Normalized Difference Vegetation Index Analysis Research Articles

Comparative analysis of the variability and impacts of tropical cyclones in flood-prone areas of Zimbabwe

Tropical cyclones (TCs) are extreme meteorological events that cause significant deaths, infrastructure damage, and financial losses around the world. In recent years, the Eastern Highlands of Zimbabwe's have become increasingly vulnerable to TCs caused by Indian Ocean tropical cyclones making landfall more frequently. There is still a limited understanding of the phenomenon and the quantification of its impacts. The aim of this research is to conduct a comparative analysis of the variability in the severity of tropical cyclones by analysing historical storm tracks and mapping the environmental impacts in Zimbabwe's Chimanimani and Chipinge districts. Results indicate that, between 1945 and 2022, the Eastern Highlands of Zimbabwe experienced 5 of the total 865 cyclones in the Southwest Indian Ocean. The maximum sustained winds from the Cyclone Idai in the Eastern Highlands were recorded as 195 km/h. Some of the remote sensing-based indices used to extract spatial information about the condition of vegetation, wetlands, built-up area, and bar land during pre and post cyclonic events included the Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI). Analysis of NDVI in the Eastern Highlands revealed that there was a significant decrease in vegetated area because of the cyclone impact, with a decrease of 2.1% and 16.68% for cyclone Japhet and Idai respectively. The MNDWI shows a 10.74% increase in water content after cyclone Eline. Field validation in 2019 confirms the research findings. An Operations Dashboard Disaster Management System was developed in order to disseminate information to the affected stakeholders about the potential risk that the face due to the occurrence of the natural phenomena.

Detection of vegetation cover change in the Southern region of Bangladesh using the Normalized Difference Vegetation Index (NDVI) and Climate Smart Agriculture (CSA) practices

Bangladesh is extremely vulnerable to climate change, and vegetation indices serve as sensitive indicators. Due to the impacts of climate change, the cropping intensity of Southern region of Bangladesh is very low. So, this study aimed to analyze the changes in vegetation cover over time using the Normalized Difference Vegetation Index (NDVI) and identify the use of Climate Smart Agriculture (CSA) technologies and the benefits of using such technologies. A questionnaire survey was carried out by purposive random sampling method to detect 120 farmers’ socioeconomic status, hazards faced by climate change, adopted climate smart agricultural practices and its benefits for assessing Adaptive Strategy Index (ASI) in Amtali upazila of Barguna district and Kalapara upazila of Patuakhali district. NDVI analysis of multi-spectral remote sensing data from 2012 and 2022 indicated the extent of sparse vegetation of Kalapara has increased. Western part of Amtali upazila, fallow areas have become lessened in 2022 (354.55 km2) compared to 2012 (368.78 km2) due to adopting different CSA practices. Saline-tolerant crop varieties, sunflowers, and watermelon cultivation were the highest ranked among the CSA practices, with 301, 300, and 296 ASI, respectively. Calculated weighted average of CSA practices indicated the reduction of production cost, increased family income (49.19%) and cropping intensity (51.67%), which impacts developed social bonding.

Index-based Approach in Relation to Built-up and LST Dynamics; A Study of Lahore, Pakistan

In 21st century, cities outpaced in size and also in density due to development of economic sector.Consequently, the wide spread expansion of urban areas is resulting in the loss of productive green cover and waterbodies. Therefore, realizing this alarming situation, the present study is aimed to investigate and evaluate the pattern ofurban expansion by considering two major land cover types (i) built-up area (ii) other classes (vegetation, waterbody,soil etc.) during the last six years (2015-2020). For this study Sentinel imagery was acquired from USGS EarthExplorer, while Modis Terra images were acquired from World View NASA. New built-up area index (NBUI),normalized difference vegetation index (NDVI), worldview water index (WV-WI) and land surface temperature (LST)were calculated in order to analyze variations in Lahore’s major land cover types and its varying temperature patterns.Spatial analysis presented the obvious impacts of land development on Lahore. NBUI indicated that the built-up areahas increased drastically from 34.0% in 2015 to 84.2% in 2020; NDVI analysis depicted a decline from 0.76% to0.73%, in the green spaces of Lahore during the study period; WV-WI portrayed inconsistent values of water bodies, agift of massive rise in the built-up area in Lahore. LST results presented that the temperature was 42.21°C in 2015,which simultaneously increased and recorded at 49.51°C in 2020. The increase in LST exhibited the alarming situationfor urban environment and can become threat to increase the air pollution level in Lahore. Therefore, this study willserve as a snapshot for policy makers to control the menace of unplanned urbanization by formulating stringent policiesto protect environment.

