Maximum Normalized Difference Vegetation Index Research Articles

The snow cover is more important than other climatic variables on the prediction of vegetation dynamics in the Pyrenees (1981–2014)

The dynamics of the mountain vegetation is governed by multiple climatic drivers including temperature, precipitation, radiation and snow cover variability. However, in the Mediterranean environment, little is known about the relative importance of each variable. In this study we assess how different snowpack indices (the maximum annual accumulation, the length of the snow season, and the melt-out date) and key climate variables (precipitation, temperature and shortwave solar radiation) control the interannual variability of the maximum Normalized Difference Vegetation Index (peak NDVI) in the Pyrenees. We use a 33 year long remote sensing dataset (1981–2014) to build a statistical model relating the annual peak NDVI with snow and climate variables. In elevated areas characterized by a well developed seasonal snowpack the melt-out date was the most important climatic variable for predicting the annual peak NDVI. However, at lower elevations where snow presence is ephemeral, shortwave solar radiation was the most important variable. This change in the relative importance of climatic variables occurs around 1300 m a.s.l. The results do not show a significant contribution of maximum snow accumulation, suggesting that indicators of snow presence (i.e. melt-out date or snow season duration), which are significantly easier to obtain than snow mass indicators from remote sensing, could be used to model the influence of the snowpack on peak NDVI at regional scale.

A new method of high-resolution NPP estimation and its implementation in caragana shrublands on the Loess Plateau, China

ABSTRACT Accurate estimation for vegetation net primary production (NPP) at high resolution is essential for ecosystem management and at local to global scales. However, no reliable methods are available to achieve fine-resolution estimation. In this study, a new NPP estimation system was developed by integrating high spatial resolution remote sensing images, tree ring measurements, and process-based model CASA. The system was illustrated by estimating the historical NPP of caragana shrublands from 2011 to 2021 in Dingbian County, China. The results showed that: (1) by employing the biomass model and tree ring measurements, an NPP-crown width model was established to enable a large number of samples for calibrating the CASA model on high spatial resolution images without continuous field measurements. The calibrated parameters, including maximum NDVI, minimum NDVI, and maximum light use efficiency, were determined to be 0.415, 0.022 and 0.025, respectively; (2) according to the estimation by the calibrated CASA model, the NPP in Dingbian County revealed an increasing trend from 2011 to 2021, and the NPP in southern hilly and gully regions was significantly higher than the other regions; (3) among the different indices, max temperature of warmest month had a significant negative effect on the NPP of cagarana shrublands, whereas annual precipitation had a positive effect. In the northern and central regions, air temperature predominantly influenced NPP, while in the southern regions, both temperature and precipitation played a joint role. The implementation in this study illustrated that the NPP estimation system could provide reliable NPP with high resolution and efficient to monitor and detect dominant factors affecting NPP for ecosystem management.

Analysis of trends in productivity metrics in assessing land degradation: A case study in the Campania region of southern Italy

Land degradation is a critical issue at a global level and its progressive increasing greatly reduces soil ecosystem services. In this context, the 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, defined the Sustainable Development Goals (SDGs) and indicated some targets of particular interest for a territory to be integrated into short- and medium-term national programs. Target 15.3, which aims to end desertification and restore degraded lands, is currently monitored by indicator 15.3.1, measured as the combination of three sub-indicators (trends in land cover change, land productivity and carbon stocks) as suggested by the United Nations Convention to Combat Desertification (UNCCD), the custodian agency for the SDG indicator. In our opinion, this assessment shows some weakness that are generally caused by a lack of information from direct field observations. The greatest limitation regards land productivity dynamics linked to the NDVI trajectory adopted by the UNCCD methodological approach. For this reason, the paper proposes an alternative approach that consists of using annual maximum NDVI value assessments instead of annual mean values for trajectory calculation. To come to these conclusions, the study addresses a reliability assessment by using remote sensing techniques via the Google Earth Engine (GEE) and analysing the NDVI evolution over time at 450 locations spread around the Campania region (southern Italy). To this end, a customised Graphical User Interface (GUI) was built on the GEE platform and a Google Earth time slider tool was applied to visualize land cover changes which occurred at each location over a period of 18 years (2001–2018). The survey was carried out on MODIS and Landsat 7 collections and showed that the new approach had a better performance than the UNCCD approach (90 % vs 62 % of successful reliability tests, up to 96 % considering results from Landsat images). The application of maximum NDVI values to assess productivity dynamics spatially shows, with regard to UNCCD data, more than double the percentages of degraded and stable lands and a drastic reduction in improved areas within the Campania region. Overall, this innovative approach appears to agree more closely with ground truth and the use of finer resolution data is more suitable for investigating land degradation processes within a regional context.

Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI Data

The normalized difference vegetation index (NDVI) can depict the status of vegetation growth and coverage in grasslands, whereas coarse spatial resolution, cloud cover, and vegetation phenology limit its applicability in fine-scale research, especially in areas covering various vegetation or in fragmented landscapes. In this study, a methodology was developed for obtaining the 30 m annual maximum NDVI to overcome these shortcomings. First, the Landsat NDVI was simulated by fusing Landsat and MODIS NDVI by using the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and then a single-peaked symmetric logistic model was employed to fit the Landsat NDVI data and derive the maximum NDVI in a year. The annual maximum NDVI was then used as a season-independent substitute to monitor grassland variation from 2001 to 2022 in a typical area covering the major vegetation types in the Qinghai Lake Basin. The major conclusions are as follows: (1) Our method for reconstructing the NDVI time series yielded higher accuracy than the existing dataset. The root mean square error (RMSE) for 91.8% of the pixels was less than 0.1. (2) The annual maximum NDVI from 2001 to 2022 exhibited spatial distribution characteristics, with higher values in the northern and southern regions and lower values in the central area. In addition, the earlier vegetation growth maximum dates were related to the vegetation type and accompanied by higher NDVI maxima in the study area. (3) The overall interannual variation showed a slight increasing trend from 2001 to 2022, and the degraded area was characterized as patches and was dominated by Alpine kobresia spp., Forb Meadow, whose change resulted from a combination of permafrost degradation, overgrazing, and rodent infestation and should be given more attention in the Qinghai Lake Basin.

Short-Term Tree-Ring Series of Pinus hartwegii Lindl. Taken at Ground Level Correlate to Normalized Difference Vegetation Index Series

In this work, we propose that small wood core samples (≤10 cm length), taken from 0.3 m above the soil, represent an accurate means of correlating forest growth with remote sensing data. Short-term correlations between the Normalized Difference Vegetation Index (NDVI) and tree-ring components of the last three to four decades were tested, using 210 Pinus hartwegii wood cores collected at two locations, Tláloc (TLA) and Jocotitlán (JOCO) in central Mexico. The NDVI time series were generated with the Google Earth Engine (GEE) using Landsat 8 images. Also, seasonal trends in NDVI (e.g., spring, summer, autumn, winter) were analyzed through longitudinal analysis. The results showed more statistically significant dendrochronological indices in TLA than in JOCO, but both locations consistently showed an NDVI decrease in 2018 and 2020, indicating a reduction in vegetation vigor. At the two locations, the minimum and maximum NDVI occurred in April and October, respectively. Seasonal NDVI changes for spring were mainly seen at TLA with a decreasing trend, which may be related to a less defined dry season. The significant correlations (p < 0.05) between tree-ring components and the NDVI occurred in the dry season, indicating that the productivity of a given year is defined by the tree vigor shown in April and May, in the case of TLA, and between January and March, for JOCO. Although the NDVI values of JOCO were higher than those of TLA, tree growth, expressed by tree-ring indices, was lower. Our proposed field method to correlate tree-ring information and the NDVI is reliable and can be used in other coniferous forests.

