High Vegetation Cover Research Articles

Construction and Zoning of Ecological Security Patterns in Yichang City

The study of ecological security patterns is of great significance to the balance between regional economic development and environmental protection. By optimizing the regional ecological security pattern through reasonable land-use planning and resource management strategies, the purpose of maintaining ecosystem stability and improving ecosystem service capacity can be achieved, and ultimately regional ecological security can be achieved. As a typical ecological civilization city in the middle reaches of the Yangtze River, Yichang City is also facing the dual challenges of urban expansion and environmental pressure. The construction and optimization of its ecological security pattern is the key to achieving the harmonious coexistence of economic development and environmental protection and ensuring regional sustainable development. Based on the ecological environment characteristics and land-use data of Yichang City, this paper uses morphological spatial pattern analysis and landscape connectivity analysis to identify core ecological sources, constructs a comprehensive ecological resistance surface based on the sensitivity–pressure–resilience (SPR) model, and combines circuit theory and Linkage Mapper tools to extract ecological corridors, ecological pinch points, and ecological barrier points and construct the ecological security pattern of Yichang City with ecological elements of points, lines, and surfaces. Finally, the community mining method was introduced and combined with habitat quality to analyze the spatial topological structure of the ecological network in Yichang City and conduct ecological security zoning management. The following conclusions were drawn: Yichang City has a good ecological background value. A total of 64 core ecological sources were screened out with a total area of 3239.5 km². In total, 157 ecological corridors in Yichang City were identified. These corridors were divided into 104 general corridors, 42 important corridors, and 11 key corridors according to the flow centrality score. In addition, 49 key ecological pinch points and 36 ecological barrier points were identified. The combination of these points, lines, and surfaces formed the ecological security pattern of Yichang City. Based on the community mining algorithm in complex networks and the principle of Thiessen polygons, Yichang City was divided into five ecological functional zones. Among them, Community No. 2 has the highest ecological security level, high vegetation coverage, close distribution of ecological sources, a large number of corridors, and high connectivity. Community No. 5 has the largest area, but it contains most of the human activity space and construction and development zones, with low habitat quality and severely squeezed ecological space. In this regard, large-scale ecological restoration projects should be implemented, such as artificial wetland construction and ecological island establishment, to supplement ecological activity space and mobility and enhance ecosystem service functions. This study aims to construct a multi-scale ecological security pattern in Yichang City, propose a dynamic zoning management strategy based on complex network analysis, and provide a scientific basis for ecological protection and restoration in rapidly urbanizing areas.

Based on Landsat8 Analysis of Urban Heat Island Effect in Mianyang City District

The accelerated urbanization process has formed heat islands in urban areas, resulting in the heat island effect, which brings about increased pollution, reduced visibility and other adverse effects, seriously affecting people's production and life. In this paper, we use Landsat8 remote sensing image data from 2013 to 2018 to analyze the heat island situation in the urbanization process, taking Mianyang city as an example. The results show that: the very strong heat island area in Mianyang city area is concentrated in the city center, and the weak heat island area changes to the strong heat island area; the non-heat island area is concentrated in the places with high vegetation cover or water bodies, and the very strong heat island is concentrated in the densely populated places and around the Fuling River; the overall heat island effect in Mianyang city area shows an increasing trend, and the intensity of heat island has a decreasing trend until 2018.

Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model

As a critical predisaster warning tool, landslide susceptibility assessment is crucial in disaster prevention and mitigation efforts. However, earlier methods for assessing landslide susceptibility have often ignored the impact of similarities in geographical attributes, restricting their feasibility in regions with diverse characteristics. The geographical-optimal-similarity (GOS) model effectively captures similarity relations within geospatial data and can isolate region-specific landslide features, thus overcoming this challenge. Consequently, a landslide susceptibility assessment method was developed by integrating the information value (IV) model with the GOS model. Huangshan City in Anhui Province, China, was selected as the study region. This research used 11 remote sensing feature factors and 657 historical landslide points, combined with the IV model, to construct a dataset for landslide prediction and susceptibility assessment using the GOS model. The findings indicate that, compared to conventional methods such as random forest, logistic regression, and radial basis function classifier, the GOS model enhances the area under the curve (AUC) value by 2.81% to 8.92%, reaching 0.846. This demonstrates superior performance and confirms the effectiveness and accuracy of the method in landslide susceptibility assessment. Furthermore, compared to the basic-configuration-similarity (BCS) model, the GOS model increases the AUC value by 9.64%, achieving 0.846. This approach substantially diminishes the effects of historical data accuracy, revealing upgraded applicability in landslide susceptibility evaluations. Landslides in Huangshan City are primarily influenced by rainfall and vegetation cover. High-susceptibility zones are predominantly located in areas with high precipitation and low vegetation cover. In contrast, low-susceptible and non-susceptible zones are primarily found in flat areas with high vegetation cover and farther from fault lines. The majority of the study region lies within landslide-prone zones, with non-susceptible areas comprising only 12.43% of the total area. Historical landslides are largely concentrated in moderate- to high-susceptibility zones, accounting for 92.24% of all landslide occurrences. Landslide density increases with the susceptibility level, with a density of 0.15 landslides per square kilometre in high-susceptibility zones. This study brings forward a reliable strategy for establishing the spatial relationship between geographical attribute similarity and landslide susceptibility, bolstering the method’s adaptability across various regions.

Research on Fine-Scale Terrain Construction in High Vegetation Coverage Areas Based on Implicit Neural Representations

Research on Fine-Scale Terrain Construction in High Vegetation Coverage Areas Based on Implicit Neural Representations

Volatile organic compounds at a highland forest site in the southeast of the Tibetan Plateau: Source apportionment and reactivity contributions

Volatile organic compounds at a highland forest site in the southeast of the Tibetan Plateau: Source apportionment and reactivity contributions

Changes in arthropod family richness, activity-density, biomass and body-size differed along a grazing intensity gradient: Modulation of plant traits and soil moisture conditions.

Changes in arthropod family richness, activity-density, biomass and body-size differed along a grazing intensity gradient: Modulation of plant traits and soil moisture conditions.

Niche Overlap Between Two Sympatric Steppe Birds in Inner Mongolia: Habitat Selection and Insights for Conservation.

The destruction and degradation of natural ecosystems is a major driver of biodiversity loss. The steppe ecosystem is under threat from human activities and habitat degradation. Fine-scale breeding habitat selection is critical for the survival of steppe birds, but understanding the factors that drive this selection remains challenging. This study uses field point-count surveys to examine factors influencing habitat selection and quantify niche overlap between two closely related steppe bird species: Jankowski's Bunting (Emberiza jankowskii) and Meadow Bunting (E. cioides) in Inner Mongolia, China. These species share similar ecological traits and overlapping habitats, making them ideal for exploring how fine-scale habitat selection and resource differentiation enable coexistence despite ecological similarity. We use generalized linear models (GLMs) and niche modeling algorithms to analyze the data. The results reveal distinct habitat preferences at both local and landscape scales. Jankowski's Bunting favors areas with higher vegetation cover and height, while Meadow Bunting prefers sites with greater edge density. GLM results show non-linear responses of both species to habitat variables, with distinct thresholds for optimal occurrence. Niche overlap analysis indicates considerable overlap (Schoener's D = 0.57), but significant differences in niche centroids suggest niche differentiation between the two species. Our findings emphasize the importance of considering fine-scale habitat characteristics and non-linear species -habitat relationships in conservation planning for steppe birds. Understanding how these species respond to habitat changes resulting from anthropogenic activities-such as land-use conversion and agricultural intensification-can help tailor conservation efforts to mitigate negative impacts and promote species coexistence in sensitive habitats.

