ASSESSMENT OF URBAN ENVIRONMENTAL QUALITY: A CASE STUDY OF CASABLANCA, MOROCCO

Abstract. By 2050, Most of the world’s population will live in cities, this demographic explosion will lead to significant urban development at the expanse of natural land which may harm the environmental quality. Consequently, assessing and modeling the urban environmental quality (UEQ) is requisite for efficient urban sprawl control and better city planning and management. The present study proposes a methodology to model and assess the environment of the urban system by developing the urban environmental quality index (UEQI) based on remote sensing data. Five environmental indicators were derived from the Landsat OLI image namely, Modified Normalized Difference Impervious Surface Index (MNDISI), Modified Normalized Difference, Water Index (MNDWI), Normalized difference vegetation Index (NDVI), Normalized difference built-up Index (NDBI) and Soil adjusted vegetation index (SAVI). Using the Principal Component Analysis (PCA) the urban environmental quality index was computed for the 17 communes of Casablanca city. The UEQI values were spatially mapped under three classes (good, moderate, and poor). The results obtained from the analysis showed a significant difference in the term of UEQI values among the communes. In addition, the environmental quality is inadequate in communes with fewer green spaces and more impervious surfaces. The outcomes of this work can serve as an efficient tool to determine the most critical interventions to be made by the authority for current and future urban planning and land/resource management.

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Comparative Study of Change Detection and Urban Expansion Using Multi-Date Multi-Source Data: A Case Study of Greater Khartoum

Journal of King Abdulaziz University-Engineering Sciences ◽

10.4197/eng.23-2.8 ◽

2012 ◽

Vol 23 (2) ◽

pp. 139-172

Author(s):

Abdullah Salman Alsalman Abdullah Salman Alsalman

Keyword(s):

Image Processing ◽

Change Detection ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Urban Expansion ◽

Remote Sensing Data ◽

Principal Component ◽

Test Area ◽

Image Differencing ◽

Erdas Imagine

Noting that Khartoum represents the most rapidly expanding city in the Sudan and taking into account that change detection operations are seldom , the present study has been initiated to attempt to produce work that synthesizes land use/land cover (LULC) to investigate change detection using GIS, remote sensing data and digital image processing techniques; estimate, evaluate and map changes that took place in the city from 1975 to 2003. The experiment used the techniques of visual inspection, write-function-memoryinsertion, image differencing, image transformation i.e. normalized difference vegetation index (NDVI), tasseled cap, principal component analysis (PCA), post-classification comparison and GIS. The results of all these various techniques were used by the authors to study change detection of the geographic locale of the test area. Image processing and GIS techniques were performed using Intergraph Image analyst 8.4 and GeoMedia professional version 6, ERDAS Imagine 8.7, and ArcGIS 9.2. Results obtained were discussed and analyzed in a comparative manner and a conclusion regarding the best method for change detection of the test area was derived.

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Inherent Reflectance Variability of Vegetation

Agriculture ◽

10.3390/agriculture9110246 ◽

2019 ◽

Vol 9 (11) ◽

pp. 246 ◽

Cited By ~ 1

Author(s):

Baabak Mamaghani ◽

M. Grady Saunders ◽

Carl Salvaggio

Keyword(s):

Remote Sensing ◽

Precision Agriculture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Time Of Day ◽

