Search results for: NDVI map

Authors: Anna Gaspard, Stéphane Boudreau, Martin Simard

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Altered temperature and precipitation regimes associated with climate change generally result in improved conditions for plant growth. For Arctic and sub-Arctic ecosystems, this new climatic context favours an increase in primary productivity, a phenomenon often referred to as "greening". The development of an erect shrub cover has been identified as the main driver of Arctic greening. Although this phenomenon has been widely documented at the circumpolar scale, little information is available at the scale of plant communities, the basic unit of the Arctic, and sub-Arctic landscape mosaic. The objective of this study is to quantify the variation of NDVI within the different plant communities of Nunavik, which will allow us to identify the plant formations that contribute the most to the increase in productivity observed in this territory. To do so, the variation of NDVI extracted from Landsat images for the period 1984 to 2020 was quantified. From the Landsat scenes, annual summer NDVI mosaics with a resolution of 30 m were generated. The ecological mapping of Northern Quebec vegetation was then overlaid on the time series of NDVI maps to calculate the average NDVI per vegetation polygon for each year. Our results show that NDVI increases are more important for the bioclimatic domains of forest tundra and erect shrub tundra, and shrubby formations. Surface deposits, variations in mean annual temperature, and variations in winter precipitation are involved in NDVI variations. This study has thus allowed us to quantify changes in Nunavik's vegetation communities, using fine spatial resolution satellite imagery data.

Keywords: climate change, latitudinal gradient, plant communities, productivity

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Authors: Cenk Aksit, Suleyman Kodal, Yusuf Ersoy Yildirim

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Normalized Difference Vegetation Index (NDVI) is a widely used index in the world that provides reference information, such as the health status of the plant, and the density of the vegetation in a certain area, by making use of the electromagnetic radiation reflected from the plant surface. On the other hand, the chlorophyll index provides reference information about the chlorophyll density in the plant by making use of electromagnetic reflections at certain wavelengths. Chlorophyll concentration is higher in healthy plants and decreases as plant health decreases. This study, it was aimed to determine the changes in Normalize Difference Vegetation Index (NDVI) and Normalize Difference Chlorophyll (NDCI) of silage corn irrigated with subsurface drip irrigation systems under different irrigation levels. In 5 days irrigation interval, the daily potential plant water consumption values were collected, and the calculated amount was applied to the full irrigation and 3 irrigation water levels as irrigation water. The changes in NDVI and NDCI of silage corn irrigated with subsurface drip irrigation systems under different irrigation levels were determined. NDVI values have changed according to the amount of irrigation water applied, and the highest NDVI value has been reached in the subject where the most water is applied. Likewise, it was observed that the chlorophyll value decreased in direct proportion to the amount of irrigation water as the plant approached the harvest.

Keywords: NDVI, NDCI, sub-surface drip irrigation, silage corn, deficit irrigation

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Authors: Sandeep Thakur, Ismail Mondal, Phani Bhusan Ghosh, Papita Das, Tarun Kumar De

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Sundarbans Reserve forest of India has been undergoing major transformations in the recent past owing to population pressure and related changes. This has brought about major changes in the spatial landscape of the region especially in the western parts. This study attempts to assess the impacts of the Landcover changes on the mangrove habitats. Time series imageries of Landsat were used to analyze the Normalized Differential Vegetation Index (NDVI) patterns over the western parts of Indian Sundarbans forest in order to assess the heath of the mangroves in the region. The images were subjected to Land use Land cover (LULC) classification using sub-pixel classification techniques in ERDAS Imagine software and the changes were mapped. The spatial proliferation of aquaculture farms during the study period was also mapped. A multivariate regression analysis was carried out between the obtained NDVI values and the LULC classes. Similarly, the observed meteorological data sets (time series rainfall and minimum and maximum temperature) were also statistically correlated for regression. The study demonstrated the application of NDVI in assessing the environmental status of mangroves as the relationship between the changes in the environmental variables and the remote sensing based indices felicitate an efficient evaluation of environmental variables, which can be used in the coastal zone monitoring and development processes.

Keywords: aquaculture farms, LULC, Mangrove, NDVI

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Authors: Muhammad Imran, Iqra Basit, Mobushir Riaz Khan, Sajid Rasheed Ahmad

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The present study investigates the space-time impact of climate change on the rice crop calendar in tropical Gujranwala, Pakistan. The climate change impact was quantified through the climatic variables, whereas the existing calendar of the rice crop was compared with the phonological stages of the crop, depicted through the time series of the Normalized Difference Vegetation Index (NDVI) derived from Landsat data for the decade 2005-2015. Local maxima were applied on the time series of NDVI to compute the rice phonological stages. Panel models with fixed and cross-section fixed effects were used to establish the relation between the climatic parameters and the time-series of NDVI across villages and across rice growing periods. Results show that the climatic parameters have significant impact on the rice crop calendar. Moreover, the fixed effect model is a significant improvement over cross-sectional fixed effect models (R-squared equal to 0.673 vs. 0.0338). We conclude that high inter-annual variability of climatic variables cause high variability of NDVI, and thus, a shift in the rice crop calendar. Moreover, inter-annual (temporal) variability of the rice crop calendar is high compared to the inter-village (spatial) variability. We suggest the local rice farmers to adapt this change in the rice crop calendar.