Land intensification use scenarios based on urban land suitability assessment of the national park

In designing national parks, it is important to balance land use with ecological protection and local socio-economic development. In the past decades, the national park has undergone landscape fragmentation and degradation of ecological functions due to climate change and land use change, especially in national parks with urban areas. Therefore, ecological environmental protection projects should include the assessment of land use when planning urban area development in the national park region. Consequently, we used the Land Suitability Evaluation (LSE) method, with 13 indicators for natural, social and ecological variables, to assess the suitability of the newly created national park for urban land use on the Tibetan Plateau. Combining results of the suitability assessment and the Cellular Automata (CA) method, three urban intensification scenarios were simulated for 2040: (1) unplanned, (2) intensified, and (3) strictly regulated urban growth boundaries (UGBs). The impact of land use on the normalized difference vegetation index (NDVI) was analyzed by remote sensing and GIS tools, and the landscape patterns of the three urban growth scenarios were calculated. Close to 87 % of the area was basically suitable, nearly 13 % was unsuitable, and only 0.06 % (43.4 km2–also included in the basically suitable) was suitable for urban land use. In 2040, the built-up areas are predicted to be 3.38, 2.71 and 2.32 times greater than in 2020 under the unplanned (without considering ecological restrictions), the intensive development (emphasizing the driving force of transportation and tourism on the expansion of built-up areas) and the strict control (maximizing ecological restrictions) scenarios, respectively. An ideal development model was used to balance ecological protection and population growth and economic development. The NDVI analysis revealed that the impact of land use on vegetation cover in the intensive development scenario was ranked as winter pastures > tourist development zones > summer pastures. It is concluded that the intensification scenario, with transport and tourism as the main spatial drivers, is the most desirable for the future coordination of ecological conservation and socio-economic development in the national park. This scenario may alleviate the current environmental pressure of livestock grazing on winter pastures in the national park on the Tibetan Plateau. However, realizing this model requires appropriate engineering design for town development. Future research should assess the impacts of urban growth on the ecological environment, with the aim to achieve sustainable urban development tailored to the various geographic regions.

Estimation of carbon stock in mixed garden tree stands in Jatigede Subdistrict, Sumedang Regency using NDVI (Normalized Difference Vegetation Index)

Carbon dioxide (CO2) is the most significant greenhouse gas causing global warming. Mixed gardens are one type of artificial vegetation in Indonesia that helps to mitigate global warming. The construction of Jatigede Reservoir leads to a reduction in agricultural area and plant cover, affecting tree stands' ability to absorb CO2 from the atmosphere. Remote sensing, GIS, and NDVI analysis were used in research to estimate stored carbon stocks in Jatigede District, Sumedang Regency. These methods can improve the accuracy and efficiency of carbon stock estimates by providing preliminary data on the role that tree stands possess in absorbing carbon in the atmosphere. The purpose of this study was to ascertain the potential for carbon storage in mixed plantation tree stands as well as the amount of carbon stored in each village within Sumedang Regency's Jatigede District. The Normalized Difference Vegetation Index (NDVI), a geographic information system, and non-destructive sampling were the quantitative methods used in the research. The research's findings indicated that the estimated carbon stock in Jatigede District, Sumedang Regency, are 55,680.17135 tons/ha. Of the 12 villages in Jatigede District, Karedok Village has the largest carbon stock, at 13,368.05 tons/ha, while Sukakersa Village has the smallest, at 156.90 tons/ha.

