Improving river medium–high flow estimation by CM Hierarchical Classification (CMHC) method using Sentinel-2 imagery

As the population grows, the development of conservation tillage offers a means of promoting the sustainability of agricultural engineering. Remote sensing images with high spatial and temporal resolutions enable the accurate monitoring of conservation tillage on a broad spatial scale, further promoting conservation tillage research. This paper describes using streamlined time series Sentinel-2 images based on the Google Earth Engine (GEE) cloud platform for mapping maize tillage practices in the Songnen Plain region of Northeast China. Based on the correlation with the normalized difference tillage index (NDTI) and maize residue coverage (MRC) data, the optimal time series and streamlining functions in the GEE cloud platform are determined. Estimates of MRC and the mapping of tillage practices in the Songnen Plain for 2019–2022 are then determined using GEE and a previous model. Geostatistical analysis using ArcGIS is applied to analyze the spatial and temporal distribution characteristics of MRC and conservation tillage over the Songnen Plain. The results show that time series images from 20–30 May achieve an r value of 0.902 and an R2 value of 0.8136 when using the median streamlining function. The mean MRC for the study area in 2022 is 2.3%, and an overall upward trend in conservation tillage is observed (from 0.08% in 2019 to 0.25% in 2022). Our analysis shows that MRC monitoring and conservation tillage mapping can be performed over a broad spatial scale using remote sensing technology based on the GEE cloud platform. Spatial and temporal information on farm practices provides a theoretical basis for agricultural development planning efforts, which can promote sustainable agricultural development.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于GEE平台的黄河流域森林植被净初级生产力时空变化特征 DOI: 10.5846/stxb202104271113 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(31960330);宁夏自然科学基金(2020AAC03112) Spatio-temporal variation characteristics of forest net primary productivity in the Yellow River Basin based on Google Earth Engine cloud platform Author: Affiliation: Fund Project: National Natural Science Foundation of China (31960330)；Ningxia Natural Science Foundation (2020AAC03112) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:黄河流域是我国重要的生态屏障,研究黄河流域森林植被净初级生产力(Net Primary Productivity,NPP)的时空变化特征及驱动机制,对解释黄河流域森林碳汇/源变化具有重要意义。基于Google Earth Engine (GEE)云平台,利用MOD17A3H V6 NPP数据、MCD12Q1 V6土地覆盖类型数据、ECMWF/ERA5气象数据和USGS/SRTMGL1_003高程数据,采用岭回归分析、Hurst指数和冗余分析(Redundancy Analysis,RDA)对黄河流域2001-2019年森林NPP的时空变化特征及影响因子进行分析。结果表明:(1)2001-2019年,黄河流域森林平均总面积为3.66万km2,其中阔叶林、针叶林、混交林平均面积分别为:2.64万km2、0.01万km2和1.01万km2,森林NPP年总量呈线性增加趋势,其均值为8.99Tg C,年均增速为0.36Tg C/a,19a增长率为173.60%;不同森林类型的NPP年总量均值分别为:4.79Tg C (阔叶林)、6.04×10-5Tg C (针叶林)和0.64Tg C (混交林),年均增速为:阔叶林(0.16Tg C/a)>混交林(0.04Tg C/a)>针叶林(6.98×10-6Tg C/a)。(2)2001-2019年,黄河流域森林年均NPP呈线性增加趋势,其均值为241.58g C m-2 a-1,年均增速为7.18g C m-2 a-1,19a增长率为108.63%;不同森林类型的年均NPP均值分别为:178.48g C m-2 a-1(阔叶林)、0.60g C m-2 a-1(针叶林)和62.49g C m-2 a-1(混交林),年均增速为:阔叶林(4.75g C m-2 a-1)>混交林(2.39g C m-2 a-1)>针叶林(0.04g C m-2 a-1)。(3)黄河流域森林NPP呈增加趋势的面积占94.50%,其中显著增加的面积占73.29%;呈减少趋势的面积占5.50%,其中显著减少的面积占1.57%。阔叶林NPP显著增加的面积最高(76.78%),其次为混交林(60.84%),针叶林最少(56.76%)。(4)黄河流域森林NPP的Hurst指数(H)介于0.38-1.00之间,平均值为0.87,其中H≥0.5的像元数约占99.34%,黄河流域森林NPP在未来一段时间内仍保持持续增加趋势。(5)归因分析表明环境因子对黄河流域森林NPP时空变化的总解释率为55.80%,显著影响的环境因子为经度(35.50%)、降水(8.00%)、气温(6.50%)和纬度(5.40%)。