Keywords: Landsat NDVI, panel models, temperature, rainfall

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Authors: Adesuyi Ayodeji Steve, Zahn Munch

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This study investigates the use of MODIS NDVI to identify agricultural land cover change areas on an annual time step (2007 - 2012) and characterize the trend in the study area. An ISODATA classification was performed on the MODIS imagery to select only the agricultural class producing 3 class groups namely: agriculture, agriculture/semi-natural, and semi-natural. NDVI signatures were created for the time series to identify areas dominated by cereals and vineyards with the aid of ancillary, pictometry and field sample data. The NDVI signature curve and training samples aided in creating a decision tree model in WEKA 3.6.9. From the training samples two classification models were built in WEKA using decision tree classifier (J48) algorithm; Model 1 included ISODATA classification and Model 2 without, both having accuracies of 90.7% and 88.3% respectively. The two models were used to classify the whole study area, thus producing two land cover maps with Model 1 and 2 having classification accuracies of 77% and 80% respectively. Model 2 was used to create change detection maps for all the other years. Subtle changes and areas of consistency (unchanged) were observed in the agricultural classes and crop practices over the years as predicted by the land cover classification. 41% of the catchment comprises of cereals with 35% possibly following a crop rotation system. Vineyard largely remained constant over the years, with some conversion to vineyard (1%) from other land cover classes. Some of the changes might be as a result of misclassification and crop rotation system.

Keywords: change detection, land cover, modis, NDVI

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Authors: Tausif A. Ishtiaque, Zarrin T. Tasin, Kazi S. Akter

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Current trend of urban expansion, especially in the developing countries has caused significant changes in land cover, which is generating great concern due to its widespread environmental degradation. Energy consumption of the cities is also increasing with the aggravated heat island effect. Distribution of land surface temperature (LST) is one of the most significant climatic parameters affected by urban land cover change. Recent increasing trend of LST is causing elevated temperature profile of the built up area with less vegetative cover. Gradual change in land cover, especially decrease in vegetative cover is enhancing the Urban Heat Island (UHI) effect in the developing cities around the world. Increase in the amount of urban vegetation cover can be a useful solution for the reduction of UHI intensity. LST and Normalized Difference Vegetation Index (NDVI) have widely been accepted as reliable indicators of UHI and vegetation abundance respectively. Chittagong, the second largest city of Bangladesh, has been a growth center due to rapid urbanization over the last several decades. This study assesses the intensity of UHI in Chittagong city by analyzing the relationship between LST and NDVI based on the type of land use/land cover (LULC) in the study area applying an integrated approach of Geographic Information System (GIS), remote sensing (RS), and regression analysis. Land cover map is prepared through an interactive supervised classification using remotely sensed data from Landsat ETM+ image along with NDVI differencing using ArcGIS. LST and NDVI values are extracted from the same image. The regression analysis between LST and NDVI indicates that within the study area, UHI is directly correlated with LST while negatively correlated with NDVI. It interprets that surface temperature reduces with increase in vegetation cover along with reduction in UHI intensity. Moreover, there are noticeable differences in the relationship between LST and NDVI based on the type of LULC. In other words, depending on the type of land usage, increase in vegetation cover has a varying impact on the UHI intensity. This analysis will contribute to the formulation of sustainable urban land use planning decisions as well as suggesting suitable actions for mitigation of UHI intensity within the study area.

Keywords: land cover change, land surface temperature, normalized difference vegetation index, urban heat island

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Authors: Amritansh Agarwal, Tejaswi Agarwal

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Forest ecosystem plays very important role in the global carbon cycle. It stores about 80% of all above ground and 40% of all below ground terrestrial organic carbon. There is much interest in the extent of tropical forests and their rates of deforestation for two reasons: greenhouse gas contributions and the impact of profoundly negative biodiversity. Deforestation has many ecological, social and economic consequences, one of which is the loss of biological diversity. The rapid deployment of remote sensing (RS) satellites and development of RS analysis techniques in the past three decades have provided a reliable, effective, and practical way to characterize terrestrial ecosystem properties. Global estimates of tropical deforestation vary widely and range from 50,000 to 170,000 km2 /yr Recent FAO tropical deforestation estimates for 1990–1995 cite 116,756km2 / yr globally. Remote Sensing can prove to be a very useful tool in monitoring of forests and associated deforestation to a sufficient level of accuracy without the need of physically surveying the forest areas as many of them are physically inaccessible. The methodology for the assessment of forest cover using digital image processing (ERDAS) has been followed. The satellite data for the study was procured from USGS website in the digital format. While procuring the satellite data, care was taken to ensure that the data was cloud and aerosol free by making using of FLAASH atmospheric correction technique. The Normalized Difference Vegetation Index (NDVI) has been used as a numerical indicator of the reduction in ground biomass. NDVI = (near I.R - Red)/ (near I.R + Red). After calculating the NDVI variations and associated mean we have analysed the change in ground biomass. Through this paper we have tried to indicate the rate of deforestation over a given period of time by comparing the forest cover at different time intervals. With the help of remote sensing and GIS techniques it is clearly shows that the total forest cover is continuously degrading and transforming into various land use/land cover category.