Spatial-temporal dynamics of paddy productivity on the north coast of Java Island, Indonesia based on the principal component analysis of MODIS NDVI anomaly data

Comprehending the dynamics of paddy productivity is imperative for enhancing the efficacy of agricultural land developments. This study provides the application of principal component analysis (PCA) as a method for visualizing the spatial-temporal changes in paddy productivity. The analysis is conducted using the 8-day NDVI (normalized difference vegetation index) anomaly data of MODIS (Moderate Resolution Imaging Spectroradiometer) data spanning the period from 2000 to 2020. The regencies of Karawang, Subang, and Indramayu on the north coast of Java island are chosen as the study area because of their top rice production areas in Indonesia. The results show that the first leading PCA of the NDVI anomaly is related to the interannual variability of paddy productivity with 3-4 year cycles. The spatial and temporal dynamics of the first mode of eigenvectors and principal component time series can generally be grouped into nine categories. Two important categories to note are category-1 (1 January – 19 March) and category-8 (12 September – 16 December). In category-1, the NDVI anomalies move from north to middle and middle to north areas in Karawang and Subang regencies, respectively. In Indramayu Regency, the NDVI anomalies relatively remain in almost all areas. In contrast, in category-8 the NDVI anomalies move from the middle to northern areas in Karawang, Subang, and Indramayu regencies.

Quantifying land degradation and desertification in semi-arid regions through environmental monitoring and assessment (A case study of the Imiter area, Toudgha River catchment, Morocco)

This study focuses on the spatiotemporal drought analysis in the driest region of southeastern Morocco, specifically the Imiter region within the eastern Anti Atlas region. The region has witnessed various climate change events in recent years, necessitating a comprehensive investigation of the relationship between climate and environmental conditions. To achieve this, Landsat 7, 8 and 9 satellite data were utilized for drought analysis. Additionally, satellite-based drought indices including the Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI), were employed to assess the extent of drought evolution. Over a period of two decades (from 1998 to 2022), 24 annual spatial maps were generated for each index to identify the most affected areas. The analysis of NDVI and VCI from 1998 to 2011 revealed significant year-to-year variability, attributed to various factors such as declining piezometric levels, deforestation and mining activities. These findings contribute to a better understanding of drought dynamics in the study area and highlight the importance of monitoring environmental conditions for effective drought management and mitigation strategies.

Spatial Analysis of Changes in Normalization Differences Vegetation Index in Protected Forest Areas of South Lore District, Poso Regency

Detection of changes in vegetation density generally uses the vegetation index parameter. The value of the vegetation index can provide information on the proportion of vegetation cover, live plant index, plant biomass, cooling capacity, and estimation of carbon dioxide absorption. This study aims to analyze changes in the level of vegetation density using Sentinel 2-A imagery in the protected forest area of South Lore District. This study used the method of calculating the Normalized Difference Vegetation Index (NDVI) to identify changes in density over 5 years. The results of the NDVI analysis are the largest in the range of -0.92960 to 0.871725. The vegetation density class in the Protected Forest Area of South Lore District in 2017 is in the dense class with an area of 15,322.24 Ha or around 47.66%, while the smallest in the non-vegetation class, which is 103.11 Ha or 0.32%, while the largest vegetation density class is in the Protected Forest Area of South Lore District in 2022, namely in the medium/quite dense class with an area of 19,948.18 Ha or 62.01% while the smallest in the non-vegetation class of 219.17 Ha or 0.68%. The largest increase in area was in the moderate/quite dense class of 4,892.33 Ha or 15.20% while the largest decrease in area was in the dense class with an area of 6,651.16 Ha or 20.67% of the total area of the Protected Forest Area of South Lore District.

NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) BASED LAND USE LAND COVER CHANGE DETECTION IN PARTS OF NAINITAL DISTRICT, KUMAUN LESSER HIMALAYA, INDIA

This study examines the transformation of surface topography in Nainital district (part of Kumaun Himalaya of Uttarakhand), India, from 1972 to 2022, shedding light on the profound changes that have occurred over this half-century period. In 1972, the land area was primarily dominated by forested areas, agriculture and water bodies, constituting 71.35%, 24.79%, and 1.19% of the total land cover, respectively. However, by 2022, these percentages have dramatically shifted to 60.73%, 6.91% and 0.46%, indicating a significant reduction in forested areas, agriculture and water bodies. In stark contrast, barren lands, settlements and grasslands, which occupied a merely 0.78%, 0.91%, and 0.98% of the land in 1972, have expanded considerably to 2.42%, 1.79% and 28.13% by 2022. These substantial changes are graphically represented, further reinforcing the alarming trend of diminishing forests, dwindling agriculture and reduced water resources, juxtaposed with the ominous rise of barren lands, settlements and extensive grasslands. Notably, the NDVI (Normalized Difference Vegetation Index) analysis unveils a clear downward trajectory in forested areas, agriculture and water bodies, accompanied by a conspicuous increase in barren lands, settlements and grasslands. This trend implies that human encroachment and deforestation have driven the intensification of light vegetation (grasslands) at the expense of dense vegetation (forests), exacerbating environmental concerns within the state. The results of this study highlight the significance of these critical land use changes to mitigate their potentially adverse consequences.

Vegetation Health and Forest Canopy Density Monitoring in The Sundarban Region Using Remote Sensing and GIS

The present study explores vegetation health and forest canopy density in the Sundarbans region using Landsat-8 images. This work analyzes changes in vegetation health using two vegetation indices, the Normalized Difference Vegetation Index (NDVI) and Forest Canopy Density (FCD) values of the Sundarbans, from 2014 to 2020. NDVI, comprising two bands, Red and Near-infrared (NIR), shows a declining trend during the period. Two NDVI land cover classification maps for 2014 and 2020 are produced, and the interest area is divided into five classes: Scanty, Low, Medium, and Densely Vegetated Regions and Water Bodies. A single-band linear gradient pseudo-color is used to assess the land cover difference between 2020 and 2014, showing marked changes in densely vegetative areas. The NDVI difference marks the coastal regions with a higher depletion rate of vegetation than the regions away from the seacoasts. FCD has been taken to compare the results of NDVI with it. FCD consists of another four models: AVI (advanced vegetative index), BI (Bare soil index), SSI (scaled shadow index), and TI (thermal index). FCD is also called crown cover or canopy coverage, which refers to the portion of an area in the field covered by the crown of trees. 2014 and 2015 FCD maps are produced with a single band linear gradient pseudocolor with five land cover classifications: bare soil, Bare Soil, Shrubs, Low, Medium, and Highly vegetated regions. Both maps bear a significant resemblance to NDVI land classification maps. Further, the FCD values of the two maps are scaled between 1 and 100, and the area of each class is calculated. To check the veracity of the NDVI and FCD analysis, a Deep Neural Network (DNN) model has been developed to classify each year’s image taken from Google Earth Engine (GEE). It classifies each year’s image with 99% accuracy. The calculation of the area of each class emphasizes the rapid decline of densely wooded vegetation. Almost 80% of the highly forested zone has been diminished and has become part of the medium-forested region. Area inflation in medium-forested regions corroborates the same. The study also analyzes the migration of vegetation density, i.e., where and how many areas are unchanged, growing, or deforested.

The Impact of Dam Construction on Downstream Vegetation Area in Dry Areas Using Satellite Remote Sensing: A Case Study

The assessment of ecosystem quality and the maintenance of optimal ecosystem function require understanding vegetation area dynamics and their relationship with climate variables. This study aims to detect vegetation area changes downstream of the Hali dam, which was built in 2009, and to understand the influence of the dam as well as climatic variables on the region’s vegetation areas from 2000 to 2020. The case study is located in an arid area with an average rainfall amount from 50 to 100 mm/year. An analysis of seasonal changes in vegetation areas was conducted using the Normalized Difference Vegetation Index (NDVI), and supervised image classification was used to evaluate changes in vegetation areas using Landsat imagery. Pearson correlation and multivariate linear regression were used to assess the response of local vegetation areas to both hydrologic changes due to dam construction and climate variability. The NDVI analysis revealed a considerable vegetation decline after the dam construction in the dry season. This is primarily associated with the impoundment of seasonal water by the dam and the increase in cropland areas due to dam irrigation. A significantly stronger correlation between vegetation changes and precipitation and temperature variations was observed before the dam construction. Furthermore, multivariant linear regression was used to evaluate the variations in equivalent water thickness (EWT), climate data, and NDVI before and after the dam construction. The results suggested that 85 percent of the variability in the mean NDVI was driven by climate variables and EWT before the dam construction. On the other hand, it was found that only 42 percent of the variations in the NDVI were driven by climate variables and EWT from 2010 to 2020 for both dry and wet seasons.

