Impact assessment of land use change on surface temperature and agricultural productivity in Peshawar-Pakistan

Monitoring agricultural drought in Peshawar Valley, Pakistan using long -term satellite and meteorological data

Monitoring agricultural drought across a large area is challenging, especially in regions with limited data availability, like the Peshawar Valley, which holds great agricultural significance in Pakistan. Although remote sensing provides biophysical variables such as precipitation (P), land surface temperature (LST), normalized difference vegetation index (NDVI), and relative soil moisture (RSM) to assess drought conditions at various spatiotemporal scales, these variables have limited capacity to capture the complex nature of agricultural drought and associated crop responses. Here, we developed a composite drought index named "Temperature Vegetation ET Dryness Index" (TVEDI) by modifying the Temperature Vegetation Precipitation Dryness Index (TVPDI) and integrating NDVI, LST, and remotely sensed evapotranspiration (ET) using 3D space and Euclidean distance. Several statistical techniques were employed to examine TVPDI and TVEDI trends and relationships with other commonly used drought indices such as the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and standardized soil moisture index (SSI), as well as crop yield, to better understand how these indices captured the spatial and temporal distribution of agricultural drought in the Peshawar valley between 1986 and 2018. Results indicated that while the temporal patterns of the 3-month SPI, SPEI, and SSI generally align with those of TVEDI and TVPDI, TVEDI was more strongly correlated with these indices (e.g., correlation coefficient, r = 0.78-0.84 from TVEDI and r = 0.73-0.79 from TVPDI). Moreover, the crop yield, a measure of crop response to agricultural drought, demonstrated a significant positive correlation with TVEDI (r = 0.60-0.80), much higher than its correlation with TVPDI (r = 0.30-0.48). These outcomes indicate that the inclusion of ET in TVEDI effectively captured changes in soil moisture, crop water status, and their impact on crop yield. Overall, TVEDI exhibited enhanced capability to identify drought impacts compared to TVPDI, showing its potential for characterizing agricultural drought in regions with limited data availability.

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.

Spatiotemporal variation in the vegetation cover of Peshawar Basin in response to climate change

Climate factors like temperature, precipitation, humidity, and sunshine time exert a profound influence on vegetation. The intricate interplay between the two is crucial to understand in the face of changing climate to develop mitigation strategies. In the current exploration, we delve how climate variability (CV) has impacted the vegetation in the Peshawar Basin (PB) using remote sensing data tools. The trend of climatic variability was investigated using the modified Mann-Kendall test and Sen's slope statistics. The changing climatic parameters were regressed on the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The NDVI was further analyzed for spatiotemporal variability under land surface temperature (LST) influence. Results revealed that among the climate factors, average annual temperature and solar radiation have a significant (p < 0.05) negative impact on vegetation while precipitation and relative humidity significantly (p < 0.05) influence NDVI positively. The overall positive trend shows that vegetation improved between 2001 and 2020 with time, however some years (2010, 2012, 2014, 2016, and 2017) with low NDVI. NDVI varied in space considerably due to climatic extremes brought on by CV and the urbanization of agricultural land. NDVI regressed on LST showed that there was no or very little vegetation in the grids with high LST. The study concluded that the region is significantly impacted by both CV-related extreme weather events and anthropogenic activities. The vegetation is improving, but it is in danger of being destroyed by deforestation due to CV and human activities that exacerbate the risk of future calamities. To protect vegetation and avoid disasters, there is an immense need for adaptation and mitigation measures to deal with the region's fast-changing environment. The study urges local authorities to create climate-resilient governmental policies and supports regional sustainable development and vegetation restoration.

Do farmers' risk perception, adaptation strategies, and their determinants benefit towards climate change? Implications for agriculture sector of Punjab, Pakistan

Due to global and regional climatic dynamics for a couple of decades, agricultural productivity, rural livelihood, and food security have been badly affected in Pakistan. This study was conducted in Punjab, Pakistan, to explore the farmers' understanding of the impacts of climate change, adaptation strategies, determinants, and benefits on agriculture using data from 1080 respondents. Perceived risks by the farmers in the rice-wheat cropping system and the cotton-wheat cropping system were weed infestation, seed rate augmented, low-quality seeds, infestation of crop diseases and pests, change of cropping pattern, increase of input use, decrease of cropping intensity and productivity, decreasing soil fertility, increasing irrigation frequency, and increase of harvesting time. To alleviate the adverse influences of climate change, the adaptation strategies used by farmers were management of crop and variety, soil and irrigation water, diversification of agriculture production systems and livelihood sources, management of fertilizer and farm operations time, spatial adaptation, access to risk reduction measures and financial assets, adoption of new technologies, institutional support, and indigenous knowledge. Moreover, the results of Binary Logistic Regression indicate that adaptation strategies are affected by different factors like age, education, household family size, off-farm income, remittances, credit access, information on climatic and natural hazards, information on weather forecasting, land acreage, the experience of growing crops and rearing of livestock, tenancy status, tube well ownership, livestock inventory, access to market information, agricultural extension services, and distance from agricultural input/output market. There is a significant difference between adapters and nonadapters. The risk management system may be created to protect crops against failures caused by extreme weather events. There is a need to develop crop varieties that are both high yielding and resistant to climate change. Moreover, cropping patterns should be revised to combat the effects of climate change. To enhance farmers' standard of living, it is necessary to provide adequate extension services and a more significant number of investment facilities. These measures will assist farmers in maintaining their standard of living and food security over the long term to adapt to the effects of climate change based on various cropping zones.