2001-2019年黄河流域森林NPP呈增加趋势,且呈现较强的可持续性;GEE云平台结合冗余分析可及时、高效获取黄河流域森林NPP的时空变化并对其进行归因分析。 Abstract:The Yellow River Basin is an important ecological barrier in China. To study the spatio-temporal variation characteristics and driving mechanisms of the Net Primary Productivity (NPP) of forest is of great significance to explain the change of forest carbon sink and source in the Yellow River Basin. Based on Google Earth Engine (GEE) cloud platform, MOD17A3H V6 NPP data, MCD12Q1 V6 land cover data, ECMWF/ERA5 weather data, USGS/SRTMGL1_003 elevation data, and ridge regression analysis, Hurst index, redundancy analysis (RDA) were used to analyze the spatio-temporal variation characteristics and influencing factors of the forest NPP in the Yellow River Basin from 2001 to 2019. The results showed that (1) from 2001 to 2019, the average total area of forest in the Yellow River Basin was 36600 km2, of which the average area of broadleaf forest, coniferous forest and mixed forest was 26400 km2, 100 km2 and 10100 km2. The annual total forest NPP showed a linear increasing trend, with an average value of 8.99 Tg C, an average annual growth rate of 0.36 Tg C/a, and a 19-year growth rate of 173.60%. The averagely annual total NPP of different forest types were:4.79 Tg C (broadleaf forest), 6.04×10-5 Tg C (coniferous forest) and 0.64 Tg C (mixed forest), and the averagely annual growth rate were:broadleaf forest (0.16 Tg C/a) > mixed forest (0.04 Tg C/a) > coniferous forest (6.98×10-6 Tg C/a). (2) From 2001 to 2019, the annual average NPP of the forest in the Yellow River Basin increased linearly, with an average value of 241.58 g C m-2 a-1, an average annual growth rate of 7.18 g C m-2 a-1, and a 19-year growth rate of 108.63%. The mean annual average NPP of different forest types were 178.48 g C m-2 a-1 (broadleaf forest), 0.60 g C m-2 a-1 (coniferous forest) and 62.49 g C m-2 a-1(mixed forest), and the averagely annual growth rate were:broadleaf forest (4.75 g C m-2 a-1) > mixed forest (2.39 g C m-2 a-1) > coniferous forest (0.04 g C m-2 a-1). (3) The area of forest NPP showed an increasing trend in the Yellow River Basin accounting for 94.50%, of which 73.29% was significantly increased; the area showed a decreasing trend accounting for 5.50%, of which the area with a significant decrease accounted for 1.57%. The area of the significantly increased of the broadleaf forest NPP was the highest (76.78%), followed by mixed forest (60.84%) and coniferous forest (56.76%). (4) The Hurst index (H) of the forest NPP in the Yellow River Basin was between 0.38-1.00, with an average value of 0.87. Among them, the amount of H ≥ 0.5 accounted for about 99.34%, and the forest NPP in the Yellow River Basin would continue to increase in the future. (5) Attribution analysis showed that the total interpretation rate of the environmental factors on the spatio-temporal variation of the forest NPP in the Yellow River Basin was 55.80%, and the environmental factors that had significant effects were longitude (35.50%), precipitation (8.00%), temperature (6.50%) and latitude (5.40%). From 2001 to 2019, the forest NPP of the Yellow River Basin increased and showed strong sustainability. The GEE cloud platform combined with redundant analysis can timely and efficiently obtain the spatio-temporal variation of the forest NPP of the Yellow River Basin and perform an attribution analysis. 参考文献 相似文献 引证文献

