Decrease In Vegetation Cover Research Articles

A systematic flood risk assessment of Bloemfontein Watershed, South Africa

Understanding the elements contributing to flood risk can create effective plans and policies for decision-makers to lessen vulnerability and boost resilience. Over the years, Bloemfontein has experienced seasonal flooding, despite the low amount of rainfall it receives compared to other towns in South Africa. This study systematically assessed flood risk in the most vulnerable watershed of Bloemfontein. The analytical hierarchy process (AHP) and GIS-based multi-criteria decision-making were utilized to map and identify potential locations that are vulnerable to floods. The relative weight of each criterion taken into account for the susceptibility mapping was determined using AHP. Geospatial techniques were used to assess the vegetation cover, moisture content, surface water, and land surface temperature (LST) from 1991 to 2023, as well as ten physical environmental factors linked to the occurrence of floods. Findings from this study revealed a decrease in vegetation cover and moisture content and an increase in the LST from 1990 to 2023. The susceptibility to flood map shows that 9.63% of the watershed is low susceptible to flooding, 79.26% is moderate, 11.07% is high, and 0.03% is very high susceptible to flooding. The built areas and major farmlands fall within the high susceptibility zone of 11%.

Assessing the impact of sediment characteristics on vegetation recovery in debris flow fans: A case study of the Ohya Region, Japan

What factors influence natural vegetation recovery in debris flow-prone areas, and how do sediment dynamics play a role? This study investigates these questions in the Ohya debris flow fan, Japan, utilizing various analytical techniques. Through the application of Support Vector Machine (SVM) classification, grain size analysis, accuracy assessment, debris flow analysis, and hotspot analysis, this study assess the distribution of sediments, vegetation classes, and the extent of debris flow events. The findings shed light on the dynamics of vegetation recovery and its relationship with sediment dynamic. The SVM classification outcomes reveal distinct trends in sediment and vegetation distribution along the flow path, particularly noting a significant decrease in vegetation cover from upstream to downstream sections. By employing SVM classification, this study successfully identified 3,282,910 sediment particles and determined their average, minimum, maximum, and standard deviation grain sizes as 7.3 cm, 0.27 cm, 415 cm, and 9.18, respectively. Accuracy assessments of image classification and grain size measurements demonstrate high levels of accuracy, with an overall classification accuracy of 98.82 % and a kappa coefficient of 0.977. Validation of grain size measurements reveals a strong correlation (R2 = 0.997 and y = 1.005× + 0.0661) between field-observed sediment sizes and sizes derived from classified images. Debris flow analysis reveals that the total area affected by debris flow in 2022 was 36,232.7 square meters, decreasing to 14,213.9 square meters in 2023. Hotspot analysis identifies regions of both high and low sediment size concentrations, providing valuable insights into sediment distribution patterns. Examining the natural recovery of vegetation, present study identifies vegetation spots across different study sections. Results show that 55 % of naturally recovering vegetation areas are located in regions unaffected by debris flow events between 2022 and 2023. Among the study sections, the area affected by debris flow in 2023 exhibits the lowest density of vegetation spots. Overall, this study highlights the new generation vegetation recovery and its association with sediment dynamic in the Ohya debris flow fan. The findings contribute valuable insights for understanding natural recovery processes in highly dynamic sedimentation areas, informing the development of effective strategies for ecosystem restoration and management in debris flow-prone regions.

Analyzing the spatio-temporal pattern of urban growth and its influence on urban heat islands in the Sekondi-Takoradi metropolis, Ghana

The rapid urbanization in Sekondi-Takoradi, Ghana has significantly transformed the land cover, resulting in the proliferation of impervious surfaces and a decline in vegetation. However, the influence of this urban growth on the development of urban heat islands (UHIs) in the metropolis remains understudied. This study aimed to fill this research gap by employing Landsat images to explore the influence of urban growth on urban heat islands in the metropolis from 1991 to 2023. The supervised random forest technique was utilized to map the land cover changes. Furthermore, the computed normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), land surface temperature (LST), and urban thermal field variance index (UTFVI) were used to analyze the influence of urban expansion on UHIs. The findings revealed a 63.07 km2 increase in built-up areas and a 60.99 km2 decrease in vegetation cover during the study period. This dramatic land use change led to a 3.1°C rise in mean LST and a 19.38 km2 expansion of areas affected by the UHI effect. The UTFVI analysis further indicated a 33.63 km2 increase in the worst ecological zone due to the temperature rise. Statistical analysis between LST, NDVI, and NDBI revealed significant variability in explaining the intensity of LST and UHI in the metropolis over the study period. The study equips city authorities and planners with the fundamental knowledge needed to prepare a sustainable development plan that alleviates adverse effects of urban growth and elevated temperature-related issues. Also, the findings contribute to the global efforts in promoting more livable and climate-resilient urban environments.