Extreme flood in Pakistan: Is Pakistan paying the cost of climate change? A short communication

Global warming is one of the foremost causes of changes in climate patterns around the world. Pakistan is among the top ten countries affected by global warming. Today, Pakistan is facing severe consequences of global warming in the form of an extreme flood. It affected 33 million people, destroyed 1.5 million homes, and caused $2.3 billion in crop damage. It has also damaged more than 2000 km of roads, cutting off connectivity to provinces and major cities. Thus, inflation in Pakistan has reached its highest level, i.e. 26 % - 27 %, and a severe food crisis is not far away. Recently, Pakistan noted a record temperature of 40 °C in several territories, notably 51 °C in Jacobabad. The study reported that high temperatures, melting glaciers, heavy monsoon rains, government inattention, and poor governance are the key reasons of this severe flood. Moreover, in 2080, the average temperature in Pakistan is predicted to increase by 4.38 degrees Celsius. The study suggested that Supply of cheap seeds and fertilizers to farmers, maintenance of water supply infrastructure, availability of food and medicines through domestic and foreign assistance, and reduction of electricity rates and taxes in flood-affected areas can be the solution to stop this crisis. Similarly, building dams, investing in technology and training, and educating the general public about environmental change should be included in the long-term goals to avoid future disasters.

Seed Priming Modulates Physiological and Agronomic Attributes of Maize (Zea mays L.) under Induced Polyethylene Glycol Osmotic Stress

Drought and osmotic stresses are major threats to agricultural crops as they affect plants during their life cycle. The seeds are more susceptible to these stresses during germination and establishment of seedlings. To cope with these abiotic stresses, various seed priming techniques have broadly been used. The present study aimed to assess seed priming techniques under osmotic stress. Osmo-priming with chitosan (1 and 2%), hydro-priming with distilled water, and thermo-priming at 4 °C were used on the physiology and agronomy of Zea mays L. under polyethylene glycol (PEG-4000)-induced osmotic stress (-0.2 and -0.4 MPa). The vegetative response, osmolyte content, and antioxidant enzymes of two varieties (Pearl and Sargodha 2002 White) were studied under induced osmotic stress. The results showed that seed germination and seedling growth were inhibited under osmotic stress and germination percentage, and the seed vigor index was enhanced in both varieties of Z. mays L. with chitosan osmo-priming. Osmo-priming with chitosan and hydro-priming with distilled water modulated the level of photosynthetic pigments and proline, which were reduced under induced osmotic stress; moreover, the activities of antioxidant enzymes were improved significantly. In conclusion, osmotic stress adversely affects the growth and physiological attributes; on the contrary, seed priming ameliorated the stress tolerance resistance of Z. mays L. cultivars to PEG-induced osmotic stress by activating the natural antioxidation enzymatic system and accumulating osmolytes.

Evaluating land use/cover change associations with urban surface temperature via machine learning and spatial modeling: Past trends and future simulations in Dera Ghazi Khan, Pakistan

While urbanization puts lots of pressure on green areas, the transition of green-to-grey surfaces under land use land cover change is directly related to increased land surface temperature–compromising livability and comfort in cities due to the heat island effect. In this context, we evaluate historical and future associations between land use land cover changes and land surface temperature in Dera Ghazi Khan–one of the top cities in Pakistan–using multi-temporal Landsat data over two decades (2002–2022). After assessing current land use changes and future predictions, their impact on land surface temperature and urban heat island effect is measured using machine learning via Multi-Layer Perceptron-Markov Chain, Artificial Neural Network and Cellular Automata. Significant changes in land use land cover were observed in the last two decades. The built-up area expanded greatly (874 ha) while agriculture land (−687 ha) and barren land (−253 ha) show decreasing trend. The water bodies were found the lowest changes (57 ha) and vegetation cover got the largest proportion in all the years. This green-grey conversion in the last two decades (8.7%) and prospect along the main corridors show the gravity of unplanned urban growth at the cost of vegetation and agricultural land (−6.8%). The land surface temperature and urban heat island effect shows a strong positive correlation between urbanization and vegetation removal. The simulation results presented in this study confirm that by 2032, the city will face a 5° C high mean temperature based on historical patterns, which could potentially lead to more challenges associated with urban heat island if no appropriate measures are taken. It is expected that due to land cover changes by 2032, ~60% of urban and peri-urban areas will experience very hot to hot temperatures (&gt; 31.5°C). Our results provide baseline information to urban managers and planners to understand the increasing trends of land surface temperature in response to land cover changes. The study is important for urban resource management, sustainable development policies, and actions to mitigate the heat island effect. It will further asset the broader audience to understand the impact of land use land cover changes on the land surface temperature and urban heat island effect in the light of historic pattern and machine learning approach.

