Vegetation Change Analysis Research Articles

Analysis of the Influence of Driving Factors on Vegetation Changes Based on the Optimal-Parameter-Based Geographical Detector Model in the Yima Mining Area

The growth of vegetation directly maintains the ecological security of coal mining areas. It is of great significance to monitor the dynamic changes in vegetation in mining areas and study the driving factors of vegetation spatial division. This study focuses on the Yima mining area in Henan Province. Utilizing MODIS and multi-dimensional explanatory variable data, the Theil–Sen Median + Mann–Kendall trend analysis, variation index, Hurst index, and optimal-parameter-based geographical detector model (OPGD) are employed to analyze the spatiotemporal changes and future trends in the EVI (enhanced vegetation index) from 2000 to 2020. This study further investigates the underlying factors that contribute to the spatial variation in vegetation. The results indicate the following: (1) During the period studied, the Yima mining area was primarily characterized by a moderate-to-low vegetation cover. The area exhibited significant spatial variation, with a notable pattern of “western improvement and eastern degradation”. This pattern indicated that the areas that experienced improvement greatly outnumbered the areas that underwent degradation. Moreover, there was an inclination towards a deterioration in vegetation in the future. (2) Based on the optimal parameter geographic detector, it was found that 2 km was the optimal spatial scale for the analysis of the driving factors of vegetation change in this area. The optimal parameter combination was determined by employing five spatial data discretization methods and selecting an interval classification range of 5–10. This approach effectively addresses the subjective bias in spatial scales and data discretization, leading to enhanced accuracy in vegetation change analysis and the identification of its driving factors. (3) The spatial heterogeneity of vegetation is influenced by various factors, such as topography, socio-economic conditions, climate, etc. Among these factors, population density and mean annual temperature were the primary driving forces in the study area, with Q > 0.29 and elevation being the strongest explanatory factor (Q = 0.326). The interaction between temperature and night light was the most powerful explanation (Q = 0.541), and the average Q value of the interaction between the average annual temperature and other driving factors was 0.478, which was the strongest cofactor among the interactions. The interactions between any two factors enhanced their impact on the vegetation’s spatial changes, and each driving factor had its suitable range for affecting vegetative growth within this region. This research provides scientific support for conserving vegetation and restoring the ecological system.

A dataset of NDVI with medium to high spatio-temporal resolutions in Tajikistan using spatio-temporal data fusion method (2010–2020)

Normalized Difference Vegetation Index (NDVI) is one of the most commonly used remote sensing indices to study the condition of vegetation. The long time series data of NDVI is of great significance to the study of vegetation change. However, due to the limitations of sensors, remote sensing data cannot obtain both high temporal resolution and high spatial resolution. Therefore, among the widely used NDVI data products, the data with high spatial and temporal resolution is still scarce. Based on the Cubist model, we integrated MODIS data with Landsat and Sentinel remote sensing data, and obtained the MODIS-Landsat fusion data of Tajikistan from 2010 to 2020, and the MODIS-Sentinel fusion data of 2020. This dataset can reflect the temporal and spatial changes of NDVI in Tajikistan from 2010 to 2020, and provide long-term data support for vegetation change analysis and ecological environment monitoring in Tajikistan.

Distentanging the late-Holocene human–environment interactions in the Altai Mountains within the Arid Central Asia

Understanding the impacts of human activities on landscapes necessitates a comprehensive analysis of historical changes in climate, vegetation, fire and land utilization. The human-environment interactions were investigated through the analysis of new charcoal data from Kelashazi Peat in the Altai Mountains, compared with the detailed paleoenvironmental records and historical human activities (e.g., agriculture and pastoralism) at other three distinct sites. The findings suggest that the late-Holocene (prior to ∼2000 years ago) fire activities were mainly influenced by temperature at higher elevations and were primarily driven by vegetation cover at lower elevations. Over the past two millennia, human activities have increasingly impacted fire dynamics. Elevated fire frequencies during the Medieval Warm Period at higher elevations were linked to warmer climates and intensified pastoral activities. Lower fire incidences at lower elevations may be attributed to population outflows during the Medieval Warm Period, while heightened fire occurrences at lower elevations might result from increasing agricultural activities during the Little Ice Age. This study underscores the intricate interplay between natural climate-vegetation-fire dynamics and anthropogenic burning trends in the late Holocene across different elevations of the Altai Mountains within the Arid Central Asia.

