Changes In Vegetation Index Research Articles

“Where the Moose Were”: Fort William First Nation’s Ancestral Land, Two–Eyed Seeing, and Industrial Impacts

A two-eyed seeing approach considered Indigenous knowledge and Western science towards eco–health, reconciliation and land back with Fort William First Nation (FWFN) in Ontario, Canada. To map traditional land use, occupancy, and ecological knowledge, we interviewed 49 FWFN members about their hunting, fishing, trapping, plant harvesting, cultural sites, and sacred gatherings on their ancestral land. Their traditional land use and occupancy includes more than 7.5 million ha of their ancestral land. The FWFN members reported many industrial impacts on their reserve and ancestral land. We analyzed the normalized difference vegetation index (NDVI) change over time on FWFN’s ancestral land and the Thunder Bay Pulp and Paper Mill (TBPP)’s National Pollutant Release Inventory data to investigate the FWFN members’ ecohealth concerns. The NDVI analysis revealed large tracts of degraded FWFN’s ancestral land due to logging areas, mining claims, settlements, and paper mills. Mining claims and greenstone belts occupy a quarter of the FWFN members’ ancestral land. The TBPP mill dumped pollution into the Kaministiquia River upstream and upwind of the FWFN community, exposing FWFN members to kilotons of cancerous and other toxic chemicals each year for over a century. Resource extraction and pollution in Northwestern Ontario negatively impacted the human health and ecosystem integrity of FWFN, requiring reconciliation by restoring damaged land and preventing pollution as the starting point for land back. The first step to land back is ending the environmental racism of the TBPP’s pollution directed downstream and downwind of FWFN and protecting ancestral land against logging, mining, and other extractive industries.

Multi-Year Hurricane Impacts Across an Urban-to-Industrial Forest Use Gradient

Coastal forests in the eastern United States are increasingly threatened by hurricanes; however, monitoring their initial impacts and subsequent recovery is challenging across scales. Understanding disturbance impacts and responses is essential for sustainable forest management, biodiversity conservation, and climate change adaptation. Using Sentinel-2 imagery, we calculated the annual Normalized Difference Vegetation Index change (∆NDVI) of forests before and after Hurricane Michael (HM) in Florida to determine how different forest use types were impacted, including the initial wind damage in 2018 and subsequent recovery or reactive management for two focal areas located near and far from the coast. We used detailed parcel data to define forest use types and characterized multi-year impacts using sampling and k-means clustering. We analyzed five years of timberland logging activity up to the fall of 2023 to identify changes in logging rates that may be attributable to post-hurricane salvage efforts. We found uniform impacts across forest use types near the coast, where winds were the most intense but differences inland. Forest use types showed a wide range of multi-year responses. Urban forests had the fastest 3-year recovery, and the timberland response was delayed, apparently due to salvage logging that increased post-hurricane, peaked in 2021–2022, and returned to the pre-hurricane rate by 2023. The initial and secondary consequences of HM on forests were complex, as they varied across local and landscape gradients. These insights reveal the importance of considering forest use types to understand the resilience of coastal forests in the face of potentially increasing hurricane activity.

Spatiotemporal analysis of land use and land cover changes, LST and NDVI in Thatta district, Sindh, Pakistan

The purpose of this work is to determine land-use and land-cover (LULC) patterns, land surface temperature (LST), and normalized difference vegetation index (NDVI) changes in Thatta district using Landsat data from 1991 to 2021 and evaluate the relationship between LST and NDVI. The research process employed the selection of the study area, data acquisition, preprocessing, and classification of remotely sensed images for the estimation of the land use land cover change (LULC), vegetation index (NDVI), and evaluation of LST using thermal bands in the Landsat dataset. The study revealed the area under built-up structures has increased from 1991 to 2021. Although the vegetation cover showed an increase, the bare soil showed a decreasing pattern, indicating a constant change in the LULC patterns in the region. The confusion matrix method for accuracy valuation of LULC data of 2021 revealed an overall accuracy of 88.24%, with a Kappa coefficient of 84.22%, while the Artificial Neural Network Multilayer Perceptron (ANN-MLP) model had a Kappa validation of 0.95 for 2021. The highest maximum temperature is observed for 2021, indicating a positive relationship between LST and built-up structures, while regression analysis found a negative correlation between LST and NDVI. This study provides a valuable monitoring framework to help resource managers develop strategies to manage land resources.

