Vegetation Conditions Research Articles

The impact of 2021 large forest fires on vegetation and soils, on the territory of distribution of light soil-forming rocks in Central Yakutia

The influence of catastrophic forest fires in 2021 on the vegetation cover and soils of the middle taiga on the territory of distribution of light soil-forming rocks of Central Yakutia has been studied. Obtained results showed the cardinal dynamics of species diversity and productivity of the larch and pine forests surface vegetation in their natural state and in burnt areas. The stock of grass-shrub cover aboveground phytomass on the control larch and pine forests is higher for 5 and 10 times than that in the burnt areas, respectively. The work carried out revealed the effect of forest fires on the morphological structure (formation of pyrogenic layer, manifestation of pyro- and cryoturbations), the depth of thawing, the water regime of permafrost soils under larch and pine forests on control and burnt sites in the first year after strong fires. The soils in the study area are predominantly permafrost illuvial-ferruginous podzols. The study revealed an increase in the permafrost table depth of these soils, an increase in the level of ground water, and a decrease in acidity in the upper part of the profile. Intense ash formation was found due to the burning of litter. The characteristic of the current state of vegetation and soil cover is given.

Hydrodynamic effects and water environment improvement of topographic reconstruction in shallow lakes

This paper focuses on a typical shallow lake, Lake Changdanghu, which is subject to frequent hydrodynamic disturbances and suspended sediment deposition, resulting in reduced water clarity and the degradation of the grass-type ecosystem. Using a combination of long-term systematic observations and numerical simulations, the impact of terrain modification on the hydrodynamic characteristics of Lake Changdanghu and its resulting environmental improvements are systematically analyzed. The results indicate that there is a significant correlation between the wind wave intensity, suspended solids concentration of the water, and water clarity. The artificial shoal project effectively reduces the local wind wave intensity, increases the water area of the weak waves, and alters the flow structure and transport path of the lake. The wave reduction effect of the artificial shoal significantly improves the transparency of the local water, especially under high wind wave conditions, with a 60% increase in the sheltered area, significantly improving the physical habitat environment. Additionally, the flow resistance and guidance roles of the artificial shoal concentrate the impact of the poor water quality of the inflow in a local area, reducing its adverse effects on the main body of the lake. Our research demonstrates that for shallow lakes, the water transparency can be improved, the physical habitat conditions for aquatic vegetation can be enhanced, and grass-type ecosystems can be gradually restored through artificial intervention and vegetation recovery projects by reducing the wave energy, flow resistance, and guidance through the construction of artificial shoals. This approach achieves long-term maintenance of eutrophication control and water quality improvement in shallow lakes.

Improving remote sensing based agricultural drought characterization in Saurashtra, Gujarat : A region-specific threshold approach

Remote sensing technology has demonstrated its significant utility in the monitoring and mapping of agricultural drought on a global scale. This study focused on the assessment of agricultural drought in the Saurashtra region of Gujarat, India, utilizing a comprehensive dataset spanning 33 years from Landsat and Sentinel satellites. It employed various vegetation indices, including NDVI (Normalized Difference Vegetation Index), Anomaly Index (NAI), Vegetation Condition Index (VCI) and NDWI Anomaly index (NDWIA), to gauge drought conditions. The performance of these indices was evaluated through the generation of drought severity maps and their correlation analysis with major Kharif crops in the region, specifically cotton and groundnut. The analysis pinpointed major agricultural drought years, such as 1986, 1987, 1991, 2000, 2002 and 2012, which corresponded to substantial crop yield losses ranging from 37% to 76% for cotton and 66% to 95% for groundnut, varying by district. Despite VCI demonstrating equivalent or superior correlations with crop yields (ranging from 0.32 to 0.73 for cotton and 0.33 to 0.75 for groundnut) compared to NAI in various districts, it tended to underestimate drought severities, designating only 2 to 9 drought years for different districts. Consequently, this study recommends revised VCI drought severity thresholds, which enhance the categorization of agricultural drought in terms of severity levels and corresponding yield losses for cotton and groundnut in the Saurashtra region of Gujarat. Furthermore, it underscores the need to establish region-specific drought severity thresholds by identifying the most suitable vegetation index for effective quantification of agricultural drought, thereby facilitating informed drought mitigation measures.

Effect of Biostimulant, Manure Stabilizer, and Manure on Soil Physical Properties and Vegetation Status.