Global digital elevation model (GDEM) product generation by correcting ASTER GDEM elevation with ICESat-2 altimeter data

Abstract. Advancements in scientific inquiry and practical applications have created a higher demand for the accuracy of global digital elevation models (GDEMs), especially for GDEMs whose main data source is optical imagery. To address this challenge, integrating GDEM and satellite laser altimeter data (global coverage and high-accuracy ranging) is an important research direction, in addition to the technological enhancement of the main data source. In this paper, we describe the datasets and algorithms used to generate a GDEM product (IC2-GDEM) by correcting ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model) elevation data with ICESat-2 altimeter data. The algorithm scheme presents the details of the strategies used for the various challenges, such as the processing of DEM boundaries, the fusion of the different data, and the geographical layout of the satellite laser altimeter data. We used a high-accuracy global elevation control point dataset and multiple high-accuracy local DEMs as the validation data for a comprehensive assessment at the global scale. The results from the validation comparison show that the elevation accuracy of IC2-GDEM is evidently superior to that of the ASTER GDEM product: (1) the RMSE reduction ratio of the corrected GDEM elevation is between 16 % and 82 %, and the average reduction ratio is about 47 %; and (2) from the analysis of the different topographies and land covers, this error reduction is effective even in areas with high topographic relief (>15°) and high vegetation cover (>60 %). ASTER GDEM has been in use for more than a decade, and many historical datasets and models are based on its elevation data. IC2-GDEM facilitates seamless integration with these historical datasets, which is essential for longitudinal studies examining long-term environmental change, land use dynamics, and climate impacts. Meanwhile, IC2-GDEM can serve as a new complementary data source for existing DEMs (such as Copernicus DEM) mainly sourced from synthetic aperture radar (SAR) observation. By cross-validating qualities, filling data gaps, and conducting multi-scale analyses, it can lead to more reliable and comprehensive scientific discoveries, thereby improving the overall quality and reliability of Earth science research. The IC2-GDEM product is openly available at https://doi.org/10.11888/RemoteSen.tpdc.301229 (Xie et al., 2024).

Monitoring the Effectiveness of Emergent Detached Offshore Structures in Mangrove Vegetation Increase: Lessons and Recommendations.

Although successful in protecting planted mangrove plants, the effectiveness of emergent detached offshore structures in increasing vegetation cover has yet to be definitively determined. We selected Tien Giang Province, Vietnam as an appropriate case study to address this question. We analyzed multiyear (2000 and 2022) shoreline changes and calculated the enhanced vegetation index (EVI) together with ground truthing in pursuit of the objectives of the study. Our findings suggest that emergent detached offshore structures have yet to lead to an increase in vegetation cover or promote mangrove growth. The vegetation growth steadily increased, as did the high level of natural mangrove growth with fully grown mangrove trees, even before the structures were constructed. By 2015, all the categories increased slightly except for low vegetation cover (LVC) and medium vegetation cover (MVC). LVC decreased from 390 ha in 2010 to 291 ha in 2015, while MVC decreased from 305 ha in 2010 to 275 ha in 2015. By 2020, all the categories decreased slightly except for non-vegetation cover-Barren lands (NVC2) and MVC. NVC2 decreased slightly from 404 ha in 2015 to 368 ha in 2015. The MVC decreased slightly from 275 ha in 2015 to 212 ha in 2020. Non-vegetation cover-Intertidal mudflats (NVC1)-LVC, and high vegetation cover (HVC) increased slightly from 2015 (326 ha, 291 ha, and 249 ha, respectively) to 2020 (368 ha, 292 ha, and 298 ha, respectively). By 2022, NVC2, MVC, and HVC remained unchanged, while NVC1 and LVC increased slightly from 368 ha and 292 ha in 2015, respectively, to 380 ha and 302 ha, respectively. The increase in vegetation cover and the natural regeneration of mangrove species were partly due to the adaptation of mangrove species to the site (river mouth areas), particularly the protection provided by Ngang Island offshore, and the construction of these structures. In addition, these structures were constructed in a rather stable area (slightly eroded and estuarine area) and therefore have yet to provide any noticeable benefits for mangrove regeneration three to five years after their construction. In the future, the morpho dynamic and hydrodynamic elements of the site should be adequately considered during the design and construction of these structures to increase vegetation cover and promote natural mangrove regeneration.