Photo-interpretation and diachronic analysis of land use and vegetation changes in the Baztan Valley (Navarre, Spain) between 1927 and 2019

Photo-interpretation and diachronic analysis of land use and vegetation changes in the Baztan Valley (Navarre, Spain) between 1927 and 2019

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.

Satellite observed dryland greening in Asian endorheic basins: Drivers and implications to sustainable development

A large portion of Central-Western Asia is made up of contiguous closed basins, collectively termed as the Asian Endorheic Basins (AEBs). As these retention basins are only being replenished by the intermittent and scarce rainfall, global warming coupled with ever-rising human demand for water is exerting unprecedented pressures on local water and ecological security. Recent studies revealed a persistent and widespread water storage decline across the AEBs, yet the response of dryland vegetation to this recent hydroclimatic trend and a spatially explicit partitioning of the impact into the hydroclimatic factors and human activities remain largely unknown. To fill in this knowledge gap, we conducted trend and partial correlation analysis of vegetation and hydroclimatic change from 2001 to 2021 using multi-satellite observations, including vegetation greenness, total water storage anomalies (TWSA) and meteorological data. Here we show that much of the AEB (65.53 %), encompassing Mongolia Plateau, Northwest China, Qinghai Tibet Plateau, and Western Asia (except the Arabian Peninsula), exhibited a significant greening trend over the past two decades. In arid AEB, precipitation dominated the vegetation productivity trend. Such a rainfall dominance gave way to TWSA dominance in the hyper-arid AEB. We further showed that the decoupling of rainfall and hyper-arid vegetation greening was largely due to a significant expansion (17.3 %) in irrigated cropland across the hyper-arid AEB. Given the extremely harsh environment in the AEB, our results therefore raised a significant concern on the ecological and societal sustainability in this region, where a mild increase in precipitation cannot catch up the rising evaporative demand and water consumption resulted from global warming and agriculture intensification.

Regional Analysis of Dominant Factors Influencing Leaf Chlorophyll Content in Complex Terrain Regions Using a Geographic Statistical Model

Chlorophyll is a vital indicator of vegetation growth; exploring its relationship with external influencing factors is essential for studies such as chlorophyll remote sensing retrieval and vegetation growth monitoring. However, there has been limited in-depth exploration of the spatial distribution of leaf chlorophyll content (LCC) and its influencing factors across large-scale areas with varying climates and terrains. To investigate the primary influencing factors and degrees of various environmental factors on LCC, this study employed the Geodetector Model (GDM) and the LCC satellite products in Sichuan Province in 2020 to investigate the impact of relationships between nine environmental factors (meteorology, topography, and vegetation types) and the ecosystem LCC at a regional scale. The results indicated the following: (1) Elevation (q-value = 49.31%) is the primary factor determining photosynthesis in Sichuan Province, followed by temperature (46.10%) and vegetation types (40.73%). The impact of topographical factors on LCC distribution is higher than that of meteorological factors and vegetation types in terrain with complex topography. The elevation effectively distinguishes the variations in climate factors and vegetation types. (2) Combining the influencing factors pairwise increased the combined q-values. The combination of elevation with other factors yielded the highest combined q-value. (3) The q-values for all influencing factors are higher in winter and spring and lowest in summer. Different influencing factors exhibited more substantial constraints on vegetation photosynthesis during winter and spring, significantly reducing influence during summer. (4) The different primary factors drive or constrain vegetation photosynthesis in different climate zones due to their distinct temperature and humidity characteristics. The findings of this study provide a basis for future research on vegetation change analysis and dynamic monitoring of vegetation LCC in different terrains.

Vegetation change analysis using Normalized Difference Vegetation Index (NDVI) in Sumedang Regency

Vegetation is a crucial element of livable and healthy cities and has been linked to a number of advantages, such as enhanced human health, habitat provision, and natural system regulation. Planning sustainable cities requires an understanding of and documentation of changes in urban vegetation. The Normalized Difference Vegetation Index (NDVI)'s spatial variance and driving force are useful for managing natural resources and protecting ecological environments. Using Sumedang Regency as the research area, the normalized vegetation index (NDVI) was computed using Landsat-7 ETM and Landsat 8 OLI/TIRS data from 2003 to 2023. The findings show that the high greenness index first declined and subsequently increased between 2003 and 2023. Sumedang Regency's high greenness index shrank in area between 2003 and 2018. In 2003, 144793.17 ha was categorised as high greenness index, but in 2018 the high greenness index was only 122392.08 ha. Furthermore, the index with non-vegetated land increases every year. This shows that Sumedang Regency continues to experience land use change into non-vegetated areas, such as settlements and bare land. This research can provide assistance for the development of a sustainable natural environment in Sumedang Regency.