Can normalized difference vegetation index and climate data be used to estimate soil carbon, nitrogen, and phosphorus and their ratios in the Xizang grasslands?

Accurately quantifying the relative effects of climate change and human activities on soil carbon, nitrogen, and phosphorus in alpine grasslands and their feedback is an important aspect of global change, and high-precision models are the key to solving this scientific problem with high quality. Therefore, nine models, the random forest model (RFM), generalized boosted regression model (GBRM), multiple linear regression model (MLRM), support vector machine model (SVMM), recursive regression tree model (RRTM), artificial neural network model (ANNM), generalized linear regression model (GLMR), conditional inference tree model (CITM), and eXtreme gradient boosting model (eXGBM), were used for modeling soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), the ratio of SOC to TN (C:N), the ratio of SOC to TP (C:P), and the ratio of TN to TP (N:P) at depths of 0–10, 10–20, and 20–30 cm under non-grazing and free-grazing scenarios in the Xizang grasslands. Annual radiation (ARad), annual precipitation (AP), and annual temperature (AT) were used as independent variables under non-grazing scenarios, whereas ARad, AP, AT, and growing season maximum normalized difference vegetation index (NDVImax) were used as independent variables under free-grazing scenarios. Overall, the RFM and GBRM were more accurate than the other seven models. However, the tree numbers of the GBRM were much larger than those of the RFM, indicating that the GBRM may have a greater model complexity and lower running speed. Therefore, the RFM had the best performance among the nine models in modeling SOC, TN, TP, C:N, C:P, and N:P in the Xizang grasslands. The RFM established in this study can not only help scientists save time and money on massive sampling and analysis, but can also be used to construct a database of SOC, TN, and TP, and their ratios, and further scientific research related to ecological and environmental issues (e.g., examining whether soil systems intensified global warming over the past few decades by exploring whether climate change and human activities altered soil organic carbon) in the grasslands of Xizang Plateau.

Extracting Shrubland in Deserts from Medium-Resolution Remote-Sensing Data at Large Scale

Shrubs are important ecological barriers in desert regions and an important component of global carbon estimation. However, the shrubland in deserts has been hardly presented, although many high-quality land cover datasets with a 10 m scale based on remote-sensing data have been publicly released products. Therefore, the underestimation of carbon storage is inevitable with the absence of desert shrublands. The existing land-cover datasets have been analyzed and compared, and it has been found that the reason for missing the shrubland in deserts is mainly indued by the absence of shrubland samples, which are easy to neglect and difficult to retrieve. In this study, we developed a semi-automatic method to extract shrubland samples in deserts as the updated input for the machine-learning method. Firstly, the initial samples of desert shrublands were identified from the very high spatial-resolution (0.3~0.5 m) imagery on GEE, and the maximum NDVI from Sentinel-2 was used for double-checking. Secondly, a feature-based method was used to learn the feature from the initial samples and a similarity-based searching method was employed to automatically expand the samples. Finally, the expanded samples and their corresponding time-series satellite images were inputted into different machine-learning methods at a large region (1.63 × 106 km2) for extracting the shrubland in the desert. It was found that different combinations of feature variables and time-series combinations have different impacts on the overall accuracy (OA) of the classification results, as well as the performance of identifying and classifying the different land-cover types. Compared to the existing global-scale land-cover products, the proposed method can better identify the shrubland in deserts and show better overall accuracy.

Landsat-observed changes in forest cover and attribution analysis over Northern China from 1996‒2020

