Land degradation is a critical global issue with profound implications for ecosystem services and environmental stability. This study investigates land degradation and vegetation dynamics in a typical semi-arid region, Ningxia Hui Autonomous Region of China, using MODIS NDVI data and integrating climatic variables to assess their long-term impact on vegetation growth. The analysis reveals substantial vegetation improvement during 2001–2022, with 73.26

Snow algae, microscopic organisms thriving in snow-covered environments, significantly affect snow albedo and broader climatic processes. This study introduces the Algae Presence Index (API), a novel spectral tool using Sentinel-2 multispectral imagery to detect and classify red and green algae on King George Island, Antarctica. From 2019 to 2023, we analyzed temporal and spatial variations in algae presence during austral summers and observed corresponding reductions in surface albedo, demonstrating how algal blooms influence snowmelt. Green algae showed a stronger albedo reduction (up to 8.46 %) compared to red algae (5.33 %), emphasizing their greater role in accelerating snowmelt. The API outperformed traditional indices, such as the red/green band ratio and Red-Green Normalized Difference. It eliminated spectral overlap and accurately distinguished algae types from algae-free snow. These findings underscore the critical role of snow algae in climate feedback mechanisms and highlight the importance of monitoring their growth during Antarctic warming. This methodology provides a robust framework for assessing algae impacts on the cryosphere, with important implications for climate models and conservation.

Understanding the drivers of vegetation dynamics in arid and semi-arid regions is critical for achieving sustainable land management, ecological restoration, and climate resilience. Vegetation dynamics influence key ecosystem processes, including energy balance, carbon sequestration, soil stabilization, and water regulation, making them pivotal for the health of terrestrial systems. In recent decades, these fragile environments have experienced rapid changes driven by both anthropogenic interventions and climate variability. This study aims to separate and evaluate the relative contributions of climate change and human activities to vegetation changes in two major dryland ecosystems: China’s Mu Us Desert and Pakistan’s Thar Desert. Using MODIS NDVI satellite data from 2001 to 2023, we analyzed spatiotemporal trends in vegetation activity. Statistical approaches, including the Mann-Kendall trend test and Sen’s slope estimator, were applied to quantify trends, while Pearson correlation and threshold segmentation distinguished the impacts of climate variability, multi-controls, and restoration efforts. Unlike earlier work, this study separately quantifies these drivers to differentiate their roles in vegetation greening. Our results reveal significant vegetation greening across 82.40 Detected vegetation increases by 82.40

Ecological vulnerability assessment is crucial for environment protection, ecological restoration and resource utilization. However, many former studies have limitations in the indicator system of the assessment, which were not comparable for different types of ecologically vulnerable areas. It is difficult to apply directly to the ecological vulnerability assessment of different types and in various regions. Aiming to solve these problems, the study proposed a well-established and comprehensive indicator system for ecological vulnerability assessment and conducted ecological vulnerability assessment application of five types of typical ecologically vulnerable areas of China based on remote sensing, meteorological, geographic and other data. The results show that the average EVIs value of Zhangbei County ranging from 0.525 to 0.559 are the highest among the five research areas during the four periods, followed by Zoige region and Xiamen bay. However, the region with the lowest average EVI value varies. In 2005 and 2015, it was Taihe County, while in 2010 and 2020, it was the Sanjiangyuan region. The variation of average EVIs in the five typical areas presents slight fluctuation and remains generally stable from 2005 to 2020. It indicates that the environmental protection measures and projects undertaken by the Chinese government in recent years have had a striking effect, curbing the trend of ecological environment deterioration.