Distribution Patterns and Habitat Preferences of Five Globally Threatened and Endemic Montane Orthoptera (Parnassiana and Oropodisma)

Greece is a European hotspot for Orthoptera (378 species), yet it has been scarcely explored. We investigated the distribution and habitat preferences of the species of two endemic Orthoptera genera, Parnassiana and Oropodisma, in the montane ecosystems of central Greece. We conducted field surveys from 2021 to 2024 in 174 sites across seven mountains. The species of both genera preferred habitats above 1500 m, with species-specific preferences for microhabitat parameters: Parnassiana species favored moderate slopes with dense shrub cover, while Oropodisma species favored substrates with intermediate stone cover and relatively high vegetation cover. Species distribution models estimated the area of suitable habitat for Parnassiana to be at 5 km2 and Oropodisma at 3.28 km2. The Normalized Difference Vegetation Index (NDVI) and potential evapotranspiration were the key environmental drivers of the habitat suitability for both genera. Generalized regression models showed that altitude positively influenced Parnassiana population density, peaking at 2200 m, whereas rock and soil cover negatively impacted Oropodisma population densities. The results emphasize the critical role of montane habitats in sustaining these species and provide essential data for future research and conservation strategies.

Spatio-temporal analysis of litterfall load in the lower reaches of Qarqan and Tarim rivers using BP neural networks

Litterfall load is crucial in maintaining ecosystem health, controlling wildfires, and estimating carbon stock in arid regions. However, there is a lack of spatiotemporal analysis of litterfall in arid riparian forests. This study aims to estimate Litterfall load using a BP neural network based on vegetation indices from Landsat 5 and 8 satellite images, litterfall inventory data, slope, and distance to major river tributaries. It also aims to analyze the spatiotemporal distribution pattern of litter in the research area by estimating and analyzing the spatiotemporal pattern of litterfall along the desert riparian forests of the lower Qarqan and Tarim Rivers from 2001 to 2021. The results show that the initiation of the ecological water transfer project has facilitated the decomposition of litterfall, leading to an initial decline. Subsequently, the vegetation gradually recovered, leading to an increase in leaf litter input. Since 2001, litterfall initially decreased until reaching its lowest value of 4.39 × 109 kg in 2005, followed by a subsequent increase, reaching its highest value of 12.5 × 109 kg in 2021. The study concludes that ecological water conveyance promotes both the decomposition and increase of litterfall. Initially, it accelerates litterfall decomposition, while later stages foster an increase in Litterfall load. Meanwhile, due to the ecological water transfer project and the higher vegetation cover along the Tarim River compared to the Qarqan River, the Tarim River basin experiences higher average Litterfall load and variation.

Assessing Temporal Variability of Turbidity in Pampean Shallow Lakes (Argentina) Using Three Decades of Landsat Imagery

ABSTRACTThe Pampean region (Argentina) encompasses thousands of permanent shallow lakes, which can be either in turbid or clear water regimes. The intensification of human activities in this region has led to a general deterioration of their water quality. Remote sensing allows the monitoring of water quality in lakes over large areas and extended periods. The objectives of this study were to analyse temporal variations in turbidity in six shallow lakes using Landsat satellite data (5‐TM, 7‐ETM+ and 8‐OLI) for the period 1987–2020 and to evaluate the impact of total crop area and El Niño Southern Oscillation (ENSO) events on these variations. A semi‐analytical turbidity algorithm using reflectance in the red and near‐infrared bands was tested against in situ turbidity measurements. The Floating Algal Index (FAI) was used to discard pixels with above‐water vegetation. The best performance of the algorithm was achieved using only the near‐infrared band for the four turbid lakes, and the red band for the two clear lakes. High vegetation coverage in clear lakes prevented the estimation of the complete turbidity series. A significant positive turbidity trend was observed for turbid lakes during the 2000s, coinciding with a sharp increase in cultivated area. Regarding ENSO, a statistically significant relationship with turbidity was observed for the entire period in one lake, but turbidity peaks coincided with La Niña events in all lakes. This study emphasizes the utility of remote sensing in complementing field studies and thus monitoring regional changes in aquatic systems over long periods.