ABSTRACT Forest dynamics provide important information on the ecological environment. The Three-North Shelter Forest Program (TNSFP) is one of the world’s largest reforestation/afforestation programs, however the actual changes in forest cover in the Three-North Regions (TNR) of China resulting from this program are highly uncertain. This study quantified changes in fractional forest cover (FFC) at 30 m using Landsat data from 1996 to 2020. Using the Google Earth Engine platform, more than 40,000 images from Landsat-5, Landsat 7 and Landsat-8 were integrated, and the annual surface reflectance was normalized based on the multi-band least squares regression and maximum normalized difference vegetation index composite method. An ensemble learning model trained using high-resolution Gao-Fen 2 satellite imagery was used to generate the FFC long time-series product. FFC showed an increasing trend with average rates of 0.022/10a in the last 25 years, and 0.03/10a after 2010 largely corresponding to the fourth and fifth phases of the TNSFP. There are significant regional differences in the relationship between FFC and air temperature ( R 2 = 0.37) and precipitation ( R 2 = 0.49). The increased air temperature in arid and less rainy areas inhibit the FFC increase, whereas the increase in precipitation had a promoting effect. FFC appeared more sensitive to changes in solar radiation and heat conditions in humid and rainy areas. The attribution analysis revealed that 34% of FFC changes were caused by climatic variables and 66% were caused by non-climatic factors. Among them, afforestation associated with the TNSFP significantly increased FFC, and forest fire is a key factor of forest change in the Greater Khingan Ranges and Lesser Khingan Ranges regions. Planting single tree species caused biological disasters in forests of Xinjiang and Inner Mongolia. Further analysis of the increased FFC using high-level satellite products demonstrated an improvement in environmental conditions with cooler land surface temperature and higher vegetation gross primary production over the TNR.

Drivers of contemporary and future changes in Arctic seasonal transition dates for a tundra site in coastal Greenland.

Climate change has had a significant impact on the seasonal transition dates of Arctic tundra ecosystems, causing diverse variations between distinct land surface classes. However, the combined effect of multiple controls as well as their individual effects on these dates remains unclear at various scales and across diverse land surface classes. Here we quantified spatiotemporal variations of three seasonal transition dates (start of spring, maximum normalized difference vegetation index (NDVImax ) day, end of fall) for five dominating land surface classes in the ice-free Greenland. Using a distributed snow model, structural equation modeling, and a random forest model, based on ground observations and remote sensing data, we assessed the indirect and direct effects of climate, snow, and terrain on seasonal transition dates. We then presented new projections of likely changes in seasonal transition dates under six future climate scenarios. The coupled climate, snow cover, and terrain conditions explained up to 61% of seasonal transition dates across different land surface classes. Snow ending day played a crucial role in the start of spring and timing of NDVImax . A warmer June and a decline in wind could advance the NDVImax day. Increased precipitation and temperature during July-August are the most important for delaying the end of fall. We projected that a 1-4.5°C increase in temperature and a 5%-20% increase in precipitation would lengthen the spring-to-fall period for all five land surface classes by 2050, thus the current order of spring-to-fall lengths for the five land surface classes could undergo notable changes. Tall shrubs and fens would have a longer spring-to-fall period under the warmest and wettest scenario, suggesting a competitive advantage for these vegetation communities. This study's results illustrate controls on seasonal transition dates and portend potential changes in vegetation composition in the Arctic under climate change.

Improved estimation of the underestimated GEDI footprint LAI in dense forests

ABSTRACT Light Detection and Ranging (LiDAR), with its ability to capture vegetation vertical profile, could be a unique technique for deriving Leaf Area Index (LAI). A global LAI product at 25-m spatial resolution was derived from the Global Ecosystem Dynamics Investigation (GEDI) LiDAR data since 2019, but it was often significantly underestimated in dense forests. Here we explored the potential for improving the estimation of the underestimated GEDI LAI in dense forests by using the Digital Elevation Model (DEM) as auxiliary data to separate ground and canopy returns in the received waveform. Dense forests were defined as forests with high vegetation greenness (annual maximum NDVI ≥ 0.8). The newly estimated GEDI footprint LAI was first validated with the ground-measured LAI at two sites in Fujian, China, and the results showed that the underestimation was significantly reduced compared to the original GEDI LAI product (r increased from −0.55 to 0.81, RMSE decreased from 3.94 to 1.43, Bias decreased from 3.17 to 0.48). To evaluate whether the improvement was applicable to other areas and forest types, the newly estimated GEDI footprint LAI for the entire Fujian and Contiguous US (CONUS) was then compared to the consistent LAI among three widely used global LAI products. The comparison results demonstrated that the use of DEM as auxiliary data could largely reduce the underestimation of GEDI footprint LAI (In Fujian, RMSE decreased from 4.75 to 2.52, and Bias decreased from 4.61 to 0.58; in CONUS, RMSE decreased from 5.24 to 1.96, and Bias decreased from 5.1 to 0.73). Overall, this study demonstrates the effectiveness of correcting the large underestimation of GEDI footprint LAI in dense forests by utilizing DEM, which has an important influence on the results, as auxiliary data.