Do changes in land use, water bodies, and grazing pastures have a detrimental influence on environmental quality? Opportunities and threats to long-term growth

Land use activities mainly for economic and agricultural purposes have converted one third to one half of our planet's land surface into urban expansion and agricultural practice, which has had significant impacts on natural ecosystems, food production, and environmental quality, attracting the attention of researchers and policymakers. Consequently, land use is emerging as a fundamental issue in global environmental change and sustainable development. This study represents an addition to the prevailing literature by investigating the asymmetric impacts of land-use and land-cover changes on environmental quality in Pakistan using time series data from 1961 to 2016. Carbon dioxide (CO2) emissions were deemed a dependent variable (a proxy for environmental quality), whereas built-up land, cropland, water bodies, and grazing land were considered independent. A nonlinear ARDL bound testing technique (NARDL) was used to investigate dynamic cointegration among the study variables. Moreover, this study used the BDS test and structural break unit root test to confirm nonlinearity and stationarity of the data set. The results confirm that the variables exhibit asymmetrical co-integration. There is a symmetric unidirectional causation, running from built-up land and grazing land towards CO2 emissions with coefficients of 10.570 and 17.045, respectively. Furthermore, asymmetric causality shows that any positive shocks to built-up land (6.134) and water bodies (20.335) significantly cause CO2 emissions. Similarly, a negative shock to grazing land (16.470) also causes CO2 emissions. By contrast, a neutral effect was found between cropland and CO2 emissions.

Spatio-temporal assessment of land use dynamics and urbanization: linking with environmental aspects and DPSIR framework approach

Urbanization is the main force of the global environmental as well as land use land cover changes (LULC). Urbanization is caused by prompt increase in population growth, migration, and urge for employment. In this study, Geographic Information System (GIS) was applied for the analysis and representation of spatio-temporal changes in LULC in Peshawar district and these results were linked with environmental aspects and Driver-Pressure-State-Impact-Response (DPSIR) framework approaches. For LULC classification, the Landsat freely available satellite imageries were used. The analysis revealed that the vegetation cover has increased from 37.8% of the total area to 71.3% during 1990-2020 and this change in vegetation is attributed to the government initiatives of Billion Tree Tsunami afforestation project after 2014 which has substantially decreased the barren land (from 66% in 1990 to 19% in 2020) in southeastern part of Peshawar district. Although, there was reduction in the vegetation cover in the past but due to extensive plantation between 2014 and 2020 resulted rapid increase in vegetation cover in the study area. The results of the present study detected a remarkable increase in built-up area which has increased almost 224.6% from 1990 to 2020. The study area population has increased from 2.12 million during 1998 to 4.26 million in 2017. The DPSIR results revealed that drivers and pressure have adverse effects on the carrying capacity of natural resources which have resulted deterioration of ecosystem. The resulted reduced capacity leading towards land degradation, loss of agricultural land, decline the groundwater level and resulted in pluvial flooding in Peshawar district. Government and environmental protection agency should implement the land use bylaws to reduce the rapid and unplanned urban growth and its negative impacts on natural environment.

The effect of climate patterns on rice productivity in Pakistan: an application of Driscoll and Kraay estimator

This study assesses climate change impact on rice productivity in 28 top rice-producing districts in Pakistan. Driscoll and Kraay estimation is applied on panel data from 1981 to 2019. The cross-sectional dependency test results reveals that climatic factors such as rainfall, humidity, and temperature have correlation issue in different selected regions. The study observes that temperature, rice productivity, and rainfall have inverted U-shape relationship. Rice productivity response is quadratic instead of linear towards average rainfall and temperature during the particular cultivation time, comprising of harvesting, flowering, and planting. The coefficient of temperature during planting time is positive and significant, while the square of temperature during planting time is negative and significant. Temperature during flowering and harvesting time is significant and positive, while the square of temperature during flowering and harvesting time is negative and significant. Rain fall during planting and flowering time are positive and significant; besides that, fertilizer usage stimulates and humidity hinders rice productivity in selected districts of Pakistan. Our empirical results considered the issues of spatial dependency, serial correlation, and heterogeneity.

