Dense Vegetation Cover Research Articles

Relationship between vegetation cover and land surface temperature in Basra, Iraq

This study aims to assess the changes in vegetation cover and its relationship with land surface temperature from 1990 to 2024 in Basra Governorate, southern Iraq. Satellite images from Landsat 5 and 8, in addition to remote sensing and GIS tools, were used to analyze the changes in vegetation cover and their effects on land surface temperature (LST). Non-supervisory classification based on normalized difference vegetation index (NDVI) threshold values was used to classify vegetation cover into four classes. The results showed a significant decrease in dense vegetation cover from 4.8% in 1990 to 1.0% in 2024. It was also noted that non-vegetation areas increased, rising from 67.4% in 1990 to 79.3% in 2024. In winter, dense vegetation decreased from 4.5% in 1990 to 0.8% ​​in 2024, and non-vegetation areas increased from 67.5% to 79.7%. In addition, the coefficient of determination decreased from 0.11 in 1990 to 0.06 in 2024, indicating a decline in the effect of vegetation cover on the surface temperature of the earth due to rapid urban expansion, which contributes to climate change. The study emphasizes the need to develop strategies to preserve the environment and reduce the effects of desertification and climate change in Basra.

Analysis of the Spatiotemporal Characteristics of Gross Primary Production and Its Influencing Factors in Arid Regions Based on Improved SIF and MLR Models

In this study of constructing gross primary production (GPP) based on solar-induced chlorophyll fluorescence (SIF) and analyzing its spatial–temporal characteristics and influencing factors, numerous challenges are encountered, especially in arid regions with fragile ecologies. Coupling SIF with other factors to construct the GPP and elucidating the influencing mechanisms of environmental factors could offer a novel theoretical method for the comprehensive analysis of GPP in arid regions. Therefore, we used the GPP station data from three different ecosystems (grasslands, farmlands, and desert vegetation) as well as the station and satellite data of environmental factors (including photosynthetically active radiation (PAR), a vapor pressure deficit (VPD), the air temperature (Tair), soil temperature (Tsoil), and soil moisture content (SWC)), and combined these with the TROPOMI SIF (RTSIF, generated through the reconstruction of SIF from the Sentinel-5P sensor), whose spatiotemporal precision was improved, the mechanistic light reaction model (MLR model), and different weather conditions. Then, we explored the spatiotemporal characteristics of GPP and its driving factors in local areas of Xinjiang. The results indicated that the intra-annual variation of GPP showed an inverted “U” shape, with the peak from June to July. The spatial attributes were positively correlated with vegetation coverage and sun radiation. Moreover, inverting GPP referred to the process of estimating the GPP of an ecosystem through models and remote sensing data. Based on the MLR model and RTSIF, the inverted GPP could capture more than 80% of the GPP changes in the three ecosystems. Furthermore, in farmland areas, PAR, VPD, Tair, and Tsoil jointly dominate GPP under sunny, cloudy, and overcast conditions. In grassland areas, PAR was the main influencing factor of GPP under all weather conditions. In desert vegetation areas, the dominant influencing factor of GPP was PAR on sunny days, VPD and Tair on cloudy days, and Tair on overcast days. Regarding the spatial correlation, the high spatial correlation between PAR, VPD, Tair, Tsoil, and GPP was observed in regions with dense vegetation coverage and low radiation. Similarly, the strong spatial correlation between SWC and GPP was found in irrigated farmland areas. The characteristics of a low spatial correlation between GPP and environmental factors were the opposite. In addition, it was worth noting that the impact of various environmental factors on GPP in farmland areas was comprehensively expressed based on a linear pattern. However, in grassland and desert vegetation areas, the impact of VPD on GPP was expressed based on a linear pattern, while the impact of other factors was more accurately represented through a non-linear pattern. This study demonstrated that SIF data combined with the MLR model effectively estimated GPP and revealed its spatial patterns and driving factors. These findings may serve as a foundation for developing targeted carbon reduction strategies in arid regions, contributing to improved regional carbon management.

