote sensing data and land use transfer matrix. The study includes interannual dynamic changes in vegetation types, quantitative analysis of interannual changes in surface vegetation types, and the land use transfer matrix analysis. In addition, the drivers of dynamic changes in vegetation types were analysed. The research indicates a notable advancement in ecological restoration within Changbai Mountain Nature Reserve between 2013 and 2022. There was a substantial reduction in the extent of barren land, which predominantly transitioned into grass and woodland areas. In particular, the year 2022 exhibited a significant increase in grassland area when compared to 2013, with a remarkable expansion of 27.18 square kilometers, primarily concentrated in the central-eastern sector of the study area. The findings of this investigation serve as a valuable scientific foundation for guiding ecological preservation and management decisions within Changbai Mountain Nature Reserve, contributing to a deeper comprehension of the evolving patterns in vegetation cover.

Vegetation Change Detection Analysis Using Multi-sensor Hyperspectral Imagery

Vegetation is a fundamental component of ecosystems that maintains carbon levels, hydrological cycles, mitigating greenhouse gases, and ensures climate stability. In recent years, the impacts of global climate change have led to changes in vegetation cover at various levels. Efforts to monitor changes in vegetation are important and beneficial for various fields such as forest monitoring, agriculture, and plantations, among others. The main objective of this research is to detect changes both increase and decrease in vegetation using multi-sensor hyperspectral imagery. The hyperspectral images used in this study are Hyperion 2014 and PRISMA 2021. The method involves creating different levels of spectral resolution simulations from hyperspectral images to detect vegetation changes. Meanwhile, the vegetation change Clustering method employs unsupervised (k-means) techniques. The cluster results can indicate vegetation changes such as vegetation degradation, vegetation, devegetation, or no change, though they currently have low accuracy. The highest accuracy is by Simulated RapidEye image simulations, is 33.5%. The low accuracy results attributed insufficient preprocessing, particularly in topographic correction. Additionally, this research indicates that the spectral resolution levels do not have a significant impact on vegetation change detection, as the differences in change classes at each level are very small.

Impacts of Land Use on Runoff and Sediment Dynamics in Tropical Watersheds: A Case Study in Bogowonto Upper Watershed

Land use changes in tropical regions have increased, leading to rising environmental stress in Java, Indonesia. Food shortages have driven land conversion and expansion, which increases peak flows during the rainy season and reduces water storage in the dry season, heightening flood risks. Research on integrated catchment hydrology is crucial. This study examines the relationship between land use, runoff, and sediment in the Bogowonto Upper Watershed using SWAT hydrological modeling. The SWAT model helps understand hydrological processes at the watershed scale and the impact of land use changes on runoff and sediment dynamics. The sensitivity of SWAT model parameters varies in the Bogowonto Upper Watershed. Runoff sensitivity analysis indicates a +62% increase with a 50% change in CN value, showing high sensitivity. A 50% change in vegetation cover results in a +50% model output, indicating moderate sensitivity. Slope, Ksat (saturated hydraulic conductivity), and bulk density are fairly sensitive, while AWC is slightly sensitive. For sediment, a 50% increase in CN value results in a +47% change, and a 50% increase in vegetation cover leads to a +58% model output, showing moderate sensitivity. The model, run from 2014-2019, shows excellent accuracy with NSE of 0.82, RRMSE of 0.43, R² of 0.83, and PBIAS of 9.8%.