The relationship between land use land cover and land surface temperature using remote sensing: systematic reviews of studies globally over the past 5 years

The land surface temperature (LST) has been increased worldwide from time to time with the rapid increase of impervious surfaces such as built-up areas, parking lots, and asphalt and concrete roads. Several studies have examined the impacts of spatial dynamics of land use land cover (LULC) on the spatial variability of LST. However, there have not been systematic reviews conducted about the relationship between LULC and LST. Therefore, this study was conducted to investigate the relationship between LULC and LST with the main objective of synthesizing the relationship between LULC and LST using remote sensing data. An extensive literature search was conducted from the most familiar electronic databases such as Science Direct, Scopus, Web of Science, and Google Scholar between 27/08/2021 and 28/08/2021. The studies that are focussed on the relationship between LULC and LST and/or the impacts of LULC change on the LST using remote sensing were included for the analysis. Besides, papers conducted over the last 5 years (January/2016 to August/2021) were selected in this systematic study since this study focused on the most recent studies. In this systematic review, 100 studies were included for the study analysis. Based on the analysis of this study, built-up land has the first highest LST from the thirteen LULC types. Besides, bare land has a higher LST next to built-up land. On the other side, snow cover has the lowest LST among the LULC types. Lastly, waterbodies have a lower LST compared to vegetation cover.

Temperature Accuracy Analysis by Land Cover According to the Angle of the Thermal Infrared Imaging Camera for Unmanned Aerial Vehicles

Land surface temperature (LST) is one of the crucial factors that is important in various fields, including the study of climate change and the urban heat island (UHI) phenomenon. The existing LST was acquired using satellite imagery, but with the development of unmanned aerial vehicles (UAV) and thermal infrared (TIR) cameras, it has become possible to acquire LST with a spatial resolution of cm. The accuracy evaluation of the existing TIR camera for UAV was conducted by shooting vertically. However, in the case of a TIR camera, the temperature value may change because the emissivity varies depending on the viewing angle. Therefore, it is necessary to evaluate the accuracy of the TIR camera according to each angle. In this study, images were simultaneously acquired at 2–min intervals for each of the three research sites by TIR camera angles (70°, 80°, 90°). Then, the temperature difference by land cover was evaluated with respect to the LST obtained by laser thermometer and the LST obtained using UAV and TIR. As a result, the image taken at 80° showed the smallest difference compared with the value obtained with a laser thermometer, and the 70° image showed a large difference of 1–6 °C. In addition, in the case of the impervious surface, there was a large temperature difference by angle, and in the case of the water-permeable surface, there was no temperature difference by angle. Through this, 80° is best when acquiring TIR data, and if it is impossible to take images at 80°, it is considered good to acquire TIR images between 80° and 90°. To obtain more accurate LST, correction studies considering the external environment, camera attitude, and shooting height are needed in future studies.

Flood hazards and agricultural production risks management practices in flood-prone areas of Punjab, Pakistan

Climate induced disasters, more specifically floods, have caused severe damage to the agriculture sector in Pakistan. These climatic risks have constrained the farming community to adopt risk management strategies to overcome such climate change risks. This research work attempted to examine the association of risk management tools with farmers' perception of risk, risk averse attitude, and various socioeconomic factors. The study has employed the sample data of 398 farmers from two high-risk flood-prone districts of Punjab, Pakistan. The multivariate probit model was used in this study to investigate the association of dependent and independent variables. The findings of the study indicated that small farmers consider heavy rains and floods severe risks to their agricultural production and are more risk averse than large farmers. Estimates of a multivariate probit model interpreted as age of farmer (0.036), heavy rains risk perception (0.597), and landholding size (0.114) were positively related with the risk management tool of depletion of assets. Farmers' education (0.056), off-farm income (3.47), age (0.018), and risk averse attitude of farmer (0.687) were positive, whereas experience of farming (-0.037) was negatively linked with reduction of consumption. Furthermore, experience of farming (0.005), risk averse attitude (0.493), heavy rains (0.481), and flood risk perception (0.536) were positively related with diversification adoption. The flood-prone farming community is more vulnerable to these climatic risks and rely on traditional strategies for risk management. There is a need to adopt crop diversification based on developing research capacity for innovative crop varieties having resistance to floods and climate change affects. Some significant policy measures, such as a more resilient scenario of climate change and floods, need to stimulate activities of enterprise diversification, opportunities of diversifying employment, and strengthening activities of off-farm employment for the sound livelihood of flood-prone farmers and to minimize severe affects of climatic risks.

