Vegetation Distribution Research Articles

Evaluating the relationship of vegetation dynamics with rainfall and land surface temperature using geospatial techniques in South Wollo zone, Ethiopia

This study was conducted in the South Wollo zone to assess vegetation dynamics and their relationship with rainfall and land surface temperature. To that end, 16-day 250-meter resolution Moderate Imaging Spectro-radiometer (MODIS) Normalized difference vegetation Index (NDVI), 5-kilometer resolution MODIS Land Surface Temperature (LST), 5-kilometer CHIRPS data were used. A Mann-Kendall (MK) trend test was used to determine the trend of each dataset using seasonal and annual time series. In addition, the Pearson correlation coefficient was used to estimate the relationship between NDVI and climatic elements. The results of the study revealed that there was no significant change in the annual and seasonal rainfall and LST during the period 2000 to 2021, except LST in the autumn season (p = 0.01) and NDVI was significant only in the mean annual (p = 0.027) and summer seasons (p = 0.03). The correlation between NDVI and rainfall was positive (r = 0.84, 0.78, 0.72 & 0.79) and obtained for the summer, autumn, winter, and spring season respectively. Similarly, the correlation between NDVI and LST was negative (r= -0.79, -0.75, -0.72, and -0.78) in the summer, autumn, winter, and spring seasons respectively. The outcome of the study indicates precipitation is the most significant factor in the distribution of vegetation and LST

Convolutional transformer attention network with few-shot learning for grassland degradation monitoring using UAV hyperspectral imagery

ABSTRACT In recent years, the desertification of grasslands has increased due to various factors, including both global warming and human activities. It is an essential basis for grassland degradation monitoring to monitor the dynamic change of desert grassland vegetation communities and distribution statistics. Although unmanned aerial vehicle (UAV) remote sensing images have allowed us to achieve dynamic real-time grassland monitoring, the distribution of desert grassland ground objects can be random and narrow, thus increasing the difficulty of sample labelling of remote sensing imagery. Therefore, to reduce the number of samples required for the model, this research proposes a convolutional transformer attention network (CTAN) to identify desert grassland ground objects and validate it on a self-collected desert grassland dataset. The network utilizes the transformer model to enhance its learning of global pixels so that it suppresses the transmission of background pixels within the network. Furthermore, the edge convolution module is designed to strengthen the network’s learning for edge pixels, improving its identification effect. The results show that the network provides 97.22% of overall accuracy (OA), 94.35% of average accuracy (AA), and 0.9398 of Kappa for ground object recognition in desert grassland. The model improves OA by 2.36–9.85% points compared to methods in the same field and 0.8–6.35% points compared to methods in hyperspectral imagery classification. The experimental results show the superior performance of the CTAN model for recognizing desert grassland objects, which helps the management and restoration of desert grasslands.

The Influence of Vegetation on Climate Elements in Northwestern China

Vegetation plays a crucial role in maintaining the balance between nature, water and soil resources. However, understanding its impact mechanisms in arid and semi-arid areas remains limited. This study aims to analyze the spatial–temporal characteristics of the vegetation leaf area index (LAI) and climate elements in typical regions of northwest China and the correlations between LAI and climate elements; it also aims to explore the influence of regional vegetation growth on climate change. The results reveal significant correlations between LAI and various climate elements. Specifically, within the same region, surface temperature, precipitation, vegetation transpiration, and total evaporation show positive correlations with the LAI, whereas surface albedo shows a negative correlation. Vegetation may affect climate through both heat and water exchange between the land and atmosphere. Increased vegetation leads to the enhanced absorption of solar radiation by the land surface, elevating surface temperature. Increased levels of vegetation also increase vegetation transpiration and total evaporation, increasing the water vapor content in the atmosphere and thus leading to increased surface precipitation. Therefore, vegetation distribution plays a role in climate change, and ecological restoration projects in the northwest region hold significant potential for addressing ecological challenges in its arid and semi-arid areas.