Addressing the impact of land use land cover changes on land surface temperature using machine learning algorithms

Over the past two and a half decades, rapid urbanization has led to significant land use and land cover (LULC) changes in Kabul province, Afghanistan. To assess the impact of LULC changes on land surface temperature (LST), Kabul province was divided into four LULC classes applying the Support Vector Machine (SVM) algorithm using the Landsat satellite images from 1998 to 2022. The LST was assessed using Landsat data from the thermal band. The Cellular Automata-Logistic Regression (CA-LR) model was applied to predict the future patterns of LULC and LST for 2034 and 2046. Results showed significant changes in LULC classes, as the built-up areas increased about 9.37%, while the bare soil and vegetation cover decreased 7.20% and 2.35%, respectively, from 1998 to 2022. The analysis of annual LST revealed that built-up areas showed the highest mean LST, followed by bare soil and vegetation. The future simulation results indicate an expected increase in built-up areas to 17.08% and 23.10% by 2034 and 2046, respectively, compared to 11.23% in 2022. Similarly, the simulation results for LST indicated that the area experiencing the highest LST class (≥ 32 °C) is expected to increase to 27.01% and 43.05% by 2034 and 2046, respectively, compared to 11.21% in 2022. The results indicate that LST increases considerably as built-up areas increase and vegetation cover decreases, revealing a direct link between urbanization and rising temperatures.

Urban expansion and vegetation dynamics: The role of protected areas in preventing vegetation loss in a growing mega city

The decrease in vegetation cover due to urban expansion poses serious challenge to urban sustainability. Protected areas (PAs) are the most effective tools to prevent the loss of urban vegetation cover and to control urban expansion. Hence, this study aims to assess the importance of PAs in protecting urban vegetation and the urban expansion in the mega city of Delhi. For this, Landsat datasets were used for land use and land cover (LULC) mapping and then land cover change rate (LCCR) and land cover intensity (LCI) were calculated. For assessing urban expansion dynamics, mean landscape expansion index (MLEI) and the area-weighted LEI (AWLEI) were calculated. To evaluate the significance of PAs in protecting vegetation cover, kernel density estimation (KDE) was applied to assess the spatial variation and concentration of vegetation cover under different PAs. The result shows that urban expansion in Delhi was initially characterized by edge expansion during 1991–2001, followed by outlying expansion of built-up area during 2001–2021, while infilling of open and vegetated areas by built-up area was consistent during 1991–2021. Vegetation cover on the other hand, has followed a fluctuating trend in the city, but has overall it has declined from 13.36% to 9.30% during 1991–2021. The vegetation cover has declined significantly in eastern, northern, and western parts of Delhi but has increased significantly in central and southern parts, especially during 2001–21. This is because the central and southern parts of Delhi are well planned and have several PAs while the western, northern, and eastern parts of Delhi are unplanned regions and have only a few PAs. The KDE chart shows that the PAs have played an important role in protecting the vegetation cover in Delhi with R2 value > 0.70. Hence, this study suggests to give special emphasis on preservation and expansion of PAs in urban planning for the long-term conservation of urban vegetation cover and sustainable urban development.

Spatio-temporal dynamic analysis of greenness base on NDVI equation for change detection from 2004 to 2024 at River Nile State, Atbara City, Sudan by using remote sensing and geographical information system

Urban regions' ability to maintain high environmental standards is largely dependent on their vegetation. A decrease in the amount of vegetated area on land has resulted from city expansion and population growth. An examination of land cover change in urban settings is necessary, particularly for urban regional planning that takes green, open space into account. The purpose of this study was to examine changes in urban vegetation cover in two Medan sub-districts from 2004 to 2014. Change analysis and the Normalized Difference Vegetation Index (NDVI) were performed in the study. The range of plant diversity in these locations was Observed. The results showed changes in vegetation cover areas in the mentioned years. In 2004, most of the areas were under a highly dense vegetation class while in 2014, they were under a low-density vegetation class with new sub-class. This indicates a decrease in vegetation cover due to changes to non-vegetation cover or land cover areas with less vegetation.