Keywords: remote sensing, deforestation, supervised classification, NDVI change detection

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Authors: Zama Mahmood

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The increasing demand of food management, monitoring of the crop growth and forecasting its yield well before harvest is very important. These days, yield assessment together with monitoring of crop development and its growth are being identified with the help of satellite and remote sensing images. Studies using remote sensing data along with field survey validation reported high correlation between vegetation indices and yield. With the development of remote sensing technique, the detection of crop and its mechanism using remote sensing data on regional or global scales have become popular topics in remote sensing applications. Punjab, specially the southern Punjab region is extremely favourable for wheat production. But measuring the exact amount of wheat production is a tedious job for the farmers and workers using traditional ground based measurements. However, remote sensing can provide the most real time information. In this study, using the Normalized Differentiate Vegetation Index (NDVI) indicator developed from Landsat satellite images, the yield of wheat has been estimated during the season of 2013-2014 for the agricultural area around Bahawalpur. The average yield of the wheat was found 35 kg/acre by analysing field survey data. The field survey data is in fair agreement with the NDVI values extracted from Landsat images. A correlation between wheat production (ton) and number of wheat pixels has also been calculated which is in proportional pattern with each other. Also a strong correlation between the NDVI and wheat area was found (R2=0.71) which represents the effectiveness of the remote sensing tools for crop monitoring and production estimation.

Keywords: landsat, NDVI, remote sensing, satellite images, yield

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Authors: Shah Fahad

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Managing the Urban Heat Island (UHI) effects is a pressing concern for achieving sustainable urban development and ensuring thermal comfort in major cities of developing nations, such as Lahore, Pakistan. The current UHI effect is mostly triggered by climate change and rapid urbanization. This study explored UHI over the Lahore district and its adjoining urban and rural-urban fringe areas. Landsat satellite data was utilized to investigate spatiotemporal patterns of Land Use and Land Cover changes (LULC), Land Surface Temperature (LST), UHI, Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and Urban Thermal Field Variance Index (UTFVI). The built-up area increased very fast, with a coverage of 22.99% in 2000, 36.06% in 2010, and 47.17% in 2020, while vegetation covered 53.21 % in 2000 and 46.16 % in 2020. It also revealed a significant increase in the mean LST, from 33°C in 2000 to 34.8°C in 2020. The results indicated a significantly positive correlation between LST and NDBI, a weak correlation was also observed between LST and NDVI. The study used scatterplots to show the correlation between NDBI and NDVI with LST, results revealed that the NDBI and LST had an R² value of 0.6831 in 2000 and 0.06541 in 2022, while NDVI and LST had an R² value of 0.0235 in 1998 and 0.0295 in 2022. Proper environmental planning is vital in specific locations to enhance quality of life, protect the ecosystem, and mitigate climate change impacts.

Keywords: land use land cover, spatio-temporal analysis, remote sensing, land surface temperature, urban heat island, lahore pakistan

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Authors: Akbar Cahyadhi Pratama Putra, Tantri Utami Widhaningtyas, M. Randy Aswin

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Indonesia as an archipelagic nation have very long coastline which have large potential marine resources, one of that is the mangrove ecosystems. Lapindo mudflow disaster in Sidoarjo, East Java requires mudflow flowed into the sea through the river Brantas and Porong. Mud material that transported by river flow is feared dangerous because they contain harmful substances such as heavy metals. This study aims to map the mangrove ecosystem seen from its density and knowing how big the impact of a disaster on the Lapindo mud to mangrove ecosystem and accompanied by efforts to address the mangrove ecosystem that maintained continuity. Mapping coastal mangrove conditions of Sidoarjo was done using remote sensing products that Landsat 7 ETM + images with dry months of recording time in 2002, 2006, 2009, and 2014. The density of mangrove detected using NDVI that uses the band 3 that is the red channel and band 4 that is near IR channel. Image processing was used to produce NDVI using ENVI 5.1 software. NDVI results were used for the detection of mangrove density is 0-1. The development of mangrove ecosystems of both area and density from year to year experienced has a significant increase. Mangrove ecosystems growths are affected by material deposition area of Lapindo mud on Porong and Brantas river estuary, where the silt is growing medium suitable mangrove ecosystem and increasingly growing. Increasing the density caused support by public awareness to prevent heavy metals in the material so that the Lapindo mud mangrove breeding done around the farm.

Keywords: archipelagic nation, mangrove, Lapindo mudflow disaster, NDVI

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Authors: T. Garba, T. O. Quddus, Y. Y. Babanyara, M. A. Modibbo

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Desertification is define as the changing of productive land into a desert as the result of ruination of land by man-induced soil erosion, which forces famers in the affected areas to move migrate or encourage into reserved areas in search of a fertile land for their farming activities. This study therefore used remote sensing imageries to determine the level of changes in the vegetative areas. To achieve that Normalized Difference of the Vegetative Index (NDVI), classified imageries and image slicing derived from landsat TM 1986, land sat ETM 1999 and Nigeria sat 1 2007 were used to determine changes in vegetations. From the Classified imageries it was discovered that there a more natural vegetation in classified images of 1986 than that of 1999 and 2007. This finding is also future in the three NDVI imageries, it was discovered that there is increased in high positive pixel value from 0.04 in 1986 to 0.22 in 1999 and to 0.32 in 2007. The figures in the three histogram also indicted that there is increased in vegetative areas from 29.15 Km2 in 1986, to 60.58 Km2 in 1999 and then to 109 Km2 in 2007. The study recommends among other things that there is need to restore natural vegetation through discouraging of farming activities in and around the natural vegetation in the study area.