Changes in extent of open-surface water bodies in China's Yellow River Basin (2000–2020) using Google Earth Engine cloud platform

This study innovatively utilizes the Google Earth Engine (GEE) cloud platform, combined with a multispectral index rule set, to address the challenges of extracting open-surface water bodies in the middle reaches of the Tarim River Basin (MROTR). To overcome the limitations of traditional index methods, particularly the reduced extraction accuracy caused by mixed pixels in arid environments, this research integrates indices such as MNDWI, NDVI, and EVI for automated water extraction, significantly enhancing the precision of water body delineation. By analyzing the spatiotemporal changes in open-surface water area (SWA) from 1990 to 2022, a notable increase in SWA was observed following the implementation of ecological gate-controlled water management, while a decreasing trend was identified within an 8-km range from the riverbanks. The results show a 93.7% accuracy in surface water identification and an SWA growth rate of 12.47 km2/year, with 96% of this growth attributed to seasonal water areas. Runoff loss decreased by 8.53% in the S1 and S2 regions but increased by 7.76% in the S3 and S4 regions. The mixed index rule method proved effective for large-scale water detection, offering new insights for managing arid region water resources.

The change detection and land use and land cover (LULC) maps are more important powerful forces behind numerous ecological systems and fallow land. The current research focuses on demarcating the spatiotemporal LULC changes, NDVI and change detections maps. These effects directly affect the ecosystem, land resources, cropping pattern and agriculture. LULC assessment and surveillance are essential for long-term planning and sustainable use of natural resources. However, we have developed the soft computing machine learning algorithm for mapping land use and land cover based on the Google earth engine (GEE) platform and change detection mapping done by SAGA GIS software. It is significantly used for ecological safety and planning under various climate variations. To accurately describe the land use and land cover classes with changes are identified in the area. This area exclusively uses the multitemporal Landsat-5 (30 m) and Sentinel-2 (10 m) imageries in LULC mapping. The GEE is a cloud-computing platform with the prevailing classification ability of random forest (RF) models to make five-year interval LULC maps for 2010, 2015 and 2020. To unique multiple RF models established as a classifier in the algorithm created by JavaScript and GEE. SAGA GIS has provided the best platform for detecting changes in land use and land cover classes. NDVI maps are created based on the cloud-based platform. These maps value ranges between −0.68 to −0.15, 0.76 to −0.29 and 0.66 to −0.11 in 2010, 2015 and 2020. Experimental outcomes indicate five classes such as water bodies, built up, barren, cropland and fallow land during 2010, 2015 and 2020. The overall accuracy of User and Producer for 2010, 2015 and 2019 years in between 86.23%, 88.34%, 85.53% and 92.51%, 94.34% and 91.54%, respectively. We have observed that (2010, 2015 − 2020) agriculture and built-up land increased by 1040.76 ha, 1246.32 ha, 1500.93 ha and 34.96 ha, 37.08 ha, 42.58 ha, respectively. Other side degraded land, fallow land, waterbodies areas (953.19 ha, 679.23 ha, 937.24 ha and 1385.73 ha, 1513.53 ha, 991.08 ha and 32.85 ha, 21.33 ha, 25.66 ha) are increased during the year of 2010, 2015 and 2020, respectively. While results have been done by GEE cloud platform and remote sensing data, this developed algorithm easily classified the land use maps from Landsat-5 and Sentinel-2 TM imagery in the machine learning approach. The determined 30-m and 10-m three-year LULC maps are made-up to deliver vital data on the changes, monitoring and understanding of which types of LULC classes and changes have occupied a place in the Rahuri area.