Precise estimation of net radiation (Rn) is fundamental to understanding surface energy balance and is critical for accurately determining crop water requirements, especially using remote sensing and geospatial techniques. The core objective of this study is to evaluate multi-satellite-based net radiations on major cropped areas of the Punjab and Sindh provinces of Pakistan. In this study, overlapping scenes from the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat 8, and Sentinel 2 were used from 2016 to 2020 along with three temperature products MOD11A1, Landsat 8 (brightness temperature), and ERA5. The multi-satellite-based net radiation estimations on overlapping days were compared with the Global Land Data Assimilation System (GLDAS) dataset. The models based on Landsat 8 and Sentinel 2 data exhibited good performance, with a Nash–Sutcliffe Efficiency (NSE) of 68.9%, a mean error (ME) of 13.918 W/m2, and a bias of 50.669 W/m2. The results indicated that Landsat 8 and Sentinel 2 data produced reliable estimations of net radiation, while MODIS data tended to overestimate due to its higher spatial resolution and broader coverage area. Landsat 8-based estimations are good compared to others, as it has good correlation coefficient and lower RMSE values. The study concludes that Landsat 8 provides the most reliable estimates of net radiation for determining crop water requirements, outperforming other datasets in accuracy. The findings underscore the importance of using high-resolution multi-satellite data for precise agricultural water management, recommending its use in future studies and water resource planning in Pakistan.

Restoration of degraded land is a significant concern in the 21st century in order to combat the impacts of climate change. For this reason, the provisional government of Khyber Pakhtunkhwa (KPK), Pakistan, initialized a Billion Tree Tsunami Project (BTTP) in 2013 and finished it in 2017. Although a few researchers have investigated the land use transitions under BTTP in the short term by merging all the vegetation types into one, analysis of the long-term benefits of the project and future persistence were missing. Furthermore, the previous studies have not discussed whether the prime objective of the BTTP was achieved. Considering the existing gaps, this research mainly involves analyzing (i) fluctuations in the green fraction by employing a land change modeler (LCM), along with the spatial location of gain-loss and exchange analysis using a high-resolution dataset (GLC30); (ii) forest cover changes under the influence of the BTTP; (iii) impacts of green fraction changes towards land surface temperature (LST) by utilizing the less-explored technique of curve fit linear regression modeling (CFLR); and finally, (iv) assessing the persistence of the NDVI and LST trends by employing the Hurst exponent. Research findings indicate that as an output of BTTP, despite the government’s claim of increasing the forest cover by 2%, a significant gain of grassland (3904.87 km2) was observed at the cost of bare land. In comparison, the overall increase in forest cover was only 0.39%, which does not satisfy the main objective of this project. On the other hand, the CFLRM-based actual contributions of land cover change (LCC) transition to LST indicate a significant decline in LST in the areas with gains in green fraction for both grassland and forest. At the same time, an increase was observed with reverse transitions. Although the results appear positive for climatic impacts in the short term, the HURST model-based persistence analysis revealed that the spatial locations of increasing vegetation and decreasing LST trends fall under the weakly persistent category, therefore these trends may not continue in the near future. Despite some positive impact on LST attributed to the green fraction increase, this project cannot be regarded as a complete success due to its failure to achieve its prime objective.

Global land cover dynamics alter energy, water, and greenhouse gas exchange between land and atmosphere, affecting local to global weather and climate change. Although reforestation can provide localized cooling, ongoing land use land cover (LULC) shifts are expected to exacerbate urban heat island impacts. In this study, we monitored spatiotemporal changes in green cover in response to land use transformation associated with the Khyber Pakhtunkhwa (KPK) provincial government’s Billion Tree Tsunami Project (BTTP) and the Ravi Urban Development Plan (RUDP) initiated by the provincial government of Punjab, both in Pakistan. The land change modeler (LCM) was used to assess the land cover changes and transformations between 2000 and 2020 across Punjab and KPK. Furthermore, a curve fit linear regression model (CFLRM) and sensitivity analysis were employed to analyze the impacts of land cover dynamics on land surface temperature (LST) and carbon emissions (CE). Results indicated a significant increase in green fraction of +5.35% under the BTTP, achieved by utilizing the bare land with an effective transition of 4375.87 km2. However, across the Punjab province, an alarming reduction in green fraction cover by −1.77% and increase in artificial surfaces by +1.26% was noted. A significant decrease in mean monthly LST by −4.3 °C was noted in response to the BTTP policy, while an increase of 5.3 °C was observed associated with the RUDP. A substantial increase in LST by 0.17 °C was observed associated with transformation of vegetation to artificial surfaces. An effective decrease in LST by −0.21 °C was observed over the opposite transition. Furthermore, sensitivity analysis suggested that LST fluctuations are affecting the % of CO2 emission. The current findings can assist policymakers in revisiting their policies to promote ecological conservation and sustainability in urban planning.

