Vegetation Disturbance Research Articles

Distinct Contributions of Climate Change and Anthropogenic Activities to Evapotranspiration and Gross Primary Production Variations over Mainland China

In recent decades, China has experienced substantial climate change and significant vegetation greenness due to the extensive implementation of artificial ecological restoration programs. However, the quantitative contributions of climatic and anthropogenic drivers to the national variations in associated evapotranspiration (ET) and gross primary productivity (GPP) over China at different climate zoning sub-regions remain unclear. Based on the analysis of climate factor and vegetation disturbance trends created by anthropogenic activities, this study constructed a remote sensing-based ecological model consisting of Penman–Monteith–Leuning (PML) and light use efficiency (LUE) components. The proposed model simulated the spatiotemporal changes in ET and GPP between 1999 and 2018 over China. The contributions of climatic factors and anthropogenic activities to ET and GPP variations were quantitatively calculated by ridge regression. The results show that (1) both interannual ET and GPP markedly increased, by 1.32 mm yr−1 and 8.01 g C m−2 yr−1, respectively; (2) vegetation changes due to anthropogenic disturbance made the dominant contribution to GPP variations over China, while the dominant factor influencing ET changes differed by sub-region due to the joint effects of vegetation and climate; (3) temperature and precipitation positively affected ET, while wind speed, humidity, and solar radiation negatively contributed to ET in most parts of Mainland China. These findings may provide a workable, scientific reference for further ecological restoration decision-making processes in China.

Importance of land use and rainfall in the dynamics of the Caatinga vegetation cover in Northeastern Brazil

Many factors, such as climate, land use, and cultural and social aspects, influence the vegetation cover dynamics. We aimed to analyze the vegetation cover dynamics in three municipalities of the Brazilian semi-arid region (Aroeiras, Boqueirão, and Congo, state of Paraíba, Brazil) and to check for the influence of land use and inter-annual variations in rainfall. We selected 18 Landsat satellite images between 1995 and 2017. We performed supervised classification based on pre-established regions of interest: Closed or conserved vegetation, Sparse or disturbed vegetation, Exposed soil, Water bodies, clouds, and shadows, according to color, shape, and texture criteria. In addition, we collected data from the IBGE's Agricultural Census and annual rainfall. Variations in land use and rainfall variations worked synergistically on vegetation cover dynamics. In the municipality of Congo, very wet years were associated with higher percentages of closed vegetation and decreased soil exposure. High percentages of land use were related to a higher cover of sparse vegetation and exposed soil. In Boqueirão, we observed the opposite phenomenon, with drier years associated with higher percentages of closed vegetation and exposed soil. In Aroeiras, we identified no clear relationship between rainfall and variations in vegetation cover. Similar to that observed in the other municipalities, the abandonment of agricultural and cattle ranching activities increased dense vegetation. Our results show that the analysis of land use and vegetation cover dynamics in the semi-arid region represents a complex task ranging from regional factors, such as climate, characterized by inter-annual rainfall variations, to local factors, such as land use intensity and demographic/social aspects.

Tracking the Vegetation Change Trajectory over Large-Surface Coal Mines in the Jungar Coalfield Using Landsat Time-Series Data

Coal mining and ecological restoration activities significantly affect land surfaces, particularly vegetation. Long-term quantitative analyses of vegetation disturbance and restoration are crucial for effective mining management and ecological environmental supervision. In this study, using the Google Earth Engine and all available Landsat images from 1987 to 2020, we employed the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm and Support Vector Machine (SVM) to conduct a comprehensive analysis of the year, intensity, duration, and pattern of vegetation disturbance and restoration in the Heidaigou and Haerwusu open-pit coal mines (H-HOCMs) in the Jungar Coalfield of China. Our findings indicate that the overall accuracy for extractions of disturbance and restoration events in the H-HOCMs area is 83% and 84.5%, respectively, with kappa coefficients of 0.82 for both. Mining in Heidaigou has continued since its beginning in the 1990s, advancing toward the south and then eastward directions, and mining in the Haerwusu has advanced from west to east since 2010. The disturbance magnitude of the vegetation greenness in the mining area is relatively low, with a duration of about 4–5 years, and the restoration magnitude and duration vary considerably. The trajectory types show that vegetation restoration (R, 44%) occupies the largest area, followed by disturbance (D, 31%), restoration–disturbance (RD, 16%), disturbance–restoration (DR, 8%), restoration–disturbance–restoration (RDR), and no change (NC). The LandTrendr algorithm effectively detected changes in vegetation disturbance and restoration in H-HOCMs. Vegetation disturbance and restoration occurred in the study area, with a cumulative disturbance-to-restoration ratio of 61.79% since 1988. Significant restoration occurred primarily in the external dumps and continued ecological recovery occurred in the surrounding area.