Response of vegetation variation to climate change and human activities in the Shiyang River Basin of China during 2001–2022

Understanding the response of vegetation variation to climate change and human activities is critical for addressing future conflicts between humans and the environment, and maintaining ecosystem stability. Here, we aimed to identify the determining factors of vegetation variation and explore the sensitivity of vegetation to temperature (SVT) and the sensitivity of vegetation to precipitation (SVP) in the Shiyang River Basin (SYRB) of China during 2001–2022. The climate data from climatic research unit (CRU), vegetation index data from Moderate Resolution Imaging Spectroradiometer (MODIS), and land use data from Landsat images were used to analyze the spatial-temporal changes in vegetation indices, climate, and land use in the SYRB and its sub-basins (i.e., upstream, midstream, and downstream basins) during 2001–2022. Linear regression analysis and correlation analysis were used to explore the SVT and SVP, revealing the driving factors of vegetation variation. Significant increasing trends (P<0.05) were detected for the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) in the SYRB during 2001–2022, with most regions (84%) experiencing significant variation in vegetation, and land use change was determined as the dominant factor of vegetation variation. Non-significant decreasing trends were detected in the SVT and SVP of the SYRB during 2001–2022. There were spatial differences in vegetation variation, SVT, and SVP. Although NDVI and EVI exhibited increasing trends in the upstream, midstream, and downstream basins, the change slope in the downstream basin was lower than those in the upstream and midstream basins, the SVT in the upstream basin was higher than those in the midstream and downstream basins, and the SVP in the downstream basin was lower than those in the upstream and midstream basins. Temperature and precipitation changes controlled vegetation variation in the upstream and midstream basins while human activities (land use change) dominated vegetation variation in the downstream basin. We concluded that there is a spatial heterogeneity in the response of vegetation variation to climate change and human activities across different sub-basins of the SYRB. These findings can enhance our understanding of the relationship among vegetation variation, climate change, and human activities, and provide a reference for addressing future conflicts between humans and the environment in the arid inland river basins.

Study on the Dynamic Changes of Vegetation Cover in Yan'an City based on Remote Sensing Data

Based on remote sensing data, we analyzed the spatial and temporal dynamic change characteristics of vegetation and meteorology in Yan'an City in the past 20 years, and explored the factors affecting the change of vegetation index. The results showed that the vegetation index of Yan'an City ranged from 0.54 to 0.75 from 2000 to 2019, and its spatial distribution showed the characteristics of high in the south and low in the north, which was consistent with the distribution of land use types in Yan'an City, and in the temporal distribution, it showed a significant upward trend, and the overall rate of increase was 0.095/(10a); during the period of 2000~2019, 79.83% of the areas of Yan'an City had an improved vegetation trend, and degraded areas accounted for 0.34% of Yan'an city. In summary, the vegetation index of Yan'an city showed an increasing trend.

Potential use of crop water stress index (CWSI) and spectral vegetation indices for black cumin under deficit irrigation

This study was carried out in 2022 to examine the yield, yield components and changes in crop water stress index (CWSI) and vegetation index in black cumin with deficit irrigation. Five different irrigation water levels (I0, I25, I50, I75, I100) were used. The amount of irrigation water applied changed between 20 and 276 mm. Plant water consumption (ET) values varied between 182 and 425 mm. The highest seed yields were obtained from I100 treatments (692 t ha−1) and the lowest from I0 treatments (25 t ha−1). Biological yield, plant height, stem diameter, first capsule height, number of capsules per plant, number of branches per plant, number of seeds per capsule and 1000-seed weight of black cumin were affected by deficit irrigation. CWSI lower limit equation to be used in irrigation scheduling was identified Tc−Ta = − 1.7524 × VPD + 0.7698 (R2 = 0.54) and the upper limit 10.9 ℃. For black cumin plants, irrigation is recommended when the CWSI value is between 0.08 and 0.12. 9 different spectral vegetation indices were evaluated in this study. It has been determined that there are significant correlations between yield, yield components and CWSI and spectral vegetation indices.