Food production sustainability is one of contemporary agriculture's fundamental challenges. Farmers are currently facing high input prices in crop production and declining organic matter in the soil. For this reason, a field experiment was established to assess the effect of the biostimulant NeOsol (NS), the manure stabilizer Z'fix (ZF), farmyard manure (FM), and their combination in farm practice. In situ measurements provided information on the change in bulk density (BD), unit draft (UD), saturated hydraulic conductivity (SHC), and cone index (CI). Furthermore, the vegetation status was investigated via vegetation indices, and the yield and quality parameters were assessed. Management of the experimental field resulted in an overall decrease in BD over time for the treated variants compared to the control (CL). The decrease with time was also verified in the case of UD and CI at the depth zone of 10-20 cm. Variants FM (by 8.0%), FM_NS (by 7.3%), and FM_ZF_NS (by 3.8%) proved to have lower UD values than CL. An overall increase in SHC and in yield was observed over time. Concerning SHC, only FM (by 58.5%) proved different from CL. The yield of all the treated variants, i.e., NS (by 8.2%), FM (by 10.8%), FM_NS (by 14.1%), FM_ZF (by 17.8%), and FM_ZF_NS (by 20.1%), surpassed CL. Simultaneously, none of the examined treatments proved to have any adverse effect either on soil or on plant-related variables.

Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm

Abstract. The processes responsible for methane (CH4) emissions from boreal wetlands are complex; hence, their model representation is complicated by a large number of parameters and parameter uncertainties. The arctic-enabled dynamic global vegetation model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator) is one such model that allows quantification and understanding of the natural wetland CH4 fluxes at various scales, ranging from local to regional and global, but with several uncertainties. The model contains detailed descriptions of the CH4 production, oxidation, and transport controlled by several process parameters. Complexities in the underlying environmental processes, warming-driven alternative paths of meteorological phenomena, and changes in hydrological and vegetation conditions highlight the need for a calibrated and optimised version of LPJ-GUESS. In this study, we formulated the parameter calibration as a Bayesian problem, using knowledge of reasonable parameters values as priors. We then used an adaptive Metropolis–Hastings (MH)-based Markov chain Monte Carlo (MCMC) algorithm to improve predictions of CH4 emission by LPJ-GUESS and to quantify uncertainties. Application of this method on uncertain parameters allows for a greater search of their posterior distribution, leading to a more complete characterisation of the posterior distribution with a reduced risk of the sample impoverishment that can occur when using other optimisation methods. For assimilation, the analysis used flux measurement data gathered during the period from 2005 to 2014 from the Siikaneva wetlands in Southern Finland with an estimation of measurement uncertainties. The data are used to constrain the processes behind the CH4 dynamics, and the posterior covariance structures are used to explain how the parameters and the processes are related. To further support the conclusions, the CH4 flux and the other component fluxes associated with the flux are examined. The results demonstrate the robustness of MCMC methods to quantitatively assess the interrelationship between objective function choices, parameter identifiability, and data support. The experiment using real observations from Siikaneva resulted in a reduction in the root-mean-square error (RMSE), from 0.044 to 0.023 gC m−2 d−1, and a 93.89 % reduction in the cost function value. As a part of this work, knowledge about how CH4 data can constrain the parameters and processes is derived. Although the optimisation is performed based on a single site's flux data from Siikaneva, the algorithm is useful for larger-scale multi-site studies for a more robust calibration of LPJ-GUESS and similar models, and the results can highlight where model improvements are needed.

Drought mitigation in sandy soil of oil palm plantation using apllication oil palm empty fruit bunch

