Testing the Potential of Vegetation Indices for Land Use/Cover Classification Using High Resolution Data

Kuzucu, A. Karakacan; Balçık, Filiz Bektaş

doi:10.5194/isprs-annals-iv-4-w4-279-2017

Cited by 13 publications

(4 citation statements)

References 16 publications

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“…Indices serve two purposes: they provide information about the health and growth of plants, and they assist in classifying various types of land (mining, forest, bare soil, pasture, water surfaces, industrial, etc.). Additionally, certain combinations of vegetation indices boost some crops' spectral traits while inhibiting others [16]. The wide use of RS imagery for monitoring land and environmental changes was proofed for soil sealing [17][18][19], human or natural factors that cause the loss of forests [20][21][22], effects of global warming [23,24], a wildfire's damage [25,26], and additional humanmade and natural dynamics.…”

Section: Introductionmentioning

confidence: 99%

Determining the Extent of Soil Degradation Processes Using Trend Analyses at a Regional Multispectral Scale

AbdelRahman

Metwalli

Gao

et al. 2023

Land

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In order to ensure the sustainability of production from agricultural lands, the degradation processes surrounding the fertile land environment must be monitored. Human-induced risk and status of soil degradation (SD) were assessed in the Northern-Eastern part of the Nile delta using trend analyses for years 2013 to 2023. SD hotspot areas were identified using time-series analysis of satellite-derived indices as a small fraction of the difference between the observed indices and the geostatistical analyses projected from the soil data. The method operated on the assumption that the negative trend of photosynthetic capacity of plants is an indicator of SD independently of climate variability. Combinations of soil, water, and vegetation’s indices were integrated to achieve the goals of the study. Thirteen soil profiles were dug in the hotspots areas. The soil was affected by salinity and alkalinity risks ranging from slight to strong, while compaction and waterlogging ranged from slight to moderate. According to the GIS-model results, 30% of the soils were subject to slight degradation threats, 50% were subject to strong risks, and 20% were subject to moderate risks. The primary human-caused sources of SD are excessive irrigation, poor conservation practices, improper utilisation of heavy machines, and insufficient drainage. Electrical conductivity (EC), exchangeable soil percentage (ESP), bulk density (BD), and water table depth were the main causes of SD in the area. Generally, chemical degradation risks were low, while physical risks were very high in the area. Trend analyses of remote sensing indices (RSI) proved to be effective and accurate tools to monitor environmental dynamic changes. Principal components analyses were used to compare and prioritise among the used RSI. RSI pixel-wise residual trend indicated SD areas were related to soil data. The spatial and temporal trends of the indices in the region followed the patterns of drought, salinity, soil moisture, and the difficulties in separating the impacts of drought and submerged on SD on vegetation photosynthetic capacity. Therefore, future studies of land degradation and desertification should proceed using indices as a factor predictor of SD analysis.

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Section: Introductionmentioning

confidence: 99%

Determining the Extent of Soil Degradation Processes Using Trend Analyses at a Regional Multispectral Scale

AbdelRahman

Metwalli

Gao

et al. 2023

Land

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“…It develops the probability function based on inputs from a data set collected from training sites. The method then considers each pixel in an image, comparing it with known pixels (training sites) and assigning unknown pixels to a LC type based on similarity and highest probability of belonging to an already known type [17,18].…”

Section: B Image Classificationmentioning

confidence: 99%

Impact of land cover change on land surface temperature over Greater Beirut Area – Lebanon