Elevation gradients alter vegetation attributes in mountain ecosystems of eastern Himalaya, India

The present study describes how vegetation (the tree layer) is shaped along the elevation gradients in the eastern part of the Indian Himalayan Region. Various vegetation attributes, distribution, population structure, and regeneration patterns of 75 tree species belonging to 31 families were studied. Tree species richness shows a low plateau (peaked between 1,300–1,500 m) with a linearly decreasing pattern above 1,500 m asl. Ericaceae was found as the dominant family, followed by Lauraceae and Rosaceae. The distributional pattern of species-to-genera ratio (S/G) did not follow any particular trends, while β-diversity increased along the elevation gradient. The Margalef index of species richness, the Menheink index of species richness, and the Fisher alpha were found to be highest at lower altitudes (1,000–1,500 m), while the Simpson index was highest at middle altitudes (2,600–3,000 m). Random distribution was shown by maximum tree species (47.3%), followed by a contagious distribution (42.9%), and regular distribution (10.8%). The regeneration of tree species was found to be good with a healthier number of seedlings (10.2%), fair (43.5%), poor (30.3%), while 16% were observed not regenerating. Acer laevigatum (1,500 m), Prunus nepalensis (3,300 m), Viburnum sympodiale (3,400 m) were among the new regenerating species at the respective altitudes. The population structure of tree species in terms of proportion of individuals in seedlings, saplings, and the adult class varied in all the elevation transects. Species with better regeneration on upper distribution limits have been recognized as probable for upward movement.

Climate changes impact on the distribution of vegetation in Wasit and Nineveh regions of Iraq

Climate changes have a direct or indirect impact on many vital systems, including human and animal, as well as vegetation. The monthly precipitation and temperature for the period (1981-2021) and vegetation images (NDVI) for the period (2000-2022) from the satellite (NASA) for the regions of Ninevah and Wasit of Iraq were used to find out their variations over the space and time. It was found that the temperature was increasing with time, but the precipitation was in a state of turbulent increase in the two study areas. The distribution of vegetation was also in a state of change with time as well as within a region. The vegetation area increased with increase in precipitation which was greater in the Ninevah region than in the Wasit region. When there was a lack of precipitation, the vegetation cover area decreased in the two study areas. The increase in temperature also resulted in a decrease in the density and area of vegetation. It was found that the change in the amount of precipitation was more influential than the change in temperature on the vegetative distribution.

Wetland Ecosystem: Plant Species Diversity, Services, Degradation Drivers, and Community Perception in Sinana District, Oromia Region, Southeast Ethiopia

Wetlands are a vital source of biodiversity and ecosystem services. The study investigated the plant species diversity and assessed the perception of the ecosystem services of the area and drivers of wetland degradation in Sinana district, Southeast Ethiopia. Vegetation inventory, household surveys, focused group discussions, and key informant interviews were employed to gather information. A total of 45 sample plots laid along transacts were inventoried. A plot size of 5 m × 5 m (25 m2) and 1 m × 1 m (1 m2) nested within the major plot was used for shrubs and herbs, respectively. A total of 137 households were surveyed to collect socioeconomic data. The study identified 20 plant species belonging to 14 families. Family Cyperaceae was dominant within the studied wetland. The Shannon diversity (H=1.15) indicates that the wetland has low vegetation diversity with an uneven distribution (E=0.385) of vegetation. A total of 20 ecosystem services thought to be underprovisioning, regulating, and cultural services were identified. According to plaintiffs, major provision services are grazing livestock (77.4%), irrigation (76.6%), and harvesting of grass for fodder (68.6%). Important drivers of wetland degradation are a shortage of cropland (70.8%), lack of awareness (69.3%), upland land degradation (65.7%), and increasing population (62%). The main driver, a shortage of cropland, was the key driver, followed by a lack of awareness and upland land degradation. Therefore, the result heightened that the studied wetland is under serious degradation due to high human pressure associated with population growth and climate change. Thus, an appropriate wetland management strategy must be designed.