Vegetation cover change and its interaction with climate are significant to study as it has impact on ecosystem stability. We used the Normalized Difference Vegetation Index (NDVI) and climatic factors (temperature and rainfall) for investigating the relationship between vegetation and climate. We also traced spatiotemporal changes in the vegetation in Pakistan from 2000 to 2020; we used the Hurst exponent to estimate future vegetation trends in Pakistan. Our results show an increase in vegetation throughout Pakistan, and the Punjab Province is showing the highest significant vegetation trend at 88.51%. Our findings reveal that the response of vegetation to climate change varies by region and is influenced by local climatic conditions. However, the relationship between rainfall and annual NDVI is stronger than the temperature in the study area-Pakistan. The Hurst exponent value is above 0.5 in all four provinces, that is, the indication of consistent vegetation trends in the future. The highest values are observed in Punjab and Khyber Pakhtunkhwa (KPK). In the Punjab Province, 88.41% of the area showed positive development, with forests in particular showing a significant positive effect on land use classes. On the other hand, the Sindh Province has the highest negative result at 2.87%, with urban areas showing the highest negative development. To sum up, the NDVI pattern and change attribute suggest vegetation restoration in Pakistan.

We presented a framework to evaluate the land use transformations over the Eurasian Steppe (EUS) driven by human activities from 2000 to 2020. Framework involves three main components: (1) evaluate the spatial-temporal dynamics of land use transitions by utilizing the land change modeler (LCM) and remote sensing data; (2) quantifying the individual contributions of climate change and human activities using improved residual trend analysis (IRTA) and pixel-based partial correlation coefficient (PCC); and (3) quantifying the contributions of land use transitions to Leaf Area Index Intensity (LAII) by using the linear regression. Research findings indicate an increase in cropland (+1.17 % = 104,217 km2) over EUS, while a - 0.80 % reduction over Uzbekistan and - 0.16 % over Tajikistan. From 2000 to 2020 a slight increase in grassland was observed over the EUS region by 0.05 %. The detailed findings confirm an increase (0.24 % = 21,248.62 km2) of grassland over the 1st half (2000-2010) and a decrease (-0.19 % = -16,490.50 km2) in the 2nd period (2011-2020), with a notable decline over Kazakhstan (-0.54 % = 13,690 km2), Tajikistan (-0.18 % = 1483 km2), and Volgograd (-0.79 % = 4346 km2). Area of surface water bodies has declined with an alarming rate over Kazakhstan (-0.40 % = 10,261 km2) and Uzbekistan (-2.22 % = 8943 km2). Additionally, dominant contributions of human activities to induced LULC transitions were observed over the Chinese region, Mongolia, Uzbekistan, and Volgograd regions, with approximately 87 %, 83 %, 92 %, and 47 %, respectively, causing effective transitions to 12,997 km2 of cropland, 24,645 km2 of grassland, 16,763 km2 of sparse vegetation in China, and 12,731.2 km2 to grassland and 15,356.1 km2 to sparse vegetation in Mongolia. Kazakhstan had mixed climate-human impact with human-driven transitions of 48,568 km2 of bare land to sparse vegetation, 27,741 km2 to grassland, and 49,789 km2 to cropland on the eastern sides. Southern regions near Uzbekistan had climatic dominancy, and 8472 km2 of water bodies turned into bare soil. LAII shows an increasing trend rate of 0.63 year-1, particularly over human-dominant regions. This study can guide knowledge of oscillations and reduce adverse impacts on ecosystems and their supply services.