High regeneration may not contribute to the forest's carbon storage: a case study in the mangrove forest of Rajang-Belawai-Paloh delta, Sarawak.

The alarming rate of the mangrove ecosystem loss poses a threat of losing valuable carbon sinks. This study was conducted to (i) determine the growth structure in different vegetation types and (ii) compare the aboveground biomass (AGB) and carbon storage in different vegetation types. The study was conducted at four vegetation types within the Rajang-Belawai-Paloh delta i.e., Matured Bakau-Berus Forest (MBBF), Bakau-Nipah Forest (BNF), Regenerating Forests (Debris pile) [RF-D], and Regenerating Forests (Machinery track) [RF-M]. Inventory plots (20 m × 20 m) are systematically located along the main waterways and smaller rivers/streams. Trees (≥ 5 cm diameter-at-breast height [DBH]), seedlings (< 2-cm stem diameter), and saplings (2-4.9-cm stem diameter) were measured. The trend of total trees per hectare is found to be decreasing across the least disturbed vegetation (MBBF) to the most disturbed vegetation (RF-M). The trends of total seedlings and saplings per hectare are found to be going upwards from the least disturbed vegetation to the most disturbed vegetation. Kruskal-Wallis H-test showed that there is a significant difference in the AGB and carbon storage between different vegetation types, χ2(2) = 43.98, p = 0.00 with the highest mean rank AGB and carbon storage in BNF (612.20 t/ha) and lowest in RF-M (287.85 t/ha). It can be concluded that although the most disturbed vegetations have higher regeneration, it may not contribute to the forest's carbon storage The naturally regenerated seedlings may not grow beyond the sapling stage unless sustainable forest management is conducted to ensure survivability and growth.

Snowmelt progression drives habitat selection and vegetation disturbance by an Arctic avian herbivore

AbstractArctic tundra vegetation is affected by rapid climatic change and fluctuating herbivore population sizes. Broad‐billed geese, after their arrival in spring, feed intensively on belowground rhizomes, thereby disturbing soil, mosses, and vascular plant vegetation. Understanding of how springtime snowmelt patterns drive goose behavior is thus key to better predict the state of Arctic tundra ecosystems. Here, we analyzed how snowmelt progression affected springtime habitat selection and vegetation disturbance by pink‐footed geese (Anser brachyrhynchus) in Svalbard during 2019. Our analysis, based on GPS telemetry data and field observations of geese, plot‐based assessments of signs of vegetation disturbance, and drone and satellite images, covered two spatial scales (fine scale: extent 0.3 km2, resolution 5 cm; valley scale: extent 30 km2, resolution 10 m). We show that pink‐footed goose habitat selection and signs of vegetation disturbance were correlated during the spring pre‐breeding period; disturbances were most prevalent in the moss tundra vegetation class and areas free from snow early in the season. The results were consistent across the spatial scales and methods (GPS telemetry and field observations). We estimated that 23.4% of moss tundra and 11.2% of dwarf‐shrub heath vegetation in the valley showed signs of disturbance by pink‐footed geese during the study period. This study demonstrates that aerial imagery and telemetry can provide data to detect disturbance hotspots caused by pink‐footed geese. Our study provides empirical evidence to general notions about implications of climate change and snow season changes that include increased variability in precipitation.