Dynamic changes and impacts of accumulated temperature on the Tibetan plateau from 1980 to 2018 based on the fusion of simulation and observation data

In the context of global warming, the thermal conditions of the Tibetan Plateau have changed significantly in recent decades. In the present study, we analysed the spatiotemporal variation in T ≥ 0 °C accumulated temperature (AT0) on the Tibetan Plateau from 1980 to 2018 and its effect on normalized difference vegetation index (NDVI) changes by fusing climate model outputs and ground observations using the High Accuracy Surface Modeling (HASM). Cross-validation revealed that the root mean square error (RMSE) and R2 of the fused data from HASM were 1.593 °C and 0.719, respectively, which were greater than the 5.864 °C and 0.385, respectively, before fusion, indicating that HASM fusion improved the accuracy and that a more accurate AT0 could be obtained. Over the past 39 years, AT0 on the Tibetan Plateau had increased at a rate of 7.53 °C/year. The growth period was extended by 0.46 days/year, while the start and end of the growth period were 0.27 days/year earlier and 0.18 days/year later, respectively. Analysis of the decadal change in AT0 revealed that the areas with AT0 < 500 °C decreased by 5 % and that the areas with AT0 > 2000 °C increased by 6.2 %. However, a slower warming trend appeared after 2010 because of the decreasing rate of the daily mean temperature increase during the growth period. Increasing AT0 also promoted vegetation growth, especially in parts of the southern and eastern plateau regions, with a Pearson's correlation coefficient of 0.46 on the entire plateau between AT0 and the average NDVI during the growth periods. However, there was a significant negative correlation with a coefficient lower than −0.4 in the Qaidam Basin, and partial correlation analysis showed that the extension of the growth period was the main factor influencing the decrease in the NDVI in the Qaidam Basin.

An Analysis on the Impact of Normalized Difference Vegetation Index (NDVI) Changes on the Land Surface Temperature (LST) using Satellite Imagery in the West Bank, Palestine

Abstract&#x0D; This study investigated the impact of the Normalized Difference Vegetation Index (NDVI) on the Land Surface Temperature (LST) in the West Bank, Palestine, using multitemporal satellite imagery. Landsat 5 TM and Landsat 8 OLI data were used in the study. The Landsat 5 TM data was acquired on March 29, 2001, while the Landsat 8 OLI data was acquired on April 05, 2021. The NDVI and the LST for the two images were then calculated from the two images using ArcGIS 10.8 software. Results of the study showed that the NDVI of the 2021 image was much higher than that of the 2001 image. The mean NDVI of the 2001 image was 0.25, while that of the 2021 image was 0.32. As for the LST, the mean LST of the 2001 image was 29.85 °C, while that of the 2021 image was 26.98 °C. Around 74.7% of the West Bank lands recorded an inverse relationship between the NDVI change and the LST change between 2001 and 2021. Around 25.3% of the West Bank lands recorded a positive relationship. In other words, the study depicted the impact of the NDVI on the LST in the West Bank. The study recommends that policymakers need to increase the green areas in the West Bank to reduce the LST as this is seen to affect the climate conditions in the area.&#x0D; Keywords: LST, NDVI, West Bank, Palestine, Landsat data.

Urban Heat Island effect and Urban Thermal Field Variance Index phenomenon at the heart of ‘Chicken's Neck Corridor’ of India

Siliguri corridor, the ‘Chicken Neck Corridor of India’, is one of the largest Municipal areas in West Bengal and is of great geo-strategic importance as it lies at the crossroads between India, Nepal, Bhutan and Bangladesh connecting the north-eastern part of India with mainland India. This study uses the LANDSAT 7 ETM+ and LANDSAT 9 TIR OLI imageries from USGS Earth Explorer to determine Land Surface Temperature (LST) changes, Normalized Differential Vegetation Index (NDVI) changes; Normalized Differential Built-up Index (NDBI) changes; Land use and Land cover (LULC) changes; Urban Heat Island (UHI) effect; Urban Hot Spots (UHS) and Urban Thermal Field Variance Index (UTFVI) phenomenon. With booming trade and increasing urbanisation, Siliguri city (Municipal Corporation) has seen rise in the mean LST by 2.92 °C resulting in increased UHI intensity and UTFVI phenomena between 2000 and 2022. The average LST in UHI and UHS has risen by approx. 3 °C causing more areas to come under bad, worse and worst thermal comfort level categories of the ecological evaluation index. An attempt is made to determine the reason for such changes in this micro climate through multi-dimensional perspectives and to exhibit the land use transformations that took place between the aforesaid time periods.