Ecosystem water use efficiency and carbon use efficiency respond oppositely to vegetation greening in China's Loess Plateau.

Ecosystem water use efficiency and carbon use efficiency respond oppositely to vegetation greening in China's Loess Plateau.

Improving Forest Canopy Height Mapping in Wuyishan National Park Through Calibration of ZiYuan-3 Stereo Imagery Using Limited Unmanned Aerial Vehicle LiDAR Data

Forest canopy height (FCH) is a critical parameter for forest management and ecosystem modeling, but there is a lack of accurate FCH distribution in large areas. To address this issue, this study selected Wuyishan National Park in China as a case study to explore the calibration method for mapping FCH in a complex subtropical mountainous region based on ZiYuan-3 (ZY3) stereo imagery and limited Unmanned Aerial Vehicle (UAV) LiDAR data. Pearson’s correlation analysis, Categorical Boosting (CatBoost) feature importance analysis, and causal effect analysis were used to examine major factors causing extraction errors of digital surface model (DSM) data from ZY3 stereo imagery. Different machine learning algorithms were compared and used to calibrate the DSM and FCH results. The results indicate that the DSM extraction accuracy based on ZY3 stereo imagery is primarily influenced by slope aspect, elevation, and vegetation characteristics. These influences were particularly notable in areas with a complex topography and dense vegetation coverage. A Bayesian-optimized CatBoost model with directly calibrating the original FCH (the difference between the DSM from ZY3 and high-precision digital elevation model (DEM) data) demonstrated the best prediction performance. This model produced the FCH map at a 4 m spatial resolution, the root mean square error (RMSE) was reduced from 6.47 m based on initial stereo imagery to 3.99 m after calibration, and the relative RMSE (rRMSE) was reduced from 36.52% to 22.53%. The study demonstrates the feasibility of using ZY3 imagery for regional forest canopy height mapping and confirms the superior performance of using the CatBoost algorithm in enhancing FCH calibration accuracy. These findings provide valuable insights into the multidimensional impacts of key environmental factors on FCH extraction, supporting precise forest monitoring and carbon stock assessment in complex terrains in subtropical regions.

Characterization of daytime resting sites of New England and eastern cottontails

For cottontails (genus: Sylvilagus), suitable resting cover provides essential food and protection, leading to higher survival rates. As such, resting cover is a common target for management actions. In the Northeastern United States, efforts to conserve New England cottontails (Sylvilagus transitionalis) have focused on habitat restoration, yet their resting cover needs remain uncharacterized. Current management practices for resting cover borrow from guidelines from eastern cottontails (Sylvilagus floridanus), risking unintentionally favoring this non-native competitor. Furthermore, invasive plants alter the composition and structure of resting habitat potentially impacting species like cottontails that rely on shrubs for forage and protection. This study characterized daytime resting site use for New England and eastern cottontails and examined the impacts of resting site use on their survival. We specifically examined factors contributing to use of a common, potentially harmful invasive shrub, Japanese barberry (Berberis thunbergii) for both species. We showed both species used a diversity of resting cover types and use was similar between species. Cottontails relied heavily on vegetative cover, with dense vegetation used more frequently than less dense vegetative cover. Invasive/ non-native plant species were used as resting cover more than other cover types. Use of Japanese barberry appeared to be linked to the lack of other suitable native cover and was associated with reduced survival. We demonstrated that recommendations for eastern cottontail resting cover may suit New England cottontails and emphasize the complex tradeoffs inherent in invasive species management. Invasive shrub cover provides crucial resources in the absence of other cover but may also increase vulnerability of at-risk populations.

