Temporal Changes In Vegetation Research Articles

Measuring Temporal Change in Scrub Vegetation Cover Using UAV-Derived Height Maps: A Case Study at Two UK Nature Reserves.

Measuring the outcome of practical interventions and actions helps to inform conservation management objectives and assess progress towards objectives and targets. Measuring success also informs future management by identifying actions that are effective and those that are not. Scrub vegetation is an important habitat type in terrestrial ecosystems, providing important shelter and food resources for biodiversity and livestock. Much of practical land management in the UK involves the monitoring and management of scrub, and current drone-based methods of scrub collection requires expensive equipment or complex methods. A 2021 paper determined a cheap and simple way to determine scrub levels, and this could potentially be used to map temporal changes, as well as identify directional change in scrub. This study looks at whether the method outlined in the 2021 study could be used to measure temporal and directional changes in scrub cover on two nature reserves in the UK: Daneway Banks in Gloucestershire and Flat Holm Island in the Severn Estuary. Scrub levels at Daneway Banks increased from 14.63% in 2015 to 16.52% in 2017, before decreasing to 14.89% in 2021 due to managed cutting and clearing. Scrub cover at Flatholm Island decreased from 10.18% in 2019 to 8.71% in 2021. The exact locations of scrub growth and loss for each site was also calculated and mapped. This approach was found to be a viable way of measuring temporal and directional change in scrub levels. The data can also be used to reframe changes in scrub levels as a shift towards vegetation succession or reduction, to better visualise how changes in scrub levels affect overall site management goals, and is a cheaper, more accessible alternative to current methods of measuring temporal vegetation changes.

Coastal Dune Vegetation Dynamism and Anthropogenic-Induced Transitions in the Mexican Caribbean during the Last Decade.

In the Mexican Caribbean, environmental changes, hydrometeorological events, and anthropogenic activities promote dynamism in the coastal vegetation cover associated with the dune; however, their pace and magnitude remain uncertain. Using Landsat 7 imagery, spatial and temporal changes in coastal dune vegetation were estimated for the 2011-2020 period in the Sian Ka'an Biosphere Reserve. The SAVI index revealed cover changes at different magnitudes and paces at the biannual, seasonal, and monthly timeframes. Climatic seasons had a significant influence on vegetation cover, with increases in cover during northerlies (SAVI: p = 0.000), while the topographic profile of the dune was relevant for structure. Distance-based multiple regressions and redundancy analysis showed that temperature had a significant effect (p < 0.05) on SAVI patterns, whereas precipitation showed little influence (p > 0.05). The Mann-Kendall tendency test indicated high dynamism in vegetation loss and recovery with no defined patterns, mostly associated with anthropogenic disturbance. High-density vegetation such as mangroves, palm trees, and shrubs was the most drastically affected, although a reduction in bare soil was also recorded. This study demonstrated that hydrometeorological events and climate variability in the long term have little influence on vegetation dynamism. Lastly, it was observed that anthropogenic activities promoted vegetation loss and transitions; however, the latter were also linked to recoveries in areas with pristine environments, relevant for tourism.

Tracking the Temporal Changes in Land Surface Temperature, Vegetation, and Built-up Patterns in Rizal Province, Philippines using Landsat Imagery

The Rizal Province was subjected to a series of natural and human-induced disturbances throughout the years. Currently, the area is undergoing urbanization which in turn results in shifts in the extent of impervious surfaces that can intensify heat-related health concerns, increase energy consumption for cooling, and alter local weather patterns. This study uses remote sensing images from to quantify the various environmental considerations that remain undocumented and unmapped for areas caused by changes in land use and land cover from Landsat Collection 1- Level 1 (Landsat 4-5 ™ C1- Level 1 &amp; Landsat 8 OLI/ TIRS C1 Level 1) and calculated three parameters namely, (i) Land surface temperature (LST), (ii) Normalized Difference Vegetation Index (NDVI), and (iii) the Normalized Difference Built-up Index (NDBI). The results showed the following: (i) an increase in the vegetation cover from 1993-2020 showed a decrease in LST from 29.34°C to 24.03°C, (ii) the relationship between LST and NDBI is directly proportional, whereas an inversely proportional relationship can be observed between LST and NDVI, and (iii) there is a fluctuating LST due to the changes in the land cover of the study site for almost three decades. This implicates the extensive shift in the ambient temperature of Rizal which further emphasizes the effects of the modification in certain land use land cover classifications, especially in vegetation cover and urban development. This highlights how human-induced and natural factors significantly contribute to the release of heat and ambient temperature, thus, accentuating the need for sustainable urban planning.