Integrating satellite and model data to explore spatial-temporal changes in aerosol optical properties and their meteorological relationships in northwest India

This study aims to analyze the temporal and spatial distribution of Aerosol Optical Properties across Northwest India using aerosol data from MODIS (Moderate Resolution Imaging Spectroradiometer) and OMI (Ozone Monitoring Instrument) sensors from 2003 to 2022. Therefore, this study investigated the decadal, interannual, and seasonal changes in aerosol optical properties, vegetation index, and meteorological parameters in the northwest Indian region (8 boxes). Using GIOVANNI (Goddard Earth Sciences Data and Information Services Center (GES DISC) Online Visualization and Analysis Infrastructure), we retrieved daily and monthly Aqua and Terra MODIS products of aerosol optical depth (AOD), Angstrom exponent (AE), normalized difference vegetation index (NDVI), and OMI aerosol index (AI) to examine the spatiotemporal variations by using statistical approaches. The results demonstrated that the decadal averages of aerosol properties showed values of AOD 0.35 (Aqua) and 0.34 (Terra) and AE 1.20 (Aqua) and 1.10 (Terra) with the highest levels during the post-monsoon. Notably, the mean interannual concentrations of AOD and NDVI consistently surpass 0.3, and AE and AI exceed 1 in most locations, underscoring the persistence of high aerosol loading. Also, the study revealed a negative decadal change in AOD of about −8.24 %, while AE, AI, and NDVI showed positive decadal changes of about 9.24 %, 15.09 %, and 12.67 %, respectively. In addition, aerosol optical properties and local meteorology strongly correlated (−0.8 to +0.8). Principal Component Analysis (PCA) identifies meteorological parameters as significant drivers, with the first three components explaining over 70 % of the variation in aerosol optical properties. The NOAA HYSPLIT trajectory model suggests that the long-distance dust transport from the Arabian Peninsula frequently penetrates Gujarat province and then to northwest India. The results contributed to air quality management strategies and provided valuable insights into regional climate and air quality with the influence of meteorology.

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.

Spatiotemporal Variation and Pattern Analysis of Air Pollution and Its Correlation with NDVI in Nanjing City, China: A Landsat-Based Study

The rapid socio-economic development and urbanization in China have led to a decline in air quality. Therefore, the spatial and temporal distribution patterns of urban air pollution, as well as its formation mechanisms and influencing factors, have become important areas of research in atmospheric environment studies. This paper focuses on nine monitoring sites in Nanjing, where concentration data for six air pollutants and vegetation index data were collected from 2013 to 2021. The objective of this study is to investigate the changes in air pollutants and vegetation index over time and space, as well as their relationship with each other, and to assess the social and environmental impacts of air pollution. The findings reveal a spatial distribution pattern of air pollution in Nanjing that exhibits significant variability, with pollutant concentrations decreasing from the city center towards the surrounding areas. Notably, the main urban area has lower air quality compared to the peripheral regions. The results obtained from best-fit linear regression models and correlation heatmaps demonstrate a strong correlation (coefficient of determination, R2 &gt; 0.5) between the normalized difference vegetation index (NDVI) and pollutants such as SO2, NO2, PM2.5, PM10, and O3 within a radial distance of 2 km from the air pollutant monitoring sites. These findings indicate that NDVI can be an effective indicator for assessing the distribution and concentrations of air pollutants. Negative correlations between NDVI and socio-economic indicators are observed under relatively consistent natural conditions, including climate and terrain. Therefore, the spatiotemporal distribution patterns of NDVI can provide valuable insights not only into socio-economic growth but also into the levels and locations of air pollution concentrations.