Analysis of Vegetation Changes Using Satellite Imagery and Normalized Difference Vegetation Index (NDVI): A Case Study in Tuntang District, Semarang District

Changes in land use from agriculture to residential and industry continue to occur in Tuntang District, and analysis is needed to identify areas experiencing the most significant changes. Development growth triggered by an increase in population can cause significant changes, such as the conversion of land from forests to agricultural land or plantations, as well as from agricultural land to residential and industrial areas. To monitor land changes, Remote Sensing is used as an effective tool. This method allows data analysis without direct contact with the object being studied. The use of Landsat-8 imagery, as a remote sensing tool, can help identify variations in vegetation. Analysis using the Normalized Difference Vegetation Index (NDVI) calculation method can provide information about the level of vegetation density in the area. The research results show significant changes in vegetation density in Tuntang District from 2019 to 2022. This change is believed to be related to the increase in population, which may be a driving force for several areas to experience development and changes in land function.

Comparative Verification of Leaf Area Index Products for Different Grassland Types in Inner Mongolia, China

Leaf area index (LAI) is a key indicator of vegetation structure and function, and its products have a wide range of applications in vegetation condition assessment and usually act as important input parameters for ecosystem modeling. Grassland plays an important role in regional climate change and the global carbon cycle and numerous studies have focused on the product-based analysis of grassland vegetation changes. However, the performance of various LAI products and their discrepancies across different grassland types in drylands remain unclear. Therefore, it is critical to assess these products prior to application. We evaluated the accuracy of four commonly used LAI products (GEOV2, GLASS, GLOBMAP, and MODIS) using LAI reference maps based on both bridging and cross-validation approaches. Under different grassland types, the GLASS LAI performed better in meadow steppe (R2 = 0.26, RMSE = 0.41 m2/m2) and typical steppe (R2 = 0.32, RMSE = 0.38 m2/m2); the GEOV2 LAI performed better in desert steppe (R2 = 0.39, RMSE = 0.30 m2/m2). When we assessed their spatial and temporal discrepancies during the period from 2010 to 2019, the four LAI products overall showed a high spatial and temporal consistency across the region. Compared with GLASS LAI, the most consistent to least consistent correlations can be ordered by GEOV2 LAI (R2 = 0.94), MODIS LAI (R2 = 0.92), and GLOBMAP LAI (R2 = 0.87). The largest differences in LAI throughout the year occurred in July for all grassland types. Limited by the location and number of sample plots, we mainly focused on spatial and temporal variations. The spatial heterogeneity of land surface is pervasive, especially in vast grassland areas with rich grassland types, and the results of this study can provide a basis for the application of the product in different grassland types. Furthermore, it is essential to develop highly accurate and reliable satellite-based LAI products focused on grassland from the regional to the global scale according to these popular approaches, which is the next step in our work plan.

Assessing changes in urban vegetation using Normalised Difference Vegetation Index (NDVI) for epidemiological studies

Urban vegetation is an important component of healthy, livable cities and has been linked to several benefits, including improved human health outcomes, natural system regulation, and habitat provision. Understanding and documenting changes to urban vegetation is essential for planning for sustainable cities. This study focuses on identifying where and when urban vegetation changed in Metro Vancouver between 2005 and 2019 using the dynamic change approach to aid longitudinal epidemiological studies in determining accurate exposure estimates. Vegetation change was detected for three time periods: 2005–2009, 2010–2014, and 2015–2019 using normalised difference vegetation index (NDVI) thresholds. A cluster analysis of vegetation change was then conducted to identify areas of change. The results show that Metro Vancouver has gained vegetation over this 15-year time period, particularly along the eastern part of the metropolitan area and along the Fraser River. Vegetation loss occurred mostly in areas under high housing demand, such as along the northern parts of the study area (e.g., the North Shore). The method demonstrated in this study provides a simple, cost-effective way of assessing vegetation change, which is an important step for understanding the relationships between urban development and vegetation and potentially related changes in human health.