The Impact of Groundwater Burial Depth on the Vegetation of the Dariyabui Oasis in the Central Desert

Vegetation and groundwater are important components of the ecological environment of oases in desert hinterlands and their relationship is crucial to ecosystem stability. In this study, Sentinel-2 data for 2016–2022 and measured groundwater burial depths were analysed for the Dariyabui Oasis in the hinterland of the Taklamakan Desert. The spatial and temporal changes in vegetation and groundwater burial depth from 2019 to 2022 were analysed based on the image–element dichotomous model of the normalised difference vegetation index, utilising the inverse distance weight interpolation method, cubic curve regression, image–element difference, slope trend analysis, and the Markov transfer matrix for determining the temporal and spatial response law between the two. Finally, the threshold value of groundwater burial depth for different vegetation cover types was clarified. The fractional vegetation cover of the Dariyabui Oasis showed a slight increase from 2016 to 2022. Vegetation in the northwest and southeast of the oasis increased, whereas vegetation decreased in the mid-north and northeast regions; 5.14% of the total area experienced increased coverage, whereas 3.35% experienced decreased coverage. The depth of groundwater in the oasis showed a pattern of gradual increase from the entrance to the end of the oasis, that is, south to north. The depth of groundwater in the oasis from 2019 to 2022 was stable, with a 4-year average depth of 4.1069 m and a maximum fluctuation of 0.4560 m. The interannual changes in the groundwater level showed an increasing trend in January–April, while groundwater levels showed a decreasing trend in May–July and August–October and remained constant in June–July and October–December. Oasis vegetation cover showed a negative correlation with groundwater depth, with a depth interval for the highest low-cover vegetation distribution of 3–6 m, and an ultimate depth threshold of 7 m. The depth interval with the highest medium-cover vegetation distribution was 3–4 m, that with the highest high-cover distribution was 2–4 m, and the ultimate depth threshold was 6 m. The depth of the oasis ranged from 3 to 6 m and the ultimate depth threshold was 7 m.

Spatial–temporal changes of shoreline and Vegetation: Impacts on mangrove cover along the Sundarbans area, Bangladesh

Spatial–temporal changes of shoreline and Vegetation: Impacts on mangrove cover along the Sundarbans area, Bangladesh

Investigating the spatio-temporal pattern evolution characteristics of vegetation change in Shendong coal mining area based on kNDVI and intensity analysis