The uncertain future of mountaintop-removal-mined landscapes 2: Modeling the influence of topography and vegetation

Erosion following human disturbance threatens ecosystem health and inhibits effective land use. Mountaintop removal/valley fill (MTR/VF) mined landscapes of the Appalachian Coalfields region, USA, provide a unique opportunity to quantify the geomorphic trajectory of disturbed lands. Here we assess how MTR/VF-induced changes to topography and vegetation influence spatiotemporal erosion patterns in five mined watersheds. We use landscape evolution models starting from pre- and post-MTR/VF topographic data to isolate the influence of mining-induced topographic change. We then constrain ranges of erodibility from incision depths of gully features on mine margins, and use those estimates to model the influence of vegetation recovery trends on erosion.Topographic alterations alone reduce total sediment export from mined catchments. Model runs that incorporate the disturbance and recovery of vegetation in mined watersheds show that complete vegetation recovery keeps millennial sediment export from mined catchments within the range of unmined catchments. If vegetation recovery is anything less than complete, vegetation disturbance drives greater total sediment export from mined catchments than unmined catchments. Full vegetation recovery causes sediment fluxes to decline over millennia beyond the recovery period, while watersheds without full recovery experience fluxes that increase over the same time period. Spatiotemporal erosion trends depend on 1) the extent of vegetation recovery and 2) the extent to which MTR/VF creates slope–area disequilibrium. Valley fills and mine scarps experience erosion rates several times higher than those found in the unmined landscapes. Rapid erosion of mined areas drives deposition in colluvial hollows, headwater stream valleys, and below scarps. Our experiments suggest that reclamation focused on maximizing vegetation recovery and reducing hotspots of slope–area disequilibrium would reduce MTR's influence on Appalachian watersheds both during and long after the vegetation recovery period. Insights from MTR/VF-influenced landscapes can inform mined land management as the renewable energy transition drives increased surface mining.

Modern pollen assemblages of agricultural land, wasteland, and natural vegetation in China and their significance for reconstructing the intensity of agricultural activity

Knowledge of the modern pollen assemblage characteristics of artificial and disturbed vegetation can help reconstruct changes in the intensity of human activity and land use/land cover (LULC) based on Holocene stratigraphic pollen records. We obtained pollen assemblage data from 217 agricultural land (cultivated cereals) sites, 79 wasteland sites, and 389 natural vegetation sites in different regions in China. The results show that the pollen of cereals Poaceae and the associated agricultural weeds in agricultural land and wasteland vegetation contexts are effective indicators of the intensity of agricultural activity, and that the pollen assemblages of natural vegetation accurately reflect the regional zonal vegetation composition. Although the cereals Poaceae proportion varied greatly among the agricultural sites in different regions, due to factors like cultivation practices, crop types, and terrain, all of them were significant indicators of agricultural activity. The pollen assemblages of wasteland and natural vegetation in different regions were also different, and the indicator taxa exhibit distinct zonal characteristics. There was no consistent linear relationship between cereals Poaceae proportion and the area of agricultural land. The cereals Poaceae proportion did not vary with increasing agricultural area in regions with higher or lower levels of agricultural production; only in Northeast and North China, when the agricultural area was below 60%, did the cereals Poaceae proportion increase with increasing agricultural area. These results provide a modern process basis for the use of stratigraphic pollen data to reconstruct past changes in agricultural activity and LULC.

Comparative Evaluation of State-of-the-Art Semantic Segmentation Networks for Long-Term Landslide Map Production.

The production of long-term landslide maps (LAM) holds crucial importance in estimating landslide activity, vegetation disturbance, and regional stability. However, the availability of LAMs remains limited in many regions, despite the application of various machine-learning methods, deep-learning (DL) models, and ensemble strategies in landslide detection. While transfer learning is considered an effective approach to tackle this challenge, there has been limited exploration and comparison of the temporal transferability of state-of-the-art deep-learning models in the context of LAM production, leaving a significant gap in the research. In this study, an extensive series of tests was conducted to evaluate the temporal transferability of typical semantic segmentation models, specifically U-Net, U-Net 3+, and TransU-Net, using a 10-year landslide-inventory dataset located near the epicenter of the Wenchuan earthquake. The experiment results disclose the feasibility and limitations of implementing transfer-learning methods for LAM production, particularly when leveraging the power of U-Net 3+. Furthermore, following an assessment of the effects of varying data volumes, patch sizes, and time intervals, this study recommends appropriate settings for LAM production, emphasizing the balance between efficiency and production performance. The findings from this study can serve as a valuable reference for devising an efficient and reliable strategy for large-scale LAM production in landslide-prone regions.