Yield Forecasting Model for Maize Using Satellite Multispectral Imagery Driven Vegetation Indices

In recent decades, the cultivation of maize (_Zea mays_ L.) has witnessed remarkable growth in Bangladesh, particularly in the northern regions. Maize, as a high-yielding grain crop with diverse applications, plays a pivotal role in the country's agricultural landscape. Traditional methods of yield prediction involve time-consuming and subjective on-site field visits, resulting in significant errors and delayed information dissemination to government authorities and decision-makers. This study explores the potential of remote sensing technology to predict maize yields before harvest, thereby enhancing agricultural decision-making processes. The research utilizes 16-day (~30 m) Landsat 8 and 10-day (~10 m) Sentinel 2A imagery of two years November 2018 to February 2019 and November 2019 to February 2020 to forecast maize yields in the Kaharole upazila of the Dinajpur district, Bangladesh. Four cloud-free images, representing the maximum normalized difference vegetation index (NDVI) for each maize growing season, are selected from the Landsat 8 and Sentinel 2A data. Regression models are established to relate NDVI values to the maize yields across 20 individual farmers' fields. The results reveal that the prediction models based on mean NDVI values for the combined growing seasons outperform those based on single growing seasons and the finer spatial resolution of Sentinel 2A contributes to its superior performance in comparison to Landsat 8, offering valuable insights for improved agricultural management and food security.

Residential greenspace and major depression among older adults living in urban and suburban areas with different climates across the United States

Background and aimResidential greenspace could alleviate depression – a leading cause of disability. Fewer studies of depression and greenspace have considered major depression, and, to our knowledge, none have considered how climate, which determines vegetation abundance and type, may change the impacts of greenspace. Our aim was to investigate whether residential greenspace is associated with major depression among older adults and explore effect modification by climate. MethodsWe used biennial interviews between 2008 and 2016 from the Health and Retirement Study. We calculated greenness within walking distance of home addresses as the maximum NDVI for the year of each participant interview averaged within a 1 km buffer. Reflecting clinical criteria, a score of ≥5 on the CIDI-SF indicated major depression in the preceding 12-months. We characterized climate using Köppen-Geiger classifications. To estimate prevalence ratios, we used Poisson regression. Our models adjusted for sociodemographic characteristics, geography, annual sunshine, and bluespace. ResultsThe 21,611 eligible participants were 65 ± 10 years old on average, 55% female, 81% White, 12% Black, 10% Hispanic/Latino, and 31% had at least a 4-year college degree. The 12-month prevalence of a major depression was 8%. In adjusted models, more residential greenspace was associated with a lower prevalence of major depression (prevalence ratio per IQR, 0.91; 95% CI, 0.84 to 0.98). There was evidence of effect modification by climate (P forinteraction, 0.062). We observed stronger associations in tropical (prevalence ratio per IQR 0.69; 95% CI, 0.47 to 1.01) and cold (prevalence ratio per IQR, 0.83; 95% CI, 0.74 to 0.93) climates compared to arid (prevalence ratio per IQR 0.99; 95% CI, 0.90 to 1.09) and temperate (prevalence ratio per IQR 0.98; 95% CI, 0.86 to 1.11) climates. ConclusionsResidential greenspace may help reduce major depression. However, climate may influence how people benefit from greenspace.