Keywords: vegetative index, classified imageries, change detection, landsat, vegetation

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Authors: Rabia Munsaf Khan, Eshrat Fatima

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The analysis of the spatial extent and temporal change of vegetation cover using remotely sensed data is of critical importance to agricultural sciences. Pakistan, being an agricultural country depends on this resource as it makes 70% of the GDP. The case study is of Lal Suhanra National Park, which is not only the biggest forest reserve of Pakistan but also of Asia. The study is performed using different temporal images of Landsat. Also, the results of Landsat are cross-checked by using Sentinel-2 imagery as it has both higher spectral and spatial resolution. Vegetation can easily be detected using NDVI which is a common and widely used index. It is an important vegetation index, widely applied in research on global environmental and climatic change. The images are then classified to observe the change occurred over 15 years. Vegetation cover maps of 2000 and 2016 are used to generate the map of vegetation change detection for the respective years and to find out the changing pattern of vegetation cover. Also, the NDVI values aided in the detection of percentage decrease in vegetation cover. The study reveals that vegetation cover of the area has decreased significantly during the year 2000 and 2016.

Keywords: Landsat, normalized difference vegetation index (NDVI), sentinel 2, Greenland monitoring

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Authors: Punwath Prum

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Superfund site is recognized publicly to be a severe environmental problem to surrounding communities and biodiversity due to its hazardous chemical waste from industrial activities. It contaminates the soil and water but also is a leading potential point-source pollution affecting ecosystem in watershed areas from chemical substances. The risks of Superfund site on watershed can be effectively measured by utilizing publicly available data and geospatial analysis by free and open source application. This study analyzed the vegetation change within high risked contaminated watersheds in Wisconsin. The high risk watersheds were measured by which watershed contained high number Superfund sites. The study identified two potential risk watersheds in Lafayette and analyzed the temporal changes of vegetation within the areas based on Normalized difference vegetation index (NDVI) analysis. The raster statistic was used to compare the change of NDVI value over the period. The analysis results showed that the NDVI value within the Superfund sites’ boundary has a significant lower value than nearby surrounding and provides an analogy for environmental hazard affect by the chemical contamination in Superfund site.

Keywords: soil contamination, spatial analysis, watershed

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Authors: Newton Muhury, Armando A. Apan, Tek N. Marasani, Gebiaw T. Ayele

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The changing climate has degraded freshwater availability in Australia that influencing vegetation growth to a great extent. This study assessed the vegetation responses to groundwater using Terra’s moderate resolution imaging spectroradiometer (MODIS), Normalised Difference Vegetation Index (NDVI), and soil water content (SWC). A hydrological model, SWAT, has been set up in a southeast Australian river catchment for groundwater analysis. The model was calibrated and validated against monthly streamflow from 2001 to 2006 and 2007 to 2010, respectively. The SWAT simulated soil water content for 43 sub-basins and monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) were applied in the machine learning tool, Waikato Environment for Knowledge Analysis (WEKA), using two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The assessment shows that different types of vegetation response and soil water content vary in the dry and wet seasons. The WEKA model generated high positive relationships (r = 0.76, 0.73, and 0.81) between NDVI values of all vegetation in the sub-basins against soil water content (SWC), the groundwater flow (GW), and the combination of these two variables, respectively, during the dry season. However, these responses were reduced by 36.8% (r = 0.48) and 13.6% (r = 0.63) against GW and SWC, respectively, in the wet season. Although the rainfall pattern is highly variable in the study area, the summer rainfall is very effective for the growth of the grass vegetation type. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

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Authors: A. K. Faizah, G. Vadamalai, S. K. Balasundram, W. L. Lim

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Pineapple (Ananas comosus) is a common crop in tropical and subtropical areas of the world. Malaysia once ranked as one of the top 3 pineapple producers in the world in the 60's and early 70's, after Hawaii and Brazil. Moreover, government’s recognition of the pineapple crop as one of priority commodities to be developed for the domestics and international markets in the National Agriculture Policy. However, pineapple industry in Malaysia still faces numerous challenges, one of which is the management of disease and pest. Red tip disease on pineapple was first recognized about 20 years ago in a commercial pineapple stand located in Simpang Renggam, Johor, Peninsular Malaysia. Since its discovery, there has been no confirmation on its causal agent of this disease. The epidemiology of red tip disease is still not fully understood. Nevertheless, the disease symptoms and the spread within the field seem to point toward viral infection. Bioassay test on nucleic acid extracted from the red tip-affected pineapple was done on Nicotiana tabacum cv. Coker by rubbing the extracted sap. Localised lesions were observed 3 weeks after inoculation. Negative staining of the fresh inoculated Nicotiana tabacum cv. Coker showed the presence of membrane-bound spherical particles with an average diameter of 94.25nm under transmission electron microscope. The shape and size of the particles were similar to tospovirus. SDS-PAGE analysis of partial purified virions from inoculated N. tabacum produced a strong and a faint protein bands with molecular mass of approximately 29 kDa and 55 kDa. Partial purified virions of symptomatic pineapple leaves from field showed bands with molecular mass of approximately 29 kDa, 39 kDa and 55kDa. These bands may indicate the nucleocapsid protein identity of tospovirus. Furthermore, a handheld sensor, Greenseeker, was used to detect red tip symptoms on pineapple non-destructively based on spectral reflectance, measured as Normalized Difference Vegetation Index (NDVI). Red tip severity was estimated and correlated with NDVI. Linear regression models were calibrated and tested developed in order to estimate red tip disease severity based on NDVI. Results showed a strong positive relationship between red tip disease severity and NDVI (r= 0.84).