Sandy soil in oil palm plantations has the potential to experience a higher water deficit during drought condition compared to other soil types. Application oil palm empty fruit bunch, which are co-product of oil palm industry, can be used to mitigate drought in sandy soil. This research aims to compare effective methods of applying empty fruit bunch on sandy soil in drought mitigation. This research was conducted in May-September 2023 at the Sei Rasau Region oil palm plantation of PT. BGA in Kendawangan, Ketapang, West Kalimantan. The research was carried out descriptively on block which are applied empty fruit bunch mulch (Block N21), applied focal feeder (Block P14), and applied combination mulch and focal feeder (Block P19). The technique for applying empty fruit bunch was observed directly and by seeking information from the company. Soil samples and plant vegetative data were also taken, while plant productivity was obtained from the company's secondary data. The results show that the implementation of empty oil palm fruit bunches as drought mitigation will be carried out intensively in 2022. Application empty fruit bunch as mulch are spread on the ground at a dose of 200 kg/tree and application as a focal feeder is carried out with empty fruit bunch inserted into a hole in the ground at a dose of 200 kg/hole. The microclimatic conditions in the three blocks of the research location are relatively same. The results of soil sample analysis show that soil with the application of empty fruit bunch mulch has the best soil moisture conditions and the lowest soil temperature. The lowest C/N ratio was in the combination application of mulch and focal feeder. The best plant vegetative conditions and plant productivity occurred in blocks with the application of empty fruit bunch mulch. So it can be concluded that the application of empty fruit bunch mulch provides the best drought mitigation benefits compared to the focal feeder applications and the combination application.

Assessment of environmental water strategies for improving fringing vegetation health by modelling vegetation condition in a connected floodplain lakes system

AbstractAcross the globe, environmental water has been allocated with the purpose of preserving the health and vitality of floodplain vegetation. However, the influences of environmental water volume and environmental water delivery strategies have not been studied widely because of shortage of on‐ground monitoring data. Remotely sensed data can bridge this gap by providing long‐term and continuous information; Landsat imagery from 1988 to 2020 was used in this research. We used the normalized difference vegetation index (NDVI) as an indicator of physiological condition of lake‐fringing trees on the Hattah Lakes floodplain, south‐east Australia. We employed the random forest (RF) regression method to model the relationship between NDVI and various climate and hydrological factors, such as the volume of water delivered to the connected lakes system as environmental water allocations or natural floods. The RF models performed well overall, with a mean R2 value of 0.73. The analysis identified the monthly total of environmental water delivered 3 months prior to the Landsat image date as a more crucial factor than natural floods over the same period for driving vegetation condition. Environmental water from 3 months previously exerts a positive influence on NDVI until the volume reaches a specific threshold. We have observed significant improvements in floodplain vegetation through the current environmental water strategy, particularly since the construction of pumping infrastructure in 2013. We suggest that managers aim to inundate the lake fringing area every 3 years, specifically from August to September, by delivering environmental water up to the modelled threshold volume. Finally, the use of infrastructure has proven to be an effective and efficient method for irrigating floodplain lakes, leading to improvements in vegetation condition while conserving water resources.

Estimating high-density aboveground biomass within a complex tropical grassland using Worldview-3 imagery

A large percentage of native grassland ecosystems have been severely degraded as a result of urbanization and intensive commercial agriculture. Extensive nitrogen-based fertilization regimes are widely used to rehabilitate and boost productivity in these grasslands. As a result, modern management frameworks rely heavily on detailed and accurate information on vegetation condition to monitor the success of these interventions. However, in high-density environments, biomass signal saturation has hampered detailed monitoring of rangeland condition. This issue stems from traditional broad-band vegetation indices (such as NDVI) responding to high levels of photosynthetically active radiation (PAR) absorption by leaf chlorophyll, which affects leaf area index (LAI) sensitivity within densely vegetative regions. Whilst alternate hyperspectral solutions may alleviate the problem to a certain degree, they are often too costly and not readily available within developing regions. To this end, this study evaluated the use of high-resolution Worldview-3 imagery in combination with modified NDVI indices and image manipulation techniques in reducing the effects of biomass signal saturation within a complex tropical grassland. Using the random forest algorithm, several modified NDVI-type indices were developed from all potential dual-band combinations of the Worldview-3 image. Thereafter, linear contrast stretching and histogram equalization were implemented in conjunction with Singular Value Decomposition (SVD) to improve high-density biomass estimation. Results demonstrated that both contrast enhancement techniques, when combined with SVD, improved high-density biomass estimation. However, linear contrast stretching, SVD, and modified NDVI indices developed from the red (630–690 nm), green (510–580 nm), and near-infrared 1 (770–895 nm) bands were found to produce the best biomass predictive model (R2 = 0.71, RMSE = 0.40 kg/m2). The results generated from this research offer a means to alleviate the biomass saturation problem. This framework provides a platform to assist rangeland managers in regionally assessing changes in vegetation condition within high-density grasslands.