The large area estimation of forest canopy closure (FCC) using remotely sensed data is of high interest in monitoring forest changes and forest health, as well as in assessing forest ecological services. The accurate estimation of FCC over the regional or global scale is challenging due to the difficulty of sample acquisition and the slow processing efficiency of large amounts of remote sensing data. To address this issue, we developed a novel bounding envelope methodology based on vegetation indices (BEVIs) for determining vegetation and bare soil endmembers using the normalized differences vegetation index (NDVI), modified bare soil index (MBSI), and bare soil index (BSI) derived from Landsat 8 OLI and Sentinel-2 image within the Google Earth Engine (GEE) platform, then combined the NDVI with the dimidiate pixel model (DPM), one of the most commonly used spectral-based unmixing methods, to map the FCC distribution over an area of more than 90,000 km(2). The key processing was the determination of the threshold parameter in BEVIs that characterizes the spectral boundary of vegetation and soil endmembers. The results demonstrated that when the threshold equals 0.1, the extraction accuracy of vegetation and bare soil endmembers is the highest with the threshold range given as (0, 0.3), and the estimated spatial distribution of FCC using both Landsat 8 and Sentinel-2 images were consistent, that is, the area with high canopy density was mainly distributed in the western mountainous region of Chifeng city. The verification was carried out using independent field plots. The proposed approach yielded reliable results when the Landsat 8 data were used (R-2 = 0.6, RMSE = 0.13, and 1-rRMSE = 80%), and the accuracy was further improved using Sentinel-2 images with higher spatial resolution (R-2 = 0.81, RMSE = 0.09, and 1-rRMSE = 86%). The findings demonstrate that the proposed method is portable among sensors with similar spectral wavebands, and can assist in mapping FCC at a regional scale while using multispectral satellite imagery.

A global challenge to sustainable development is land degradation, and to achieve land degradation neutrality, monitoring, mapping, and impact assessment of ongoing ecological restoration efforts is necessary. Here, we analyze the desertification process and role of restoration projects at a spatial and temporal scale in Mu Us Desert from 2001 to 2018. We used 17 years of data to (1) assess the vegetation trend including its significance and map land degradation based on Sustainable Development Goal Indicator 15.3.1 (2) address how vegetation activity has changed under the influence of restoration programs and climate change (3) estimate how successful are the positive human interventions to achieve Land degradation neutrality. Results showed an overall increasing vegetation trend (85.69% significant increasing) and a partial decreasing vegetation trend (1.33% significant decreasing) in Mu Us desert. Ecological restoration activities are found to be one of the key driving forces of vegetation restoration in the desert, however, limited impact of climatic factors on vegetation cover change was observed. Results revealed that 41.42% of total significant restoration is attributed to ecological restoration programs out of which 40.42% area has shown improvement in all three sub-indicators of land degradation.

The authors wish to make the following corrections to this paper [...]