Improving Neural Network classification of native forest in New Zealand with phenological features

ABSTRACT Changes in Vegetation Indices (VIs) over time can describe vegetation phenology; however, it is not known which phenological features contribute most to land cover classification. Feature selection could potentially solve this problem. In this study, phenological feature importance and selection were evaluated by using two-year Sentinel-2 (S-2) data and single-date PlanetScope (PS) to classify a 50 km2 native podocarp forest in New Zealand. The study area was classified into nine classes. Single-date PS and S-2 data were fused to a base image with the same spatial resolution as PS and 8 bands containing spectral data from S-2; this image was used to produce 30 Vegetation Indices (VIs). Phenological features – amplitude (AMP) and phase (PH) were extracted from these VIs using time-series S-2 only, and harmonic analysis in Google Earth Engine. For accurately classifying forests and identifying the most important features, three classification scenarios (fused bands & VIs, fused bands & phenological features, fused bands & VIs & phenological features) were developed using a Neural Network. Variable Selection Using Random Forest (VSURF) was applied on these scenarios to evaluate the impact of feature selection. Results indicate that VSURF could reduce the time needed for the classification while maintaining a comparable level of accuracy. Phenological features improved accuracy from 90% to 94%, driven mostly by Red-Edge Triangulated Vegetation Index-AMP&PH, Normalised Near-Infrared-PH, Greenness Index-PH, Water Body Index-PH, Normalised Difference Vegetation Index-PH, Normalised Green-PH, Red-Edge Normalised Difference Vegetation Index-PH, Leaf Chlorophyll Content-AMP, and Simple Near-Infrared and Blue Ratio-PH. These features reflect changes in the structure, biochemical, and physiological characteristics of the canopy. A lack of ground-based measurements precluded an evaluation of the accuracy of these phenological aspects and an explanation of their distinctive contribution to the model. Overall, the findings show that specific phenological features can improve the classification of New Zealand’s indigenous podocarp forests.

Vegetation Indices for Spatio-Temporal Analysis of the Quality of Can Gio Mangrove for Biodiversity and Conservation

Mangrove forest has an important role in protection against coastal erosion and carbon dioxide sequestration for climate change mitigation. The natural increase in vegetation quality can explain the increase in its health and biodiversity. This study aimed to examine the health of Can Gio Mangrove vegetation to determine its potential for preventing coastal erosion and maintaining biosphere’s conservation function. Algorithms in GIS and remote sensing were used along with the time-series temporal Landsat images in the 2010-2020 period to investigate the changes of vegetation indices: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Normalized Difference Water Index (NDWI). The results showed positive changes of vegetation values between dry season and rain season, and large distribution of their good values in the 2010-2020 period. Decrease in the vegetation area and stability of surface waters area indicate the expansion of urbanized area. These results may support modification of the policy to control the urbanization, to maintain the mangrove biosphere reserve, and to enhance coastline stabilization.

Improving Railway Alignment Selection in Mountainous Areas with Complex Vegetation: A Multisource Data Landslide Identification Approach for Assisted Decision-Making Research

In order to provide important assistance for the scientific and effective route selection of future planned railways in the research area and to quickly and accurately identify the distribution range of landslides, thereby proactively mitigating the impact of geological hazards on railways under earthquake conditions, this study aims to shift the risk threshold for geological hazards and provide a scientific basis for the accurate planning and route selection of railways in mountainous areas. Jiuzhaigou was selected as the research area and postearthquake surface deformation information in the study area was obtained through Sentinel-1 satellite radar data. Based on Sentinel-2 optical remote sensing imagery, the changes in vegetation indices in the study area before and after the earthquake were analyzed in depth. The concept of vegetation index difference was proposed as a characteristic parameter for landslide information interpretation and a method combining surface deformation information was developed for landslide information interpretation. According to this method, the study area experienced a deformation subsidence of up to 14.93 cm under the influence of the earthquake, with some areas experiencing an uplift of approximately 6.0 cm. The vegetation index difference in the research area ranged from −1.83502 to 1.45366. The total number of landslides extracted is 12.034 km2 and 164 landslide points are marked, with an overall recognition accuracy of 92.6% and a Kappa coefficient of 0.876. The research results provide new research ideas for landslide information interpretation and can be used to assist in the decision-making of mountain railroad alignment options.