Tree Crown Detection using UAV-captured High-Resolution Aerial Images for Baghdad University Campus

The research paper's objective is to develop an automated system that uses high-resolution aerial images captured by Unmanned Aerial Vehicles (UAVs) to accurately detect and analyze tree crowns on the Baghdad University campus. This analysis of tree crowns is crucial for assessing vegetation health and distribution in urban areas, with applications in urban planning, ecology, and environmental studies. The proposed system utilizes computer vision and machine learning techniques to detect and delineate tree crowns, facilitating objective analysis of tree canopy coverage. The study employed a Phantom Four drone flying at a height of 55 meters with an 80% overlap in image coverage. Ground control points were established using the Topcon_HIPER2 device and Static GPS Surveying Techniques. The flight plan was designed with PIX4Dcapture software, and the resulting ortho-map had a 2 cm resolution. The research used the YOLOv3 artificial intelligence technique for tree detection after training a customized dataset. The model achieved impressive performance metrics, including a low loss function value of 0.023, a high SDC value of 0.984, and a robust Jaccard Index value of 0.943.

Assessing vegetation distribution based on geometrical and morphological characteristics of the urban fabric to provide thermal comfort for pedestrians: A case study in Sanandaj

In previous studies, geometrical aspects of the buildings have been mainly considered in designing of the models, while the impacts of vegetation distribution have not been taken into account. The influence of building geometry on vegetation distribution to create a comfortable thermal environment for pedestrians has not received much attention. An examination of Sanandaj, a city in western Iran, in three fabrics, closed, linear, and open, has been conducted in this study. By using ENVI-met simulator, we studied the orientations, height-to-distance ratio of the buildings, and the geometric shapes of vegetation distributions. There have been two phases to the current research. The first phase includes evaluating the factors that may affect the modeling results in addition to validating ENVI-met software in selected fabrics. A comparison is also made of three model fabrics with and without vegetation in the first phase. The most important environmental factors in this research are air temperature, radiant temperature, wind speed, and physiological equivalent temperature (PET). The results show that there is a positive correlation between orientation and the level of comfort. In a closed fabric, the model with an east-west direction, height ratio of 0.12 and wholly vegetation distribution showed 15.31 % improvement in comfort level compared to the original fabric. The linear fabric with an east-west direction, height ratio of 0.42 and a central vegetation distribution indicated a 22.53 % improvement in the comfort level and in the open fabric, the model with height ratio of 0.75 and the central vegetation distribution improved comfort by 10.42 %.

Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets

As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500 m) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future.

Study of plant biodiversity and spatial distribution of spontaneous vegetation in semi-arid region (Tebessa Province. Northeast Algeria)

The semi-arid is a climate characterized by precipitation that is. insufficient to maintain crops and where evaporation often exceeds rainfall. Vegetation is one of the most sensitive indicators of environmental changes understanding the patterns of biodiversity distribution and what influences them is a fundamental pre-requisite for effective conservation and sustainable utilization of biodiversity. In this study. our focus was on examining the vegetation diversity in the semi-arid region of Tebessa. which falls within the Eastern Saharan Atlas domain in North Africa’s semi-arid zone. Plants were sampled at 15 sites distributed across the study area. The quadrat method was used to conduct floral surveys. The sampling area of each sample was 100 square meters 10 m × 10 m (quadrat). Each quadrat was measured for species richness (number of species). abundance (number of individuals). and Richness generic (plant cover). Based on the floristic research. 48 species were found. classified into 21 families. with Asteraceae accounting for 34.69% of the species and Poaceae accounting for 14.28%.

Sparse vegetation height estimation based on non‐local sample selection with generalised inner product