Keywords: pineapple, diagnosis, virus, NDVI

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Authors: Abdelaziz Moujane, Abedelali Boulli, Abdellah Ouigmane

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The present work focuses on the assessment of forestlandscape changes in the region of ZaouitAhansal, usingmultitemporal satellite images at high spatial resolution.Severalremotesensingmethodswereappliednamely: The supervised classification algorithm and NDVI whichwerecombined in a GIS environment to quantify the extent and change in density of forest stands (holmoak, juniper, thya, Aleppo pine, crops, and others).The resultsobtainedshowedthat the forest of ZaouitAhansal has undergonesignificantdegradationresulting in a decrease in the area of juniper, cedar, and zeenoak, as well as an increase in the area of baresoil and agricultural land. The remotesensing data providedsatisfactoryresults for identifying and quantifying changes in forestcover. In addition, thisstudycould serve as a reference for the development of management strategies and restoration programs.

Keywords: remote sensing, GIS, satellite image, NDVI, deforestation, zaouit ahansal

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Authors: Tejaswi Agarwal, Amritansh Agarwal

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Forest ecosystem plays very important role in the global carbon cycle. It stores about 80% of all above ground and 40% of all below ground terrestrial organic carbon. There is much interest in the extent of tropical forests and their rates of deforestation for two reasons: greenhouse gas contributions and the impact of profoundly negative biodiversity. Deforestation has many ecological, social and economic consequences, one of which is the loss of biological diversity. The rapid deployment of remote sensing (RS) satellites and development of RS analysis techniques in the past three decades have provided a reliable, effective, and practical way to characterize terrestrial ecosystem properties. Global estimates of tropical deforestation vary widely and range from 50,000 to 170,000km2 /yr Recent FAO tropical deforestation estimates for 1990–1995 cite 116,756km2 / yr globally. Remote Sensing can prove to be a very useful tool in monitoring of forests and associated deforestation to a sufficient level of accuracy without the need of physically surveying the forest areas as many of them are physically inaccessible. The methodology for the assessment of forest cover using digital image processing (ERDAS) has been followed. The satellite data for the study was procured from Indian institute of remote Sensing (IIRS), Dehradoon in the digital format. While procuring the satellite data, care was taken to ensure that the data was cloud free and did not belong to dry and leafless season. The Normalized Difference Vegetation Index (NDVI) has been used as a numerical indicator of the reduction in ground biomass. NDVI = (near I.R - Red)/ (near I.R + Red). After calculating the NDVI variations and associated mean, we have analysed the change in ground biomass. Through this paper, we have tried to indicate the rate of deforestation over a given period of time by comparing the forest cover at different time intervals. With the help of remote sensing and GIS techniques, it is clearly shown that the total forest cover is continuously degrading and transforming into various land use/land cover category.

Keywords: remote sensing, deforestation, supervised classification, NDVI, change detection

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Authors: Antti Nurminen, Avleen Malhi

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Significant progress in intelligent systems in combination with exceedingly wide application domains having machine learning as the core technology are usually opaque, non-intuitive, and commonly complex for human users. We use innovative IoT technology which monitors and analyzes moisture, humidity, luminosity and temperature levels to assist end users for optimization of environmental conditions for their houseplants. For plant health monitoring, we construct a system yielding the Normalized Difference Vegetation Index (NDVI), supported by visual validation by users. We run the system for a selected plant, basil, in varying environmental conditions to cater for typical home conditions, and bootstrap our AI with the acquired data. For end users, we implement a web based user interface which provides both instructions and explanations.

Keywords: explainable artificial intelligence, intelligent agent, IoT, NDVI

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Authors: Andrew J. Zacharias

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The forest garden approach (FGA) was established by Trees for the Future (TREES) over the organization’s 30 years of agroforestry projects in Sub-Saharan Africa. This method transforms traditional agricultural systems into highly managed gardens that produce food and marketable products year-round. The effects of the FGA on food security, dietary diversity, and economic resilience have been measured closely, and TREES has begun to closely monitor the environmental impacts through the use of sensors mounted on unmanned aerial vehicles, commonly known as 'drones'. These drones collect thousands of pictures to create 3-D models in both the visible and the near-infrared wavelengths. Analysis of these models provides TREES with quantitative and qualitative evidence of improvements to the annual above-ground biomass and leaf area indices, as measured in-situ using NDVI calculations.

Keywords: agroforestry, biomass, drones, NDVI

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Authors: Srushti R. Joshi, Ujjwal Rakesh, Spoorthi Sripad

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The rapid advancement of remote sensing technologies has revolutionized plant health monitoring, offering valuable insights for precision agriculture and environmental management. This paper presents a comprehensive comparative analysis between the widely employed normalized difference vegetation index (NDVI) and state-of-the-art hyperspectral sensors in the context of plant health assessment. The study aims to elucidate the weigh ups of spectral resolution. Employing a diverse range of vegetative environments, the research utilizes simulated datasets to evaluate the performance of NDVI and hyperspectral sensors in detecting subtle variations indicative of plant stress, disease, and overall vitality. Through meticulous data analysis and statistical validation, this study highlights the superior performance of hyperspectral sensors across the parameters used.