Analysis of the seasonal dynamics of the NDVI index of potato agrocenosis in the conditions of the dry steppe zone of the Volgograd region

Timely assessment of the state of agrocenoses is very important in agricultural production. In this regard, geographic information systems (GIS) are widely used, since they make it possible to collect, process, store and transmit data on the studied territorial objects. In particular, the data on plant development obtained from satellite images showing the Normalized Difference Vegetation Index (NDVI) changes are widely used in agriculture. Studies were conducted over two years, in 2020 in the village Tumak Sredneakhtubinsky district of Volgograd Region and in 2021 in the village of Mayskoye, near the city of Volgograd. The changes in the NDVI index were compared during different timings of potato growing seasons. The NDVI index readings obtained with the OneSoil software product were synchronized in time and location with the ground surveys. The contrast NDVI was used to determine the heterogeneity of the surface of the crop being grown. The results of the study of dynamics of values of vegetation index NDVI for potatoes grown in the dry steppe zone of the Volgograd region were presented. It was established that during the “germination - budding” period, the NDVI value increased from 0.12 to 0.51 in 2020 and from 0.12 to 0.72 in 2021. During the flowering period, the index value increased on average to 0.64 over the research period. The maximum value of the index was observed during the period of “tops growth cessation - tops wilting” and amounted to 0.75. Further, the index value decreased and in the period of full ripeness of potato tubers, it was equal to 0.6 on an average over the research period. The use of the contrast NDVI determined the heterogeneity of the index values distribution more clearly and completely, which allowed us to identify problem zones in a particular area for taking prompt measures on reduction or elimination of this heterogeneity.

Measuring climate action readiness in maintaining ecological resilience using satellite imagery and field research in Garang Watershed, Central Java, Indonesia

Abstract. Sudarmanto B, Suranto S, Suntoro S, Sutrisno J. 2023. Measuring climate action readiness in maintaining ecological resilience using satellite imagery and field research in Garang Watershed, Central Java, Indonesia. Biodiversitas 24: 2958-2974. The long-term viability of vegetation as a representation of land cover in a watershed is critical. However, more work is needed to develop vegetation management in the form of a climate action model that considers the existence of ecological and social values as a unified system, particularly in times of climate change. Moran's Index measurements and statistical correlation analysis were employed in this study to quantify geographical patterns. These measurements provide real-world judgment in developing aggressive climate response scenarios. Ecological values were derived from shifting vegetation trend indices due to mutual interaction between living beings that build a life cycle in harmony with the environment. Meanwhile, social values are determined by assessing individual internal factors such as attitudes, knowledge, and responses and external factors represented by local government institutions. The findings reveal that the distribution of residents and residential areas is dispersed. With a high confidence level, a linear index declines with R2 = 0.5872 for inhabitants and R2 = 0.9171 for residential areas. In the dry season, there is a significant relationship between the spatial pattern of vegetation indices and the inhabitant's index. The community's knowledge and attitudes considerably impact changes in vegetation indices, particularly during the rainy season, with R2 = 0.207 for SAVI and 0.232 for NDVI. Community readiness values, which elaborate on knowledge, attitudes, responses, and the role of external institutions, show that community readiness in the upstream area of the watershed, specifically in the Kendal District, is in the best position, with a value of 6.627976. On the contrary, the Semarang District, the upstream area, has the lowest value of 4.257092.

Investigation of the Detectability of Corn Smut Fungus (Ustilago maydis DC. Corda) Infection Based on UAV Multispectral Technology

Corn smut fungus (Ustilago maydis [DC.] Corda) is a globally widespread pathogen affecting both forage and sweet maize hybrids, with higher significance in sweet maize. Remote sensing technologies demonstrated favorable results for disease monitoring on the field scale. The study focused on the changes in vegetation index (VI) values influenced by the pathogen. In this study, four hybrids, two forage maize and two sweet maize hybrids were examined. Artificial infection was carried out at three different doses: a low (2500 sporidium number/mL), medium (5000 sporidium number/mL) and high dose (10,000 sporidium number/mL) with a non-infected control plot for each hybrid. The experimental plots were monitored using a multispectral UAV sensor of five monochrome channels on three different dates, i.e., 7, 14 and 21 days after infection. Five different indices (NDVI, GNDVI, ENDVI, LCI, and NDRE) were determined in Quantum GIS 3.20. The obtained results demonstrated that the infection had a significant effect on the VI values in sweet maize hybrids. A high-dose infection in the Dessert R 73 hybrid resulted in significantly lower values compared to the non-infected hybrids in three indices (NDVI, LCI and GNDVI). In the case of the NOA hybrids, GNDVI and ENDVI were able to show significant differences between the values of the infection levels.