Long Time Series Spatiotemporal Variations in NPP Based on the CASA Model in the Eco-Urban Agglomeration Around Poyang Lake, China

The ecological urban agglomeration around Poyang Lake represents a critical development area in the Yangtze River basin. The spatiotemporal characteristics of the net primary productivity (NPP) of vegetation are explored from the perspective of the city’s functional position, providing important insights for the city to achieve the dual-carbon target and green development. The study evaluates the spatiotemporal variations in NPP from 2003 to 2022 in the eco-urban agglomeration around Poyang Lake, using the CASA model. Its variation characteristics were explored in detail from a completely new perspective and scope using indicators such as cycle amplitudes, CV coefficients, Hurst indices, and others. Results indicate seasonal fluctuations and significant variations between urban areas and vegetation, with implications for sustainable development. The annual NPP ranged from 200 to 800 gC/(m2·a), with a change rate of 0.58 gC/(m2·a) and evident seasonal fluctuations in the study area. Notably, urban core cities like Jiujiang and Nanchang exhibit lower NPP and decreasing trends. Scenic areas showed high forest cover and vigorous NPP changes, highlighting the need for targeted urban ecological management to enhance green development. Additionally, the seasonal fluctuations in NPP were notably influenced by specific land use types and local economic conditions. In areas with high vegetation cover, the seasonal characteristics of NPP are pronounced, while they are less evident in regions with strong urban economic conditions.

Monitoring Spatial-Temporal Variability of Vegetation Coverage and Its Influencing Factors in the Yellow River Source Region from 2000 to 2020

As a vital conservation area for water sources in the Yellow River Basin, understanding the spatial-temporal dynamics of vegetation coverage is crucial, along with the factors that affect it, to ensure ecological preservation and sustainable development of the Yellow River Source Region (YRSR). In this paper, we utilized Landsat surface reflectance data from 2000 to 2020 using de-clouding and masking methods implementing the Google Earth Engine (GEE) cloud platform. We investigated spatial-temporal changes in vegetation coverage by combining the maximum value composite (MVC), the dimidiate pixel model (DPM), the Theil–Sen median slope, and the Mann–Kendall test. The influencing factors on vegetation coverage were quantitatively analyzed using a geographic detector, and future tendencies in vegetation coverage were predicted utilizing the Future Land Use Simulation (FLUS) model. The outcomes suggested the following: (1) On the temporal scale, vegetation coverage exhibited a general upward trend between 2000 and 2020, with the YRSR showing a yearly growth rate of 0.23% (p < 0.001). In comparison to 2000, the area designated as having extremely high vegetation coverage increased by 19.3% in 2020. (2) Spatially, the central and southeast regions have higher values of vegetation coverage, whereas the northwest has lower values. In the study area, 75.5% of the region demonstrated a significant improvement trend, primarily in Xinghai County, Zeku County, and Dari County in the south and the northern portion of the YRSR; conversely, a notable tendency of degradation was identified in 11.8% of the area, mostly in the southeastern areas of Qumalai County, Chenduo County, Shiqu County, and scattered areas in the southeastern region. (3) With an explanatory power of exceeding 45%, the three influencing factors that had the biggest effects on vegetation coverage were mean annual temperature, elevation, and mean annual precipitation. Mean annual precipitation has been shown to have a major impact on vegetation covering; the interconnections involving these factors have increased the explanatory power of vegetation coverage’s regional distribution. (4) Predictions for 2030 show that the vegetation coverage is trending upward in the YRSR, with a notable recovery trend in the northwestern region. This study supplies a theoretical foundation to formulate strategies to promote sustainable development and ecological environmental preservation in the YRSR.