Contemporary Dynamics in Vegetation of the Polar and Subpolar Regions of Western Siberia Using Remote Sensing Data

Abstract—Trends in the climatogenic dynamics of phytomass on the sites located along the borders of Western Siberia’s natural zones (the border of frozen and unfrozen bogs, the border of taiga and open boreal woodlands, and the northern boundary of the distribution of woody vegetation) were spotted based on the vegetation index NDVI derived using the sensors of the MODIS-Terra satellite for the period 2000–2020. The assessment of the vegetation dynamics and the factors that determine it was conducted with the use of the mean and maximum NDVI values and the relation of the index with basic climatic variables. A positive trend of NDVI caused by an increase in air temperature and precipitation was revealed in all the studied areas. The phytomass of open boreal larch woodlands grew the most strongly, and the greatest interannual variability was noted there. A correlation analysis conducted on the basis of the NDVI mean values displayed that improvements in vegetation growth conditions in tundra are mainly caused by the impact of the average summer temperature. The increase in phytomass in taiga is mostly connected with the presence of a tree stand that better retains snow cover, which contributes to less freezing of the ground during the cold season.

The Impact of Anthropogenic Activities and Natural Factors on the Grassland over the Agro-Pastoral Ecotone of Inner Mongolia

The agro-pastoral ecotone serves as an important ecological transition zone between grassland and cropland. It plays a crucial role in providing forage and maintaining the ecological security of Northern China. China has initiated many ecological restoration projects in the agro-pastoral ecotone. Separating the affecting factors of anthropogenic activities and natural factors change on grassland may help guide future ecological restoration projects. This study investigated the grassland change over the agro-pastoral ecotone in Inner Mongolia (APEI) using land use data, vegetation index, and monthly climate data, social and economic data from 2010 to 2020. Two indicators were introduced to evaluate the grassland change in quality and quantity. The average annual maximum NDVI of grassland within each sample was used as a proxy for grassland quality, and the proportion of grassland within each sample was used as a proxy for grassland quantity. The driving mechanisms of grassland quality were examined on different types of grassland. The results showed that (1) from 2010 to 2020, the quantity of grassland in APEI slightly decreased while the quality of the grassland significantly improved; (2) the change in grassland quality was jointly caused by natural factors and human activities, with 66.77% being attributed to natural factors and 33.23% to human activities. In some areas, the alteration in grassland quality was solely determined by either the impacts of natural factors or human-induced disturbances; and (3) temperature and precipitation played different roles in affecting grassland quality among different types of grassland. In some areas, anthropogenic activities, such as farmland and population, would affect grassland quality. The findings of this study would contribute to the understanding of the mechanism of ecological environmental changes in the agro-pastoral ecotone and guide the planning and implementation of future ecological restoration projects in agro-pastoral areas.

Above-treeline ecosystems facing drought: lessons from the 2022 European summer heat wave

Abstract. In 2022, a large part of Europe experienced an extremely dry and hot summer. In the Alps, this episode occurred after an unusually low-snowfall winter, which aggravated the dryness of soils. This study examines the impact of this particular year on the canopy greenness of above-treeline ecosystems by comparison with previous heat waves that hit the Alps during the last 2 decades. Normalized difference vegetation index (NDVI) time series derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were processed to extract the temporal variability in yearly maximum NDVI (NDVImax). The responsiveness of NDVImax to snow cover duration and growing-season weather conditions was evaluated in contrasting hydroclimate regions of the Alps using linear mixed-effect models. The year 2022 was unique in that the summer heat wave led to a widespread negative anomaly of NDVImax. The magnitude of this anomaly was unprecedented in the southwestern, driest part of the Alps, where vegetation activity was found to be particularly responsive to snow cover duration and early summer precipitation. In the colder and wetter regions, all warm to very warm summers before 2022 had led to increased canopy greenness, but the combination of a reduced snow cover and low early summer precipitation counteracted this expected beneficial effect in 2022. This study provides evidence that the control of canopy greenness by temperature and water balance differs markedly across regions of the Alps and that the year 2022 bears witness to a shift toward an increasing importance of moisture availability for regulating plant growth at high elevation. This is viewed as a warning sign of what could become the new norm in the years ahead in the context of increasing frequency and intensity of extreme droughts throughout temperate mountain ecosystems.