Comparison of different drought monitoring indices in different climatic conditions in Iran

This study evaluates drought in different climate zones (Rasht, Shiraz, and Birjand) in Iran, using meteorological, agricultural, and remote sensing drought indices. For this purpose, NDVI, SAVI, and SR were extracted from Landsat images for 2002 and 2014-2020. Then, these indices were compared with the SPI, SPEI, and PDSI. The results indicate an increase in drought and a decrease in vegetation cover in the study area. In Rasht, where the vegetation cover is high, NDVI and SAVI were equal. In Shiraz and Birjand, where the soil effect is more significant, the distance between these two indices increased, which shows that SAVI performs better than NDVI for Shiraz and Birjand. The results also show that the drought severity could grow with decreasing rainfall and more water demand due to temperature increases, according to SPI, SPEI, and PDSI criteria. The comparison of drought indices showed that the highest correlations were between NDVI plus SAVI and SPI in Rasht, SR and SPEI in Shiraz, and NDVI and SPEI in Birjand. Based on the results of the Mann-Kendall test, the increasing trend of drought in the studied area is confirmed based on the SPI, SPEI, and PDSI. Therefore, it is suggested that remote sensing techniques combined with drought indices can be considered a suitable tool for optimal management of water resources, land use planning, and reduction of costs due to drought.

Developing simple indicators of nitrogen and phosphorus removal in constructed stormwater wetlands

Quantifying pollutant removal by stormwater wetlands requires intensive sampling which is cost-prohibitive for authorities responsible for a large number of wetlands. Wetland managers require simple indicators that provide a practical means of estimating performance and prioritising maintenance works across their asset base. We therefore aimed to develop vegetation cover and metrics derived from monitoring water level, as simple indicators of likely nutrient pollutant removal from stormwater wetlands. Over a two-year period, we measured vegetation cover and water levels at 17 wetlands and used both to predict nitrogen (N) and phosphorus (P) removal. Vegetation cover explained 48 % of variation in total nitrogen (TN) removal; with a linear relationship suggesting an approximate 9 % loss in TN removal per 10 % decrease in vegetation cover. Vegetation cover is therefore a useful indicator of TN removal. Further development of remotely-sensed data on vegetation configuration, species and condition will likely improve the accuracy of TN removal estimates. Total phosphorus (TP) removal was not predicted by vegetation cover, but was weakly related to the median water level which explained 25 % of variation TP removal. Despite weak prediction of TP removal, metrics derived from water level sensors identified faults such as excessive inflow and inefficient outflow, which in combination explained 50 % of the variation in the median water level. Monitoring water levels therefore has the potential to detect faults prior to loss of vegetation cover and therefore TN removal, as well as inform the corrective action required.

Assessing the impact of land use land cover changes on soil moisture and vegetation cover in Southern Punjab, Pakistan using multi-temporal satellite data

ABSTRACT Change in land use land cover (LULC) is driven by human activities and drives changes that limit the accessibility of services and products for livestock and humans. This study aimed to develop the spatio-temporal changes in LULC, vegetation cover, and moisture index using multi-temporal satellite data in Southern Punjab, Pakistan. Analysis of satellite data was accomplished using ArcGIS 10.4 software. Based on ground-truthing, the supervised classification technique (maximum likelihood algorithm) was used to achieve the LULC classification. The LULC change analysis revealed that vegetation area converted to the build-up area by 7.17 % and bare soil converted to urban areas by 1.68 % from 2000 to 2021. Average NDVI values were calculated at 0.23, 0.17, 0.19, and 0.14 for 2000, 2007, 2014, and 2021, respectively. In the study area, average NDMI values were observed at 0.28, 0.25, 0.2, and 0.15 for 2000, 2007, 2014, and 2021, respectively. Based on our study, the general trend in Southern Punjab is a decrease in vegetation cover, moisture index, and forest area due to an increase in build-up areas. Assessments of LULC and NDMI changes, as well as estimates of the effect on the environment, are necessary for many policy decisions and planning.