The novel coronavirus (COVID-19) infectious respiratory disease becomes a global pandemic in few weeks from its start in December 2019 to early 2020. Various countries across the world including China went to lockdown and several caution were implemented to reduce the further spread of this infectious disease. Wuhan (China) was the first city to impose the lockdown for controlling the impact of COVID-19. The lockdown unexpectedly gives the scientific community a chance to investigate the influence of the human activity on air pollution in real world scenario. The present study attempted to investigate the impact of lockdown during the ongoing viral disease on the changes of fine particulate matters and some unhealthy gases i.e. PM2.5, PM10, SO2, CO, O3, AQI and NO2 over Hubei province of China, by using ground station data and TROPOMI satellite data. The air pollutants were compared as, (i) pre COVID-19 period (i.e. October-December 2019), (ii) throughout the lockdown in Hubei province (i.e. January 2020-March 2020) and Post lockdown duration (i.e. April 2020-June 2020). Results clearly showed that air quality was not secured due to high emission of CO, SO2, NO2, O3, PM2.5, and PM10 on Pre COVID-19 times, but under the lockdown continuously decrease in NO2 from (54 mg/cm3 to 26 mg/cm3), SO2 (10.5 mg/cm3 to 7.77 mg/cm3) PM2.5 (49.22 mg/cm3 to 44.34 mg/cm3), PM10 concentrations (80.83 mg/cm3 to 57.04 mg/cm3) and AQI (72.95 mg/cm3 to 49.64 mg/cm3) has been observed. Because lockdown shuts all anthropogenic activities like industrial work, traffic vehicles and various socio-economic activities, which developed a healthy change on air quality. Emission of unhealthy gases and particulates were quite clear during the lockdown but again increase after finishing the lockdown period. However, we don’t support the lockdown as a measure for the betterment of air quality as this has severely posed negative impacts on the socio-economic processes and progress, but changes in human behavior of using industries and vehicles can help us to improve the air quality.

This study emphasizes the awareness and training of farmers on sustainable agricultural practices for restrictive use of pesticides to reduce water pollution caused by pesticides in rural areas. For this assessment, 150 feasible farmers were selected randomly using a convenient sampling technique. Data was collected through interviews (n=30) and questionnaires (n=120). Data was scrutinized in SPSS software. To examine freshwater contamination with pesticides, freshwater samples were collected from open water bodies (5 wells) in the area of tehsil Hasilpur (29.6902° N, 72.5796° E) and Qaimpur of Bahawalpur, Punjab, and analyzed for eight pesticides which are frequently used. ECD: Electron Capture Detector was used to detect the organochlorine pesticides, herbicides, and various halogenated hydrocarbons. NPD: Nitrogen Phosphorus Detector was used to detect nitrogen- or phosphorus[1]containing compounds. Approximately 80% of farmers revealed that they never got any training or informative 64 sessions for pest management other than using chemical pesticides. The remaining 20% were trained by some private non-toxic pesticide manufacturing firms operating in Pakistan. Although non-significant cooperation from the government bodies of the state, the correlation among respondents’ profiles and the extent of training they have received in total was positive. Results for pesticide contamination were astonishing as 6 out of 8 pesticides were detected in water samples with alarming concentrations of Carbofuran (23.1µg/l) and Monocrotophos (8.3µg/l) which can be fatal for animals and humans in prolonged usage. The use of pesticides in a preventive and sustainable way gives more protection against pests and minimizes pollution such as water pollution or air pollution, etc., caused by the chemicals (pesticides). Hence such sustainable practices must be adopted for better production and conservation of the environment.

Accurate information on tree species is in high demand for forestry management and further investigations on biodiversity and environmental monitoring. Over regional or large areas, distinguishing tree species at high resolutions faces the challenges of a lack of representative features and computational power. A novel methodology was proposed to delineate the explicit spatial distribution of six dominant tree species (Pinus tabulaeformis, Quercus mongolia, Betula spp., Populus spp., Larix spp., and Armeniaca sibirica) and one residual class at 10 m resolution. Their spatial patterns were analyzed over an area covering over 90,000 km(2) using the analysis-ready large volume of multisensor imagery within the Google Earth engine (GEE) platform afterwards. Random forest algorithm built into GEE was used together with the 20th and 80th percentiles of multitemporal features extracted from Sentinel-1/2, and topographic features. The composition of tree species in natural forests and plantations at the city and county-level were performed in detail afterwards. The classification achieved a reliable accuracy (77.5% overall accuracy, 0.71 kappa), and the spatial distribution revealed that plantations (Pinus tabulaeformis, Populus spp., Larix spp., and Armeniaca sibirica) outnumber natural forests (Quercus mongolia and Betula spp.) by 6% and were mainly concentrated in the northern and southern regions. Arhorchin had the largest forest area of over 4500 km(2), while Hexingten and Aohan ranked first in natural forest and plantation area. Additionally, the class proportion of the number of tree species in Karqin and Ningcheng was more balanced. We suggest focusing more on the suitable areas modeling for tree species using species' distribution models and environmental factors based on the classification results rather than field survey plots in further studies.