Keywords: normalized difference vegetation index, hyperspectral sensor, spectral resolution, infrared

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Authors: W. Riahi, M. L. Mansour

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Using tools of data processing such as geographic information system (GIS) for the contribution of the space management becomes more and more frequent. It allows collecting and analyzing diverse natural information relative to the same territory. Space technologies play crucial role in agricultural phenomenon analysis. For this, satellite images treatment were used to classify vegetation density and particularly agricultural areas in Setif province by making recourse to the Normalized Difference Vegetation Index (NDVI). This step was completed by mapping agricultural activities of the province by using ArcGIS.10 software in order to display an overall view and to realize spatial analysis of various themes combined between them which are chosen according to their strategic importance in different thematic maps. The synthesis map elaborately showed that geographic information system can contribute significantly to agricultural management by describing potentialities and development opportunities of production systems and agricultural sectors.

Keywords: GIS, satellite image, agriculture, NDVI, thematic map

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Authors: Arafan Traore, Teiji Watanabe

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Conakry, the capital city of the Republic of Guinea, has experienced a rapid urban expansion and population increased in the last two decades, which has resulted in remarkable local weather and climate change, raise energy demand and pollution and treating social, economic and environmental development. In this study, the spatiotemporal variation of the land surface temperature (LST) is retrieved to characterize the effect of urban growth on the thermal environment and quantify its relationship with biophysical indices, a normalized difference vegetation index (NDVI) and a normalized difference built up Index (NDBI). Landsat data TM and OLI/TIRS acquired respectively in 1986, 2000 and 2016 were used for LST retrieval and Land use/cover change analysis. A quantitative analysis based on the integration of a remote sensing and a geography information system (GIS) has revealed an important increased in the LST pattern in the average from 25.21°C in 1986 to 27.06°C in 2000 and 29.34°C in 2016, which was quite eminent with an average gain in surface temperature of 4.13°C over 30 years study period. Additionally, an analysis using a Pearson correlation (r) between (LST) and the biophysical indices, normalized difference vegetation index (NDVI) and a normalized difference built-up Index (NDBI) has revealed a negative relationship between LST and NDVI and a strong positive relationship between LST and NDBI. Which implies that an increase in the NDVI value can reduce the LST intensity; conversely increase in NDBI value may strengthen LST intensity in the study area. Although Landsat data were found efficient in assessing the thermal environment in Conakry, however, the method needs to be refined with in situ measurements of LST in the future studies. The results of this study may assist urban planners, scientists and policies makers concerned about climate variability to make decisions that will enhance sustainable environmental practices in Conakry.

Keywords: Conakry, land surface temperature, urban heat island, geography information system, remote sensing, land use/cover change

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Authors: Tahir Ali Akbar, Hirra Jabbar

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Land cover change analysis plays a significant role in understanding the trends of urban sprawl and land use transformation due to anthropogenic activities. In this study, the spatio-temporal dynamics of major land covers were analyzed in the last twenty years (1988-2016) for District Lahore located in the Punjab Province of Pakistan. The Landsat satellite imageries were downloaded from USGS Global Visualization Viewer of Earth Resources Observation and Science Center located in Sioux Falls, South Dakota USA. The imageries included: (i) Landsat TM-5 for 1988 and 2001; and (ii) Landsat-8 OLI for 2016. The raw digital numbers of Landsat-5 images were converted into spectral radiance and then planetary reflectance. The digital numbers of Landsat-8 image were directly converted into planetary reflectance. The normalized difference vegetation index (NDVI) was used to classify the processed images into six major classes of water, buit-up, barren land, shrub and grassland, sparse vegetation and dense vegetation. The NDVI output results were improved by visual interpretation using high-resolution satellite imageries. The results indicated that the built-up areas were increased to 21% in 2016 from 10% in 1988. The decrease in % areas was found in case of water, barren land and shrub & grassland. There were improvements in percentage of areas for the vegetation. The increasing trend of urban sprawl for Lahore requires implementation of GIS based spatial planning, monitoring and management system for its sustainable development.

Keywords: land cover changes, NDVI, remote sensing, urban sprawl

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Authors: Walelgn Dilnesa Cherie, Fasikaw Atanaw Zimale, Bekalu W. Asres

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The most catastrophic condition for agricultural production is a drought event, which is also one of the most hydro-metrological-related hazards. According to the combined susceptibility of plants to meteorological and hydrological conditions, agricultural drought is defined as the magnitude, severity, and duration of a drought that affects crop production. The accurate and timely assessment of agricultural drought can lead to the development of risk management strategies, appropriate proactive mechanisms for the protection of farmers, and the improvement of food security. The evaluation of agricultural drought in the East Gojjam zone was the primary subject of this study. To identify the agricultural drought, soil moisture anomalies, soil moisture deficit indices, and Normalized Difference Vegetation Indices (NDVI) are used. The measured welting point, field capacity, and soil moisture were utilized to validate the soil water deficit indices computed from the satellite data. The soil moisture and soil water deficit indices in 2013 in all woredas were minimum; this makes vegetation stress also in all woredas. The soil moisture content decreased in 2013/2014/2019, and 2021 in Dejen, 2014, and 2019 in Awobel Woreda. The max/ min values of NDVI in 2013 are minimum; it dominantly shows vegetation stress and an observed agricultural drought that happened in all woredas. The validation process of satellite and in-situ soil moisture and soil water deficit indices shows a good agreement with a value of R²=0.87 and 0.56, respectively. The study area becomes drought detected region, so government officials, policymakers, and environmentalists pay attention to the protection of drought effects.