Monitoring Drought Using Spectral Drought Indices: A Case Study Canaan Forest Area, Diyala, Iraq

Drought determination is essential for many environmental and agricultural applications. To further understand drought, this study presented the spatio-temporal variations of drought based on the Landsat 8 index as a vegetation condition index (VCI)., vegetation health index (VHI), Thermal condition index (TCI), Water Supply Vegetation Index (WSVI) and normalized vegetation indices (NDVI) in the Canaan forest (mana-made forest) area within the administrative boundaries of the Canaan district of Diyala Governorate for the years 2016, 2019 and 2023.The results showed a clear difference between the years of study in terms of the spatial distribution of drought degrees (very severe, severe, moderate, light and non-dry). The NDVI maps also showed that there is a clear decrease in the density and areas of vegetation cover in the study area due to drought, which requires taking the necessary precautions to reduce the negative effects of drought with the help of remote sensing techniques because they shorten time and cost. According to NDVI, the vegetation cover occupied an area of 7.438 km2 for the year 2016, while the area of 5.735 km2 was for 2023. The Extreme Drought class occupied an area of 44.180 km2 in 2016, while it was 44.250 km2 in 2023 according to the VCI. In general, the year 2023 was drier than the rest of the study years.

Mapping Soil Properties in Tropical Rainforest Regions Using Integrated UAV-Based Hyperspectral Images and LiDAR Points

For tropical rainforest regions with dense vegetation cover, the development of effective large-scale soil mapping methods is crucial to improve soil management practices to replace the time-consuming and laborious conventional approaches. While machine learning (ML) algorithms demonstrate superior predictability of soil properties over linear models, their practical and automated application for predicting soil properties using remote sensing data requires further assessment. Therefore, this study aims to integrate Unmanned Aerial Vehicles (UAVs)-based hyperspectral images and Light Detection and Ranging (LiDAR) points to predict the soil properties indirectly in two tropical rainforest mountains (Diaoluo and Limu) in Hainan Province, China. A total of 175 features, including texture features, vegetation indices, and forest parameters, were extracted from two study sites. Six ML models, Partial Least Squares Regression (PLSR), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Decision Trees (GBDT), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP), were constructed to predict soil properties, including soil acidity (pH), total nitrogen (TN), soil organic carbon (SOC), and total phosphorus (TP). To enhance model performance, a Bayesian optimization algorithm (BOA) was introduced to obtain optimal model hyperparameters. The results showed that compared with the default parameter tuning method, BOA always improved models’ performances in predicting soil properties, achieving average R2 improvements of 202.93%, 121.48%, 8.90%, and 38.41% for soil pH, SOC, TN, and TP, respectively. In general, BOA effectively determined the complex interactions between hyperparameters and prediction features, leading to an improved model performance of ML methods compared to default parameter tuning models. The GBDT model generally outperformed other ML methods in predicting the soil pH and TN, while the XGBoost model achieved the highest prediction accuracy for SOC and TP. The fusion of hyperspectral images and LiDAR data resulted in better prediction of soil properties compared to using each single data source. The models utilizing the integration of features derived from hyperspectral images and LiDAR data outperformed those relying on one single data source. In summary, this study highlights the promising combination of UAV-based hyperspectral images with LiDAR data points to advance digital soil property mapping in forested areas, achieving large-scale soil management and monitoring.

Greening the arid: the impact of urban vegetation on the outdoor thermal comfort in hot and dry city

This article examines the impact of urban vegetation on outdoor thermal comfort in the hot and dry city of Biskra, Algeria. The study employs the ENVI-met model to simulate various vegetation arrangement scenarios and evaluate their effectiveness in improving microclimate parameters and thermal comfort. The results show that the scenario with the densest and most extensive vegetation cover (90% of the sample area) provided the greatest reduction in air temperature, mean radiant temperature, and duration of severe thermal stress, compared to the baseline scenario with minimal vegetation. However, even the best-performing scenario did not achieve a thermally comfortable outdoor environment, likely due to the heat-absorbing properties of the asphalt ground and the reduced wind speeds caused by the dense vegetation. The study highlights the importance of considering vegetation type, cover density, and strategic placement in urban planning to mitigate the harsh climatic conditions in hot and dry cities like Biskra.