Should time-lag and time-accumulation effects of climate be considered in attribution of vegetation dynamics? Case study of China's temperate grassland region.

Although the time-lag and time-accumulation effects (TLTAEs) of climatic factors on vegetation growth have been investigated extensively, the uncertainties caused by disregarding TLTAEs in the attribution analysis of long-term changes in vegetation remain unclear. This hinders our understanding of the associated changes in ecosystems and the effects of climate change. In this study, using multiple methods, we evaluate the biases of attribution analyses of vegetation dynamics caused by the non-consideration of TLTAEs in the temperate grassland region (TGR) of China from 2000 to 2019. Based on the datasets of the normalized difference vegetation index (NDVI), temperature (TMP), precipitation (PRE), and solar radiation (SR), the temporal reaction patterns of vegetation are analyzed, and the relationships among these variables under two scenarios (considering and disregarding TLTAEs) are compared. The results indicate that most areas of the TGR show a greening trend. A time-lag or time-accumulation effect of the three climatic variables is observed in most areas with significant spatial differences. The lagged times of the vegetation response to PRE are particularly prominent, with an average of 2.12 months in the TGR. When the TLTAE is considered, the areas where changes in the NDVI are affected by climatic factors expanded significantly, whereas the explanatory power of climate change on NDVI change increased by an average of 9.3% in the TGR; these improvements are more prominent in relatively arid areas. This study highlights the importance of including TLTAEs in the attribution of vegetation dynamics and the assessment of climatic effects on ecosystems.

Super resolution of historic Landsat imagery using a dual generative adversarial network (GAN) model with CubeSat constellation imagery for spatially enhanced long-term vegetation monitoring

Detailed spatial representations of terrestrial vegetation are essential for precision agricultural applications and the monitoring of land cover changes in heterogeneous landscapes. The advent of satellite-based remote sensing has facilitated daily observations of the Earth’s surface with high spatial resolution. In particular, a data fusion product such as Planet Fusion has realized the delivery of daily, gap-free surface reflectance data with 3-m pixel resolution through full utilization of relatively recent (i.e., 2018-) CubeSat constellation data. However, the spatial resolution of past satellite sensors (i.e., 30–60 m for Landsat) has restricted the detailed spatial analysis of past changes in vegetation. In order to overcome the spatial resolution constraint of Landsat data for long-term vegetation monitoring, we propose a dual remote-sensing super-resolution generative adversarial network (dual RSS-GAN) approach combining Planet Fusion and Landsat 8 data to simulate spatially enhanced long-term time-series of the normalized difference vegetation index (NDVI) and near-infrared reflectance from vegetation (NIRv). We evaluated the performance of the dual RSS-GAN against in situ tower-based continuous measurements (up to 8 years) and remotely piloted aerial system-based maps of cropland and deciduous forest in the Republic of Korea. The dual RSS-GAN enhanced spatial representations in Landsat 8 images and captured seasonal variation in vegetation indices (R2 > 0.90, for the dual RSS-GAN maps vs in situ data from all sites). Overall, the dual RSS-GAN reduced Landsat 8 vegetation index underestimations compared with in situ measurements; relative bias values of NDVI ranged from − 5.8 % to 0.3 % and − 12.4 % to − 3.7 % for the dual RSS-GAN and Landsat 8, respectively. This improvement was caused by spatial enhancement through the dual RSS-GAN, which reflects fine-scale information from Planet Fusion. Finally, the dual RSS-GAN maps showed both spatial enhancement and reducing the underestimation of vegetation index in historic Landsat dataset from 1984. This study presents a new approach for resolving sub-pixel spatial information in Landsat images.

A dataset of vegetation phenology and change trends with a resolution of 500m of on the Mongolian Plateau (2001–2019)

Located in the highland of Eurasia, the Mongolian Plateau, a typical arid and semi-arid region, is highly sensitive to climate change. Vegetation phenology is the most intuitive and sensitive biological indicator of seasonal and interannual changes of climatic conditions. Vegetation phenology data can be used to explore the ecological situation and climate change of the Mongolian Plateau. Based on THE MODIS land cover Dynamic Product (MCD12Q2 C6), in this paper, we used non-parametric Theil-Sen Median trend analysis and Mann-Kendall significance test to extract sub-datasets, Mosaic, projection transformation and cropping. In this way, we obtained the data of four phenological periods (namely the beginning time, the end time, the length of the growing season and the peak time of the growing season) and the change trend data of vegetation of 500m resolution on the Mongolian Plateau from 2001 to 2019. This dataset can reflect the spatio-temporal changes of vegetation phenology in 19 years on the Mongolian Plateau. Combined with climate factors such as temperature and precipitation, it can be used to explore the response and feedback mechanism of vegetation phenology change to environmental factors, and provide data support for vegetation change analysis, climate change, carbon cycle and other studies.