Unmanned Aircraft ◽

P Value ◽

Ground Sample ◽

Significant Difference

With the inception of small unmanned aircraft systems (sUAS), remotely sensed images have been captured much closer to the ground, which has meant better resolution and smaller ground sample distances (GSDs). This has provided the precision agriculture community with the ability to analyze individual plants, and in certain cases, individual leaves on those plants. This has also allowed for a dramatic increase in data acquisition for agricultural analysis. Because satellite and manned aircraft remote sensing data collections had larger GSDs, self-shadowing was not seen as an issue for agricultural remote sensing. However, sUAS are able to image these shadows which can cause issues in data analysis. This paper investigates the inherent reflectance variability of vegetation by analyzing six Coneflower plants, as a surrogate for other cash crops, across different variables. These plants were measured under different forecasts (cloudy and sunny), at different times (08:00 a.m., 09:00 a.m., 10:00 a.m., 11:00 a.m. and 12:00 p.m.), and at different GSDs (2, 4 and 8 cm) using a field portable spectroradiometer (ASD Field Spec). In addition, a leafclip spectrometer was utilized to measure individual leaves on each plant in a controlled lab environment. These spectra were analyzed to determine if there was any significant difference in the health of the various plants measured. Finally, a MicaSense RedEdge-3 multispectral camera was utilized to capture images of the plants every hour to analyze the variability produced by a sensor designed for agricultural remote sensing. The RedEdge-3 was held stationary at 1.5 m above the plants while collecting all images, which produced a GSD of 0.1 cm/pixel. To produce 2, 4, and 8 cm GSD, the MicaSense RedEdge-3 would need to be at an altitude of 30.5 m, 61 m and 122 m respectively. This study did not take background effects into consideration for either the ASD or MicaSense. Results showed that GSD produced a statistically significant difference (p < 0.001) in Normalized Difference Vegetation Index (NDVI, a commonly used metric to determine vegetation health), R 2 values demonstrated a low correlation between time of day and NDVI, and a one-way ANOVA test showed no statistically significant difference in the NDVI computed from the leafclip probe (p-value of 0.018). Ultimately, it was determined that the best condition for measuring vegetation reflectance was on cloudy days near noon. Sunny days produced self-shadowing on the plants which increased the variability of the measured reflectance values (higher standard deviations in all five RedEdge-3 channels), and the shadowing of the plants decreased as time approached noon. This high reflectance variability in the coneflower plants made it difficult to accurately measure the NDVI.

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Variations in Persistence and Regenerative Zones in Coastal Forests Triggered by Sea Level Rise and Storms

Remote Sensing ◽

10.3390/rs11172019 ◽

2019 ◽

Vol 11 (17) ◽

pp. 2019 ◽

Cited By ~ 3

Author(s):

Sergio Fagherazzi ◽

Giovanna Nordio ◽

Keila Munz ◽

Daniele Catucci ◽

William S. Kearney

Keyword(s):

Sea Level Rise ◽

Sea Level ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Forest Disturbance ◽

Remote Sensing Data ◽

Storm Events ◽

Coastal Forests ◽

The North ◽

Significant Difference

Retreat of coastal forests in relation to sea level rise has been widely documented. Recent work indicates that coastal forests on the Delmarva Peninsula, United States, can be differentiated into persistence and regenerative zones as a function of sea-level rise and storm events. In the lower persistence zone trees cannot regenerate because of frequent flooding and high soil salinity. This study aims to verify the existence of these zones using spectral remote sensing data, and determine whether the effect of large storm events that cause damage to these forests can be detected from satellite images. Spectral analysis confirms a significant difference in average Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) values in the proposed persistence and regenerative zones. Both NDVI and NDWI indexes decrease after storms triggering a surge above 1.3 m with respect to the North American Vertical Datum of 1988 (NAVD88). NDWI values decrease more, suggesting that this index is better suited to detect the effect of hurricanes on coastal forests. In the regenerative zone, both NDVI and NDWI values recover three years after a storm, while in the persistence zone the NDVI and NDWI values keep decreasing, possibly due to sea level rise causing vegetation stress. As a result, the forest resilience to storms in the persistence zone is lower than in the regenerative zone. Our findings corroborate the ecological ratchet model of coastal forest disturbance.

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Modeling the Radial Stem Growth of the Pine (Pinus sylvestris L.) Forests Using the Satellite-Derived NDVI and LST (MODIS/AQUA) Data

Atmosphere ◽

10.3390/atmos12010012 ◽

2020 ◽

Vol 12 (1) ◽

pp. 12

Author(s):

Yulia Ivanova ◽

Anton Kovalev ◽

Vlad Soukhovolsky

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Principal Component ◽

Stem Growth ◽

Forest Growth ◽

Parabolic Approximation ◽

New Approach ◽

The Relationship ◽

Tree Increment

The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year.