Effects of climate change and anthropogenic activities on soil pH in grassland regions on the Tibetan Plateau

Although soil pH is an important indicator of soil quality, the effects of climate change and anthropogenic activities on soil pH remain controversial. Here, we quantified the potential soil pH at three depths (0–10, 10–20, and 20–30 cm) using annual temperature (AT), annual precipitation (AP) and annual radiation (ARad), and the actual soil pH at three depths using AT, AP, ARad and maximum normalized difference vegetation index based on random forest models over the grassland regions on the Tibetan Plateau in 2000–2020. Overall, climate change caused soil alkalinization at 0–10 cm, and soil acidification at 10–20 and 20–30 cm. Under climate change conditions, 36.84%, 29.87% and 23.71% regions showed soil alkalinization, whereas 45.52%, 44.49% and 21.43% regions showed soil acidification at 0–10, 10–20 and 20–30 cm, respectively. Climate change caused soil acidification in alpine meadows, but soil alkalinization in alpine steppes. The impacts of precipitation change on soil pH were not always greater than those of climate warming, depending on soil depth, and radiation change had some exclusive impacts on soil pH. Overall, anthropogenic activities caused soil alkalinization, but more than 1/10 regions showed soil acidification. Anthropogenic activities caused soil acidification in alpine meadows, but soil alkalinization in montane meadows at 10–20 cm. Therefore, the impacts of climate change and anthropogenic activities on soil pH did not always cause soil acidification or alkalization, but varied with grassland type and soil depth. Both climate change and anthropogenic activities reconstructed the spatial distribution pattern of soil pH. This study also cautioned that the impact of radiation change on soil pH should not be ignored.

Assessment of Karachi as an Urban Heat Island Threat through Remote Sensing and GIS Techniques

The present study aimed to assess the threat of transformation of Karachi into an Urban Heat Island, so, ambit was having calculated temperature, buildup areas, and normalized difference vegetation index through remote sensing and GIS techniques. The Landsat satellite data was used to differentiate the temperature in different years. These images were processed through Envi 4.7, Erdas Imagine, and ArcGIS 10.3.1. The results revealed that the maximum temperature was found up to 30.52, 35.25, 33.60, 46.73 °C; the buildup area was 23, 34, 26, 45 %; the NDVI results showed ranging from 0.224-1, 0.07-0.43, 0.201-1, 0.29-0.7 during this years. The average spatial land use temperature and buildup area increased by 1.03 and 1.9 times from 1990 to 2019. The maximum NDVI was observed during 2019, because of heavy rainfall as a result which supports promoting more greenery. With an increase in the buildup area, a significant change in the temperature of the territory was simultaneously observed. Therefore, this indicates a major task for urban developers extenuating the subsequent urban heat island occurrence. That is, for the first time it is scientifically substantiated and confirmed by the results that when creating a city development plan, it is extremely important to exclude the possibility of the urban heat island occurrence through preliminary studies. The practical value of the study lies in sound recommendations, one of which is the need for future urban development to emphasize urban plantings, including vertical forests to prevent UHI occurrence in the area of Karachi city.