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.

Monitoring soil arsenic content in densely vegetated agricultural areas using UAV hyperspectral, satellite multispectral and SAR data

Monitoring soil arsenic content in densely vegetated agricultural areas using UAV hyperspectral, satellite multispectral and SAR data

Optimizing GEDI Canopy Height Estimation and Analyzing Error Impact Factors Under Highly Complex Terrain and High-Density Vegetation Conditions

The Global Ecosystem Dynamics Investigation (GEDI) system provides essential data for estimating forest canopy height on a global scale. However, factors such as complex topography and dense canopy can significantly reduce the accuracy of GEDI canopy height estimations. We selected the South Taihang region of Henan Province, China, as our study area and proposed an optimization framework to improve GEDI canopy height estimation accuracy. This framework includes correcting geolocation errors in GEDI footprints, screening and analyzing features that affect estimation errors, and combining two regression models with feature selection methods. Our findings reveal a geolocation error of 4 to 6 m in GEDI footprints at the orbital scale, along with an overestimation of GEDI canopy height in the South Taihang region. Relative height (RH), waveform characteristics, topographic features, and canopy cover significantly influenced the estimation error. Some studies have suggested that GEDI canopy height estimates for areas with high canopy cover lead to underestimation, However, our study found that accuracy increased with higher canopy cover in complex terrain and dense vegetation. The model’s performance improved significantly after incorporating the canopy cover parameter into the optimization model. Overall, the R2 of the best-optimized model was improved from 0.06 to 0.61, the RMSE was decreased from 8.73 m to 2.23 m, and the rRMSE decreased from 65% to 17%, resulting in an accuracy improvement of 74.45%. In general, this study reveals the factors affecting the accuracy of GEDI canopy height estimation in areas with complex terrain and dense vegetation cover, on the premise of minimizing GEDI geolocation errors. Employing the proposed optimization framework significantly enhanced the accuracy of GEDI canopy height estimates. This study also highlighted the crucial role of canopy cover in improving the precision of GEDI canopy height estimation, providing an effective approach for forest monitoring in such regions and vegetation conditions. Future studies should further improve the classification of tree species and expand the diversity of sample tree species to test the accuracy of canopy height estimated by GEDI in different forest structures, consider the distortion of optical remote sensing images caused by rugged terrain, and further mine the information in GEDI waveforms so as to enhance the applicability of the optimization framework in more diverse forest environments.

Climate and Land‐Use Change Leading to Niche Expansion and Shifts in Birds

ABSTRACTAimThe realised niches of species change following environmental and distributional shifts. Still, the magnitude of niche change and its consequences are determined by how different species cope with environmental changes, which ultimately depends on their ecology and traits. We assessed how environmental and distributional shifts have led to changes in the realised breeding niches of 121 species of North American birds over the last four decades and how the magnitude of niche change was associated with species traits.LocationNorth America.TaxonBirds.MethodsWe calculated geographic and niche overlap, breadth and shift, and estimated the temporal trends of the different niche metrics from 1980 to 2018 for each species. The slopes of these temporal trends were then modelled as a function of species traits using Bayesian models that accounted for phylogenetic relatedness and the uncertainty of the temporal slopes.ResultsWe found that the niche of 57% of the bird species diverged through time as they experienced widespread environmental change, including changes in both niche breadth and position. Most niches expanded due to increasing environmental variability within their ranges, but habitat specialists showed a tendency for niche contraction. Niche shifts were larger in species that live in habitats with denser vegetation cover. However, species showing larger average geographical shifts were able to track their reference niche more effectively.Main ConclusionsBird species were in general increasingly exposed to higher environmental variation that led to changes in their realised niches over a relatively short time span (four decades), while the niches of many others remained stable. The differences in the magnitude of niche change were to some extent related to species traits, providing clues about how different species respond to widespread environmental change in both geographical and niche space.