Alterations in vegetation cover serve as a significant indicator of land ecology. The Shendong Coal Mining Area, being the largest coal base globally, holds significant importance for national energy security. Moreover, it has gained recognition for its environmentally conscious approach to coal mining, characterized by the simultaneous implementation of mining activities and effective governance measures. In order to assess the ongoing vegetation recovery and the temporal changes in vegetation within the Shendong Coal Mining Area, we initially utilized Landsat TM/ETM+/OLI remote sensing data. Using the Google Earth Engine (GEE), we developed a novel kernel-normalized vegetation index (kNDVI) and subsequently generated a comprehensive kNDVI dataset spanning the years 2000 to 2020. In addition, the Sen (Theil-Sen median) trend analysis method and MK (Mann-Kendall) test were utilized to examine the temporal trends over a span of 21 years. Furthermore, the Hurst exponent model was employed to forecast the persistent changing patterns of kNDVI. The utilization of the intensity analysis model was ultimately employed to unveil the magnitude of vegetation dynamics. The findings indicated a notable positive trend in the overall kNDVI of vegetation within the study area. In relation to the analysis of changing trends, the vegetation in the region underwent a slight improvement from 2000 to 2010, followed by a significant improvement from 2010 to 2020. During this transition period, a total of 289.07 km2, which represents 32.36% of the overall transition area, experienced a shift in vegetation. The predictive findings from the Hurst model indicate that while the majority of areas within the mining region will exhibit an upward trend in vegetation growth, there will be certain areas that will demonstrate a decline. These declining areas account for 39.08% of the total transition area. Furthermore, the intensity analysis results reveal notable disparities in the characteristics of vegetation growth and evolution between the periods of 2000-2010 and 2010-2020. Throughout the entirety of the transformation process, the transition from slight improvement to significant improvement prevails in terms of both relative intensity and absolute intensity, surpassing alternative transformation processes. Various trend transitions display diverse intensity characteristics that adhere to the overarching principles governing shifts in vegetation growth. Furthermore, the utilization of the intensity analysis framework and intensity spectrum employed in this study demonstrates their efficacy in elucidating the temporal dynamics of vegetation changes. Furthermore, this study plays a pivotal role in the surveillance and assessment of the efficacy of ecological restoration in mining regions. It carries substantial implications for comparable land ecological restoration efforts in mining and reclamation, thereby furnishing a theoretical foundation.

Spatial and Temporal Changes of Typical Vegetation in the Yellow River Delta Based on Zhuhai-1 Hyperspectral Data

Spatial and Temporal Changes of Typical Vegetation in the Yellow River Delta Based on Zhuhai-1 Hyperspectral Data

The plant community of a stretch of riparian vegetation in the upper Paraná River after 20 years of secondary succession (Batayporã, Mato Grosso do Sul, Brazil)

The study of temporal changes in riparian vegetation is essential for understanding the paths taken by the ecosystem during ecological succession. In order to evaluate the changes in floristic composition, structure, and dynamics of the arboreal vegetation in an area of Semideciduous Seasonal Alluvial Forest, we aim to understand the secondary succession that occurs in the Paraná River floodplain. Based on a study conducted 20 years ago, in the municipality of Batayporã, Mato Grosso do Sul, a re-sampling was carried out in the same area, using the same methodology. Phytosociological and ecological parameters between the two temporal samplings were collected and compared using Student's ‘t’ test, where it was possible to find statistically significant variations (p&lt;0.05) in the vegetation structure. A total of 603 individuals were sampled, distributed among 51 species and grouped in 46 genera and 31 families. The analysis of the distribution of abundance and importance values (IV) of species visualized in the histogram indicated an expected pattern in forest communities, which is the substitution of early successional species for late successional species. For the successional categories, the comparison of IVs revealed higher values for secondary species in both surveys and changes in the positions of climax and pioneer species, directly influencing the IVs values of these categories for the zones. When compared to the result obtained in the previous study, a replacement of the IV values of pioneer species with climax and dead species is noted. The comparisons showed that this phytocoenosis is evolving to a mature successional stage, a result of the decrease in the main anthropic impacts in the area. The results obtained highlight the importance of temporal monitoring of forest areas to expand the understanding of the pathways of ecological succession in this type of environment and provide information to assist in biodiversity conservation.

Temporal changes in boreal vegetation under 70 years of conservation

Biodiversity conservation through protected areas (PAs) is often based on the idea that biodiversity is relatively static. This assumption is increasingly being challenged as species and communities shift their distributions in response to changing environmental conditions. Empirical evidence on the performance of PAs over decades is still sparse or lacking from several environments, although it is needed to understand species dynamics, support modelling of PA performance, assist PA management and ultimately, to achieve global biodiversity conservation goals. In 2021, we resurveyed vegetation of five boreal habitat types (heath forests, paludified forests, sun-exposed sites, mires and eulittoral sites) in Rokua National Park in Finland, where one of the conservation targets is to preserve the flora characteristic of the area. The study sites were originally surveyed in 1945-49, just before the National Park was established. Study sites have also remained free from the disturbances (forest fires and reindeer grazing) typical of boreal regions. We show that the compositional similarity of plant communities between habitat types has increased over time and is associated with the increase of forest species in several habitat types and the loss of many habitat-specific species. Drivers of change were most often linked to ongoing succession (understory closure) and changes in moisture conditions. Our results suggest that without natural disturbance or appropriate management efforts, the original conservation targets may be compromised over the decades. Our study demonstrates that resurvey of historical vegetation data can be effectively used to estimate long-term PA performance, helping to fill in missing temporal evidence.