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Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

Remote Sensing ◽

10.3390/rs13061131 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1131

Author(s):

Tao Yu ◽

Pengju Liu ◽

Qiang Zhang ◽

Yi Ren ◽

Jingning Yao

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Net Primary Production ◽

Remote Sensing Data ◽

Forest Degradation ◽

Observation Data ◽

Area Index ◽

Sensing Data ◽

Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

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Consideration of NDVI thematic changes in density analysis and floristic composition of Wadi Yalamlam, Saudi Arabia

Geoscientific Instrumentation Methods and Data Systems ◽

10.5194/gi-7-297-2018 ◽

2018 ◽

Vol 7 (4) ◽

pp. 297-306 ◽

Cited By ~ 4

Author(s):

Amal Y. Aldhebiani ◽

Mohamed Elhag ◽

Ahmad K. Hegazy ◽

Hanaa K. Galal ◽

Norah S. Mufareh

Keyword(s):

Saudi Arabia ◽

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Floristic Composition ◽

Water Source ◽

Shannon Index ◽

Landsat 8 ◽

Main Stream

Abstract. Wadi Yalamlam is known as one of the significant wadis in the west of Saudi Arabia. It is a very important water source for the western region of the country. Thus, it supplies the holy places in Mecca and the surrounding areas with drinking water. The floristic composition of Wadi Yalamlam has not been comprehensively studied. For that reason, this work aimed to assess the wadi vegetation cover, life-form presence, chorotype, diversity, and community structure using temporal remote sensing data. Temporal datasets spanning 4 years were acquired from the Landsat 8 sensor in 2013 as an early acquisition and in 2017 as a late acquisition to estimate normalized difference vegetation index (NDVI) changes. The wadi was divided into seven stands. Stands 7, 1, and 3 were the richest with the highest Shannon index values of 2.98, 2.69, and 2.64, respectively. On the other hand, stand 6 has the least plant biodiversity with a Shannon index of 1.8. The study also revealed the presence of 48 different plant species belonging to 24 families. Fabaceae (17 %) and Poaceae (13 %) were the main families that form most of the vegetation in the study area, while many families were represented by only 2 % of the vegetation of the wadi. NDVI analysis showed that the wadi suffers from various types of degradation of the vegetation cover along with the wadi main stream.

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New 30 m resolution Hong Kong climate, vegetation, and topography rasters indicate greater spatial variation than global grids within an urban mosaic

Earth System Science Data ◽

10.5194/essd-11-1083-2019 ◽

2019 ◽

Vol 11 (3) ◽

pp. 1083-1098 ◽

Cited By ~ 4

Author(s):

Brett Morgan ◽

Benoit Guénard

Keyword(s):

Hong Kong ◽

Precision Agriculture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

City Planning ◽

Rapid Urbanization ◽

Local Climate ◽

Spatial Understanding ◽

Environmental And Ecological Studies ◽

Relevant Variables

Abstract. The recent proliferation of high-quality global gridded environmental datasets has spurred a renaissance of studies in many fields, including biogeography. However, these data, often 1 km at the finest scale available, are too coarse for applications such as precise designation of conservation priority areas and regional species distribution modeling, or purposes outside of biology such as city planning and precision agriculture. Further, these global datasets likely underestimate local climate variations because they do not incorporate locally relevant variables. Here we describe a comprehensive set of 30 m resolution rasters for Hong Kong, a small tropical territory with highly variable terrain where intense anthropogenic disturbance meets a robust protected area system. The data include topographic variables, a Normalized Difference Vegetation Index raster, and interpolated climate variables based on weather station observations. We present validation statistics that convey each climate variable's reliability and compare our results to a widely used global dataset, finding that our models consistently reflect greater climatic variation. To our knowledge, this is the first set of published environmental rasters specific to Hong Kong. We hope this diverse suite of geographic data will facilitate future environmental and ecological studies in this region of the world, where a spatial understanding of rapid urbanization, introduced species pressure, and conservation efforts is critical. The dataset (Morgan and Guénard, 2018) is accessible at https://doi.org/10.6084/m9.figshare.6791276.