Mapping impervious surface change from remote sensing and GIS data: A case study in Hochiminh city, Vietnam

Impervious surface is artificial surfaces that prevent water from entering the soil. The increase in impervious surface area has led to negative impacts on the urban environment, including an increase in the risk of flooding, a decrease in vegetation cover, and the formation of urban heat islands. This paper presents the results of building a predictive model of impervious surfaces in Hochiminh city from remote sensing and GIS data. Landsat and Sentinel 2 satellite images for the period 2002–2022 are used to classify impervious surfaces and extract input layers about vegetation cover, land surface temperature, combined with GIS data (elevation, slope, aspect, distance to road, distance to hydrology, population density) for modeling and predicting impervious surface changes in future. 03 machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), Classification and Regression Trees (CART) and maximum likelihood method are used to classify impervious surfaces from Landsat satellite images, then select the method with the highest accuracy. To predict the future distribution of impervious surface, this study uses Cellular Automata (CA) model and 02 artificial intelligence algorithms (Artificial Neural Network - ANN, Logistic Regression - LR). The results obtained in the study can be effectively used for urban planning, minimizing the impact of the process of increasing the impervious surface on the urban environment

Dynamic of land use and vegetation change in the eastern bank of Bénoué (North Cameroon).

The eastern part of the Benoue River bank is undergoing degradation marked by a significant decrease in vegetation cover and woody resources due to anthropogenic activities and climatic. The main objective of this study is to analyze the farmers' knowledge of vegetation evolution and the dynamics of land use using satellite images in the east of the bank of the Benoue. The methodological approach used is an integrated one combining field surveys, remote sensing, mapping, and modeling. The results obtained show that 88% of the population surveyed believe that the area covered by vegetation has decreased. The reasons for this decrease are numerous, but the main one remains the strong anthropic activity that would be at the origin of the progressive degradation of the land. The evolutionary trend of plant formations is essentially regressive for natural formations from 1991 to 2021. The analysis of the evolution of land use showed that in the Rey-Bouba district during 1991, 58.24% of the area formerly made up of dense woody formations regressed considerably to 25.77% in 2021. The same is true for the Bibemi district where the area of wooded zone has decreased from 65.47% in 1991 to 28.45% of the total area in 2021. This regression of the surface area of wooded formations was done to the benefit of anthropized occupation classes whose area has increased. They suggest an effective awareness in the monitoring of the dynamics of the vegetation cover subjected to anthropic pressures and climatic variations for a better-integrated management of the vegetation of this area.

Monitoring the Expansion of Unplanned Urbanization and its Impact on Climate Change based on Google Earth Engine Service, a Case Study of Baghdad / Iraq

The earth's surface comprises different kinds of land cover, water resources, and soil, which create environmental factors for varied animals, plants, and humans. Knowing the significant effects of land cover is crucial for long-term development, climate change modeling, and preserving ecosystems. In this research, the Google Earth Engine platform and freely available Landsat imagery were used to investigate the impact of the expansion and degradation in urbanized areas, watersheds, and vegetative cover on the land surface temperature in Baghdad from 2004 to 2021. Land cover indices such as the Normalized Difference Vegetation Index, Normalized Difference Water Index, and Normalized Difference Built-up Index (NDVI, NDWI, and NDBI) were determined to examine the effects of land cover changes. In addition, the land surface temperature was calculated to assess urbanization expansion's impact on Baghdad's climate warming. The results showed a drastic decrease in vegetative cover and green land, on the other hand, a significant expansion in urbanized areas. Hence, from 2004 to 2021, the urbanized areas and open land rose by 37% and 3%, respectively, while the vegetative cover decreased by 41%. The maximum land surface temperature has risen 4° C, and the minimum land surface temperature has risen 2.5°C.