Введение. Водоохранные (защитные) леса являются уязвимыми экосистемами с характерными растительными сообществами, произрастающими на относительно богатых пойменных землях, что отличает их от других лесных насаждений. Оценка данных о лесном покрове водоохранных зон представляет собой ключевую информацию для понимания антропогенного и природного влияния на эти экосистемы. Для этих целей широко используются спутниковые изображения, которые доступны на облачной платформе Google Earth Engine (GEE) по обработке глобальных данных. Цель исследования – оценить применение спектральных каналов разносезонных снимков спутника Sentinel-2 для картографирования и оценки лесного покрова водоохранных зон рек в GEE. Объектами исследования явились лесные насаждения, произрастающие в двухсотметровой буферной (водоохранной) зоне участков рек Рутка, Парат, Большая Кокшага, Большой Кундыш, Волга, Малая Кокшага, Малый Кундыш, Илеть, Юшут, Ошла, Большая Ошла Республики Марий Эл в рамках спутниковой сцены T38VPH Sentinel-2. Методы исследования. В работе была проведена управляемая классификация разносезонных спутниковых изображений Sentinel-2 методом «случайного леса» (RF англ. - Random Forest) в GEE по 9 классам наземного покрова (8 классов древесных пород и 1–нелесные территории). На первом этапе исследования созданы и классифицированы четыре разносезонных (зима, весна, лето, осень) изображения Sentinel-2 с использованием нескольких спектральных каналов. После этого выбраны по пять наиболее значимых каналов изображений каждого исследуемого сезона и создано одно комбинированное изображение на их основе. Результаты и выводы. Общая точность проведённой классификации составила 87,4 %, а коэффициент Каппа – 0,84. Наименьшая точность наблюдается по классам «Ольха» и «Осина» – около 64 %. Исследование показало, что для классификации породного состава водоохранных лесов наиболее приемлемы следующие спектральные каналы спутника Sentinel-2 – вегетационный крайний красный летнего и осеннего сезонов, а также коротковолновый инфракрасный диапазон весеннего и осеннего сезона. Использование облачных вычислений значительно упрощает процесс обработки и анализа спутниковых изображений, а применение разносезонных изображений и выбор подходящих каналов могут повысить точность классификации водоохранных лесов. Introduction. Riparian forests are vulnerable ecosystems with specific plant communities growing in relatively rich floodplain soils, which distinguishes them from other forest plantations. The assessment of data on forest cover in riparian zones provides the key information for understanding anthropogenic and natural impacts on these ecosystems. For these purposes, satellite images are widely used, which are available on the Google Earth Engine (GEE) cloud platform for global data processing. The aim of the study is to evaluate the use of spectral bands of Sentinel-2 satellite multi-season images for mapping and assessing the forest cover of riparian zones in GEE. The objects of the study were forest plantations growing in a 200-meter buffer (water protection) zone of the rivers Rutka, Parat, Bolshaya Kokshaga, Bolshoy Kundysh, Volga, Malaya Kokshaga, Maly Kundysh, Ilet, Yushut, Oshla, and Bolshaya Oshla of the Republic of Mari El, within the framework of Sentinel-2 satellite scene T38VPH. Research methods. Supervised classification of Sentinel-2 satellite multi-season images was carried out on the basis of the random forest (RF) method in GEE according to 9 classes of land cover (8 classes of tree species and 1 - non-forest areas). At the first stage of the study, four multi-season (winter, spring, summer, autumn) Sentinel-2 images were created and classified using several spectral bands. After that, five most significant bands of the images for each studied season were selected and one combined image was created based on them. Results and conclusions. The overall accuracy of the classification was 87.4%, and the Kappa coefficient was 0.84. The lowest accuracy rate of approximately 64% was found for the Alder and Aspen classes. The study showed that the following spectral bands of the Sentinel-2 satellite are most suitable for classifying the species composition of riparian forests: the vegetation red edge band of the summer and autumn seasons, as well as the short-wave infrared range of the spring and autumn seasons. The use of cloud computing greatly simplifies satellite image processing and analysis, while the use of multi-season images and the selection of appropriate bands can improve the accuracy of riparian forest classification.