AbstractWith the assumption of densely distributed vegetation in PolInSAR processing, the height parameters can be inversed by the conventional random volume over ground (RVoG) method. During the procedure of RVoG, the samples used to estimate the PolInSAR coherence are selected directly from the neighbouring areas. However, for sparsely distributed vegetation, the scattering statistics are different from those of densely distributed vegetation. Therefore, the inversion performance will be deteriorated if the samples are selected directly in the neighbouring areas. A new phase‐based method is proposed to select samples, whose positions represent the volume scattering pixels, for sparsely distributed vegetation height inversion. By analysing the scattering characteristics of sparsely distributed vegetation, the processing data vector is formulated based on the amplitude‐normalised interferograms. With the PolSAR classification, the generalised inner product (GIP) is iteratively used to select the non‐local samples based on the formulated phase data vectors, which are utilised for sparsely distributed vegetation height inversion. For the selected sample sets, the vegetation distribution can be approximately regarded as “dense distribution”, and then the vegetation height parameters can be inversed by RVoG method. Compared to the height inversion performance based on different sample selection methods, the effectiveness of the proposed method is validated by the PolSARPro simulated data and the real airborne L‐band PolInSAR data.

Alpine vegetation community patterns in the Khumbu region, Nepalese Himalaya

ABSTRACT The Himalayan alpine zone (HAZ)—a high-altitude zone above approximately 4,100 m.a.s.l., is projected to experience strong eco-environmental changes with climate change. As plants expand their range in this region, other processes are likely to be impacted; for example, flows and stores of water. A first vital step in conceptualizing HAZ ecohydrology is to understand the distribution pattern of HAZ vegetation communities. Satellite remote sensing provides one means of doing so, but the often patchy distribution of alpine vegetation creates challenges when using coarse-grained satellite data whose pixels are typically coarser than the grain of vegetation pattern. Here we use fine spatial resolution satellite imagery from WorldView-2 (2 m2 per pixel) coupled with elevation model data from the Copernicus GLO-30 product to produce a land cover classification for the HAZ. Field data captured during in situ surveys in the Gokyo valley, Nepal, were used to drive and then test a random forest classifier. Grassy meadows and dwarf shrubs belonging to the Rhododendron and Juniperus families dominate the ecology of the alpine zone in this region, so we created three vegetation classes for mapping indicative major plant communities dominated by these species. We found that altitude and aspect were dominant drivers of vegetation distribution in the HAZ and that the average vegetation cover of Rhododendron spp. and Juniperus spp. reduces with increasing altitude, as expected. South- and east-facing slopes were dominated by Juniperus spp., whereas north- and west-facing slopes were dominated by Rhododendron spp., and the growth extent of Rhododendron spp. (between 4,010 and 4,820 m.a.s.l.) and meadow (between 4,010 and 4,680 m.a.s.l.) were vertically wider than that of Juniperus spp. (between 4,010 and 4,660 m.a.s.l.). Results from this study demonstrate the vegetation distribution pattern in HAZ at the plant community level and provide an impetus for further studies that seek to understand ecohydrological interactions between dwarf plants and water flows and stores in the HAZ.

Modelling of potential vegetation identifies diverging expectable outcomes of river floodplain widening

River re-naturalisations are at the forefront of conservation efforts. The hope is that these interventions will benefit both local ecosystems and facilitate flood mitigation. While hydrological modelling has been a standard procedure in assessing the outcomes of river re-naturalisations, vegetation modelling has not always been performed as part of these assessments. We hypothesised that the use of potential vegetation modelling, i.e. the modelling of self-sustainable vegetation that can survive after the intervention, can provide insight into vegetation outcome of river re-naturalisation and can thus support vegetation restoration and conservation planning. We investigated the utility of potential vegetation modelling under a specific, widespread element of river re-naturalisation: river floodplain widening at three study sites along the Tisza River in Hungary. We applied potential vegetation modelling, in addition to hydrological and groundwater modelling, to assess the expected vegetation outcome of the river floodplain widening. Flood frequency and duration were assessed by the HEC-RAS hydrological model. Based on the output this hydrological model provided, expected values of the water-related explanatory variables (including groundwater level) were calculated. Statistical relationships encompassed by the existing mulitple potential vegetation (MPV) models of Hungary were applied to the environmental variable sets corresponding to conditions before and after floodplain widening (pre- and post-treatment, respectively), including water-related and other explanatory variables. This resulted in predicted potential vegetation distribution for pre-treatment and post-treatment conditions, which were then compared via ordinations and PERMANOVA. At two of the study sites, post-treatment potential vegetation prediction typically showed vegetation types requiring wetter and less saline conditions when compared to the pre-treatment potential vegetation distribution. This pattern corresponds to general expectations given river floodplain widening. However, at one of the sites, the potentiality of saline and non-saline steppe vegetation was actually more pronounced under the expected post-treatment conditions than those before widening. The strengthening of the potentiality of dry vegetation types can be explained by the minor environmental differences related to the microrelief. As the studied sites were all located in the lowlands where geomorphological variation is small, the effect of these minor geomorphological differences on post-treatment potential vegetation would have remained hidden without applying MPV models. In conclusion, scenarios that employ MPV models help predict river restoration outcomes more accurately and can help identify factors that might otherwise be overlooked. Thus, when combined with physical modelling of river flow, their use can aid in the restoration and landscape planning decisions in river re-naturalisation projects.