The Restoration of Degraded Landscapes along the Urban–Rural Gradient of Lubumbashi City (Democratic Republic of the Congo) by Acacia auriculiformis Plantations: Their Spatial Dynamics and Impact on Plant Diversity

This study examines the spatio-temporal dynamics of Acacia auriculiformis in Lubumbashi city, southeastern Democratic Republic of Congo, in the context of rapid urbanization following the liberalization of the mining sector. The city has experienced significant demographic growth and unplanned spatial expansion, resulting in a decrease in vegetation cover. The introduction and proliferation of A. auriculiformis, an exotic tree species, have occurred without strategic planning or monitoring. Utilizing digitized remote sensing imagery from 2006, 2014, and 2021, we quantified the expansion of A. auriculiformis along the urban–rural gradient. Additionally, a floristic inventory conducted in 2021 provided insights into tree diversity within A. auriculiformis plantations. Our findings indicate a substantial increase in the number and area of A. auriculiformis patches, predominantly in urban zones. However, the patch values, highest in 2006, were shown to decline by 2021, especially in urban areas. The floristic inventory identified 39 tree species within A. auriculiformis plantations, including predominant species such as Albizia lebbeck, Albizia alba, and Leucaena leucocephala. Notably, 20 of these species are exotic, with half being invasive. In contrast, the 19 indigenous species were primarily found in peri-urban areas. While a greater number of tree species were observed in urban zones, larger average diameters were recorded in peri-urban zones. The persistence and expansion of A. auriculiformis in a landscape characterized by declining tree cover suggest its potential sustainability in this setting. However, A. auriculiformis plantations have facilitated the establishment of predominantly exotic and potentially invasive species. These findings highlight the need for the strategic management of A. auriculiformis and associated exotic flora to mitigate their spread and to consider their role in the restoration of degraded lands.

Spatio-temporal analysis of land use land cover change and its impact on land surface temperature of Sialkot City, Pakistan

The dynamic interplay between urbanization and its impacts on climate is a subject of recent concern, particularly in rapidly urbanizing cities of Pakistan. This research investigated the spatio-temporal effects of urban growth in terms of Land Use Land Cover changes on the thermal environment (Land Surface Temperature) of the Sialkot city, Pakistan using satellite data spanning four distinct time periods (1989, 2000, 2009 and 2020) and predicted changes for year 2030 by employing Cellular Automata Markov Chain Model. Satellite imagery (Landsat 5, 7 and 8) was processed, and maximum likelihood supervised classification was done to generate LULC maps for each of the aforementioned years. In addition to LULC classification, thermal bands of satellite data (for summer and winter) were processed to compute Land Surface Temperature (LST) of the city. The prediction of LULC changes and LST was done for year 2030 using Cellular Automata Markov Chain Model. The accuracy of classified and prediction maps was checked using Kappa Index. The LULC analysis revealed 4.14% increase in the built-up area and 3.43% decrease in vegetation cover of the city during 1989 to 2020. Both land covers are expected to change in the future (year 2030) by + 1.31% (built-up) and − 1.1% (vegetation). Furthermore, a declining trend in the barren land and water bodies was also observed over time. These LULC changes were found affecting the LST of study area. The transformation of vegetation cover into built-up area resulted in an increase in LST over time. A notable rise of 4.5 °C (summer) and 5.7 °C (winter) in the mean LST of Sialkot was observed during 1989 to 2020 and further increases are anticipated in year 2030. This study calls for attention of the policy makers to reduce human impact on the local climate of the city. The study will also help city developers in analyzing the urban population growth trend, finding suitable location to built new infrastructure by governmental authorities and how the rising temperature can affect energy demand and agriculture production of the city in future.

Grazing for biodiversity: Assessing the effects of cattle management practices on wetlands and amphibian communities in central Argentina