The vegetation–topography heterogeneity coupling in the Loess Plateau, China

As a result of adaptation to the environment, the great environmental spatial heterogeneity leads to the high spatial heterogeneity of vegetation status. This coupling may be more apparent in water-limited drylands, where topography is the main determinant of small-scale variation in water availability and energy. Metrics describing this coupling may contribute to the detection of the extension of vegetation reshaped by human intervention and other driven forces. In this study, the heterogeneity index of coupling (HIC) was developed to indicate the coupling between spatial heterogeneity of vegetation status (Hv) and the spatial heterogeneity of topography (HT) in the Loess Plateau in northern China. The 16-day composed MODIS normalized vegetation index (NDVI) with a resolution of 250 m and SRTM DEM were employed to quantify the heterogeneity of vegetation status and the topographical heterogeneity. The results show that HIC varies among geomorphic zones, land cover types, and land cover change categories. Among all land cover types, HIC of sandy areas was the largest, followed by the HIC of the forest, shrub, farmland, and grassland. Among geomorphic zones, the highest HIC value appeared in plains with dense residential areas, followed by sandy land that is frequently reshaped by wind, rocky mountainous areas, hilly and gully loess plateaus, and loess tableland. It was revealed that the alternation of vegetation by human activities and natural disturbances shaped greater HIC. Results of this study approved the effectiveness of the HIC in reflecting the coupling of the vegetation status with topography at regional scale.

Investigating revealed preferences for urban waterway conditions: A hedonic property valuation study

AbstractThis study examines the impact of urban waterway conditions on property market prices. In general, similar revealed preference studies typically focus on identifying the value associated with changes in attributes such as riparian vegetation or water quality. Using an index that classifies waterways based on the vegetation and channel conditions, we analyse both attributes. Our spatial hedonic property price model findings indicate that buyers are willing to pay premiums ranging from 2.7% to 8.5%, depending on vegetation and channel conditions. However, when the proximity to the waterway is accounted for, we found that properties adjacent to the highest‐ranked vegetation and channel conditions attract a higher premium of 12.8%. Overall, the implicit marginal effects for the distance–condition interaction variables indicate that for lower‐ranked waterway conditions, there is a relative aversion to being adjacent to waterways. The results suggest that there are significant gains to be realised from removing concrete‐lined channels and replacing them with stones for banks, or re‐creating unmodified channels, even if there is only limited scope for increasing vegetation.

Seasonal movements of Black Coucals Centropus grillii in Nigeria

The scale of movement associated with the migration of many intra-African bird species is still poorly known even with the increasing availability of tracking devices. In this study, we tracked the movements of Black Coucals Centropus grillii breeding in Nigeria from late July using satellite telemetry. Individuals remained on the breeding site for several months; two individuals transmitted after October and these two moved shorter distances (< 100 km) away from the breeding site in early December. One of these was tracked for a full year, moving to a site 175 km south of the breeding site in early January and returning to the breeding site in late May. The bird migrated faster during the return journey (58 km day−1) as compared to the post-breeding journey (5.9 km day−1). The overall home range (90% kernel density) during breeding was 20.4 ± 3.3 km2 (mean ± s.d.) and the core (50%) 5.0 ± 1.6 km2 with no apparent clear change outside of the breeding season. Vegetation conditions at the distant site were apparently poorer although in an average year the move would have led to improved conditions. Short-distance seasonal migration of Black Coucals might be widespread in drier seasonal habitats.

Selected methods for determining inconclusively identifiable shorelines of watercourses and lakes

Abstract The determination of a lake or natural watercourse shoreline is the subject of various administrative proceedings relating to, among others, the engineering of riverbeds, construction of hydro-technical facilities, remediation work, land division, or delimitation of parcels. The provisions of law, while laying out the rules for determining shorelines, do not explicitly specify the measurement method to follow. All the more so, as many shores of lakes and watercourses are among terrain details that are difficult to measure due to their varied accessibility, which depends on the terrain, vegetation, and water conditions. The purpose of this paper is to compare selected methods for determining the shoreline of watercourses and lakes in terms of their applicability under different environmental conditions under current legislation. This study comprises an assessment of the suitability of the applied methods of shoreline measurement under varying field conditions and their applicability in surveying work on shoreline determination. Surveys were conducted on 3 reservoirs and one watercourse using geodetic, photogrammetric, and remote sensing techniques, and the suitability of the various methods was evaluated with respect to the field conditions of the measurements.

Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery

Abstract Climate change poses an urgent global challenge in water resource management, with drought emerging as a pervasive threat worldwide. Over the past two decades, Iraq has faced severe droughts, categorized into meteorological, agricultural, hydrological, and socioeconomic types. Agricultural drought, marked by prolonged soil moisture deficits due to insufficient rainfall, often leads to crop failures. This comprehensive study delves into the impact of drought on Iraq’s vegetation cover from 2000 to 2022, utilizing advanced tools like remote sensing (RS) and geographic information systems. The normalized difference vegetation index (NDVI) facilitated the creation of multitemporal drought maps. Employing Landsat satellite imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, the study revealed 2000, 2008, 2009, 2010, 2012, and 2022 as the most drought-prone years. In specific years such as 2000, 2008, 2010, and 2009, Landsat imagery showcased the lowest vegetation cover percentages (3.356, 4.984, 5.112, and 5.409%, respectively), while MODIS data indicated the lowest percentages in 2008, 2000, 2009, 2012, and 2022 (11.122, 11.260, 12.580, 13.026, and 14.445%, respectively). This study underscores the pivotal role of RS, particularly NDVI, as a valuable tool for agricultural drought early warning systems. The findings highlight the effectiveness of NDVI as a simple and cost-efficient index for monitoring changes in vegetation conditions and assessing the impact of droughts on agriculture.

EVALUATION OF VEGETATION COVER DYNAMICS IN A SUB-TROPICAL CLIMATE USING HIGH-RESOLUTION MULTISPECTRAL DATA FROM UAVS

The study of vegetation status is of great importance as it reveals environmental factors and their relationship with the availability of water resources, components that are relevant in the planning, drafting and execution of numerous civil engineering projects. Detecting changes in vegetation cover often requires data from space-based platforms, which may have limitations in terms of spatial or temporal resolution and high costs for acquiring high-resolution data. For relatively small areas, the use of Unmanned Aerial Vehicles (UAVs) provides a solution. These devices facilitate the acquisition of land cover information with high spatial and temporal resolution through the use of photographic cameras or specialized sensors. This research analyzes the vegetation status within a study area characterized by distinct climatic seasons (dry and humid) within a warm sub-tropical climate. The primary focus lies in detecting changes in vegetation cover and evaluating the overall state of the vegetation. To accomplish this objective, a multirotor UAV equipped with a multispectral camera that captures RED and NIR bands is deployed. Various vegetation indices based on distance and slope are applied to access the vegetation’s condition. The analysis of the results indicates that it is possible to evaluate the vegetation cover dynamics using the very high-resolution data obtained with the VANT approach (10 cm per pixel). Notably, the distance-based vegetation indices demonstrate superior efficacy in delineating the vegetation status and the stress induced by the scarcity of water resources during the transition between dry and humid seasons.

Vegetation and Dormancy States Identification in Coniferous Plants Based on Hyperspectral Imaging Data

Conifers are a common type of plant used in ornamental horticulture. The prompt diagnosis of the phenological state of coniferous plants using remote sensing is crucial for forecasting the consequences of extreme weather events. This is the first study to identify the “Vegetation” and “Dormancy” states in coniferous plants by analyzing their annual time series of spectral characteristics. The study analyzed Platycladus orientalis, Thuja occidentalis and T. plicata using time series values of 81 vegetation indices and 125 spectral bands. Linear discriminant analysis (LDA) was used to identify “Vegetation” and “Dormancy” states. The model contained three to four independent variables and achieved a high level of correctness (92.3 to 96.1%) and test accuracy (92.1 to 96.0%). The LDA model assigns the highest weight to vegetation indices that are sensitive to photosynthetic pigments, such as the photochemical reflectance index (PRI), normalized PRI (PRI_norm), the ratio of PRI to coloration index 2 (PRI/CI2), and derivative index 2 (D2). The random forest method also diagnoses the “Vegetation” and “Dormancy” states with high accuracy (97.3%). The vegetation indices chlorophyll/carotenoid index (CCI), PRI, PRI_norm and PRI/CI2 contribute the most to the mean decrease accuracy and mean decrease Gini. Diagnosing the phenological state of conifers throughout the annual cycle will allow for the effective planning of management measures in conifer plantations.

Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output

Abstract. Land–atmosphere (L–A) interactions are important for understanding convective processes, climate feedbacks, the development and perpetuation of droughts, heatwaves, pluvials, and other land-centered climate anomalies. Local L–A coupling (LoCo) metrics capture relevant L–A processes, highlighting the impact of soil and vegetation states on surface flux partitioning and the impact of surface fluxes on boundary layer (BL) growth and development and the entrainment of air above the BL. A primary goal of the Climate Process Team in the Coupling Land and Atmospheric Subgrid Parameterizations (CLASP) project is parameterizing and characterizing the impact of subgrid heterogeneity in global and regional Earth system models (ESMs) to improve the connection between land and atmospheric states and processes. A critical step in achieving that aim is the incorporation of L–A metrics, especially LoCo metrics, into climate model diagnostic process streams. However, because land–atmosphere interactions span timescales of minutes (e.g., turbulent fluxes), hours (e.g., BL growth and decay), days (e.g., soil moisture memory), and seasons (e.g., variability in behavioral regimes between soil moisture and latent heat flux), with multiple processes of interest happening in different geographic regions at different times of year, there is not a single metric that captures all the modes, means, and methods of interaction between the land and the atmosphere. And while monthly means of most of the LoCo-relevant variables are routinely saved from ESM simulations, data storage constraints typically preclude routine archival of the hourly data that would enable the calculation of all LoCo metrics. Here, we outline a reasonable data request that would allow for adequate characterization of sub-daily coupling processes between the land and the atmosphere, preserving enough sub-daily output to describe, analyze, and better understand L–A coupling in modern climate models. A secondary request involves embedding calculations within the models to determine mean properties in and above the BL to further improve characterization of model behavior. Higher-frequency model output will (i) allow for more direct comparison with observational field campaigns on process-relevant timescales, (ii) enable demonstration of inter-model spread in L–A coupling processes, and (iii) aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.

LANDSCAPE AND ECOLOGICAL POTENTIAL OF VACANT LANDS IN THE CITY OF ASTRAKHAN

The article provides a multicriterial assessment of urban vacant lands in the city of Astrakhan. Urban vacant lands are defined as undeveloped and unused sites, not attributed to green zones in the city plan. The purpose of the assessment is to recognize the most valuable vacant sites. Their conservation and rehabilitation could enhance the quality and attractiveness of urban environment, while less valuable sites could be recommended for urban development. The work evaluates four landscape functions that correspond to the main problems of the city: 1) runoff regulation function is assessed by the means of morphometric analysis of flow channels and field data on erosion and water logging; 2) climate regulation function is assessed using InVEST urban cooling model; 3) recreational function is evaluated by field and remote sensing data about the current state of vegetation on the vacant sites and by the demand of recreational zones estimated by walking time to the nearest park; 4) air pollution mitigation function is evaluated by the rate of environment child diseases and the density of green spaces in the city districts. Integral assessment of vacant sites helps to provide recommendations on their preferable use. Landscape and ecological potential of vacant lands varies significantly between different parts of the city. Vacant sites of the northeastern outskirts of the city, located within the lower part of the Volga delta, received the highest values, while the small sites in the city center and the industrial zone in the eastern outskirts received the lowest values. As a result, 2570 ha of vacant lands were recommended for greening and rehabilitation, and 327,9 ha for construction.

Aboveground Biomass Estimation and Time Series Analyses in Mongolian Grasslands Utilizing PlanetScope Imagery

Mongolia, situated in central Asia and bordered by Russia to the north and China to the south, experiences a semi-arid climate across most of its territory. Grasslands are pivotal in Mongolia’s agricultural sustainability and food security, facing rapid changes in the last two decades that underscore the ongoing need for innovative approaches to assess vegetation conditions. This study aims to evaluate grassland biomass measurement and prediction through the analysis of high-resolution satellite data. By conducting a time series assessment of grazing-induced changes in vegetation dynamics at the long-term monitoring sites of the Botanic Garden and Research Institute, Mongolian Academy of Sciences, we seek to refine our understanding. The investigation covers biomass estimation across various Mongolian grassland landscapes, encompassing desert, steppe, and mountain regions. Spanning the grassland growing season from May 2020 to October 2023, the research leveraged diverse ground data types, including surface reflectance measurements, geographic coordinates for satellite data correction, and aboveground dry biomass. These components were instrumental in developing a biomass estimation model reliant on establishing correlations between the satellite-derived Normalized Difference Vegetation Index and biomass. The predicted biomass facilitated the time series map analysis and dynamic analysis. The PlanetScope surface reflectance correlates strongly at 0.97 with field measurements, indicating robust relations. Biomass and the Normalized Difference Vegetation Index show correlations of 0.82 for dry grassland, 0.80 for mountain grassland, and 0.65 for desert grassland, with a combined correlation coefficient of 0.62, revealing distinct characteristics across these grasslands. Time series dynamic analysis reveals rising biomass differences between grazed and ungrazed areas, suggesting potential grassland degradation. Variations in the slope coefficient of biomass differences among grassland types indicate differing degradation patterns, emphasizing the need for effective grazing management practices to sustain and conserve Mongolian grasslands. This highlights the potential of remote sensing in monitoring and managing grassland ecosystems.