Spatial and Temporal Variation in Vegetation Response to Runoff in the Ebinur Lake Basin

The response of spatial and temporal vegetation changes to runoff is a complex process involving the interaction of several factors and mechanisms. Timely and accurate vegetation and runoff change information is an important reference for the water cycle and water resource security. The Ebinur Lake Basin is representative of arid areas worldwide. This basin has been affected by climate change and human activities for a long time, resulting in the destruction of the basin’s ecological environment, and especially its vegetation. However, there have been few studies that have focused on watershed vegetation and runoff changes. Therefore, we combined Generalized Information System and remote sensing technology, used SWAT and InVEST models based on the Google Earth Engine platform, and used the vegetation normalization index method to calculate the spatial distribution of vegetation and water production from 2000 to 2020 in Ebinur Lake. Sen’s trend analysis and the M–K test were used to calculate vegetation and runoff trends. The relationship between vegetation and runoff variation was studied using bivariate spatial autocorrelation based on sub-basins and plant types. The results showed that the Z parameter in the InVEST model spanned from 1–2. The spatial distribution of the water yield in a watershed is similar to the elevation of the watershed, showing a trend of higher altitude leading to a higher water yield. Its water yield capacity tends to saturate at elevations greater than 3500 m. The local spatial distribution of the Normalized Difference Vegetation Index(NDVI) values and water yield clustering in the watershed were consistent and reproducible. Interannual runoff based on sub-basins correlated positively with the overall NDVI, whereas interannual runoff based on plant type correlated negatively with the overall NDVI.

Quantitative analysis of spatiotemporal changes and driving forces of vegetation net primary productivity (NPP) in the Qimeng region of Inner Mongolia

Vegetation is an essential component of terrestrial ecosystems, and understanding the drivers of vegetation change is of great importance for ecological management. In recent years, vegetation growth has increased under the combined effect of global warming and human activities in Inner Mongolia. The net primary productivity (NPP) was used as an indicator to study the spatial and temporal changes in vegetation in the Qimeng Region (QR). The residual trend analysis method was used to analyze the relative contributions of climate variations (CV) and human activities (HA) to NPP changes across the QR, while their drivers were explored using a geographical detector approach to quantify the driving forces of NPP. The results show that (1) NPP exhibited a fluctuating growth trend from 2003 to 2020, with an overall growth rate of 2.91%/year. (2) Precipitation, GDP and population density were the dominant driving factors for the spatial distribution of NPP. The combined explanatory power of any two dominant factors exceeded the power of any dominant individual factor, and the interaction between climate and human factors had a significant effect on NPP. (3) The change in NPP was influenced by the combined effect of HA and CV, accounting for 37.69% of the total area, with the relative contribution of HA being 51.75%. Finally, the relative contribution of human activities was slightly higher than that of climate change, confirming the initial success of the Grain to Green Program as well as ecological conservation projects. This paper provides a scientific basis for the local government to carry out the conversion of cropland to forest.

Monitoring the dynamic changes in vegetation cover and driving factors from 2000 to 2020 in the Maoershan Forest Farm region, China, using satellite remote sensing data