Vegetation and Built-Up Area Monitoring in Bandung City Using Multitemporal Imagery

Bandung is West Java's largest metropolitan city and Indonesia's third largest. The city of Bandung is very strategic in various aspects, such as accessibility, communication, public facilities, and the economy. The Increased population in Bandung indicates more complex ongoing human activities, which can then affect changes in land use. The land covers in urban areas tends to change more drastically over a short period e than in rural areas because of rapid urbanization. Therefore, urban phenomenon changes are ideally monitored and detected from satellite images with a multitemporal resolution. Vegetation greenness and built-up areas can identify through multitemporal remote sensing imagery. Changes in vegetation and built-up area can monitor using remote sensing with multitemporal imagery. The analysis of changes in vegetation and built-up area studied in Bandung City represents an area with rapid population growth. This study aims to: 1) Identify changes in vegetation greenness in Bandung City between 2014 and 2021, 2) Identify built-up area changes in Bandung City between 2014 and 2021, 3) Analyze the relevance between vegetation greenness and the built-up area in Bandung City—the correlation between NDBI and NDVI through selected samples is representative of all data in Landsat 8 imagery. The proportion between the values of NDBI and NDVI samples is 0.9034. So, it is concluded that the two variables are positively correlated. Therefore, the study’s results recommend preserving vegetated land cover to conserve natural resources and prevent increased land surface temperature.Keywords: Remote Sensing Imagery, Built-Up Area, Vegetation Greenness, Bandung City

Analysis of vegetation changes in land area of Syrdarya region using GIS technology and remote sensing data

This article presents a map of vegetative changes in the Syrdarya region based on remote sensing data. Landsat 8 and Landsat 9 satellite images were used for analysis during the vegetation active period. The study examines the vegetation state of the selected area from 2000 to 2022 and analyzes the changes. The Normalized Difference Vegetation Index (NDVI) was calculated using ArcGIS 10.6 software and documented sequentially. The number of color-coded pixels on the map indicating the health and unhealthiness of the crops and the areas they occupy was determined through NDVI analysis. The study revealed a decrease in the vegetation layer in the Syrdarya region, and the reasons for this phenomenon were discussed. The article demonstrates the usefulness of remote sensing in analyzing vegetational changes over time and its potential applications in monitoring the health and productivity of crops in different regions. Overall, this research is valuable for developing strategies to mitigate the impact of vegetation loss in the Syrdarya region and similar regions facing similar challenges.

Application of Remote Sensing to the analysis of changes in high-altitude vegetation of the ‘Pantanal’ (Brazil) using multitemporal Landsat data

Devido ao clima frio e altitude, identificar as tendências dinâmicas da vegetação e os principais fatores que contribuem para mudanças de cobertura vegetal em áreas de Campos de Altitude é essencial para entender alterações climáticas em regiões montanhosas. Imagens Landsat-8 OLI e Landsat-1 MSS de 1973 a 2022 da Morraria do Urucum e Serra do Amolar foram pré-processadas na plataforma de nuvem GEE e QGIS. O método de reamostragem por pixel na escala de valores definidos para vegetação Campos de Altitude foi empregado para evidenciar mudanças da cobertura da vegetação e suas dinâmicas através do índice NDVI. Em ambas as áreas de estudo foi verificada tendência contínua de redução significativa da vegetação de campos de altitude em 50 anos. O decréscimo médio foi de 49% para Urucum (menos 2.164 hectares) e 43% para o Amolar (menos 3.959 hectares). O uso do GEE para obter dados de sensoriamento remoto combinado com a análise de imagens temporais oferece o potencial de perceber rapidamente tendências de mudança de cobertura da vegetação em grande e pequena escala

Multidecadal Trend Analysis of Armenian Mountainous Grassland and Its Relationship to Climate Change Using Multi-Sensor NDVI Time-Series