Analisis Potensi Kawasan Pengembangan Lahan Pertanian Tanaman Padi Sawah di Kabupaten Tanah Datar

This study aims to determine 1) the level of suitability of wet-rice land on food agricultural land in Tanah Datar Regency, 2) existing land cover in Tanah Datar Regency and 3) potential wet-rice land development areas in Tanah Datar Regency. The method used is a descriptive approach with quantitative research. Data was processed using fuzzy logic, supervised classification, and spatial data analysis. The results of this study show 1) Tanah Datar Regency has 3 levels of land suitability with an index value of 0-0.59 which is classified at the level of unsuitable to less suitable for rice paddy agricultural crops, with an area of 7,583.9 Ha with a percentage of 5.5%. The second category of land suitability with an index value of 0.6-0.79 is classified as moderately suitable to suitable, with an area of 98,955.7 Ha with a percentage of 71.8%. The third category of land suitability with an index value of 0.8-1.0 which is classified as moderately - highly suitable for wetland agricultural crops, with an area of 31,267.7 Ha with a percentage of 22.7% of the total area of the study site. 2) Analysis of existing land cover using Landsat 8 satellite images in 2023 in Tanah Datar District shows that the structure of land cover in 2023 is as follows: dense vegetation cover covering 93,385.5 Ha with a percentage of 67.9%, followed by agricultural land covering 35,630.6 Ha with a percentage of 25.8%, settlements covering 1,828.3 Ha with a percentage of 1.4%, open land covering 137.6 Ha with a percentage of 0.09%, and waters.

Roads and Woody Vegetation Structure Are Associated With Chimpanzee (Pan troglodytes) Occurrence at Its Northern Range Limit

ABSTRACTAimThe purported defining features of chimpanzee (Pan troglodytes) biogeography are sometimes simplistic and contradictory, yet we rely on these metrics to shape conservation and management planning for this endangered ape. We analysed the cover and configuration of woody vegetation at the northern margins of the species' range in Senegal to elucidate key predictors of chimpanzee biogeography.LocationSoutheastern Senegal.TaxaWestern chimpanzee (Pan troglodytes verus).MethodsWe combined reconnaissance surveys and interviews with published accounts of chimpanzee presence to verify previous estimates of the range limits of the species. High‐resolution, multispectral satellite images were used to quantify the configuration of trees and shrubs at each site. Woody vegetation cover and physiognomy, river density and road density were compared within and outside of the species range with generalised linear mixed models.ResultsOur surveys and interviews support the current estimates of the northern limits of the species range. Chimpanzee‐occupied sites had more patchily‐distributed vegetation, greater patch edge complexity, and slightly higher river density than unoccupied sites. Unoccupied sites had more woody vegetation cover and higher road density. Of all predictors, roads had the highest impact on chimpanzee occupancy.Main ConclusionsIn line with previous findings, anthropogenic modifications of the landscape had a strong impact on chimpanzee occupancy, but surprisingly, woody vegetation cover was negatively associated with chimpanzee occupancy in this region. Instead, more specific metrics of vegetation configuration were positively associated with chimpanzee site occupancy, indicating that precise measures are needed for assessing chimpanzee biogeography at the landscape scale.

Establishing perennial strips in a cultivated floodplain for habitat restoration: monitoring over 4 years and success drivers