UTILIZATION OF LANDSAT 7 AND 8 IMAGES FOR MAPPING OF CHANGES IN THE NORTH COAST MANGROVE AREA, AESESA DISTRICT

<p>In 2019 there was damage to the mangrove forest which was converted into fish ponds by cutting down 200 hectares of mangrove land in Aeramo Village, which is the northern coastal area of Aesesa District, Nagekeo Regency. This study aims to map changes in mangrove areas and determine mangrove density values.This study uses the NDVI (Normalized Difference Vegetation Index) analysis technique to determine the density of mangrove vegetation in 2011, 2016 and 2021 using Landsat 7 and Landsat 8 imagery and uses an overlay analysis technique from the results of classifying mangrove density classes to determine changes in the area of mangrove areas. The results of the study show that the mangrove area on the north coast of Aesesa District shows density values that vary with The range of vegetation density index in Landsat 7 imagery in 2011 is from -0.573770464 to 0.54666682, in 2016 it has a vegetation density index range from -0.291984737 to 0.609456897 and in 2021 it has a vegetation density index range from -0.067266561 to 0.579148531 which is divided into 5 density classes namely; very rare, rare, moderate, rare and very tight. Based on the classification results, the results of changes in the mangrove area decreased to the medium class with forest area from 102.35 hectares to 66.86 hectares from 2011 to 2016 and experienced the largest increase in the very dense class in 2011-2016 where the forest area was in the very dense class. meeting from 128.86 hectares to 210.65 hectares.</p>

Vejetasyon İndeksleri, Ana Bileşenler Analizi ve Google Earth Engine Kullanılarak Tarımsal Alan Sınıflandırması: Söke/Aydın Örneği

Land use and land cover (LULC) classification is known to be one of the most widely used indicators of environmental change and degradation all over the world. There are various algorithms and methods for LULC classification, whereby reliability of the classification maps presents the principal concern. The study focused on evaluation of accuracies of LULC maps produced from original bands of Sentinel-2 imageries together with Normalized Difference Vegetation Index (NDVI), Green NDVI (GNDVI), and Principal Component Analysis (PCA) using Google Earth Engine (GEE) platform to identify best enhancing method for agricultural land classification. Moreover, short-term LULC changes aimed to be identified in the specified area. To achieve the aims, all available imageries acquired in the same month of different years with less than 10% cloud contamination were used to compose averaged images for May 2018 and May 2022 for generating LULC2018 and LULC2022 maps. The area has separated into seven main classes, namely, olive (O), perennial cultivation (P), non-perennial cultivation (NP), forest (F), natural vegetation (N), settled area-bare land (S), and water surface (W) via random forest algorithym. Reliabilities of LULC maps were evaluated through accuracy assessment procedures considering stratified randomized control points. Transitions between each LULC classes were identified.