Khyami

2022

JASTT

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Remote sensing (RS) technology has been used together with geographic information systems (GIS) to determine the LC types, retrieve LST, and analyze their relationships. The term Greater Beirut Area (GBA) is used to refer to the city of Beirut and its suburbs, which witnessed rapid urban growth after the end of the civil war in the last decade of the twentieth century, due to the increase in the number of its inhabitants, and the prosperity and development of sectors such as; industrial, trade, tourism, and construction. These factors led to a wide change in land cover (LC) types and increased land surface temperature (LST). The results showed an increase in built-up areas by 29.1% and agricultural lands by 6%, while bare land, forests, and seawater decreased by 28.5%, 4.9%, and 1.9%, respectively. These changes caused large differences in the LST between built-up areas and other LC types. The highest LST recorded was in built-up areas (33.03°C in 1985, and 34.01°C in 2020), followed by bare lands (32.61 °C in 1985 and 33.49°C in 2020), cropland (31.23°C in 1985 and 32.17°C in 2020), forest (30.08°C in 1985 and 30.47°C in 2020), and water (24.97°C in 1985 and 28.15°C in 2020). Consequently, converting different LC types into built-up areas led to increases in LST and a changed microclimate.

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“…Vegetation indices are numerical values of the results of spectral transformation of image-forming bands that are formulated to assess the photosynthetic activity of the vegetation canopy layer which correlates to plant types, water content in tissues, and photosynthetic activities. Vegetation Indices are mathematical combination of different spectral bands, and are designed to visualize various features of different images ( Karakacan Kuzucu & Bektas Balcik, 2017 ). The vegetation index is dimensionless radiometric measurements acquired from linear combination of the red spectrum and NIR, which is calculated based on the light reflectance from satellites, or light reflectance from various objects on the earth surface ( Gherghina, Maftei & Filip, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia

Prasetyo

Hartomo

Paseleng

2021

PeerJ Computer Science

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This study aims to develop a software framework for predicting aridity using vegetation indices (VI) from LANDSAT 8 OLI images. VI data are predicted using machine learning (ml): Random Forest (RF) and Correlation and Regression Trees (CART). Comparison of prediction using Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest neighbors (k-nn) and Multivariate Adaptive Regression Spline (MARS). Prediction results are interpolated using Inverse Distance Weight (IDW). This study was conducted in stages: (1) Image preprocessing; (2) calculating numerical data extracted from the LANDSAT band imagery using vegetation indices; (3) analyzing correlation coefficients between VI; (4) prediction using RF and CART; (5) comparing performances between RF and CART using ANN, SVM, k-nn, and MARS; (6) testing the accuracy of prediction using Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE); (7) interpolating with IDW. Correlation coefficient of VI data shows a positive correlation, the lowest r (0.07) and the highest r (0.98). The experiments show that the RF and CART algorithms have efficiency and effectivity in determining the aridity areas better than the ANN, SVM, k-nn, and MARS algorithm. RF has a difference between the predicted results and 1.04% survey data MAPE and the smallest value close to zero is 0.05 MSE. CART has a difference between the predicted results and 1.05% survey data MAPE and the smallest value approaching to zero which is 0.05 MSE. The prediction results of VI show that in 2020 most of the study areas were low vegetation areas with the Normalized Difference Vegetation Index (NDVI) < 0.21, had an indication of drought with the Vegetation Health Index (VHI) < 31.10, had a Vegetation Condition Index (VCI) in some areas between 35%–50% (moderate drought) and < 35% (high drought). The Burn Area Index (dBAI) values are between −3, 971 and −2,376 that show the areas have a low fire risk, and index values are between −0, 208 and −0,412 that show the areas are starting vegetation growth. The result of this study shows that the machine learning algorithms is an accurate and stable algorithm in predicting the risks of drought and land fire based on the VI data extracted from the LANDSAT 8 OLL imagery. The VI data contain the record of vegetation condition and its environment, including humidity, temperatures, and the environmental vegetation health.

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Testing the Potential of Vegetation Indices for Land Use/Cover Classification Using High Resolution Data

Cited by 13 publications

References 16 publications

Determining the Extent of Soil Degradation Processes Using Trend Analyses at a Regional Multispectral Scale

Determining the Extent of Soil Degradation Processes Using Trend Analyses at a Regional Multispectral Scale

Impact of land cover change on land surface temperature over Greater Beirut Area – Lebanon

Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia

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