For proper forest management, accurate detection and mapping of burned areas are needed, yet the practice is difficult to perform due to the lack of an appropriate method, time, and expense. It is also critical to obtain accurate information about the density and distribution of burned areas in a large forest and vegetated areas. For the most efficient and up-to-date mapping of large areas, remote sensing is one of the best technologies. However, the complex image scenario and the similar spectral behavior of classes in multispectral satellite images may lead to many false-positive mistakes, making it challenging to extract the burned areas accurately. This research aims to develop an automated framework in the Google Earth Engine (GEE) cloud computing platform for detecting burned areas in Andika and Behbahan, located in the south and southwest of Iran, using Sentinel-2 time-series images. After importing the images and applying the necessary preprocessing, the Sentinel-2 Burned Areas Index (BAIS2) was used to create a map of the Primary Burned Areas (PBA). Detection accuracy was then improved by masking out disturbing classes (vegetation and water) on the PBA map, which resulted in Final Burned Areas (FBA). The unimodal method is used to calculate the ideal thresholds of indices to make the proposed method automatic. The final results demonstrated that the proposed method performed well in both homogeneous and heterogeneous areas for detecting the burned areas. Based on a test dataset, maps of burned areas were produced in the Andika and Behbahan regions with an overall accuracy of 90.11% and 92.40% and a kappa coefficient of 0.87 and 0.88, respectively, which were highly accurate when compared to the BAIS2, Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Mid-Infrared Bispectral Index (MIRBI), and Normalized Difference SWIR (NDSWIR) indices. Based on the results, accurate determination of vegetation classes and water zones and eliminating them from the map of burned areas led to a considerable increase in the accuracy of the obtained final map from the BAIS2 spectral index.

The spatio-temporal change of the surface water is very important to agricultural, economic, and social development in the Hetao Plain, as well as the structure and function of the ecosystem. To understand the long-term changes of the surface water area in the Hetao Plain, we used all available Landsat images (7534 scenes) and adopted the modified Normalized Difference Water Index (mNDWI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) to map the open-surface water from 1989 to 2019 in the Google Earth Engine (GEE) cloud platform. We further analyzed precipitation, temperature, and irrigated area, revealing the impact of climate change and human activities on long-term surface water changes. The results show the following. (1) In the last 31 years, the maximum, seasonal, and annual average water body area values in the Hetao Plain have exhibited a downward trend. Meanwhile, the number of maximum, seasonal, and permanent water bodies displayed a significant upward trend. (2) The variation of the surface water area in the Hetao Plain is mainly affected by the maximum water body area, while the variation of the water body number is mainly affected by the number of minimum water bodies. (3) Precipitation has statistically significant positive effects on the water body area and water body number, which has statistically significant negative effects with temperature and irrigation. The findings of this study can be used to help the policy-makers and farmers understand changing water resources and its driving mechanism and provide a reference for water resources management, agricultural irrigation, and ecological protection.

Under the background of intensified human activities and global climate warming, the frequency and intensity of flood disasters have increased, causing many casualties and economic losses every year. Given the difficulty of mountain shadow removal from large-scale watershed flood monitoring based on Sentinel-1 SAR images and the Google Earth Engine (GEE) cloud platform, this paper first adopted the Support Vector Machine (SVM) to extract the water body information during flooding. Then, a function model was proposed based on the mountain shadow samples to remove the mountain shadows from the flood maps. Finally, this paper analyzed the flood disasters in the middle and lower basin of the Yangtze River (MLB) in 2020. The main results showed that: (1) compared with the other two methods, the SVM model had the highest accuracy. The accuracy and kappa coefficients of the trained SVM model in the testing dataset were 97.77% and 0.9521, respectively. (2) The function model proposed based on the samples achieved the best effect compared with other shadow removal methods with a shadow recognition rate of 75.46%, and it alleviated the interference of mountain shadows for flood monitoring in a large basin. (3) The flood inundated area was 8526 km2, among which, cropland was severely affected (6160 km2). This study could provide effective suggestions for relevant stakeholders in policy making.