Environmental quality and the asymmetrical nonlinear consequences of energy consumption, trade openness and economic development: prospects for environmental management and carbon neutrality

Economic expansion gives rise to modern and energy-efficient technologies and, thus, contributes to a decline in energy usage. Developing countries, including Pakistan, require tremendous efforts to sustain economic growth. However, to attain economic growth, these countries have to cope with greenhouse gas (GHG) emissions and other environmental problems. This research focuses primarily on the asymmetric impacts of energy consumption and economic growth on Pakistan's environmental quality. Accordingly, secondary data spanning from 1971 to 2018 was used, and carbon dioxide emission (CO2) was considered a target variable (a proxy for environmental quality), whereas energy consumption (E) and gross domestic product (GDP) as a proxy for economic growth, and trade accessibility (TR) and foreign direct investment (FDI) as control variables. The nonlinear autoregressive distributed lag (NARDL) approach is used to verify the asymmetric co-integration between the variables selected. Moreover, to examine data stationarity and nonlinearity, we used the Zivot-Andrews structural break unit root and BDS tests, respectively. The findings confirmed the asymmetric and symmetric co-integrations among the considered variables. In addition, the causality analysis reveals that only negative shocks to TR have an effect on CO2 emissions. Similarly, negative shocks to FDI asymmetrically cause CO2 emissions. Meanwhile, GDP symmetrically affects CO2 emissions. Finally, a neutral causal response was observed between E and CO2 emissions. These findings have policy implications in terms of environmental management and carbon neutrality, and they serve as a baseline for future research.

An impact of climate change and groundwater salinity on shadow price of water, farmers' revenue, and socioeconomic and environmental indicators in district Kohat-Pakistan

Globally, agricultural productivity is adversely impacted due to climatic changes as the temperatures rises and precipitation decreases, and especially in Pakistan, which ultimately enhanced groundwater salinity and harmed water quality in the country. However, the impacts of groundwater salinity and climate change on farmers' revenue have not been fully understood in Pakistan. Therefore, the focus of current research is the assessment of shadow price of water, farmers' revenue, and socioeconomic and environmental indicators affected by variations in groundwater salinity, precipitation, and temperature. The estimation of crop yield sensitivity to groundwater salinity, precipitation, and temperature and their prediction for 2030, 2040, and 2050 time periods was accomplished through the technique of General Maximum Entropy and Response-Yield function. Moreover, the assessment of groundwater quality and climate variable impacts on socioeconomic and environmental indicators was obtained through Target Motad-PMP model. In the end, the most suitable climate change scenario in the study area was established by applying a multi-criteria decision-making method. The results revealed that groundwater salinity and temperature expressed a significantly increasing trend with the Z values of 5.82 and 2.15, respectively. While the precipitation depicted a significantly decreasing trend (Z value = -3.37). The negative impact of climatic changes and groundwater salinity was revealed for revenue risk and shadow prices of water. The most negative impact on income risk and shadow prices is during 2050 horizon with a decrease by 11.4 and 19.4% respectively. The environmental index is the most important with a priority of 43.4% compared to the socio-economic indicators. The sub-index water use is also significant in the study area with a priority of 28.1%. A2 is the most appropriate climate scenario conferring to the TOPSIS ranking method. Therefore, the A2 scenario should be taken into account for the policy of adaptation to the climate change wonder in district Kohat.

The Effect of Urban Land-Use Change on Runoff Water Quality: A Case Study in Hangzhou City

The main functions of this research are to guide the proportion of urban land that is used and the layout of the facilities on it, help understand the changes to surface runoff that are caused by land being used in urban development, and thus solve surface runoff pollution. Hangzhou City, China has been selected for the experiment, and the way in which its land is utilized as well as the grading of urban construction projects in the demonstration area are specifically analyzed. This study systematically distinguishes the definitions of impervious area based on the Sutherland equation and analyzes the impact of different impervious area subtypes on surface runoff water quality. Then, we compare the impact of impervious area subtypes with the impact of other land-use patterns on surface runoff water quality. This study shows the relationship between different land-use types and runoff water bodies: Land-use index can affect runoff water quality; Greening activities, impervious surface, and the water quality index are negatively correlated; the effective impervious area rate is positively correlated with the water quality index. The paper suggests that increasing the proportion of green spaces and permeable roads in build-up land reduces the effective impervious area (EIA) and thus controls land runoff pollution and improves runoff water quality.