Argentina is widely recognized as a key player in the production and consumption of beef, occupying a prominent position in the global context. However, there is a lack of comprehensive research on the environmental consequences of beef production, particularly concerning wetlands and aquatic biodiversity. We aim to assess the effects of cattle management practices (grazing types and stocking density) on wetland attributes and associated amphibian diversity in central Argentina. During two amphibian breeding seasons, we surveyed 117 wetland sites in paddocks with different cattle grazing types (continuous grazing, glyphosate-promoted pastures and rotational grazing) and stocking densities (low and high). We analyzed the effects of cattle management practices on wetland attributes, including water quality parameters, nutrient concentrations, wetland morphometry, and vegetation cover. Effects on amphibian communities were explored using richness, abundance, and species occurrence. General Mixed-effects Models revealed significant increases in nutrients (total solid dissolved, total nitrogen and soluble reactive phosphorous) and a significant decrease in vegetation cover in sites with high cattle stocking density. Likewise, we observed increased soluble reactive solids in wetlands associated with rotational grazing and decreased vegetation cover in those wetlands related to glyphosate-promoted pastures. Generalized Mixed-effects Models revealed that amphibian communities were negatively affected mainly by high stocking density, while abundance and species occurrence of hylids (Boana pulchella and Scinax squalirostris) were also affected by glyphosate-promoted pastures and rotational grazing treatments. Our results indicate that different types of grazing at low stocking density favor amphibian diversity and improve wetland water quality, therefore, reduced stocking density may result in improved wetland conditions and more diverse amphibian communities. Our results also suggest that glyphosate-promoted pastures and rotational grazing, although beneficial in terms of pasture management, may have unintended consequences on water quality in wetlands and amphibian communities. This study will contribute to our understanding of how cattle management practices influence wetland ecosystems and aquatic biodiversity in the most important cattle breeding area of South America, providing valuable insights for conservation efforts in cattle ranching landscapes.

Estimation and simulation of carbon sequestration in typical dryland areas of China under future climate change scenarios

Climate anomalies and human disturbances exert complex effects on regional carbon sequestration (CS), causing ecosystem CS to either increase or decrease due to factors such as vegetation greening, climate extremes, land use/land cover (LULC) changes, and farming systems. This study employs the patch-level land use simulation model to forecast changes in various land-use types in the Xinjiang Uyghur Autonomous Region, China, between 2020 and 2060 under different climatic conditions. The Intergovernmental Panel on Climate Change has proposed shared socioeconomic pathways (SSPs) as potential socioeconomic and environmental trajectories that might influence the region’s future land-use patterns. Three salient findings emerged: (1) LULC displayed considerable variation across future climate scenarios, with a notable rise in forest and grassland cover and a marked decrease in cropland areas under the SSP126 scenario. Conversely, the SSP585 scenario witnessed a substantial cropland expansion, paralleled by a decrease in forest and grassland areas; (2) CS exhibited significant disparities across different future climate scenarios. Xinjiang achieved a peak CS of 9.81 Pg between 2020 and 2060 under the SSP126 scenario, marking an increase of 335.22 Tg in 2060 compared to that in 2020. The SSP585 scenario registered the lowest CS at 9.41 Pg, indicating a decline of 50.07 Tg in 2060 relative to 2020; (3) The impacts of longitude and latitude on CS in Xinjiang predominantly arose from shifts in forest area and vegetation cover based on latitude, which positively influenced CS. In contrast, a decrease in vegetation cover and a subsequent decline in CS were evident with increasing longitude, particularly in the Altai, Tianshan, and Kunlun Mountains. These findings hold significant implications for formulating CS management strategies for terrestrial ecosystems and enhancing ecological preservation in Xinjiang.

Assessing long-term trends in vegetation cover change in the Xilin River Basin: Potential for monitoring grassland degradation and restoration

Under the influence of climate change and human activities, the problem of grassland degradation is becoming increasingly severe. Detection of changes in vegetation cover is crucial for a better understanding of the interaction between humans and ecosystems. This study maps changes in vegetation cover using the Google Earth Engine (GEE). We used 36 years of Landsat satellite imagery (1985–2020) in the Xilin River Basin, China, to classify grassland conditions and validated the results with field observation data. The overall classification of the model accuracy assessment was 83.3%. The Dynamic Reference Vegetation Cover Method (DRCM) was adopted to remove the effect of interannual variation of rainfall, allowing to focus on the impact of human activities on vegetation cover changes. The results identify five categories of vegetation cover changes: significantly increased, potentially increased, stable, potentially decreased, and significantly decreased. The reference level is derived from the most persistent land surface coverage across different grassland types and all years. Overall, 9.3% of the study area had a significant increase in vegetation cover, 14.2% a potential increase, 48.6% of the area showed a stable vegetation condition, 9.8% showed a potential decrease, and 18.1% a significant decrease in vegetation cover. The largest proportion of combined potential and significant reduction was 35.2% for desert grassland, where the vegetation faced the most severe reduction. This study will provide a basis for identifying grassland degradation and developing scientific management policies.