Urbanization is an increasing phenomenon around the world, causing many adverse effects in urban areas. Urban heat island is are of the most well-known phenomena. In the present study, surface urban heat islands (SUHI) were studied for seven megacities of the South Asian countries from 2000–2019. The urban thermal environment and relationship between land surface temperature (LST), land use landcover (LULC) and vegetation were examined. The connection was explored with remote-sensing indices such as urban thermal field variance (UTFVI), surface urban heat island intensity (SUHII) and normal difference vegetation index (NDVI). LULC maps are classified using a CART machine learning classifier, and an accuracy table was generated. The LULC change matrix shows that the vegetated areas of all the cities decreased with an increase in the urban areas during the 20 years. The average LST in the rural areas is increasing compared to the urban core, and the difference is in the range of 1–2 (°C). The SUHII linear trend is increasing in Delhi, Karachi, Kathmandu, and Thimphu, while decreasing in Colombo, Dhaka, and Kabul from 2000–2019. UTFVI has shown the poor ecological conditions in all urban buffers due to high LST and urban infrastructures. In addition, a strong negative correlation between LST and NDVI can be seen in a range of −0.1 to −0.6.

The novel infectious disease (COVID-19) took only a few weeks from its official inception in December 2019 to become a global pandemic in early 2020. Countries across the world went to lockdown, and various strict measures were implemented to reduce the further spread of the infection. Although, the strict lockdown measures were aimed at stopping the spread of COVID-19, however, Its positive implications were also observed for the environmental conditions across the global regions. The present study attempted to explore the eco-restoration of coastal marine system in response to reduced deposition of atmospheric nitrogen (NO2) emission during the substantial shift in human activities across the global metropolitan cities. Remotely data of NO2 emission were taken from Ozone Monitoring Instrument and the coastal water quality along the marine system was estimated from MODIS-Aqua Level-3 using Semi-Analytic Sediment Model (SASM). The changes in tropospheric NO2 in 2020s were also compared with the long-term average changes over the baseline period 2015 − 2019. A significant reduction in anthropogenic mobility (85 − 90%) has been observed in almost all countries over different places, especially grocery, parks, workplaces, and transit stations. A massive reduction in tropospheric NO2 was detected in Wuhan (53%), Berlin (42%), London (41%), Karachi (40%), Paris (38%), Santiago (35%), and Chennai (34%) during the strict lockdown period of the early 2020 as compared to the last five years. However, after the partial lockdown was lifted, tropospheric NO2 values bounced back and slightly increased over Karachi (6%) and Bremen (12%). For water turbidity, the rate of reduction was found to be the highest along the different coastal regions of the Mediterranean Sea and Black Sea (51%), West Atlantic Ocean (32%), East Atlantic Ocean (29%), and Indian Ocean (21%) from Apr to Jun 2020. The monthly comparison of overland-runoff in 2020 compared to 2019 across the different costal watersheds indicates that the observed decline in turbidity might have been due to the reduced deposition of atmospheric nitrogen. The findings of this study suggest that the recent decline in tropospheric NO2 and water turbidity might be associated with reduced emissions from fossil fuels and road transports followed by COVID-19 forced restrictions in the twenty-first century. The inferences made here highlight the hope of improving the global environmental quality by reducing greenhouse gas emissions using innovative periodic confinement measures on heavy transport and industries while securing public health and socioeconomics.