Exploring the Influence of Slope Gradient on Tree Species Richness and Forest Structure

Slope gradient is a critical environment factor influencing the abundance, distribution and diversity of vegetation. The objective of this study was to exploring the influence of slope gradient on tree species richness and forest structure in Kpatawee forest, Liberia. Tree diameter was measured at breast height for species more than 5 cm in diameter and over 3 m in height. Tree species diversity was analyzed using Shannon diversity index (Hˈ). Basal area (BA) which is the cross-sectional area of tree stems was measured through the diameter at breast height that is 1.3 m above ground level. Results showed that a total of 26 tree species were identified from the total sampling area (0.36 ha). The three richest families: Moraceae, Rutaceae and Euphorbiaceae appear to be well represented in the Kpatawee forest. These families become outnumbered in the herbaceous except for the Rutaceae family, which is ranked second. This is mainly due to their adaptation potential to wider agro-ecologies. Tree species richness was higher in the lower altitude. High species abundance was recorded in the lower altitude followed by the middle and upper. The lowest Shannon diversity index value (H' = 1.85) and the highest value (H' = 2.28) were recorded in the upper, and the lower altitudes, respectively. Despite their values, the biodiversity did not vary greatly from altitude to altitude. Diameter class distribution showed that more species were in the lower diameter classes and decreased gradually towards the higher classes. It is recommended that further studies on the entire Kpatawee forest is needed to fully understand the species richness in the forest.

Low-Cost Plant-Protection Unmanned Ground Vehicle System for Variable Weeding Using Machine Vision.

This study presents a machine vision-based variable weeding system for plant- protection unmanned ground vehicles (UGVs) to address the issues of pesticide waste and environmental pollution that are readily caused by traditional spraying agricultural machinery. The system utilizes fuzzy rules to achieve adaptive modification of the Kp, Ki, and Kd adjustment parameters of the PID control algorithm and combines them with an interleaved period PWM controller to reduce the impact of nonlinear variations in water pressure on the performance of the system, and to improve the stability and control accuracy of the system. After testing various image threshold segmentation and image graying algorithms, the normalized super green algorithm (2G-R-B) and the fast iterative threshold segmentation method were adopted as the best combination. This combination effectively distinguished between the vegetation and the background, and thus improved the accuracy of the pixel extraction algorithm for vegetation distribution. The results of orthogonal testing by selected four representative spraying duty cycles-25%, 50%, 75%, and 100%-showed that the pressure variation was less than 0.05 MPa, the average spraying error was less than 2%, and the highest error was less than 5% throughout the test. Finally, the performance of the system was comprehensively evaluated through field trials. The evaluation showed that the system was able to adjust the corresponding spraying volume in real time according to the vegetation distribution under the decision-making based on machine vision algorithms, which proved the low cost and effectiveness of the designed variable weed control system.