Ecosystem service mapping (ESM) studies are receiving increasing attention due to the imbalance between the supply of and demand for ecosystem services (ES). Global scale ESM is still scarce, but the high computing power of the Google Earth Engine (GEE) cloud platform significantly increases the efficiency. Based on global-scale land cover datasets and the GEE, an ES matrix model based-expert is constructed in this paper to map the ES supply, demand, and relative budgets. The net primary productivity (NPP), enhanced vegetation index (EVI), nighttime light (NTL), and world population (Pop) were acquired, and the NPP and EVI and the NTL and Pop datasets were used to revise the supply of and demand for ESs, respectively. We discovered that the ES supply capacity exhibits a double-peaked distribution with latitude, and the peaks are located at the equator and 50° N. The global ESs have a high spatial heterogeneity and the global supply of ESs is 2.405 times higher than the demand; however, the demand exhibits an increasing trend of about 3.36% per decade, and only southern Asia has more ES demand than supply. The imbalance between the ES supply and demand produced a push-pull effect, that is, it forced humans to move closer to the ES surplus regions (ESSRs) and farther away from the ES deficit regions (ESDRs), and the destruction of the ecological environment promoted this phenomenon. The global terrestrial area is divided into eight ES sub-regions, and targeted land management, urban planning, and environmental remediation policies are proposed.

Greenhouses used for agricultural production have been expanding around the world because it significantly increases crop yield. Meanwhile, it brings a series of environmental problems that should be considered in agricultural planning and management. The advent of the Google Earth Engine (GEE) cloud platform makes remote sensing image processing more convenient and efficient. It has been widely applied in multiple disciplines, but few studies have investigated the detection of greenhouses. In this research, four different classification methods were applied for comparing their performance in monitoring greenhouses in the Guanzhong Plain, Shaanxi, China using GEE: the Minimum Distance Classifier (MDC), the Support Vector Machine with three kernel functions (linear, SVM-L, polynomial, SVM-P, and radial basis function variations, SVM-R), the Classification and Regression Trees (CART), and the Random Forest (RF). Our results illustrate that these classification techniques’ overall accuracy is >84%. The most accurate classification results were obtained by the SVM-R classifier, with an overall accuracy of 94%, followed by the RF and CART classifier, while the MDC performed worst among these four classifiers. These results would be useful for greenhouse extraction in long time series and large-scale areas, which provides solid information for decision-makers and practitioners for agriculture planning and management.

While access to discharge data is key to hydrologic studies, it is a serious obstacle in ungauged basins. Currently, Sentinel-2 imagery at high spatiotemporal resolution offers a unique opportunity to infer the relation between pixel-based discharge rate and surface reflectance. One promising approach in this respect has been to find the complex relationship between river discharge and the spectral ratio between two benchmark pixels, namely the wet and dry pixels, whose dynamics resembles river discharge variation. However, this has been challenging due to the adverse impact of soil moisture and mixed land cover on the spectral behavior of the dry pixel. The selection of the wet pixel must also guarantee sufficient sensitivity of its spectral response to water depth fluctuations. To tackle the above issues, in this study, we developed a novel framework that automatizes the selection of the wet and dry pixels by using Sentinel-2 imagery. We also introduced the Normalized Difference Discharge Index (NDDI), as the best band combination, to predict river discharge. We used linear regression with leave-one-out cross-validation as the prediction model, which leverages limited satellite data due to the cloud cover. By implementing the developed framework at multiple gauged points across the continental United States, the best location of the dry pixel was consistently found in urban pixels whose longwave reflectance fall within a certain range. By analyzing the pixel-wised correlation coefficient between surface reflectance at NIR band and river discharge across the studied river widths, we found that the best wet pixels are located along river banks with shallow water depth. These pixels were characterized by the average reflectance higher than the 98th percentile in the green band. Finally, by testing over 4000 band combinations as input to the river discharge prediction model, we found that the normalized difference between B11 and B4 for the wet pixel, as well as the B11 ratioing between the dry and wet pixels yielded the most accurate predictions with R2 = 0.88 and R2 = 0.73, respectively.