Aim of study: Natural climate change is a central driver of global ecosystem and forest change. Climate change and topographical factors have had the greatest impact on different types of forests around the world. We used remote sensing technology to detect and analyze the temporal and spatial changes of forest vegetation to provide reference for regional management. Area of study: Maoershan Forest Farm, China. Material and methods: The Landsat images were preprocessed using ArcGIS and ENVI software. The normalized difference vegetation index (NDVI) was calculated to identify vegetation changes from 2000 to 2020. In addition, the vegetation fraction cover (VFC) was calculated using the pixel binary model. The driving factors and their influences on vegetation changes in this region were determined using the random forest algorithm and Pearson correlation analysis method. Main results: From 2000 to 2020, the NDVI showed an overall increasing trend. The results indicated that compared with the climatic factors, topographic factors were more important to vegetation growth in the study area. Among the topographic factors, elevation was the most important factor affecting vegetation growth and both showed a negative correlation. Among the climatic factors, relative humidity was the primary driving factor affecting vegetation growth and both showed a positive correlation. Research highlights: Accurate and timely assessment of vegetation change and its relationship to climate and topographical changes can provide very useful information for policy makers, governments and planners in formulating management policies.

Investigation of Horizontal Vertical and Temporal Changes of Vegetation with NDVI and DEM Data (Example of Mardin Savur District)

Uzaktan Algılama ile Coğrafi Bilgi Sistemleri teknikleri yeryüzünün doğal özelliklerini, yer ve zamana göre değişiminin gözlemlenmesinde kullanılan yaygın ve güvenilir yöntemlerdendir. Bu yöntemlerin birlikte kullanıldığı en yaygın uygulamalardan biri NDVI (Normalize Edilmiş Fark Bitki Örtüsü İndeksi) analizleridir. NDVI analizi genellikle kuraklığı izlemek, tarımsal verimliliği tahmin etmek veya bölgelere göre bitki örtüsündeki tahribatı belirlemek için yapılan çalışmalarda kullanılır. Böylece doğal döngü açısından önemli bir yere sahip olan bitkilerin ve bitki örtüsünün artış ve azalışının denetlenmesi ve izlenmesi mümkün olabilmektedir. Bu çalışmada, Mardin ili Savur ilçe sınırları içindeki alanı kapsayan 1995-2021 yılları arasındaki 30 m çözünürlüklü Landsat 5, Landsat 8 ve 2000-2021 yılları arasındaki 500 metre çözünürlüklü MODIS (Moderate Resolution Imaging Spectroradiometer) uydu görüntülerinden yararlanılarak NDVI ve DEM (Sayısal Yükselti Modeli) veri katmanları ile sahanın bitki örtüsünün yatay ve dikey yönde dağılımının ve değişiminin belirlenmesi amaçlanmıştır. Bulgular harita ve diyagramlarla değerlendirilmiştir.

A Spatio-Temporal Neural Network Learning System for City-Scale Carbon Storage Capacity Estimating

Carbon storage capacity can be estimated to establish evaluation standards and statistics for carbon neutrality. Existing estimation methods including machine learning system have weakness modeling ability, and they are unable to deal with the complex topographies and temporal changes in vegetation in urban zones. However, a deep neural network has the potential ability to face such complex scenes because of its nonlinear fitting properties which has been widely used in industry. In this study, a novel and powerful neural network learning system named CiSL-NPP is proposed, to integrate multi-source data as an accurate, efficient, simple, long-term, city-scale carbon storage capacity measurement method. We use an unsupervised generation model spatio-temporal Masked AutoEncoder (st-MAE) and lightweight RNN model to efficiently obtain high-quality data that contain time-series information such as seasons; this minimizes any undue impact of meteorological, temporal, and spatial factors on the measurement owing to the multi-source, multi-modal nature of the data. In the MAE, we add seasonal coding to make it time-series sensitive; we also embed road network information to accurately perceive the complex topography of the city. Results for 16 cities in China and Europe revealed that the proposed method shows: 1) Higher-quality generated data (MSE is 0.13-0.29); 2) Accurate coverage of time series and complex geographical features; 3) Satisfaction of estimation demands with only 316 RMB cost; 4) Capability to evolve a long-term trend of urban vegetation carbon storage capacity in 4.2 days; and 5) Easily interpretable results which could, in practice, provide sound guidance for urban planning and decision-making processes.