Spatiotemporal Variation of Hourly Scale Extreme Rainstorms in the Huang-Huai-Hai Plain and Its Impact on NDVI

This paper utilizes high-resolution ERA5 hourly data from 1980 to 2020 and long-term normalized difference vegetation index (NDVI) time series obtained from remote sensing and applies trend analysis, correlation analysis, lag analysis, and other methods to study the spatiotemporal characteristics of extreme rainfall at daily and hourly scales in the Huang-Huai-Hai Plain. The paper explores the NDVI’s variability and its relationship with extreme hourly precipitation and analyzes the main factors affecting it. The study made the following observations: (1) The extreme daily precipitation in the Huang-Huai-Hai Plain shows a decreasing trend, with a 13.6 mm/yr reduction rate. In contrast, the proportion of extreme rainfall to total precipitation generally exceeds 20%, and the intensity of extreme rain has gradually increased. The spatial distribution pattern of extreme rainfall follows the distribution pattern of China’s rain belts, with the terrain being an important influencing factor. The high-incidence areas for extreme rainfall are the Huaihe River region and the Shandong Peninsula. (2) The observed significant increase in hourly extreme precipitation events in the Shandong and Henan provinces of the Huang-Huai-Hai Plain has led to an increased risk of flooding, while the corresponding events in the northwest region of the Plain have exhibited a gradual weakening trend over time. (3) The extreme hourly precipitation in the Huang-Huai-Hai plain shows a frequent and scattered pattern, with decreasing intensity over time. Extreme precipitation mainly occurs in the first half of the night, especially between 19:00 and 21:00, with extreme hourly rainfall intensity fluctuating between 0.2 and 0.25 and the proportion of rainfall to total precipitation reaching as high as 10%. The spatial distribution of extreme hourly rainstorms during the peak period (19:00–21:00) exhibits a high rainfall volume, intensity, and frequency pattern in the eastern region, while the western part exhibits low rainfall volume, intensity, and frequency. (4) The incidence of extremely heavy rainfall in an hour has exhibited a more significant increase compared to extreme daily events in the Huang-Huai-Hai Plain, primarily in the form of backward-type precipitation. Hourly extreme precipitation events in the Huang-Huai-Hai Plain are affected by terrain and land use/cover change (LUCC), with the micro-topography of hilly areas leading to a concentrated distribution of precipitation and LUCC suppressing extreme precipitation events in arid climates. (5) At the ten-day scale, the spatial distribution of the NDVI shows a gradually increasing trend from northwest to southeast, with the highest NDVI value reaching up to 0.6 in the southern part of the study area. For extreme hourly precipitation, there is no significant change observed at the multi-year ten-day scale; while the NDVI in the northern and central parts of the Huang-Huai-Hai Plain shows a significant decreasing trend, in contrast, it presents a significant increasing trend in the southern region. (6) Finally, the correlation between NDVI at the ten-day scale and extreme hourly precipitation exhibits a decreasing pattern from north to south, with a correlation coefficient decreasing from 0.48 to 0.08. The lagged correlation analysis of extreme hourly rainfall and NDVI for one, two, and three ten-day periods shows that the lagged effect of extreme hourly precipitation on NDVI is negligible. Analyzing the correlation between extreme hourly rainfall and NDVI for different months, the impact of extreme hourly precipitation on NDVI is predominantly negative, except for June, which shows a positive correlation (0.35), passing the significance test. This study offers a scientific foundation for enhancing disaster warning accuracy and timeliness and strengthening the research on disaster reduction techniques.

Analysis of the Biophysical Environmental Impact of Sand Mining in Mawasangka District, Central Buton Regency

The rise of illegal or unlicensed sand mining by irresponsible individuals has caused damage along the coast of Mawasangka District, Central Buton Regency. This study aims to identify the biophysical environmental impacts due to illegal sand mining in the coastal District of Mawasangka Buton and recommendations for post-sand mining land management. The method used in this study was direct observation to determine changes in the coastline and types of damaged vegetation, normalized difference vegetation index (NDVI) analysis to determine the density of vegetation on post-mining land, and measuring the depth of excavation. The results showed that there had been 321 m of beach abrasion and the types of vegetation damaged were coconut, acacisa and jaran wood trees. The results of the NDVI analysis show that non-green space around the mining area is 33.63 ha, while very low vegetation density is 41.91 ha, and low vegetation is 330.05 ha, while the rest is moderate and high vegetation covering 605.47 ha. Measurement of the depth of excavation ranged from 0.6 m – 2.3 m with good and moderate categories. Management of coastal post-mining land in the Mawasangkan sub-district is directed as pond land and revegetation.