The impacts of armed conflict on vegetation cover degradation in Tigray, northern Ethiopia

Efforts made to restore the degraded landscape of the Tigray region, Northern Ethiopia, over the last three decades have been relatively successful. However, an armed conflict that broke out in the region in November 2020 has significantly destroyed the restored vegetation, either directly associated with conflict (environment, pollution, fire) or indirectly (agricultural abandonment). This study aimed at assessing spatio-temporal changes in vegetation cover in a 50 km radius zone centered on Mekelle city, Tigray. Vegetation cover dynamics was evaluated using Landsat Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) datasets for the years 2000, 2020, and 2022 and analysed using ENVI 5.3 and ArcGIS 10.8.1 software. These data were analysed using the Modified Normalized Difference Vegetation Index (MNDVI), Optimized Soil Adjusted Vegetation Index (OSAVI), and Moisture Adjusted Vegetation Index (MAVI). Based on the MNDVI, results show that vegetation cover increased in the period 2000–2020 by 179 km2 or 2% of the area, whereas in the period 2020–2022, there was a decrease in vegetation cover by 403 km2 or 5% of the area. This was accompanied by a decrease in vegetation density. These vegetation changes in 2020–2022 are attributed to the impact of armed conflict on the land surface which can include farmlands and village abandonment, spread of weeds and scrub vegetation, or failure to harvest crops. Monitoring vegetation change using Landsat data can help understand the environmental impacts of armed conflict in rural agricultural landscapes, including potential food security risks.

Relative performance evaluation of machine learning algorithms for land use classification using multispectral moderate resolution data

Analyses of spatial and temporal patterns of land use and land cover through multi-resolution remote sensing data provide valuable insights into landscape dynamics. Land use changes leading to land degradation and deforestation have been a prime mover for changes in the climate. This necessitates accurately assessing land use dynamics using a machine-learning algorithm’s temporal remote sensing data. The current study investigates land use using the temporal Landsat data from 1973 to 2021 in Chikamagaluru district, Karnataka. The land cover analysis showed 2.77% decrease in vegetation cover. The performance of three supervised learning techniques, namely Random Forest (RF), Support Vector Machine (SVM), and Maximum Likelihood classifier (MLC) were assessed, and results reveal that RF has performed better with an overall accuracy of 90.22% and a kappa value of 0.85. Land use classification has been performed with supervised machine learning classifier Random Forest (RF), which showed a decrease in the forest cover (48.91%) with an increase of agriculture (6.13%), horticulture (43.14%) and built-up cover (2.10%). Forests have been shrinking due to anthropogenic forces, especially forest encroachment for agriculture and industrial development, resulting in forest fragmentation and habitat loss. The fragmentation analysis provided the structural change in the forest cover, where interior forest cover was lost by 27.67% from 1973 to 2021, which highlights intense anthropogenic pressure even in the core Western Ghats regions with dense forests. Temporal details of the extent and condition of land use form an information base for decision-makers.

Dynamic monitoring of vegetation coverage in weibei dry plateau based on remote sensing

Taking Fu County, a typical area of Weibei dry plateau, as the research object, the normalized difference vegetation index ( NDVI ) was calculated by using Landsat 8 OLI remote sensing image. On this basis, the vegetation coverage in the study area was estimated and graded according to the binary pixel model, and the dynamic changes of vegetation coverage in the study area from 2013 to 2017 were quantitatively analyzed. Results showed that the overall NDVI value in the study area increased from 2013 to 2017. The vegetation cover in the study area is dominated by extremely high vegetation cover. The coverage area of low, medium and high vegetation cover decreases, and the coverage area of extremely high and extremely low vegetation cover increases. The coverage area of high vegetation and extremely high vegetation cover accounts for more than 75% of the study area. The study area showed a transfer trend of extremely low vegetation→low vegetation→medium vegetation→high vegetation coverage. The vegetation coverage generally developed to a good trend, but there was also a transfer of high vegetation coverage to medium vegetation coverage. The areas with the greatest changes in the study area are mainly concentrated in the northwest and near the southern Damagou, and the high NDVI area of Ziwuling National Nature Reserve has moved eastward and narrowed. Accuracy assessment indicates that the dynamic monitoring using the fused image time series produces results with relatively high accuracy. Bangladesh J. Bot. 52(2): 479-486, 2023 (June) Special