Biome- and timescale-dependence of Holocene vegetation variability in the Northern Hemisphere.

Global climatic changes expected in the next centuries are likely to cause unparalleled vegetation disturbances, which in turn impact ecosystem services. To assess the significance of disturbances, it is necessary to characterize and understand typical natural vegetation variability on multi-decadal timescales and longer. We investigate this in the Holocene vegetation by examining a taxonomically harmonized and temporally standardized global fossil pollen dataset. Using principal component analysis, we characterize the variability in pollen assemblages, which are a proxy for vegetation composition, and derive timescale-dependent estimates of variability using the first-order Haar structure function. We find, on average, increasing fluctuations in vegetation composition from centennial to millennial timescales, as well as spatially coherent patterns of variability. We further relate these variations to pairwise comparisons between biome classes based on vegetation composition. As such, higher variability is identified for open-land vegetation compared to forests. This is consistent with the more active fire regimes of open-land biomes fostering variability. Needleleaf forests are more variable than broadleaf forests on shorter (centennial) timescales, but the inverse is true on longer (millennial) timescales. This inversion could also be explained by the fire characteristics of the biomes as fire disturbances would increase vegetation variability on shorter timescales, but stabilize vegetation composition on longer timecales by preventing the migration of less fire-adapted species.

Burrowing crabs' influence on tidal marsh vegetation species composition and abundance in a temperate back-barrier marsh, Cape Cod, Massachusetts, USA

Emergent vegetation in coastal marshes across the Northeastern United States have been declining and changing in relative species composition due to abiotic factors such as accelerated sea level rise, storm intensity, excess nutrients, and other anthropogenic disturbances. A possible biotic factor in marsh decline is increased perturbation from burrowing crabs. Uca pugilator and Minuca pugnax (fiddler crabs) whose populations are increasing in many marshes in the eastern US, likely due to disruptions in food webs and expanding habitat from sea level rise. High densities of crab burrows contribute to creek bank erosion and possibly in other factors of marsh peat degradation. When burrowing crabs were excluded from large scale (9 m2) plots in a Cape Cod, MA, marsh for five months, vegetation species richness increased significantly, as did abundance of woody shrubs and the forb, Suaeda maritima (L.). Conversely, burrowing crab exclusion resulted in less Spartina alterniflora (Loisel) and less bare ground - indicating that the other types of vegetation were able to occupy more space in the absence of crab burrowing and sediment sorting activity. The experimental enclosure results were compared with vegetation and burrow density monitoring data taken from nearby long-term monitoring plots. Of the sixteen analyses, we found only one similar result between the 5 month exclusions and monitoring plots; high burrow densities were associated with higher density of S. alterniflora. In contrast to the exclusions, high burrow densities were positively correlated with abundance of Iva frutescens and Suaeda maritima. Six other species (or groupings of rare plants) were negatively associated with burrow densities, four of these are sensitive to inundation and are considered characteristic of the high marsh zone. Different interpretations of the effects of burrowing crabs will result depending on whether correlations are observed from monitoring plots or longer term, large-scale exclusions are conducted; both are needed to assess the rapid vegetation and faunal shifts that are occurring in temperate salt marshes. We suggest that Uca pugilator and Minuca sp. will benefit from sea level rise induced habitat expansion at the expense of the abundance and species richness of flooding-sensitive plants. The erosion and vegetation disturbance that high burrowing crab densities lead to will further exacerbate the decline of these species that are receding at the seaward edge of their distributions.