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Hydrological model parameterization using NDVI values to account for the effects of land cover change on the rainfall–runoff response

Hydrology Research ◽

10.2166/nh.2017.249 ◽

2017 ◽

Vol 48 (6) ◽

pp. 1455-1473 ◽

Cited By ~ 5

Author(s):

Vahid Nourani ◽

Ahmad Fakheri Fard ◽

Hoshin V. Gupta ◽

David C. Goodrich ◽

Faegheh Niazi

Keyword(s):

Land Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Model Parameters ◽

Rainfall Runoff ◽

Remotely Sensed Data ◽

Efficiency Criterion ◽

Mean Values ◽

Model Parameter

Abstract Classic rainfall–runoff models usually use historical data to estimate model parameters and mean values of parameters are considered for predictions. However, due to climate changes and human effects, model parameters change temporally. To overcome this problem, normalized difference vegetation index (NDVI) derived from remotely sensed data was used in this study to investigate the effect of land cover variations on hydrological response of watersheds using a conceptual rainfall–runoff model. The study area consists of two sub-watersheds (Hervi and Lighvan) with varied land cover conditions. Obtained results show that the one-parameter model generates runoff forecasts with acceptable level of the considered criteria. Remote sensing data were employed to relate land cover properties of the watershed to the model parameter. While a power form of the regression equation could be best fitted to the parameter values using available images of Hervi sub-watershed, for the Lighvan sub-watershed the fitted equation shows somewhat lower correlation due to higher fluctuations of the model parameter. The average values of the Nash–Sutcliffe efficiency criterion of the model were obtained as 0.87 and 0.55, respectively, for Hervi and Lighvan sub-watersheds. Applying this methodology, the model's parameters might be determined using temporal NDVI values.

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An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

Earth Science Informatics ◽

10.1007/s12145-015-0217-3 ◽

2015 ◽

Vol 8 (2) ◽

pp. 327-335 ◽

Cited By ~ 37

Author(s):

Daniel Hölbling ◽

Barbara Friedl ◽

Clemens Eisank

Keyword(s):

Change Detection ◽

Spatial Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Detection Methods ◽

Detection And Attribution ◽

Object Based ◽

Wide Range ◽

Northern Taiwan

Abstract Earth observation (EO) data are very useful for the detection of landslides after triggering events, especially if they occur in remote and hardly accessible terrain. To fully exploit the potential of the wide range of existing remote sensing data, innovative and reliable landslide (change) detection methods are needed. Recently, object-based image analysis (OBIA) has been employed for EO-based landslide (change) mapping. The proposed object-based approach has been tested for a sub-area of the Baichi catchment in northern Taiwan. The focus is on the mapping of landslides and debris flows/sediment transport areas caused by the Typhoons Aere in 2004 and Matsa in 2005. For both events, pre- and post-disaster optical satellite images (SPOT-5 with 2.5 m spatial resolution) were analysed. A Digital Elevation Model (DEM) with 5 m spatial resolution and its derived products, i.e., slope and curvature, were additionally integrated in the analysis to support the semi-automated object-based landslide mapping. Changes were identified by comparing the normalised values of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI) of segmentation-derived image objects between pre- and post-event images and attributed to landslide classes.

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Estimation of different data compositions for early-season crop type classification

PeerJ ◽

10.7717/peerj.4834 ◽

2018 ◽

Vol 6 ◽

pp. e4834 ◽

Cited By ~ 10

Author(s):

Pengyu Hao ◽

Mingquan Wu ◽

Zheng Niu ◽

Li Wang ◽

Yulin Zhan

Keyword(s):

Time Series ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Classification Performance ◽

Distribution Maps ◽

Production Forecasting ◽

Crop Type ◽

Ndvi Time Series ◽

Multi Temporal

Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer’s accuracies (PAs) and user’s accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study.

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