Floodplains, one of the most biologically diverse and productive ecosystems, are under threat from intensive crop production. Implementing perennial strips alongside agricultural ditches and streams could reduce negative impacts of intensive agriculture and restore wildlife habitats in cultivated floodplains. To successfully set up perennial strips, it is important to understand the parameters that drive their establishment. Here we assessed the establishment success of reed canarygrass (RCG; Phalaris arundinacea) strips in the lake Saint Pierre (LSP) floodplain, Québec, Canada by monitoring RCG biomass and vegetation height over 4 years and identify the factors driving its establishment. A total of 26 RCG strips across six municipalities of LSP were monitored. Biomass and vegetation height of RCG increased over time to reach an average of 5048 kg/ha in year 4 and 104 cm in year 3 in established strips. The RCG established successfully in 62% of surveyed plots and three environmental parameters explained 61% of this success. Establishment of RCG was most successful when a first rain came right after seeding (<3 days). High clay content and low elevation were associated with establishment failures. Overall, our results highlight the ability of RCG strips to restore dense perennial vegetation cover in cultivated floodplain, thereby providing suitable habitat for fish spawning during spring floods. This study provides significant insight into the drivers of establishment of perennial grass strips in highly constrained cultivated areas such as floodplains.

Warming and disturbances affect Arctic-boreal vegetation resilience across northwestern North America.

Rapid warming and increasing disturbances in high-latitude regions have caused extensive vegetation shifts and uncertainty in future carbon budgets. Better predictions of vegetation dynamics and functions require characterizing resilience, which indicates the capability of an ecosystem to recover from perturbations. Here, using temporal autocorrelation of remotely sensed greenness, we quantify time-varying vegetation resilience during 2000-2019 across northwestern North American Arctic-boreal ecosystems. We find that vegetation resilience significantly decreased in southern boreal forests, including forests showing greening trends, while it increased in most of the Arctic tundra. Warm and dry areas with high elevation and dense vegetation cover were among the hotspots of reduced resilience. Resilience further declined both before and after forest losses and fires, especially in southern boreal forests. These findings indicate that warming and disturbance have been altering vegetation resilience, potentially undermining the expected long-term increase of high-latitude carbon uptake under future climate.

Enhancing the Capacity of Tourism Awareness Groups in Climate Change Adaptation and Mitigation at the Tanjung Batu Mangrove Ecotourism, Sekotong Tengah Village, West Lombok Regency

Mangrove Ecotourism in Tanjung Batu is one of the unique tourist attractions on Lombok Island, featuring a scenic 11-hectare mangrove area with dense vegetation coverage. The site is equipped with several facilities, including a 417-meter wooden bridge, several gazebos, selfie spots, a homestay, and a café. The ecotourism management is handled by the Tourism Awareness Group (Pokdarwis) of Sekotong Tengah Village. This destination is quite popular, with no fewer than 300 tourists visiting each month. However, this area is prone to climate-related disaster risks such as tidal floods, strong winds, high waves, floods, and droughts. Additionally, the ecotourism management has mainly focused on physical aspects and lacks sufficient managerial skills for implementing a sustainable governance system. The proposed solution is to strengthen the Pokdarwis management to improve ecotourism governance, optimizing its potential to deliver greater social, economic, and environmental value while contributing to climate change adaptation and mitigation efforts. The conclusion from the community service activity is that to address these challenges, the community is committed to preserving the mangrove ecosystem, preventing disturbances from both internal and external factors, and managing ecotourism in a way that benefits the surrounding community by reducing climate-related disaster risks. Priority activities include mangrove rehabilitation and enrichment, beach cleanup, monthly management meetings, semi-annual evaluations, and promptly developing plans to strengthen the fishermen's economy through involvement in marine tourism packages

Effect of Land Cover Variations on the Fertility Status of Soils of Umuhu in Ngor-Okpala Area of Imo State