A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

This paper synthesizes research studies on spatial forest assessment and mapping using remote sensing data and techniques in Pakistan. The synthesis states that 73 peer-reviewed research articles were published in the past 28 years (1993–2021). Out of all studies, three were conducted in Azad Jammu & Kashmir, one in Balochistan, three in Gilgit-Baltistan, twelve in Islamabad Capital Territory, thirty-one in Khyber Pakhtunkhwa, six in Punjab, ten in Sindh, and the remaining seven studies were conducted on national/regional scales. This review discusses the remote sensing classification methods, algorithms, published papers’ citations, limitations, and challenges of forest mapping in Pakistan. The literature review suggested that the supervised image classification method and maximum likelihood classifier were among the most frequently used image classification and classification algorithms. The review also compared studies before and after the 18th constitutional amendment in Pakistan. Very few studies were conducted before this constitutional amendment, while a steep increase was observed afterward. The image classification accuracies of published papers were also assessed on local, regional, and national scales. The spatial forest assessment and mapping in Pakistan were evaluated only once using active remote sensing data (i.e., SAR). Advanced satellite imageries, the latest tools, and techniques need to be incorporated for forest mapping in Pakistan to facilitate forest stakeholders in managing the forests and undertaking national projects like UN’s REDD+ effectively.

Impact of land use/land cover changes on water quality and human health in district Peshawar Pakistan

The quality and quantity of groundwater resources are affected by landuse/landcover (LULC) dynamics, particularly the increasing urbanization coupled with high household wastewater discharge and decreasing open lands. This study evaluates temporal changes of groundwater quality for 2012 and 2019, its relation to Landuse/landcover, and its impact on Peshawar's residents (study area), Pakistan. A total of 105 and 112 groundwater samples were collected from tube wells in 2012 and 2019. Samples were then analyzed for seven standard water quality parameters (i.e., pH, electric conductivity (EC), turbidity, chloride, calcium, magnesium, and nitrate). Patient data for waterborne diseases were also collected for the years 2012 and 2019 to relate the impact of groundwater quality on human health. Landsat satellite images were classified for the years 2012 and 2019 to observe landuse/landcover dynamics concerning groundwater quality. Results manifested a decrease in groundwater quality for the year 2019 compared to 2012 and were more highlighted in highly populated areas. The nitrate concentration level was found high in the vicinity of agricultural areas due to the excessive use of nitrogenous fertilizers and pesticides, and thus the methemoglobinemia patients ratio increased by 14% (48-62% for the year 2012 and 2019, respectively). Besides, Urinary Tract Infections, Peptic Ulcer, and Dental Caries diseases increased due to the high calcium and magnesium concentration. The overall results indicate that anthropogenic activities were the main driver of Spatio-temporal variability in groundwater quality of the study area. The study could help district health administration understand groundwater quality trends, make appropriate site-specific policies, and formulate future health regulations.

An asymmetric analysis of the impacts of energy use on carbon dioxide emissions in the G7 countries

The foremost theme of the paper is to explore the asymmetric/symmetric impact of energy consumption on the carbon dioxide emission of G7 countries (Germany, Canada, USA, Italy, France, Italy, UK, and Japan). The nonlinear autoregressive distributed lag is used to measure asymmetric/symmetric cointegration by using annual data of G7 countries from 1965 to 2019. The augmented Dickey-Fuller and structural break unit root test is employed to measure the stationarity in variables while the Brock, Dechert, and Scheinkman test is used for measuring nonlinearity and the Wald test is used to figure out short- and long-run asymmetries/symmetries, respectively. The estimated findings of the nonlinear autoregressive distribution lag model show a significant effect of energy use on the ecological footprint. The asymmetric causality test provides evidence of unidirectional, bidirectional, and asymmetrical/symmetrical causality among the variables of G7 nations. The finding of the study suggested policy for the government of Canada and France to use coal instead of oil and gas while the USA, Germany, Italy, UK, and Japan are required to consume gas as compared to oil and coal. Similarly, the study also suggests using modern technology, renewable energy, and preventive measurement for ensuring environmental betterment.

Climate change impact assessment, flood management, and mitigation strategies in Pakistan for sustainable future

In recent years, flooding has not only disrupted social growth but has also hampered economic development. In many nations, this global epidemic has affected lives, property, and financial damage. Pakistan has experienced many floods in the past several years. Due to economic, social, and climate change, Pakistan is at risk of flooding. In order to overcome this problem, the institutions of the country have taken various measures. However, these measures are not sufficient enough to ensure the safety of communities and areas that are prone to disasters with a rapid onset. Hence, it is imperative to forecast future flood-related risks and take necessary measures to mitigate the adverse impacts and losses caused by floods. This article is aimed at exploring floods in Pakistan, analyze the adverse effects of floods on humans and the environment, and propose possible sustainable options for the future. The aqueduct flood analyzer software was used to examine the impact of floods on gross domestic product (GDP), urban damage, and people livelihood, with several years of flood protection plans. To adequately assess the future changes, various flood protection levels and three scenarios for each level of protection were employed, which represent the socio-economic and climate change. The findings revealed that if there is no flood protection, a 2-year flood has a 50% probability of flood occurrence in any given area and may cause no significant impact on GDP, population, and urban damage. Similarly, the probability of a flood occurrence in a five-year flood is 20%, which may cause the country's GDP about $20.4 billion, with 8.4 million population at risk and $1.4 billion urban damage. Furthermore, a 10-year flood has a 10% probability of flood occurrence and may affect the national GDP by $28.9 billion, with 11.9 million affected population and $2.4 billion urban damage in Pakistan. The government of Pakistan should devise appropriate climate change policies, improve disaster preparedness, build new dams, and update relevant departments to mitigate the adverse effects of flooding.