Assessment of Forest Fire Severity for a Management Conceptual Model: Case Study in Vilcabamba, Ecuador

Wildfires are affecting natural ecosystems worldwide, causing economic and human losses and exacerbated by climate change. Models of fire severity and fire susceptibility are crucial tools for fire monitoring. This case study analyses a fire event on 3 September 2019 in Vilcabamba parish, Loja province, Ecuador. This article aims to assess the severity and susceptibility of a fire through spectral indices and multi-criteria methods for establishing a fire action plan proposal. The methodology comprises the following: (i) the acquisition of Sentinel-2A products for the calculation of spectral indices; (ii) a fire severity model using differentiated indices (dNBR and dNDVI) and a fire susceptibility model using the Analytic Hierarchy Process (AHP) method; (iii) model validation using Logistic Regression (LR) and Non-metric Multidimensional Scaling (NMDS) algorithms; (iv) the proposal of an action plan for fire management. The Normalised Burn Ratio (NBR) index revealed that 10.98% of the fire perimeter has burned areas with moderate-high severity in post-fire scenes (2019) and decreased to 0.01% for post-fire scenes in 2021. The Normalised Difference Vegetation Index (NDVI) identified 67.28% of the fire perimeter with null photosynthetic activity in the post-fire scene (2019) and 5.88% in the post-fire scene (2021). The Normalised Difference Moisture Index (NDMI) applied in the pre-fire scene identified that 52.62% has low and dry vegetation (northeast), and 8.27% has high vegetation cover (southwest). The dNDVI identified 10.11% of unburned areas and 7.91% using the dNBR. The fire susceptibility model identified 11.44% of the fire perimeter with null fire susceptibility. These results evidence the vegetation recovery after two years of the fire event. The models demonstrated excellent performance for fire severity models and were a good fit for the AHP model. We used the Root Mean Square Error (RMSE) and area under the curve (AUC); dNBR and dNDVI have an RMSE of 0.006, and the AHP model has an RMSE of 0.032. The AUC = 1.0 for fire severity models and AUC = 0.6 for fire susceptibility. This study represents a holistic approach by combining Google Earth Engine (GEE), Geographic Information System (GIS), and remote sensing tools for proposing a fire action plan that supports decision making. This study provides escape routes that considered the most significant fire triggers, the AHP, and fire severity approaches for monitoring wildfires in Andean regions.

Dataset Construction for Monitoring the Effects of Ecosystem Restoration Projects

Climate change have increased the necessity for developing climate adaptation strategies. Ecological restoration projects have been implemented to restore the structure and function of degraded natural environments, thus enabling adaptation to the climate crisis. However, due to the lack of subsequent monitoring and evaluation, the effectiveness and success of these restoration efforts remain unverified. Verification of restoration effectiveness is essential for establishing ecological restoration and adaptation policies. Therefore, this study catalogs 338 cases of ecosystem restoration projects (including Ecosystem Conservation Fund Return Projects, Jayeon Madang Restoration Projects, and Urban Ecological Corridor Restoration Projects) conducted between 2010 and 2023 to effectively select monitoring sites. Using satellite-based spatial data, we quantified the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference moisture index (NDMI) and land surface temperature (LST). This study shows that higher NDVI values significantly lower LST and NDMI, indicating that the more vegetation restored in ecosystems, the more effectively it reduces surface temperatures. The NDVI across all land cover types averaged above 0.2, corresponding to a high vegetation cover density. Specifically, forests exhibited significantly higher NDVI and NDMI, whereas bare land and used area showed significantly higher NDBI and LST. Over the time series, NDVI and NDMI increased, and NDBI decreased, suggesting the positive effects of restoration. The ranges of NDVI, NDBI, NDMI and LST values by land cover type for the 338 restoration project sites provided in this study can be utilized for selecting specific monitoring sites and verifying effectiveness.