The study was conducted to ascertain the effect of four different land covers (cassava cultivated field (CCF), oil palm plantation (OPP), plantain plantation (PP) and forest land (FL)) on the fertility status of soils of Umuhu in Ngor-Okpala area of Imo State, Nigeria. Random sampling technique was used in the collection of soil samples from the four different land covers. The sampling was done at the beginning, middle and end of each land cover. Twelve (12) composite samples were collected from all the land cover types with 3 per land cover. The composite samples were prepared for routine laboratory analyses. Data generated were analyzed statistically using analysis of variance. The textural classes of all the sampled soils were of loamy sand. The soil which had plantain plantation as its cover had the highest pH (6.4), Total N (0.229 %), Soil organic matter 4.70%, Available P (27.05 mg kg-1), Exch. Ca (6.492 cmolc kg-1), Exch. Mg (3.942 cmolc kg-1), Exch. K (0.953 cmolc kg-1), Exch. Na (0.06 cmolc kg-1), Exch. H (1.2 cmolc kg-1) and CEC (28.4 cmolc kg-1) with its Exch. Al in trace amount (<0.001 cmolc kg-1). The soil under CCF, despite being under low cover, did not show much degeneration due to the shifting cultivation being practiced in the study location. However, the result obtained from the study indicated that the different land covers especially that under plantain plantation (PP) have affected the soils fertility at different rates. This observed factor is strongly controlled by high content of organic materials, a dense vegetative cover which mitigates erosion effects and the addition of ash as a means of fertilizing the soils under plantain plantation being practiced in the study area.

The Environmental Legal Framework of Mexican Caribbean Dunes: A Retrospective Case Study of Vegetation and Coastal Dune Loss in the Sian Ka’an Biosphere Reserve

The Mexican Caribbean coastal dune is protected by national and international environmental legislation. However, through permits, concessions and authorizations for changes in land use, the coastal dune has been fragmented or suppressed, mainly for touristic activities, causing a decline in protective and ecological ecosystem services. In this study, we evaluated the strength and weakness of Mexican legislation to protect the Caribbean coastal dune ecosystem and estimated the historical and current effects on coastal dune vegetation and dune geomorphology, associated with legal allowances of land use change in the Sian Ka’an Biosphere Reserve (SKBR). Legislation at the federal, state and local level were critically reviewed, and with remote sensing techniques and the Remotely Piloted Aircraft System (RPAS), we conducted a case study in the SKBR to estimate coastal dune vegetation alteration trends during the period 2011–2020 and modifications on the dune geomorphology associated with land use change allowances. At the federal (four laws), state (eight laws) and local (nine Local and Territorial Planning Programs (POEL and POET) levels, we found a lack of consensus and alignment between regulations, starting with a lack of definition of ecosystems subject to protection. For coastal dunes, none of them consider topography, ecological function and a way to identify it in the field, making the surveillance highly complex and favoring land use changes, the removal of vegetation and dune geomorphology alteration. Remote sensing techniques showed that areas with land use authorizations exhibit negative vegetation cover trends (Mann–Kendall <−0.4), indicating a decline in vegetation cover density that is mostly anthropogenically induced. The RPAS analysis demonstrated drastic alterations to complete elimination of the coastal dune geomorphology in areas with land use change. In the Mexican Caribbean, the loss of coastal dune and associated ecosystem by the lack of congruent legislation threatens the environmental stability of the coastal areas.

Impact of land cover changes on land surface temperature variations in Adama City, Ethiopia

ABSTRACT Land surface temperature (LST) is becoming a serious environmental issue, since it is an essential controller of city climate. Currently, addressing this challenge in urban planning has gained worldwide importance. The study aims to analyze the effect of land cover dynamics on LST using geographic information system and remote sensing techniques. In this study, Landsat 7 ETM+ (2002) and Landsat 8 TIRS (2022) data were used. The LST was retrieved from Landsat datasets. The correlation analyses were conducted on LST, normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI). The findings suggested that there was a negative correlation between LST and the NDVI, while a positive correlation existed with the NDBI. Moreover, NDBI was found to be a better predictor of LST than NDVI. Additionally, it was revealed that the proportion of dense and sparse vegetation cover was reduced from 24.14 km2 (17.47%) in 2002 to 18.17 km2 (13.15%) in 2022. Similarly, the average LST increased from 28.25 to 31.78°C. This indicated that the rapid growth of urban development was the primary factor behind the rise in LST. Therefore, it was deemed crucial to create a smart urban land use plan to mitigate the impacts of microclimate change.