Desert Locust (Schistocerca gregaria) Invasion Risk and Vegetation Damage in a Key Upsurge Area

In the recent past, the Horn of Africa witnessed an upsurge in the desert locust (Schistocerca gregaria) invasion. This has raised major concerns over the massive food insecurity, socioeconomic impacts, and livelihood losses caused by these recurring invasions. This study determined the potential vegetation damage due to desert locusts (DLs) and predicted the suitable habitat at high risk of invasion by the DLs using current and future climate change scenarios in Kenya. The normalized difference vegetation index (NDVI) for the period 2018–2020 was computed using multi-date Sentinel-2 imagery in the Google Earth Engine platform. This was performed to assess the vegetation changes that occurred between May and July of the year 2020 when northern Kenya was the hotspot of the DL upsurge. The maximum entropy (MaxEnt) algorithm was used together with 646 DL occurrence records and six bioclimatic variables to predict DL habitat suitability. The current (2020) and two future climatic scenarios for the shared socioeconomic pathways SSP2-4.5 and SSP5-8.5 from the model for interdisciplinary research on climate (MIROC6) were utilized to predict the future potential distribution of DLs for the year 2030 (average for 2021–2040). Using Turkana County as a case, the NDVI analysis indicated the highest vegetation damage between May and July 2020. The MaxEnt model produced an area under the curve (AUC) value of 0.87 and a true skill statistic (TSS) of 0.61, while temperature seasonality (Bio4), mean diurnal range (Bio2), and precipitation of the warmest quarter (Bio18) were the most important bioclimatic variables in predicting the DL invasion suitability. Further analysis demonstrated that currently 27% of the total area in Turkana County is highly suitable for DL invasion, and the habitat coverage is predicted to potentially decrease to 20% in the future using the worst-case climate change scenario (SSP5-8.5). These results have demonstrated the potential of remotely sensed data to pinpoint the magnitude and location of vegetation damage caused by the DLs and the potential future risk of invasion in the region due to the available favorable vegetational and climatic conditions. This study provides a scalable approach as well as baseline information useful for surveillance, development of control programs, and monitoring of DL invasions at local and regional scales.

A GIS Assessment of the Green Space Percentage in a Big Industrial City (Dnipro, Ukraine)

Abstract Green spaces in an industrially polluted city are extremely important for the physical activity and mental health of its inhabitants. The study aim was to analyze the urban green space dynamics and the greening degree of different functional zones typical for a big Ukrainian industrial city (Dnipro, Ukraine). This article used the remote sensing methods and geographic information systems (GIS) to assess the percentage of green spaces in the city of Dnipro, its eight districts, parks, ravines, and sanitary protection zones. The Normalized Difference Vegetation Index (NDVI) was used for mapping the urban green spaces within the ravines, parks, and sanitary protection zones. According to our results, 67% of Dnipro city is greened (NDVI >0.3). On average, there are 219 m2 of green spaces per person, which is 24 and 4 times more than the minimum and optimal values recommended by the World Health Organization (WHO), respectively. It was found that the NDVI values were significantly lower in the territories of sanitary protection zones, compared to the other functional zones. The results of NDVI analysis indicate a significant variation of the studied parameter during the vegetative season, with two typical peaks of the green space vegetative activity. The peak of plant vegetative activity occurs in the first decade of June, followed by slight decline for 2–3 weeks, then there is another increase in the activity, reaching the maximum in mid-July. Even in autumn, the photosynthetic activity remains relatively high for most of the urban areas, declining only from mid-October. In addition, a method and algorithm for estimating and standardizing the level of the urban area greening based on the remote sensing methods and zonal statistics tools are proposed.