NDVI-Based Assessment of Land Degradation Trends in Balochistan, Pakistan, and Analysis of the Drivers

Land degradation destroys human habitats, and vegetation is a marker reflecting land degradation. In this article, the Balochistan Province of Pakistan, which has a fragile ecological environment, was selected as a typical case to analyze its land degradation over 21 years. Relevant studies that used the NDVI and remote sensing data to monitor land degradation already existed. Based on the data product of MODIS, this study obtained the spatio-temporal trends of the normalized difference vegetation index (NDVI) changes from 2000 to 2020 using the sen+ Mann–Kendall (MK) test and Hurst index and analyzed the driving factors of land degradation and restoration by employing the multiple stepwise regression method. The residual analysis method was an effective tool for distinguishing between anthropogenic and climatic impacts, given that not all regions have a significant correlation between the NDVI and rainfall. The main climatic drivers of the NDVI were derived based on the Geodetector analysis and stripped of the main climatic factors by residual analysis to explore the influence of anthropogenic factors on the NDVI. The results show the following: (1) Balochistan is dominated by land restoration. Land restoration is mainly dominated by climate as well as both climate and human factors, and land degradation is mainly dominated by climate and human factors. (2) The Geodetector-based study found high correlations between the NDVI and TMP, MAP, AET and PET, complementing most previous residual analyses that considered only precipitation and temperature. In Balochistan, TMP, AET, PET and MAP were the dominant climatic factors affecting the spatial distribution of the NDVI; TMP with MAP and TMP with AET were the main interactive factors in the spatial distribution of the NDVI. (3) The article quantifies the impact of the anthropogenic drivers on land degradation. Human activities positively influenced the NDVI in 91.02% of the area and negatively influenced it in 8.98% of the area. (4) The overall trend of the NDVI was mainly stable, with stronger improvement than degradation, and showed strong persistence. The above findings enrich our understanding of the climatic impacts of land degradation and human impacts in arid or semi-arid regions and provide a scientific basis for ecological engineering to achieve ecological conservation and quality development in Balochistan, Pakistan.

Identifying the relationship between changes in vegetation indices and the anthropogenic activities in the tropical Semarang City of Indonesia using space imagery

The existence of vegetation in the watershed area as a function of maintaining the continuity of the process of soil infiltration, erosion resistance, and landslide resistance is essential. Studies that measure the existence of vegetation through vegetation index parameters by utilizing optical-based satellite imagery maps, as well as radiometric waves or Radio Detection and Ranging (RADAR), have been widely carried out. However, attempts to test it to formulate it into vegetation resilience indices on the scale of functional areas such as watersheds are still a challenge in itself. In this study, the vegetation resilience index was measured by looking for spatial change patterns from Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI) in 2015-2021. This spatial pattern change is associated with anthropogenic factors represented by the dynamics of changes in vegetation to housing and population distribution. The use of residential land is obtained from the interpretation of optical-based satellite imagery maps from Google Earth sources mitigated by the administrative boundaries of villages. The population change was obtained from statistical data on the village’s population in the same year. The change in vegetation index, residential, and inhabitants were examined by the Spatial Autocorrelation (SA) using the Global Moran’s Index (GMI). Finally, the changes in the spatial patterns in vegetation indices, residentials index, and inhabitant index were conducted with correlation analysis to determine the relationship between these parameters. The results of GMI indicate a tendency towards dispersion in NDVI and SAVI, which showed a linear index decline in the dry season but weak linear indices incline in the wet season. The trend of dispersion patterns also occurs in residential and inhabitants, showing a linear index decline with a high confidence level (R2=0.9171 in residentials and R2=0.5872 in inhabitants). The relationship between pattern changes in NDVI and SAVI and Residential and Inhabitant found by the following formula; NDVI = 0.214173 -3.12952 * Residential + 6.457788 * Inhabitant; SAVI = 0.101544 + 0.120294 * Residential + 1.963223 * Inhabitant.