Biophysical factors control the interannual variability of evapotranspiration in an alpine meadow on the eastern Tibetan Plateau

Evapotranspiration (ET) is a key parameter regulating land–atmosphere interaction processes and the water cycle. The seasonal and interannual variability of ET and its environmental controls over an alpine meadow in a subfrigid humid zone of the Tibetan Plateau (TP) are reported on. Direct measurements were made using the eddy covariance method over a 10-year period with a significant increase in growing season length (GSL). The results showed that annual ET for the alpine meadow site was 492 ± 66 mm in comparison with 635 ± 88 mm of precipitation (P), with a ratio of ET/P ranging from 0.63 (2017) to 1.04 (2010). The path analysis and Priestley–Taylor coefficient (ET/ETeq) revealed that the daily ET experienced energy-limited and water-limited seasons within the year. During the water-limited non-growing season, the daily surface conductance (Gs) and ET/ETeq increased positively with soil water content (SWC). During the energy-limited growing season, the 16-day average ET, ET/ETeq, and Gs scaled positively with the normalized difference vegetation index (NDVI). ET/ETeq increased nonlinearly with an increase in Gs, but was insensitive to increases in Gs greater than the threshold Gs*. The mean Gs and Gs* were strongly regulated by the maximum NDVI. The maximum NDVI, Gs*, and annual mean Gs explained 46%, 80%, and 68%, respectively, of interannual variation in annual ET. Thus, we concluded that biophysical factors, rather than P and GSL, mainly controlled the interannual variability in annual ET. These findings are critical for understanding the response mechanism of ET to the changing biotic and abiotic conditions in the TP.

The imprint of urbanization on vegetation in the ecologically fragile area: A case study from China’s Loess Plateau

Rapid urbanization significantly affects vegetation growth in urban environments. Responses of vegetation to urbanization at high spatial resolutions and long temporal ranges are still unclear, especially in ecologically fragile areas. Here, we explored urbanization impacts on vegetation across China’s Loess Plateau (CLP) from 2000 to 2020 at a spatial resolution of 30 m. Urbanization and annual maximum Normalized Difference Vegetation Index (NDVI) in urban agglomerations, provincial capital cities, also the whole CLP experienced a rapid increase over this period; the annual maximum NDVI (NDVImax) in urban areas decreased before 2011 but increased afterward to reach its maximum value in 2018, and in non-urban areas, a significant increase was found between 2000 and 2020. NDVImax with significant reduction areas were mostly distributed in the urbanization area. A total of 97.56%, 53.66%, 80.49%, and 100% of all cities’ urban core, urbanization, urban, and non-urban areas, respectively, indicated an increase in NDVImax during 2000–2020. For 97.56% (40/41) of the cities in the CLP, the increases in NDVImax in the non-urban areas were stronger than the NDVImax increases detected in the urban areas of these cities. In general, the urban areas of most cities (29/41) in the CLP had positive NDVI trends of greater than 50%. The NDVImax generally decreased with increasing urbanization development intensity (UDI) in 2000 and 2020 in the CLP along with five urban agglomerations. These findings have important implications for understanding urbanization impacts on vegetation, urbanization management, and development in ecologically fragile areas.

Greening rate in North Korea doubles South Korea

South and North Korea have experienced contrasting economic developments since the 1950s while sharing similar climates. Previous studies revealed an overall long-term greening trend across the Korean Peninsula based on greenness data from coarse-resolution satellite images. However, there has been no comprehensive comparison of the greenness patterns and the driving mechanisms between two countries due to the limitations of coarse-resolution satellite data. Here, we performed cross-calibration among Landsat sensors and adopted a phenology-based approach to generate Landsat annual maximum Normalized Difference Vegetation Index (NDVImax) time series for each pixel from 1986 to 2017. We found that over 1986–2017, the greening rate in North Korea was almost twice that of South Korea. Cropland in South Korea is the main source of the greening discrepancy. The expansion of agricultural facilities in the stable cropland area and urbanization in the cropland loss area of South Korea contributed 57% to the significant negative NDVImax trend, which was dominant over the forest NDVImax increase resulting from rising temperatures, CO2 fertilization effects and afforestation projects. However, in North Korea, CO2 fertilization effects in the stable cropland area and transition from grassland to cropland promoted an increase in NDVImax, despite decreasing NDVImax in forest areas due to deforestation. Our results highlight the need for delineating fine-scale land-use changes to advance our understanding of regional vegetation dynamics.