Ecological benefits of artificial vegetation restoration on local climate condition in abandoned mining area

Ecological benefits of artificial vegetation restoration on local climate condition in abandoned mining area

Biosphere reserves in the megadiverse Cape Floristic Region are effective in conserving arthropod diversity

AbstractBiosphere reserves (BRs) aim to protect global biodiversity alongside social and economic development. Each BR is composed of a core area where biodiversity conservation is maximal. Surrounding this zone is a buffer and then a transition zone where agroecological farming and other low‐intensity land‐uses are undertaken to an increasing degree. The Cape Floristic Region (CFR) is a biodiversity hotspot at the southern tip of Africa, extremely rich in endemic plants and arthropods. We review the instigation of four CFR BRs for effective conservation to protect this unique biodiversity, and where new species are regularly being discovered. Core areas protect many endemic and rare arthropod species with impacts from human activities in these critical areas being softened by the outer zones. Within the buffer and transition zones, agroecological approaches are advancing arthropod conservation, largely through maintenance of natural patches in the agricultural matrix. While larger patches are better, small patches also have high value for arthropods, especially when functionally connected. Other biodiversity‐friendly farming methods such as high vegetation cover between vine rows are also proving to be effective for indigenous arthropod conservation, as is an ecologically sensitive pest management strategy. Furthermore, a national programme aimed at removal of invasive alien trees is enabling arthropod recovery, especially endemic stream insects. Although fire is a natural CFR phenomenon, increased fire frequency is of concern. Nevertheless, natural fire refuges remain vitally important and are playing a critical role, especially for vulnerable species‐rich groups like pollinators. Overall, there is optimism that most arthropod species will remain at effective population levels in this megadiversity hotspot by putting in place effective precautionary measures. Here we show that the BR model contributes towards the protection of the CFR's endemic arthropod fauna, with potential to expand the BRs beyond the four official reserves.

Temporal Analysis of Mangrove Canopy Cover of High Resolution Satellite Imagery on the West Coast of Bangkalan Regency, Madura East Java

Mangroves are plants that live in muddy substrates, muddy sand, sand to tidal areas. These plants have important roles for marine organisms, carbon sequestration, as shoreline protectors from waves or erosion and sediment filters before entering the sea. The influence of human activity in the form of conversion of mangrove land to other forms or caused by natural disasters is a dangerous threat to the mangrove ecosystem. The magnitude of mangrove cover change is an important component so information is needed on changes in the spatial and temporal distribution of mangrove cover. This study aims to temporally analyze mangrove cover using high-resolution satellite imagery on the West Coast of Bangkalan Regency. This research uses secondary data in the form of Planetscope high-resolution satellite images from 2018 - 2023. The data will be processed to determine changes in mangrove area using Fv (Fractional Vegetation Cover). Calculation of the Fv (Fractional Vegetation Cover) value is done from the results of the minimum and maximum values of NDVI (Normalized Difference Vegetation Index). The results showed that there was a change in mangrove cover which was initially included in the FVC (Full Vegetation Coverage) class in 2022 with an area of 41.12 Ha to the HVC (High Vegetation Coverage) class with an area of 84.82 Ha in 2020. At the Arosbaya location, the highest Fv value occurred in 2019 with an area of 206.48 Ha. Reduction of mangrove area occurred in Sepulu and Arosbaya sub-districts due to land use change and sedimentation.

Multi-species ecological network based on asymmetric movement: Application in an urban rural fringe

Multi-species ecological network based on asymmetric movement: Application in an urban rural fringe