Biological soil crusts as a major ecosystem component in sandization areas of the Brazilian Pampa

In the Brazilian Pampa, vegetation disturbance and soil degradation led to an expansion of sediment remobilization areas, a process locally termed as sandization. Transported sands generate severe environmental problems and hamper the growth of vegetation. However, parts of these sandization areas are quickly covered by biological soil crusts (biocrusts), which have not been investigated in this biome before.This study describes biocrust occurence in sandization areas of the Brazilian Pampa for the first time and analyses their distribution patterns related to the local topography. We studied biocrusts at two research sites where their presence appeared to be essential for soil protection. First, the occurrence of different biocrust types were assessed and a taxonomic survey was conducted. Second, UAV-based imagery was created and biocrusts were classified, using a random forest approach to understand the relation of their abundance to topography.We observed that biocrusts occurred widely in areas prone to sandization with an approx. 25% distribution on the land surface. They are mostly dominated by cyanobacteria and secondarily by bryophytes. Among the observed genera, the cyanobacterial genus Stigonema sp. was dominating in both study sites. Campylopus pilifer was the dominating moss species. The mapped biocrusts represented all major morphological types and were classified as rolling, pinnacled, rugose, and smooth crusts. Local topography was a major driver for biocrust distribution, but also the establishment of vascular plants. Slope and aspect had a strong influence on biocrust development, but the presence of protective topographic positions against atmospheric influences most prominently facilitated their occurrence.This pilot study proves that biocrusts can potentially play a key role as ecosystem engineers in the Brazilian Pampa with a positive effect on general vegetation growth and soil-stabilization. In this biome, however, they have been largely overlooked compared to other areas globally. Thus, biocrusts need to be further studied and their effects considered in land management to constrain further soil degradation.

Lakeshore vegetation: More resilient towards human recreation than we think?

AbstractLakes and their shoreline vegetation are rich in biodiversity and provide multiple functions and habitats for fauna and flora. Humans are attracted by the scenic beauty of these ecosystems and the possibilities for recreational activities they offer. However, the use of lakes for recreational activities can lead to disturbance of vegetation, threatening the integrity and functionality of shoreline areas. Recent literature reviews revealed that impacts of the seemingly harmless activities bathing and lingering on the shore on lakeshore vegetation are poorly understood. In this study, we analysed the effects of shoreline use connected with bathing on the structure, composition and diversity of lakeshore vegetation. Vegetation relevés were recorded in 10 bathing and 10 adjacent control sites in the nature park ‘Dahme‐Heideseen’ (Brandenburg, Germany). In addition visitor counts were performed. The species composition and the cover of herbaceous and shrub vegetation differed between bathing and control sites, but all sites had a high percentage of plant species not typical for the community. The vegetation parameters did not correlate with visitor counts. The results indicate that the present visitor intensity in the nature park does not impact the vegetation severely.

Diversity of hymenoptera parasitoid wasp in oil palm with the presence and absence of non crop vegetation

The diversity of hymenopteran parasitoid wasps is influenced by the presence of the host, the number and type of flowering vegetation as a food source for adult parasitoids. This study aims to determine the effect of reducing non-crop vegetation by cattle grazing disturbances on the impact of beneficial ecosystem services, especially parasitoids wasp in oil palm plantations. This disturbance phenomenon is important to study, so that it can see the response of each parasitoid wasp due to reduced vegetation and become the basis for strategies for habitat enrichment in oil palm plantations. Sampling was carried out in six blocks, including six oil palm blocks consisting of three blocks with non-plant vegetation disturbance and three blocks without disturbance. Insect collection uses yellow pot traps, malaise traps, and sweeping nets. In total, 4273 individuals belonging to 185 morphospecies, 24 families, and 9 superfamilies were collected from all sampling methods. The diversity and abundance of Hymenopteran parasitoids wasp are strongly influenced by the presence or absence of non-plant vegetation, especially flowering herbs. The reduced non-crop vegetation was followed by a decrease in the population of several Hymenopteran parasitoid wasps both in terms of abundance and richness.