This paper presents a comprehensive analysis of links between satellite-measured vegetation vigor and climate variables in Armenian mountain grassland ecosystems in the years 1984–2018. NDVI is derived from MODIS and LANDSAT data, temperature and precipitation data are from meteorological stations. Two study sites were selected, representing arid and semi-arid grassland vegetation types, respectively. Various trend estimators including Mann–Kendall (MK) and derivatives were combined for vegetation change analysis at different time scales. Results suggest that temperature and precipitation had negative and positive impacts on vegetation growth, respectively, in both areas. NDVI-to-precipitation correlation was significant but with an apparent time-lag effect that was further investigated. No significant general changes were observed in vegetation along the observed period. Further comparisons between results from corrected and uncorrected data led us to conclude that MODIS and LANDSAT data with BRDF, topographic and atmospheric corrections applied are best suited for analyzing relationships between NDVI and climatic factors for the 2000–2018 period in grassland at a very local scale; however, in the absence of correction tools and information, uncorrected data can still provide meaningful results. Future refinements will include removal of anthropogenic impact, and deeper investigation of time-lag effects of climatic factors on vegetation dynamics.

Spatiotemporal Analysis of Land Surface Temperature and Vegetation Changes in Duhok District, Kurdistan Region, Iraq

The temperature rise has become a serious environmental concern affected by both human and natural factors. Worldwide, rising land surface temperatures have emerged as the most pressing issue facing the twenty-first century. In the last two decades, a curious change was realized in temperature in the Duhok district of Iraq. Hence, this study examined the spatiotemporal land surface temperature distribution and Modified Soil Adjusted Vegetation Index (MSAVI2) and the correlation between them in the Duhok district in three different years 2001, 2011, and 2021 using Landsat satellite images. Air temperature data from seven weather stations were used to validate the land surface temperature results. The study's findings revealed that the Duhok district’s LST has risen during the study period. In general, the average land surface temperature has been increasing at a rate of 0.15 °C per year. Other findings showed that the vegetation cover of the Duhok district has changed dynamically. In all three years of study, the regression analysis results indicated that there was a negative correlation between LST and MSAVI2. This method of evaluation will be useful in guiding future urban management work and local government strategies.

Optimal selection of wavelet transform parameters for spatio-temporal analysis based on non-stationary NDVI MODIS time series in Mediterranean region

The multi-resolution analysis based on wavelet transform (WT) has widely proved its effectiveness for the analysis of non-stationary time series (TS) in the remote sensing field. However, there are three essential parameters affecting wavelet-aided data in vegetation dynamics detection. This study proposes a new methodology that serves for the proper selection of the most critical parameters to carry out an optimal analysis for vegetation seasonal and long-term dynamics, specifically adapted to NDVI TS. The specific objectives are: (1) to simulate a TS dataset to understand the best WT parameter performance for trend and seasonal changes detection, and (2) to assess the impact of those WT parameters using NDVI MODIS TS in the Mediterranean area along the period 2001–2019. Different types of trends and WT were considered (i.e., Discrete WT (DWT) and Maximal Overlap DWT (MODWT)) in the simulated dataset. The best WT parameters were calculated based on an iterative algorithm that computes the trend and seasonal components using the optimal level of decomposition for each TS and mother wavelet (MW). Then, the findings obtained from the simulated data were exploited using Earth observation data. In the step, the Theil–Sen’s slope (Q) derived for the best MWs was calculated to provide the trend magnitude and direction. The z-score and energy to Shannon entropy ratio (R(S)) were computed to select the most adequate MW based on pixel characterization. On one hand, the simulated results proved the Multi-resolution analysis DWT (MRA-DWT) as the most optimal wavelet type. Level 5 of decomposition was obtained in 99% and 72% of simulated TS according to the trend and seasonal analysis, respectively. Symlet family gave the optimal results in 92% (trend) and 64% (seasonal) of simulated TS. On the other hand, based on R(S), sym12 MW from Symlet family showed the most dominant wavelet for forest, savanna and mosaic land covers, followed by db14 MW from Daubechies family which was dominated in cropland. From more practical adaptation, sym12 was revealed as an effective solution for all the pixels in the Mediterranean area with a relative error lower than 15%. The vegetation change analysis in the Mediterranean area showed a relationship between QNDVI and precipitation in almost 37% of the area, being Turkey, Greece and North Africa the regions with a high correspondence with precipitation. Around 5% of the study area showed to be possibly affected by land degradation processes.