Monitoring of Land Surface Temperature from Landsat Imagery: A Case Study of Al-Anbar Governorate in Iraq

Land surface temperature (LST) estimation is a crucial topic for many applications related to climate, land cover, and hydrology. In this research, LST estimation and monitoring of the main part of Al-Anbar Governorate in Iraq is presented using Landsat imagery from five years (2005, 2010, 2015, 2016 and 2020). Images of the years 2005 and 2010 were captured by Landsat 5 (TM) and the others were captured by Landsat 8 (OLI/TIRS). The Single Channel Algorithm was applied to retrieve the LST from Landsat 5 and Landsat 8 images. Moreover, the land use/land cover (LULC) maps were developed for the five years using the maximum likelihood classifier. The difference in the LST and normalized difference vegetation index (NDVI) values over this period was observed due to the changes in LULC. Finally, a regression analysis was conducted to model the relationship between the LST and NDVI. The results showed that the highest LST of the study area was recorded in 2016 (min = 21.1°C, max = 53.2°C and mean = 40.8°C). This was attributed to the fact that many people were displaced and had left their agricultural fields. Therefore, thousands of hectares of land which had previously been green land became desertified. This conclusion was supported by comparing the agricultural land areas registered throughout the presented years. The polynomial regression analysis of LST and NDVI revealed a better coefficient of determination (R2) than the linear regression analysis with an average R2 of 0.423.

Latitudes and land use: Global biome shifts in vegetation persistence across three decades

Introduction: The dynamics of terrestrial vegetation are shifting globally due to environmental changes, with potential repercussions for the proper functioning of the Earth system. However, the response of global vegetation, and the variability of the responses to their changing environment, is highly variable. In addition, the study of such changes and the methods used to monitor them, have in of themselves, been found to significantly impact the findings.Methods: This research builds on a recently developed vegetation persistence metric, which is simple to use, is user‐controlled to assess levels of statistical significance, and is readily reproducible, all designed to avoid these potential pitfalls. This study uses this vegetation persistence metric to present a global exploration of vegetation responses to climatic, latitudinal, and land‐use changes at a biomes level across three decades (1982–2010) of seasonal vegetation activity via the Normalized Difference Vegetation Index (NDVI).Results: Results demonstrated that positive vegetation persistence was found to be greater in June, July, August (JJA), and September, October, November (SON), with an increasing vegetation persistence found in the Northern Hemisphere (NH) over the Southern Hemisphere (SH). While vegetation showed positive persistence overall, this was not constant across all studied biomes. Overall forested biomes along with mangroves showed positive responses towards enhanced vegetation persistence in both the northern hemisphere and southern hemisphere. Contrastingly, desert, xeric shrubs, and savannas exhibited no significant persistence patterns, but the grassland biomes showed more negative persistence patterns and much higher variability over seasons, compared to the other biomes. The main drivers of changes appear to relate to climate, with tropical biomes linking to the availability of seasonal moisture, whereas the northern hemisphere forested biomes are driven more by temperature. Grasslands respond to moisture also, with high precipitation seasonality driving the persistence patterns. Land-use change also affected biomes and their responses, with many biomes having been significantly impacted by humans such that the vegetation response matched land use and not biome type.Discussion: The use here of a novel statistical time series analysis of NDVI at a pixel level, and looking historically back in time, highlights the utility and power of such techniques within global change studies. Overall, the findings match greening trends of other research but within a finer scale both temporally and spatially which is a critical new development in understanding global vegetation shifts.

Analysis of vegetation changes in land area of Syrdarya region using GIS technology and remote sensing data

This article presents a map of vegetative changes in the Syrdarya region based on remote sensing data. Landsat 8 and Landsat 9 satellite images were used for analysis during the vegetation active period. The study examines the vegetation state of the selected area from 2000 to 2022 and analyzes the changes. The Normalized Difference Vegetation Index (NDVI) was calculated using ArcGIS 10.6 software and documented sequentially. The number of color-coded pixels on the map indicating the health and unhealthiness of the crops and the areas they occupy was determined through NDVI analysis. The study revealed a decrease in the vegetation layer in the Syrdarya region, and the reasons for this phenomenon were discussed. The article demonstrates the usefulness of remote sensing in analyzing vegetational changes over time and its potential applications in monitoring the health and productivity of crops in different regions. Overall, this research is valuable for developing strategies to mitigate the impact of vegetation loss in the Syrdarya region and similar regions facing similar challenges.