Keywords: NDVI, agricultural drought, SWDI, soil moisture

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Authors: Vivek Ganesh

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Tropical cyclones are some of the most damaging events. They occur in yearly cycles and affect the coastal population with three dangerous effects: heavy rain, strong wind and storm surge. In order to estimate the area and the population affected by a cyclone, all the three types of physical impacts must be taken into account. Storm surge is an abnormal rise of water above the astronomical tides, generated by strong winds and drop in the atmospheric pressure. The main aim of the study is to identify the impact by comparing three different months data. The technique used here is NDVI classification technique for change detection and other techniques like storm surge modelling for finding the tide height. Current study emphasize on recent very severe cyclonic storm Hud Hud of category 3 hurricane which had developed on 8 October 2014 and hit the coast on 12 October 2014 which caused significant changes on land and coast of Visakhapatnam, Andhra Pradesh. In the present study, we have used Remote Sensing and GIS tools for investigating and quantifying the changes in vegetation and settlement.

Keywords: inundation map, NDVI map, storm tide map, track map

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Authors: Yangyang Zhou

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Rapid urbanization has caused the urban heat island effect, which will threaten the physical and mental health of urban dwellers, and blue-green spaces can mitigate the thermal environment effectively. In this study, we calculated the average LST from 2013 to 2022, Northeastmonsoon and Southwestmonsoon of Singapore, and compared the cooling effect differences of the four blue-green spaces. Then, spatial correlation and spatial autoregression model were conducted between cooling distance intensity (CDI) and 11 independent variables. The results reveal that (1) the highest mean land surface temperature (LST) in all years, Northeast monsoon and Southwest monsoon can reach 42.8 ℃, 41.6 ℃, and 42.9 ℃, respectively. (2) the temperature-changing tendency in the three time periods is similar to each other, while the overall LST changing trends of the Southwest monsoon are lower than all year and Northeast monsoon. (3) the cooling distance of the sea can reach 1200 m, and CEI is highly positively correlated with NDBI and BuildD and highly negatively correlated with SVF, NDVI and TreeH. LISA maps showed that the zones that passed the significance test between CDI, NDBI and BuildD were nearly the same locations; the same phenomenon also happened between CDI and SVF, NDVI and TreeH. (4) SLM had better regression results than SEM in all the regions; only 3 independent variables passed the significance test in region 1, and most independent variables can pass the significance test in other regions. Variables DIST and NDBI were significantly affecting the CDI in all the regions. In the whole region, all the variables passed the significance test, and NDBI (1.61), SVF (0.95) and NDVI (0.5) had the strongest influence on CDI.

Keywords: cooling effect, land surface temperature, thermal environment mitigation, spatial autoregression model

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Authors: Newton Muhury, Armando A. Apan, Tek Maraseni

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This study modelled the relationships between vegetation response and available water below the soil surface using the Terra’s Moderate Resolution Imaging Spectroradiometer (MODIS) generated Normalised Difference Vegetation Index (NDVI) and soil water content (SWC) data. The Soil & Water Assessment Tool (SWAT) interface known as ArcSWAT was used in ArcGIS for the groundwater analysis. The SWAT model was calibrated and validated in SWAT-CUP software using 10 years (2001-2010) of monthly streamflow data. The average Nash-Sutcliffe Efficiency during the calibration and validation was 0.54 and 0.51, respectively, indicating that the model performances were good. Twenty years (2001-2020) of monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) and soil water content for 43 sub-basins were analysed using the WEKA, machine learning tool with a selection of two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The modelling results show that different types of vegetation response and soil water content vary in the dry and wet season. For example, the model generated high positive relationships (r=0.76, 0.73, and 0.81) between the measured and predicted NDVI values of all vegetation in the study area against the groundwater flow (GW), soil water content (SWC), and the combination of these two variables, respectively, during the dry season. However, these relationships were reduced by 36.8% (r=0.48) and 13.6% (r=0.63) against GW and SWC, respectively, in the wet season. On the other hand, the model predicted a moderate positive relationship (r=0.63) between shrub vegetation type and soil water content during the dry season, which was reduced by 31.7% (r=0.43) during the wet season. Our models also predicted that vegetation in the top location (upper part) of the sub-basin is highly responsive to GW and SWC (r=0.78, and 0.70) during the dry season. The results of this study indicate the study region is suitable for seasonal crop production in dry season. Moreover, the results predicted that the growth of vegetation in the top-point location is highly dependent on groundwater flow in both dry and wet seasons, and any instability or long-term drought can negatively affect these floodplain vegetation communities. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

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Authors: Virginia Venturini, Elisabet Walker, Aylen Carrasco-Millan