Spatial and Temporal Characteristics of NDVI in the Weihe River Basin and Its Correlation with Terrestrial Water Storage

The Weihe River Basin (WRB) of China is located in an arid and water-scarce semi-arid region with a fragile ecological environment, and it is meaningful to study the spatial and temporal changes in vegetation and terrestrial water storage changes in a small-scale inland basin. This study uses normalized difference vegetation index (NDVI) data and Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) time-variable gravity field models to derive changes in vegetation cover and water storage in the WRB from 2002 to 2020. Firstly, taking NDVI as the breakthrough point, the temporal and spatial characteristics of vegetation were analyzed by trend analysis method and F-test. Then, GRACE and GRACE-FO were used to derive water storage variations. Finally, the correlation between NDVI and water storage variations is discussed using the Pearson correlation analysis. The results show that the overall trend of NDVI is increasing, and the increasing trend is more evident before 2014, and after that, there is a significant fluctuation. The spatial distribution shows a large spatial variability, but the growing area still accounts for the majority, and the change varies by vegetation type, among which the cultivated vegetation is more influential. The overall change in terrestrial water storage showed a decreasing trend of −0.09 cm/yr, and also reflected a solid intra-annual regular cycle, i.e., reaching a trough from October to November and a peak from May to June each year. The correlation is 0.6 on the time scale, and there was a 3-month lag between NDVI and TWS. On the spatial scale, the percentage of areas with a negative correlation was about 95.4%, probably due to increased water consumption and evapotranspiration. The study’s results can help to understand the relationship between vegetation and water storage in the WRB and provide scientific support for local environmental management.

Vegetation change on mountaintops in northern Sweden: Stable vascular‐plant but reordering of lichen and bryophyte communities

AbstractAlpine ecosystems harbor remarkably diverse and distinct plant communities that are characteristically limited to harsh, and cold climatic conditions. As a result of thermal limitation to species occurrence, mountainous ecosystems are considered to be particularly sensitive to climate change. Our understanding of the impact of climate change is mainly based on vascular plants however, whereas cryptogams (i.e., lichens and bryophytes) are generally neglected or simply considered as one functional group. Here we aimed to improve our understanding of the drivers underlying temporal changes in vegetation of alpine ecosystems. To this end, we repeatedly surveyed the vegetation on four mountain summits along an elevational gradient in northern Sweden spanning a 19‐year period. Our results show that the vascular plant communities remained relatively stable throughout the study period, despite fluctuations in terms of ground cover and species richness of shrubs and graminoids. In contrast, both lichens and bryophytes substantially decreased in cover and diversity, leading to alterations in community composition that were unrelated to vascular plant cover. Thermophilization of the vascular plant community was found only on the two intermediate summits. Our findings are only partially consistent with (long‐term) climate‐change impacts, and we argue that local non‐climatic drivers such as herbivory might offset vegetation responses to warming. Hence, we underline the importance of considering local non‐climatic drivers when evaluating temporal vegetation change in biologically complex systems.

A CNN-LSTM Model for Soil Organic Carbon Content Prediction with Long Time Series of MODIS-Based Phenological Variables

The spatial distribution of soil organic carbon (SOC) serves as critical geographic information for assessing ecosystem services, climate change mitigation, and optimal agriculture management. Digital mapping of SOC is challenging due to the complex relationships between the soil and its environment. Except for the well-known terrain and climate environmental covariates, vegetation that interacts with soils influences SOC significantly over long periods. Although several remote-sensing-based vegetation indices have been widely adopted in digital soil mapping, variables indicating long term vegetation growth have been less used. Vegetation phenology, an indicator of vegetation growth characteristics, can be used as a potential time series environmental covariate for SOC prediction. A CNN-LSTM model was developed for SOC prediction with inputs of static and dynamic environmental variables in Xuancheng City, China. The spatially contextual features in static variables (e.g., topographic variables) were extracted by the convolutional neural network (CNN), while the temporal features in dynamic variables (e.g., vegetation phenology over a long period of time) were extracted by a long short-term memory (LSTM) network. The ten-year phenological variables derived from moderate-resolution imaging spectroradiometer (MODIS) observations were adopted as predictors with historical temporal changes in vegetation in addition to the commonly used static variables. The random forest (RF) model was used as a reference model for comparison. Our results indicate that adding phenological variables can produce a more accurate map, as tested by the five-fold cross-validation, and demonstrate that CNN-LSTM is a potentially effective model for predicting SOC at a regional spatial scale with long-term historical vegetation phenology information as an extra input. We highlight the great potential of hybrid deep learning models, which can simultaneously extract spatial and temporal features from different types of environmental variables, for future applications in digital soil mapping.