Spatio-Temporal Dynamics of Normalized Difference Vegetation Index and Its Response to Climate Change in Xinjiang, 2000–2022

Based on the NDVI and climate data from 2000 to 2022, this study systematically investigated the spatial and temporal patterns, trend characteristics, and stability of the NDVI in Xinjiang using the one-way linear regression method, Theil–Sen Median trend analysis, the Mann–Kendall significance test, and the coefficient of variation. Meanwhile, the persistence of the NDVI distribution was analyzed by combining the trend results and Hurst index. Finally, partial correlation analysis was used to deeply explore the response mechanisms of interannual and seasonal-scale NDVI and climatic factors in Xinjiang, and the characteristics of multi-year vegetation distribution were comprehensively analyzed with the help of human footprint data. The findings indicate the following: (1) The NDVI of interannual and seasonal vegetation in Xinjiang showed a significant increasing trend during the 23-year period, but the spatial distribution was heterogeneous, and the improvement of the vegetation condition in the southern part of the region was remarkable. (2) The NDVI is relatively stable across the region. Unlike in other regions, in general, it is difficult to maintain the existing trend in NDVI in the study area for a long period of time, and the reverse trend is more persistent. (3) On the interannual scale, both precipitation and temperature are positively correlated with the NDVI, and the influence of temperature (80.94%) is greater than that of precipitation (63.82%). Precipitation was dominantly positively correlated with the NDVI in spring, summer, and the growing season, while it was negatively correlated with it in autumn. Temperature and NDVI were positively correlated, with the greatest influence in the spring. (4) Human activities had the greatest impact on the areas with low vegetation cover and areas with medium–low vegetation cover, and there was a high degree of overlap between the areas where the interannual human footprints and NDVI showed an increasing trend. The percentage of human footprints that significantly correlated with interannual NDVI was 34.79%. In the future, the protection and management of ecologically fragile areas should be increased to increase desert-vegetation cover.

Evaluasi Kesesuaian RTH terhadap RTRW Kota Pariaman

This research is related to assessing the reasonableness of the area of green open space (RTH) towards the development strategy of Pariaman City as stated in the Regional Regulation (Perda) of the Regional Spatial Planning (RTRW) of Pariaman City for 2010-2030, namely 30% and aims to, 1) Find out distribution and extent of availability of green open space (RTH) in Pariaman City. 2) Knowing the suitability between green open space (RTH) and Regional Spatial Planning (RTRW). This research is a quantitative exploration using spatial methodology. Using Sentinel-2 satellite imagery and the Normalized Difference Vegetation Index (NDVI) identification method, the analysis used focuses on the distribution of green vegetation throughout Pariaman City. The vegetation distribution obtained was overlaid with shapefile data (SHP) obtained from the digitization of the Regional Regulations (Perda) of the Regional Spatial Planning (RTRW) of Pariaman City for 2010-2030, so that the area, distribution and suitability are known. The results of identifying 3946.09 Ha of green open space (RTH) from 4118.76 Ha which were planned in detail, namely; City Park RTH, planned area 3214.16 Ha and area in the field 3087.12 Ha, City Forest RTH, planned area 174.8 Ha and area in the field 174.23 Ha, Cemetery Open Space, planned area 4.72 Ha and area in field 4.67 Ha, RTH Road Route, planned area 504.14 Ha and area in the field 472.53 Ha and RTH Tourism Area, planned area 220.94 Ha and area in the field 207.54 Ha. The percentage of Pariaman City's green open space to the area is 59.57% and has fulfilled the provisions of the RTRW Regional Regulation, namely a minimum green open space of 30%.

Resilience of an Urban Coastal Ecosystem in the Caribbean: A Remote Sensing Approach in Western Puerto Rico