Yield gap analysis of major food crops in Pakistan: prospects for food security

The concept of yield gap analysis can be used to meet the growing demands for agricultural products. Sustainable development goals (e.g. SDG1 and SDG2) to end poverty and hunger require sustainable increases in agricultural production in developing countries, particularly in South Asia and Africa. The aim of this paper is to assess the spatial heterogeneity of the potential and actual yields of major food crops and to determine the yield gaps at the provincial and national levels in Pakistan. The regional test yields of the registered cultivars were used to determine the potential grain yields and total potential grain production levels in each province of Pakistan. The yield gaps were then calculated by comparing the potential and actual yields of the selected major food crops. With a substantial exploitation rate of 0.38 in total grain production, a significant potential gap was found at the national level. The results revealed that the national potential of total grain production is 100,933 Mg, and the actual national grain production from 2015 to 2016 was 38,227 Mg, with a production gap of 62,706 Mg. The results further revealed that the maize crop had the highest potential gap on a national basis, followed by wheat and rice. Meanwhile, on provincial basis, the total production gaps of Punjab, Sindh, Khyber Pakhtunkhwa and Balochistan are 43,776 Mg, 9649.3 Mg, 6772.1 Mg and 2510.7 Mg, respectively. Moreover, based on climate, temperature, rainfall and topography, the yield gaps varied from crop to crop and from province to province. To increase grain yields and close the yield gaps of the major crops, the farmland infrastructure should be strengthened and resource allocations should be optimized. The findings of this study can provide conclusive data for national grain production, protection and food security.

Monitoring responses of vegetation phenology and productivity to extreme climatic conditions using remote sensing across different sub-regions of China

Drought is a major natural disaster that significantly impacts the susceptibility and flexibility of the ecosystem by changing vegetation phenology and productivity. This study aimed to investigate the impact of extreme climatic variation on vegetation phenology and productivity over the four sub-regions of China from 2000 to 2017. Daily rain gauge precipitation and air temperature datasets were used to estimate the trends, and to compute the standardized precipitation-evapotranspiration index (SPEI). Remote sensing-based Enhanced Vegetation Index (EVI) data from a moderate resolution imaging spectroradiometer (MODIS) was used to characterize vegetation phenology. The results revealed that (1) air temperature had significant increasing trends (P < 0.05) in all sub-regions. Precipitation showed a non-significant increasing trend in Northwest China (NWC) and insignificant decreasing trends in North China (NC), Qinghai Tibet area (QTA), and South China (SC). (2) Integrated enhanced vegetation index (iEVI) and SPEI variations depicted that 2011 and 2016 were the extremely driest and wettest years during 2000-2017. (3) Rapid changes were observed in the vegetation phenology and productivity between 2011 and 2016. In 2011, changes in the vegetation phenology with the length of the growing season (ΔLGS) = was - 14 ± 36 days. In 2016, the overall net effect changed at the onset and end of the growing season with ΔLGS of 34 ± 71 days. The change in iEVI per SPEI increased rapidly with a changing rate of 0.16 from arid (NWC, and QTA) to semi-arid (NWC, QTA and NC) and declined with a rate of - 0.04 from semi-humid (QTA, NC, and SC) to humid (SC) region. A higher association was observed between iEVI and SPEI as compared to iEVI and precipitation. Our finding exposed that north China is more sensitive to climatic variation.

An empirical study on spatial-temporal dynamics and influencing factors of apple production in China

In the context of supply-side structural reform, revealing the characteristics of spatial–temporal dynamics and influencing factors of China’s apple production layout is of great significance to ensure apple supply and demand balance and timely adjustment of industrial policies and regional layout strategies. Based on national and provincial apple production data from 1978 to 2016, this study used the apple production concentration index to analyse the evolution characteristics of regional apple production patterns in China. A theoretical analysis framework was established and a spatial econometric model was used to quantitatively explore the influencing factors of China’s apple production layout. The results showed that, first, since the reform and opening-up policy, a general trend of fluctuating growth was found for apple production in China. The centre of apple production layout moved in the southwest direction, with the shift from the Bohai Bay region to the Loess Plateau region. Second, apple production had a significant spatial correlation, while the degree of spatial agglomeration gradually decreased. Third, these changes were significantly influenced by apple comparative income, infrastructure, policies, and climatic conditions. Therefore, it is necessary to continue optimizing and adjusting the apple spatial layout to enhance the technological progress and economic effect of the apple industry and to ensure the stability and balance of regional supply and demand.