Temporal Changes in Land Use, Vegetation, and Productivity in Southwest China

In recent decades, vegetation coverage and land use/land cover (LULC) have constantly changed, especially in southwest China. Therefore, it is necessary to conduct in-depth research into the temporal–spatial variation patterns of vegetation greening, LULC, and gross primary productivity (GPP). Here, we used remote sensing to analyze the spatial and temporal variation in the normalized difference vegetation index (NDVI) and GPP in the growing season under different LULCs in southwest China. Results showed: (1) From 2000–2019, the forest area in southwest China had increased by 2.1%, while the area of cropland and grassland had decreased by 3.2% and 5.5%, respectively. Furthermore, there are significant differences in spatial variation patterns. (2) NDVI and GPP in the growing season showed a general increasing trend (p &lt; 0.01); vegetation coverage is dominated by high coverage to highest coverage and medium coverage to high coverage transfer. (3) Under different LULCs, the migration directions of NDVI and GPP were different. The center of gravity migration of highest and medium coverage shifted to the southeast by 1.69° and to the northwest by 1.81°, respectively. The results showed the ecosystem evolution and will help to guide the maintenance measure of ecosystem balance and sustainable development.

GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube

With the application of big data in Earth observation, satellite imagery data are gradually becoming important means of observation for monitoring changes in vegetation, water bodies, and urbanization. Therefore, new satellite imagery data organization and management paradigms are urgently needed to fully mine the useful information from these data and provide new ways to better quantify and serve the sustainable development of resources and the environment. In this paper, a framework for processing and analyzing Chinese GF-1 satellite imagery data was developed using the latest technologies such as Open Data Cube (ODC) grids, Analysis Ready Data (ARD) generation, and space subdivision, which extended the data loading and processing capacities of the ODC grids for Chinese satellite imagery data. Using the proposed framework, we conducted a case study to investigate the spatial and temporal changes in vegetation and water mapping with GF-1 data collected from 2014 to 2021 covering the Miyun Reservoir, Beijing, China. The experimental results showed that the proposed framework had significantly improved temporal and spatial efficiency compared with the traditional scene-based data management approach, thus demonstrating the advantages and potential of the ODC grids as a new data management paradigm.

Observer‐driven pseudoturnover in vegetation monitoring is context‐dependent but does not affect ecological inference

AbstractAimsResurveys of vegetation plots are prone to several errors that can result in misleading conclusions. Minimizing such errors and finding alternative approaches for analyzing resurvey data are therefore important. We focused on inter‐observer error and excluded other sources of variation. Our main questions were: How large is the inter‐observer error (i.e. pseudoturnover) in vegetation surveys, and can it be reduced by simple data aggregation approaches? Which factors are affecting pseudoturnover and does it vary between morphological species groups or change over time? Is ecological inference robust against inter‐observer differences?LocationSwitzerland.MethodsOver seven years, we double‐surveyed a total of 224 plots that were marked once in the field and then sampled by two observers independently on the same day. Both observers conducted full vegetation surveys, recording all vascular plant species, their cover, and additional plot information. We then calculated mean ecological indicator values and pseudoturnover.ResultsAverage pseudoturnover was 29% when raw species lists were compared. However, by applying simple aggregation steps to the species list, pseudoturnover was reduced to 17%. Pseudoturnover further varied among habitat types and declined over the years, indicating a training effect among observers. Most overlooked taxa, responsible for pseudoturnover, had low cover values. Mean ecological indicator values were robust against inter‐observer differences.ConclusionsTo minimize pseudoturnover, we suggest continuous training of observers and species‐list aggregation prior to analysis. As mean ecological indicator values were robust against inter‐observer differences, we conclude that they can provide a reliable estimate of temporal vegetation and ecological changes.