Utilization of remote sensing-derived meteorological data is a valuable alternative for tropical insular territories such as Puerto Rico (PR). The study of ecosystem resilience in insular territories is an underdeveloped area of investigation. Little research has focused on studying how an ecosystem in PR responds to and recovers from unique meteorological events (e.g., hurricanes). This work aims to investigate how an ecosystem in Western Puerto Rico responds to extreme climate events and fluctuations, with a specific focus on evaluating its innate resilience. The Antillean islands in the Caribbean and Atlantic are vulnerable to intense weather phenomena, such as hurricanes. Due to the distinct tropical conditions inherent to this region, and the ongoing urban development of coastal areas, their ecosystems are constantly affected. Key indicators, including gross primary production (GPP), normalized difference vegetation index (NDVI), actual evapotranspiration (ET), and land surface temperature (LST), are examined to comprehend the interplay between these factors within the context of the Culebrinas River Watershed (CRW) ecosystem over the past decade during the peak of hurricane season. Data processing and analyses were performed on datasets provided by Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8–9 OLI TRIS, supplemented by information sourced from Puerto Rico Water and Energy Balance (PRWEB)—a dataset derived from Geostationary Operational Environmental Satellite (GOES) data. The findings revealed a complex interrelationship among atmospheric events and anthropogenic activities within the CRW, a region prone to recurrent atmospheric disruptions. NDVI and ET values from 2015 to 2019 showed the ecosystem’s capacity to recover after a prolonged drought period (2015) and Hurricanes Irma and Maria (2017). In 2015, the NDVI average was 0.79; after Hurricanes Irma and Maria in 2017, the NDVI dropped to 0.6, while in 2019, it had already increased to 0.8. Similarly, average ET values went from 3.2339 kg/m2/day in 2017 to 2.6513 kg/m2/day in 2018. Meanwhile, by 2019, the average ET was estimated to be 3.8105 kg/m2/day. Data geoprocessing of LST, NDVI, GPP, and ET, coupled with correlation analyses, revealed positive correlations among ET, NDVI, and GPP. Our results showed that areas with little anthropogenic impact displayed a more rapid and resilient restoration of the ecosystem. The spatial distribution of vegetation and impervious surfaces further highlights that areas closer to mountains have shown higher resilience while urban coastal areas have faced greater challenges in recovering from atmospheric events, thus showing the importance of preserving native vegetation, particularly mangroves, for long-term ecosystem stability. This study contributes to a deeper understanding of the dynamic interactions within urban coastal ecosystems in insular territories, emphasizing their resilience in the context of both natural atmospheric events and human activity. The insights gained from this research offer valuable guidance for managing and safeguarding ecosystems in similar regions characterized by their susceptibility to extreme weather phenomena.

Analyzing anthropogenic determinants of vegetation distributions pattern in high-elevation forests of the Himalayas

Analyzing anthropogenic determinants of vegetation distributions pattern in high-elevation forests of the Himalayas

Assessing the Distribution and Driving Effects of Net Primary Productivity along an Elevation Gradient in Subtropical Regions of China

Globally, forest ecosystems, especially subtropical forests, play a central role in biogeochemical cycles and climate regulation, demonstrating their irreplaceable function. The subtropical region of China, characterized by its unique forest ecosystem, complex terrain, climate heterogeneity, diverse vegetation types, and frequent human activities, underscores the importance of the in-depth study of its net primary productivity (NPP). This paper employs the eddy covariance–light use efficiency (EC-LUE) model to quantitatively estimate the gross primary productivity (GPP) of this region from 2001 to 2018, followed by an estimation of the actual net primary productivity (ANPP) using the carbon use efficiency (CUE). The results showed that over these 18 years, the annual average ANPP was 677.17 gC m−2 a−1, exhibiting an overall increasing trend, particularly in mountainous areas, reserves, and the cultivated lands of the northeastern plains, whereas a significant decrease was observed around the urban agglomerations on the southeast coast. Furthermore, the Thornthwaite memorial model was applied to calculate the potential net primary productivity (PNPP), and diverse scenarios were set to quantitatively evaluate the impact of climate change and human activities on the vegetation productivity in the study area. It was found that in areas where the ANPP increased, both human activities and climate change jointly influenced ANPP dynamics; in areas with a decreased ANPP, the impact of human activities was particularly significant. Additionally, the heterogeneous distribution of ANPP across different altitudinal gradients and the driving effects of various climatic factors were analyzed. Finally, a partial correlation analysis was used to examine the relationships between the temperature, precipitation, and ANPP. This study indicated that temperature and precipitation have a substantial impact on the growth and distribution of vegetation in the region, yet the extent of this influence shows considerable variation among different areas. This provides a robust scientific basis for further research and understanding of the carbon dynamics of subtropical forest ecosystems and their role in the global carbon cycle.