Citizen science can complement professional invasive plant surveys and improve estimates of suitable habitat

AbstractAimCitizen science is a cost‐effective potential source of invasive species occurrence data. However, data quality issues due to unstructured sampling approaches may discourage the use of these observations by science and conservation professionals. This study explored the utility of low‐structure iNaturalist citizen science data in invasive plant monitoring. We first examined the prevalence of invasive taxa in iNaturalist plant observations and sampling biases associated with these data. Using four invasive species as examples, we then compared iNaturalist and professional agency observations and used the two datasets to model suitable habitat for each species.LocationHawai'i, USA.MethodsTo estimate the prevalence of invasive plant data, we compared the number of species and observations recorded in iNaturalist to botanical checklists for Hawai'i. Sampling bias was quantified along gradients of site accessibility, protective status and vegetation disturbance using a bias index. Habitat suitability for four invasive species was modelled in Maxent, using observations from iNaturalist, professional agencies and stratified subsets of iNaturalist data.ResultsiNaturalist plant observations were biased towards invasive species, which were frequently recorded in areas with higher road/trail density and vegetation disturbance. Professional observations of four example invasive species tended to occur in less accessible, native‐dominated sites. Habitat suitability models based on iNaturalist versus professional data showed moderate overlap and different distributions of suitable habitat across vegetation disturbance classes. Stratifying iNaturalist observations had little effect on how suitable habitat was distributed for the species modelled in this study.Main ConclusionsOpportunistic iNaturalist observations have the potential to complement and expand professional invasive plant monitoring, which we found was often affected by inverse sampling biases. Invasive species represented a high proportion of iNaturalist plant observations, and were recorded in environments that were not captured by professional surveys. Combining the datasets thus led to more comprehensive estimates of suitable habitat.

A novel index combining temperature and vegetation conditions for monitoring surface mining disturbance using Landsat time series

The exploitation and utilization of mineral resources have significantly contributed to economic and social development; however, the procession has also negatively impacted the ecological environment. Thus, it is necessary to effectively monitor coalfields, an important basis for reasonable development and utilization of coalfields and following ecological reclamation. Based on MODIS Global Damage Index, We couple the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), and proposal the Mine Landscape Damage Index (MLDI) and Mine Landscape Reclamation Index (MLRI) to supervise the disturbed vegetation of mines. Huolinhe coalfield, one of the largest mines in China, is monitored in this research. 3675 Landsat imageries from 1990 to 2020 are analyzed using the LandTrendr algorithm with MLDI and MLRI time series data on Google Earth Engine. The results demonstrate that this method could distinguish the region and disturbing process of damage and reclamation due to its effectiveness proved by visual interpretation of high-resolution imageries and verification of field investigation. The total detected area of vegetation damage was 16505.10 ha, and the vegetation damage in the mining was active from 2006 to 2011, with average annual damage of more than 1000 ha. The total area of vegetation restoration in the mine is 8640.81 ha, accounting for 52.35% of the vegetation damage. Vegetation restoration has increased gradually since 2010. In the past three years, vegetation restoration accounted for 47.61% of the total reclamation area. A novel index for monitoring the effects of surface damage in multi-open-pit mining areas is provided to us, and the detection is reliable. It can give the government an effective practical data reference in the sustainable development of utilization management and ecological reclamation in coalfield.

Automatically identifying the vegetation destruction and restoration of various open-pit mines utilizing remotely sensed images: Auto-VDR