Spatiotemporal disturbances and attribution analysis of mangrove in southern China from 1986 to 2020 based on time-series Landsat imagery

Due to the cold climate and dramatically undulating altitude, the identification of dynamic vegetation trends and main drivers is essential to maintain the ecological balance in Tibet. The normalized difference vegetation index (NDVI), as the most commonly used greenness index, can effectively evaluate vegetation health and spatial patterns. MODIS-NDVI (Moderate-resolution Imaging Spectroradiometer-NDVI) data for Tibet from 2001 to 2020 were obtained and preprocessed on the Google Earth Engine (GEE) cloud platform. The Theil–Sen median method and Mann–Kendall test method were employed to investigate dynamic NDVI changes, and the Hurst exponent was used to predict future vegetation trends. In addition, the main drivers of NDVI changes were analyzed. The results indicated that (1) the vegetation NDVI in Tibet significantly increased from 2001 to 2020, and the annual average NDVI value fluctuated between 0.31 and 0.34 at an increase rate of 0.0007 year−1; (2) the vegetation improvement area accounted for the largest share of the study area at 56.6%, followed by stable unchanged and degraded areas, with proportions of 27.5 and 15.9%, respectively. The overall variation coefficient of the NDVI in Tibet was low, with a mean value of 0.13; (3) The mean value of the Hurst exponent was 0.53, and the area of continuously improving regions accounted for 41.2% of the study area, indicating that the vegetation change trend was continuous in most areas; (4) The NDVI in Tibet indicated a high degree of spatial agglomeration. However, there existed obvious differences in the spatial distribution of NDVI aggregation areas, and the aggregation types mainly included the high-high and low-low types; and (5) Precipitation and population growth significantly contributed to vegetation cover improvement in western Tibet. In addition, the use of the GEE to obtain remote sensing data combined with time-series data analysis provides the potential to quickly obtain large-scale vegetation change trends.

In this study, the distribution and alterations of ozone concentrations in Tehran, Iran, in 2021 were investigated. The impacts of precursors (i.e., CO, NO2, and NO) on ozone were examined using the data collected over 12 months (i.e., January 2021 to December 2021) from 21 stations of the Air Quality Control Company (AQCC). The results of monthly heat mapping of tropospheric ozone concentrations indicated the lowest value in December and the highest value in July. The lowest and highest seasonal concentrations were in winter and summer, respectively. Moreover, there was a negative correlation between ozone and its precursors. The Inverse Distance Weighting (IDW) method was then implemented to obtain air pollution zoning maps. Then, ozone concentration modeled by the IDW method was compared with the average monthly change of total column density of ozone derived from Sentinel-5 satellite data in the Google Earth Engine (GEE) cloud platform. A good agreement was discovered despite the harsh circumstances that both ground-based and satellite measurements were subjected to. The results obtained from both datasets showed that the west of the city of Tehran had the highest averaged O3 concentration. In this study, the status of the concentration of ozone precursors and tropospheric ozone in 2022 was also predicted. For this purpose, the Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) approach was implemented to predict the monthly air quality parameters. Overall, it was observed that the SARIMA approach was an efficient tool for forecasting air quality. Finally, the results showed that the trends of ozone obtained from terrestrial and satellite observations throughout 2021 were slightly different due to the contribution of the tropospheric ozone precursor concentration and meteorology conditions.