Hierarchical regression approach to quantify farm households' pro-environmental behavior

The conflict which exists between a households' self-interest and pro-environmental behavior has led to emotional barriers in ecological conservation. We propose that self-construal affects preferences in this conflicting choice, and self-control plays the mediating role. This study provides conflicting decisions associated with the households' green pesticide adoption behavior in China. Individuals were inquired to select between environmental (pro-environmental) and cost-effective (self-interested) pesticide for food security. Hierarchical regression analysis was employed to progress standard regression estimates by adding a second-stage prior regression to an ordinary model and provides a practical method to evaluate multiple exposures. The results show that the hierarchical regression model may improve environmental protection behavioral studies by including socioeconomic, demographic, and psychological factors. Moreover, our study aims to examine the mediating effect of self-control on the relationship between "self-construal" (SC) and "conflict of self-interest and pro-environmental behavior" (CIPB). Results indicate that self-control (coefficient, - 0.0739; significant level, 5%) partially mediates the relationship between SC and CIPB. Our findings suggest that compared with independent self-construal (coefficient, - 0.05; significant level, 5%), the respondents with interdependent self-construal (coefficient, - 0.09; significant level, 1%) favor pro-environmental choices as they are better in applying self-control and perform pro-environmental behavior. The finding reveals that the hierarchical regression approach provides significant advantages in studying a rural households' pro-environmental decision-making. The current research has policy implications for the adoption of environment-friendly pesticide and organic fertilizer.

Symmetric and asymmetric effect of energy consumption and CO2 intensity on environmental quality: using nonlinear and asymmetric approach

This paper investigates the asymmetric impact of energy consumption and carbon dioxide (CO₂) intensity on the environmental quality in Russia. The ecological footprint (a proxy for environmental quality) was used as a dependent variable, while independent variables include energy consumption, CO₂ intensity, and gross fixed capital formation as a proxy of capital for time series data spanning from 1992 to 2016. To examine the asymmetric cointegration between the selected variables, the nonlinear autoregressive distributed lag (NARDL) method was used. The structural break unit root and BDS tests were used to check data stationarity and nonlinearity, respectively. Besides, the asymmetric causality test in Gauss software was employed to check the causal relationship among the variables. Among the considered variables, the asymmetric cointegration was found. A significant unidirectional symmetric causality was found running from energy consumption to ecological footprint (Wald test = 3.956*) and from ecological footprint to capital (Wald test = 10.115**), while asymmetrically, ecological footprint granger cause energy consumption (Wald test = 6.054**) and capital (Wald test = 6.739**) respectively. On the contrary, a neutral effect was found between environmental quality and CO₂ intensity. From these findings, environmental policies are also discussed. Specifically, policymakers should support modern, controlled emission technologies, including nuclear and renewable energy sources and green portfolio investment. Graphical abstract.

The inhabitants' dual interest preferences and their impact on pro-environmental behavior in China

The land use changes and farmers' unreasonable land use behaviors continue to threaten China's agricultural land, exacerbating the impact of pollution. The factors that persuade farm households to perform pro-environmental actions are preliminary efforts to strengthen environmental protection. The current study aims to better understand how the dual interest preferences of rural households are interrelated and influence their environmental behavior. A structured questionnaire was employed to collect the primary data from 4 provinces in China to develop new methods to measure the dual interest preferences of farmers and to study their impact on pro-environmental behaviors. The structural equation model (SEM) in Stata14 was used to analyze the relationship between latent and observed variables and to understand their impact on farmers' environmental behavior. The results showed that all the observed variables have the expected signs and have a significant relationship with their latent variables. With the coefficients of 0.76, 0.88, and 0.64, the underlying variables related to the households' dual interest preferences are statistically significantly correlated. The coefficient 0.34 of the latent variable ensures a direct and significant impact on farm households' pro-environmental behavior, suggesting that non-uniformity preferences or conflicts exist between the short- and long-term economic interests. Similarly, a positive and significant coefficient of 0.28 suggests the non-uniformity of preferences in short-term economic and social interests. All the fitness indices ensured that our model fits well. To improve the environment and land quality, the current research has policy implications for the adoption of environment-friendly pesticide and organic fertilizers.