The spatio-temporal pattern of vegetation disturbance by open-pit mining activities such as the disturbance area and magnitude are important for coordinated development of surface mining and environmental protection. This paper proposes an automatic method (Auto-VDR) for identifying vegetation destruction and restoration of various open-pit mines. The proposed method consists of three steps: construction of the sample dataset, identification of disturbance types, and extraction of disturbance time and magnitude. First, a sample dataset with three types was constructed according to the interannual trends of Normalized Difference Vegetation Index (NDVI) in mining areas: “Restoration after destruction (RAD)”, “Destruction without restoration (DWR)” and “No destruction (ND)”. Second, in order to accommodate to various open-pit mines with different background conditions (original vegetation cover prior to mining, loss rate and magnitude of vegetation due to mining, etc.), the “Shape-based distance” was used to match the NDVI curves of each pixel to the three disturbance types. Third, the disturbance time and magnitude in the destroyed regions were extracted based on the vegetated/bare ground threshold (α). The Shendong coal base in China was selected as the study area consisting of 133 open-pit coal mines. Annual NDVI was generated using Landsat time-series data. The maps of vegetation destruction and restoration caused by surface mining during 1990–2021 were drawn with accuracies of 0.82 and 0.87, respectively. The identification accuracy of the Auto-VDR is higher than Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) and Continuous Change Detection and Classification (CCDC). The results show that from 1990 to 2021, a total of 400.08 km2 of vegetation has been destroyed, accounting for 35.93% of the study area. Furthermore, restoration activities mainly began in 2010, lagging mining activities by approximately three years. The accumulated area of restoration was 177.91 km2 and accounts for 15.98% of the study area. The vegetation destruction at the Shendong coal base has gone through four stages, with 2008, 2015 and 2018 as splits, corresponding to the developing stage of the China coal industry. In particular, the area of vegetation destruction per year reached the maximum of 61.16 km2 in 2008, which was 1.38 times the total area mined over the past 17 years. There were 14% of the 133 open-pit mines in Shendong coal base with a restoration rate of 0.8 and 59% of the mines with a restoration rate of 0.5. This method has been demonstrated to be able to extract vegetation destruction and restoration processes accurately and rapidly for various open-pit mines at a scale of coal base, which provides an important reference for supervising the impact of mining on the environment and assessing the effectiveness of ecological restoration.

Extent, Severity, and Temporal Patterns of Damage to Cuba’s Ecosystems following Hurricane Irma: MODIS and Sentinel-2 Hurricane Disturbance Vegetation Anomaly (HDVA)

Mangrove forests provide a range of ecosystem services but may be increasingly threatened by climate change in the North Atlantic due to high-intensity storms. Hurricane Irma (Category 5) hit the northern coast of Cuba in September 2017, causing widespread damage to mangroves; losses have not yet been extensively documented due to financial and logistical constraints for local scientists. Our team estimated Irma’s impacts on Cuban ecosystems in a coastal and upland study area spanning over 1.7 million ha. We developed a multi-resolution time series “vegetation anomaly” approach, where post-disturbance observations in photosynthetically active vegetation (Enhanced Vegetation Index, EVI) were normalized to the reference period (dry season mean over a historical time series). The Hurricane Disturbance Vegetation Anomaly (HDVA) was used to estimate the extent, severity, and temporal patterns of ecological changes with Sentinel-2 and MODIS data and used vicarious validation with microsatellite interpretation (Planet). HDVA values were classed to convey qualitative labels useful for local scientists: (1) Catastrophic, (2) Severe, (3) Moderate, (4) Mild, and (5) No Loss. Sentinel-2 had a limited reference period (2015–2017) compared to MODIS (2000–2017), yet the HDVA patterns were similar. Mangrove and wetlands (&gt;265,000 ha) sustained widespread damages, with a staggering 78% showing damage, largely severe to catastrophic (0–0.81 HDVA; &gt;207,000 ha). The damaged area is 24 times greater than impacts from Irma as documented elsewhere. Caguanes National Park (&gt;8400 ha, excluding marine zones) experienced concentrated, severe mangrove and wetland damages (nearly 4000 ha). The phenological declines from Irma’s impacts took up to 17 months to fully actualize, a much longer period than previously suggested. In contrast, dry forests saw rapid green flushes post-hurricane. With the increase of high-intensity storm events and other threats to ecosystems, the HDVA methods outlined here can be used to assess intense to low-level damages.