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Latin America stands at the front of climate change impacts, with forecasts projecting accelerated temperature and sea level rises compared to the global average. These changes are set to trigger a cascade of effects, including coastal retreat, intensified droughts in some nations, and heightened flood risks in others. In Argentina, wildfires historically affected forests, but since 2004, wetland fires have emerged as a pressing concern. By 2021, the wetlands of the Paraná River faced a dangerous situation. In fact, during the year 2021, a high-risk scenario was naturally formed in the wetlands of the Paraná River, in Argentina. Very low water levels in the rivers, and excessive standing dead plant material (fuel), triggered most of the fires recorded in the vast wetland region of the Paraná during 2020-2021. During 2008 fire events devastated nearly 15% of the Paraná Delta, and by late 2021 new fires burned more than 300,000 ha of these same wetlands. Therefore, the goal of this work is to explore remote sensing tools to monitor environmental conditions and the severity of prescribed burns in the Paraná River wetlands. Thus, two prescribed burning experiments were carried out in the study area (31°40’ 05’’ S, 60° 34’ 40’’ W) during September 2023. The first experiment was carried out on Sept. 13th, in a plot of 0.5 ha which dominant vegetation were Echinochloa sp., and Thalia, while the second trial was done on Sept 29th in a plot of 0.7 ha, next to the first burned parcel; here the dominant vegetation species were Echinochloa sp. and Solanum glaucophyllum. Field campaigns were conducted between September 8th and November 8th to assess the severity of the prescribed burns. Flight surveys were conducted utilizing a DJI® Inspire II drone equipped with a Sentera® NDVI camera. Then, burn severity was quantified by analyzing images captured by the Sentera camera along with data from the Sentinel 2 satellite mission. This involved subtracting the NDVI images obtained before and after the burn experiments. The results from both data sources demonstrate a highly heterogeneous impact of fire within the patch. Mean severity values obtained with drone NDVI images of the first experience were about 0.16 and 0.18 with Sentinel images. For the second experiment, mean values obtained with the drone were approximately 0.17 and 0.16 with Sentinel images. Thus, most of the pixels showed low fire severity and only a few pixels presented moderated burn severity, based on the wildfire scale. The undisturbed plots maintained consistent mean NDVI values throughout the experiments. Moreover, the severity assessment of each experiment revealed that the vegetation was not completely dry, despite experiencing extreme drought conditions.

Keywords: prescribed-burn, severity, NDVI, wetlands

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Authors: B. L. Gadiga

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This study assesses the major ecological zones in Nigeria with the view to understanding the spatial pattern of vegetation zones and the implications on conservation within the period of sixteen (16) years. Satellite images used for this study were acquired from the SPOT-VEGETATION between 1998 and 2013. The annual NDVI images selected for this study were derived from SPOT-4 sensor and were acquired within the same season (November) in order to reduce differences in spectral reflectance due to seasonal variations. The images were sliced into five classes based on literatures and knowledge of the area (i.e. <0.16 Non-Vegetated areas; 0.16-0.22 Sahel Savannah; 0.22-0.40 Sudan Savannah, 0.40-0.47 Guinea Savannah and >0.47 Forest Zone). Classification of the 1998 and 2013 images into forested and non forested areas showed that forested area decrease from 511,691 km² in 1998 to 478,360 km² in 2013. Differencing change detection method was performed on 1998 and 2013 NDVI images to identify areas of ecological concern. The result shows that areas undergoing vegetation degradation covers an area of 73,062 km² while areas witnessing some form restoration cover an area of 86,315 km². The result also shows that there is a weak correlation between rainfall and the vegetation zones. The non-vegetated areas have a correlation coefficient (r) of 0.0088, Sahel Savannah belt 0.1988, Sudan Savannah belt -0.3343, Guinea Savannah belt 0.0328 and Forest belt 0.2635. The low correlation can be associated with the encroachment of the Sudan Savannah belt into the forest belt of South-eastern part of the country as revealed by the image analysis. The degradation of the forest vegetation is therefore responsible for the serious erosion problems witnessed in the South-east. The study recommends constant monitoring of vegetation and strict enforcement of environmental laws in the country.

Keywords: vegetation, NDVI, SPOT-vegetation, ecology, degradation

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Authors: Khaled Missaoui, Amina Beldjazia, Rachid Gharzouli, Yamna Djellouli

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Optical remote sensing makes use of visible, near infrared and short-wave infrared sensors to form images of the earth's surface by detecting the solar radiation reflected from targets on the ground. Different materials reflect and absorb differently at different wavelengths. Thus, the targets can be differentiated by their spectral reflectance signatures in the remotely sensed images. In this work, we are interested to study the distribution of vegetation in the massif forest of Boutaleb (North East of Algeria) which suffered between 1998 and 1999 very large fires. In this case, we use remote sensing with Landsat images from two dates (1984 and 2000) to see the results of these fires. Vegetation has a unique spectral signature which enables it to be distinguished readily from other types of land cover in an optical/near-infrared image. Normalized Difference Vegetation Index (NDVI) is calculated with ENVI 4.7 from Band 3 and 4. The results showed a very important floristic diversity in this forest. The comparison of NDVI from the two dates confirms that there is a decrease of the density of vegetation in this area due to repeated fires.

Keywords: remote sensing, boutaleb, diversity, forest

Procedia PDF Downloads 560