ANALISIS TINGKAT KERUSAKAN PANTAI AKIBAT BENCANA ABRASI DAN UPAYA PENANGANANNYA (PESISIR DESA STOWE BRANG)

Abrasion that occurs in Sumbawa Regency, Utan District, Stowe Brang Village is caused by wave factors, land characteristics which are generally in the form of peat, silt and sand are very vulnerable to erosion due to waves. In addition to natural factors, abrasion can also be blamed on human factors. The variables used in this study consist of 2 of them, namely: (1) Natural Factors, (2) Human Factors. The purpose of this study is to describe the coastal condition of Stowe Brang Village, Utan District, Sumbawa Regency. The analysis method used is Descriptive Analysis with a combination of Quantitative and Qualitative. To assess the condition of damage to coastal areas, it is necessary to survey the location of mangrove vegetation conditions, how far the coastline is to the pond, and the condition of the river estuary at the site. Furthermore, based on the value of the coastal damage index, the prioritization of handling refers to SE PU No. 11 of 2021. Based on the results of the analysis, there are several factors that affect the occurrence of abrasion at the study site, including factors still lack of vegetation on the beach, flat land slope factors, sandy beach type factors and the absence of coral reefs, sea wave factors that are large enough. To get wave plan data requires wind data for 10 years, tidal data, topographic data and bathymetry. Based on the results of data analysis, the wave height of the 10-year plan with the Gumbel method is 0.4 m. The form of handling efforts that can be done is the addition of mangrove vegetation and can also be done by building coastal protection constructions such as breakwater construction with plain concrete blocks

Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data

Climate change has led to various adverse consequences, with natural disasters being one of the most striking outcomes. Natural disasters negatively impact life, causing significant disruptions to the ecosystem. Prompt identification of affected areas and initiation of the rehabilitation process are imperative to address the disturbances in the ecosystem. Satellite imagery is employed for the rapid and cost-effective detection of damages caused by natural disasters. In this conducted study, the outputs of climate change wildfire, forest change detection, and drought analysis, have been examined, all of which worsens the impacts on the ecosystem. The analysis of drought involved using MODIS data, while Sentinel-2A satellite images were utilized to identify wildfire areas and changes in forested regions caused by windthrow. The research focused on Ganja, Azerbaijan, as the area for drought analysis. The driest June between 2005 and 2018 was assessed using the Vegetation Condition Index (VCI) in conjunction with data from the National Centers for Environmental Information (NOAA). At the Düzce Tatlıdere Forest Management Directorate, the Normalized Difference Red Edge Index (NDRE) was utilized between the years 2018 and 2019 to detect the changes occurring in forested areas due to windthrow. The NDRE synthetic band was added to satellite images for the years 2018 and 2019, and a Random Forest (RF) algorithm was employed to classify the data. The classification results were evaluated using Total Accuracy and Kappa statistics. The study includes the detection of the Normalized Burn Ratio (NBR) applied to determine the extent of the wildfire that occurred in the Solquca village of the Qabala region in Azerbaijan in 2021. According to the analysis of the VCI and NOAA, June 2014 was identified as the driest month in Ganja. In the Tatlıdere region, the analysis indicated that 4.22 hectares experienced reforestation, while 24 hectares experienced deforestation. The NBR analysis has revealed that ~1007 hectares of land were burned in the Solquca village of Qabala. The analyses conducted provide information regarding the use of satellite imagery in relation to changes in forest areas due to drought, wildfire, and windthrow.