A guide to assess distance from ecological baselines and change over time in palaeoecological records

Palaeoecology provides critical data for establishing ecological ‘baselines’, which can guide restoration efforts and be used to assess ecosystem change. However, statistical analyses can be challenging because of the large number of methods available for establishing palaeoecological baselines combined with a lack of practical guidance, particularly around quantifying baselines that include natural variability. We contribute one solution by providing guidance and an R package baselines for using palaeoecological data to (i) define baselines and (ii) test for change over time that incorporates variability. These methods provide an alternative to single-taxon analyses and allow ecosystem complexity to be captured. We use published pollen records as case studies to demonstrate how to establish vegetation baselines for seven localities in New Zealand where relatively recent (c. 1280 AD) and near-simultaneous human settlement across the country allows background environmental disturbance in the pre-human era to be distinguished from anthropogenic disturbance. We present methods for calculating distance from initial sample, distance from baseline in ordination space, allowing incorporation of ecosystem variability, and analysis of rates of change over time using principal curves. We found conventional and Bayesian ordination methods yielded similar results and were effective at identifying change following human settlement, despite the potential for a positive mean-variance relationship to confound results. Principal response curves were most sensitive to a known period of vegetation disturbance caused by volcanic eruptions at two sites with tephra deposits. Our proposed methods, case study and R package baselines are designed to provide a suite of tools to encourage and enable palaeoecological data to be used by palaeoecologists to assess trajectories and change over time, and monitor whether historical management actions have facilitated a change in direction towards a desired baseline state.

Continuous Monitoring of the Mining Activities, Restoration Vegetation Status and Solar Farm Growth in Coal Mine Region Using Remote Sensing Data

Abstract Land reclamation of previously mined regions has been incorporated in the mining process as awareness of environmental protection has grown. In this study, we used the open-pit coal mine Oslomej in R. N. Macedonia to demonstrate the activities related to the monitoring process of the study area. We combined the Google Earth Engine (GEE) computing platform with the Landsat time-series data, Normalized Difference Vegetation Index (NDVI), Random Forest (RF) algorithm, and the LandTrendr algorithm to monitor the mining impacts, land reclamation, and the solar farm growth of the coalfield region between 1984 and 2021. The data from the sequential Landsat archive that was used to construct the spatiotemporal variability of the NDVI over the Oslomej mine site (1984-2021) and the pixel-based trajectories from the LandTrendr algorithm were used to achieve accurate measurements and analysis of vegetation disturbances. The different land use/land cover (LULC) classes herbaceous, water, mine, bare land, and solar farm in the Oslomej coalfield area were identified, and the effects of LULC changes on the mining environment were discussed. The RF classification algorithm was capable of separating these LULC classes with accuracies exceeding 90 %. We also validated our results using random sample points, field knowledge, imagery, and Google Earth. Our methodology, which is based on GEE, effectively captured information on mining, reclamation, and solar farm change, providing annual data (maps and change attributes) that can help local planners, policymakers, and environmentalists to better understand environmental influences connected to the ongoing conversion of the mining areas.

Relative importance of influencing factor‐driven soil enzyme activity during the early plantation stage in the hilly‐gully Loess regions

AbstractWidespread and rapid land‐use changes have changed the ecosystem structure and threatened soil functions. Soil enzymes are the important driving force and sensitive indicators for soil ecological function. However, the effect of afforestation on soil enzyme activity and the relative importance of the influencing factors (plants, soil, structure, and nutrition) are still inconclusive, especially in the early stage of afforestation. Here, the enzymatic activities and influencing factors of five types of plantations (18 years old) and natural grassland topsoil (0–20 cm) in the hilly‐gully region of the Loess Plateau were studied. The results showed that the soil enzyme activity of the plantations was significantly lower than that of the natural grassland, and the activities of invertase, urease, and alkaline phosphatase were decreased by 32%, 26%, and 10% on average, respectively. Partial correlation analysis and multiple regression analysis showed that the influence of soil and nutrient factors on invertase and alkaline phosphatase was more important than that of plant and structural factors, while urease was more influenced by plant and nutrition factors. Our results suggest that soil and nutrient characteristics were important factors affecting soil enzyme activity in plantations, but the disturbance of vegetation may be the main reason for the decrease in enzyme activity in the early stage of restoration.