Background Accurate information on tree species is much in demand for forestry management and further investigations on biodiversity and forest ecosystem services. Over regional or large areas, discriminating tree species at high resolution is deemed challenging by lack of representative features and computational power. Methods A novel methodology to delineate the explicit spatial distribution of dominated six tree species (Pinus, Quercus, Betula, Populus, Larch, and Apricot) and one residual class using the analysis-ready large volume multi-sensor imagery within Google Earth Engine (GEE) platform is demonstrated and used to map a 10 m classification with detail analysis of spatial pattern for an area covering over 90,000 km 2 between 41° N and 45° N. Random Forest (RF) algorithm built into GEE was used for tree species mapping, together with the multi-temporal features extracted from Sentinel-1/2 and topographic imagery data. The composition of tree species in natural forests and plantations in city and county-level were performed in detail afterwards. Results The proposed model achieved a reliable overall agreement (77.5%, 0.71 kappa), and the detailed analysis on the spatial distributing of targeted species indicated that the plantations (Pinus, Populus, Larch, and Apricot) outnumber natural forests (Quercus and Betula) by 6%, and they were mainly grown in the northern and southern regions, respectively. Moreover, Arhorchin had the largest total forest area of over 4,500 km 2 , while Hexingten and Aohan ranked first in natural forest and plantation area, and the class proportion of the number of tree species in Karqin and Ningcheng was more balanced. Conclusions It is our belief that combined multi-source information of the machine learning algorithm within cloud platforms is beneficial to map a reliable spatial tree species over large areas on a fine scale. High-resolution tree species information based on online tools could be more easily considered for practical forestry management and further studies on forest ecosystems.

Aerosol pollution has become an increasingly serious problem in China. Among the multiple factors causing aerosol pollution, wildfires in China are occurring more frequently and have gradually become one of the most important contributing factors. However, little is known about their potential causality trends or spatial characteristics. In this research, satellite data of fire events and atmospheric aerosol datasets from 2001 to 2016 were applied in the geographical, statistical "Geodetector model" (GDM) to better understand their causal relationship. From long term observation data in China, we found that the increase in wildfires over the study period greatly enhanced their impacts on the aerosol optical depth (AOD) in recent years. The contribution of burning areas to AOD was 18.29% in 2001 and increased to 38.94% in 2016, and the contribution of fire radiative power (FRP) was 18.80% in 2001 and became 36.05% in 2016. In addition, seasonal research suggested that wildfires contributed rapidly to aerosol pollution, usually from April to September. The regional results in China showed that wildfires can be a relatively dominant factor for aerosol pollution in the southern regions, so that more importance should be attached to the complicated pollution conditions. Overall, our findings highlight the causal effects of wildfires on atmospheric aerosol pollution in China. We suggest that the rising contributions of wildfires to AOD in China should be noticed, and attention should be given to adaptions to local conditions regarding wildfires and aerosol pollution management.

Land use and land cover transition is continue process due to various anthropogenic activities and altering the landscape pattern of the metropolitan area of Lahore over the last two decade. LULC has been emerging masses of environmental problems including land use issues for the inhabitants, city planners, and managers. This research has been focused on the LULC change from 1998 to 2018 and their impact to forecast the landscape pattern of 2023 and 2028 in metropolitan area of Lahore. Numerous satellite imageries including land sat 5 TM and landsat 8 OLI has been undergone by supervised classification for the preparation of LULC map, land change moderler MCL to calculate the transition in LULC and CA-Markov model for predicting upcoming transitions. Results have validated by transition matrix, Google Earth data and kappa statistics. Derived results has depicted the decreased in water bodies 2.70% to 0.60%, vegetation 24.90% to 22.60% and barren land 42.50% to 35.70% and increased in built up area 29.80 % to 41% between 1998 to 2018. Finding clearly represents the loss of ecological and barren landscape over the last two decade and therefore urban expansion will likely to continue the change in landscape. This study will provide a baseline reference to urban planners and policymakers to make informed decision for management of land resources, urban land planning and for maintain sustainable land development.