scholarly journals SPATIOTEMPORAL ANALYSIS OF URBAN LAND COVER CHANGES IN KIGALI, RWANDA USING MULTITEMPORAL LANDSAT DATA AND LANDSCAPE METRICS

2017 ◽  
Vol XLII-3/W2 ◽  
pp. 137-144
Author(s):  
T. Mugiraneza ◽  
J. Haas ◽  
Y. Ban
Keyword(s):  
Land Cover ◽  
Landscape Metrics ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Early Warning Systems ◽  
Landscape Configuration ◽  
Support Vector ◽  
Landsat Data ◽  
Environment Impact ◽  
Landsat Images

Mapping urbanization and ensuing environmental impacts using satellite data combined with landscape metrics has become a hot research topic. The objectives of the study are to analyze the spatio-temporal evolution of urbanization patterns of Kigali, Rwanda over the last three decades (from 1984 to 2015) using multitemporal Landsat data and to assess the associated environmental impact using landscape metrics. Landsat images, Normalized Difference Vegetation Index (NDVI), Grey Level Co-occurrence Matrix (GLCM) variance texture and digital elevation model (DEM) data were classified using a support vector machine (SVM). Eight landscape indices were derived from classified images for urbanization environment impact assessment. Seven land cover classes were derived with an overall accuracy exceeding 88 % with Kappa Coefficients around 0.8. As most prominent changes, cropland was reduced considerably in favour of built-up areas that increased from 2,349 ha to 11,579 ha between 1984 and 2015. During those 31 years, the increased number of patches in most land cover classes illustrated landscape fragmentation, especially for forest. The landscape configuration indices demonstrate that in general the land cover pattern remained stable for cropland but it was highly changed in built-up areas. Satellite-based analysis and quantification of urbanization and its effects using landscape metrics are found to be interesting for grassroots and provide a cost-effective method for urban information production. This information can be used for e.g. potential design and implementation of early warning systems that cater for urbanization effects.

scholarly journals Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Long Zhao ◽  
Pan Zhang ◽  
Xiaoyi Ma ◽  
Zhuokun Pan
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Information Extraction ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Land Resource ◽  
Spectral Angle Mapper ◽  
Ndvi Time Series

A timely and accurate understanding of land cover change has great significance in management of area resources. To explore the application of a daily normalized difference vegetation index (NDVI) time series in land cover classification, the present study used HJ-1 data to derive a daily NDVI time series by pretreatment. Different classifiers were then applied to classify the daily NDVI time series. Finally, the daily NDVI time series were classified based on multiclassifier combination. The results indicate that support vector machine (SVM), spectral angle mapper, and classification and regression tree classifiers can be used to classify daily NDVI time series, with SVM providing the optimal classification. The classifiers of K-means and Mahalanobis distance are not suited for classification because of their classification accuracy and mechanism, respectively. This study proposes a method of dimensionality reduction based on the statistical features of daily NDVI time series for classification. The method can be applied to land resource information extraction. In addition, an improved multiclassifier combination is proposed. The classification results indicate that the improved multiclassifier combination is superior to different single classifier combinations, particularly regarding subclassifiers with greater differences.

scholarly journals INVESTIGATING MANGROVE FRAGMENTATION CHANGES USING LANDSCAPE METRICS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 159-162
Author(s):  
M. Baharlouii ◽  
D. Mafi Gholami ◽  
M. Abbasi
Keyword(s):  
Climate Change ◽  
Landscape Metrics ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Images ◽  
Surrounding Water ◽  
Maximum Likelihood Classification ◽  
High Degree ◽  
Drought Occurrence

Abstract. Generally, investigation of long-term mangroves fragmentation changes can be used as an important tool in assessing sensitivity and vulnerability of these ecosystems to the multiple environmental hazards. Therefore, the aim of this study was to reveal the trend of mangroves fragmentation changes in Khamir habitat using satellite imagery and Fragstats software during a 30-year period (1986–2016). To this end, Landsat images of 1986, 1998, and 2016 were used and after computing the normalized difference vegetation index (NDVI) to distinguish mangroves from surrounding water and land areas, images were further processed and classified into two types of land cover (i.e., mangrove and non-mangrove areas) using the maximum likelihood classification method. By determining the extent of mangroves in the Khamir habitat in the years of 1986, 1998 and 2017, the trend of fragmentation changes was quantified using CA, NP, PD and LPI landscape metrics. The results showed that the extent of mangroves in Khamir habitat (CA) decreased in the period post-1998 (1998–2016). The results also showed that, the NP and PD increased in the period of post-1998 and in contrast, the LPI decrease in this period. These results revealed the high degree of vulnerability of mangroves in Khamir habitat to the drought occurrence and are thus threatened by climate change. We hope that the results of this study stimulate further climate change adaptation planning efforts and help decision-makers prioritize and implement conservative measures in the mangrove ecosystems on the northern coasts of the PG and the GO and elsewhere.

scholarly journals Monitoring Land Cover Change Using Remote Sensing and GIS Techniques: a Case Study of Al-Dalmaj Marsh, Iraq

2018 ◽  
Vol 24 (9) ◽  
pp. 96 ◽  
Author(s):  
Marwah Moojid Kadhim
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accurate Information ◽  
Past Century ◽  
Landsat Images ◽  
Gis And Remote Sensing ◽  
Vegetation Water ◽  
Set Up

Al-Dalmaj marsh and the near surrounding area is a very promising area for energy resources, tourism, agricultural and industrial activities. Over the past century, the Al-Dalmaje marsh and near surroundings area endrous from a number of changes. The current study highlights the spatial and temporal changes detection in land cover for Al-Dalmaj marsh and near surroundings area using different analyses methods the supervised maximum likelihood classification method, the Normalized  Difference Vegetation Index (NDVI), Geographic Information Systems(GIS),  and Remote Sensing (RS). Techniques spectral indices were used in this study to determine the change of wetlands and drylands area and of other land classes, through analyses Landsat images for different three years (1990, 2003, 2016). The results indicated that there was an annual increase in vegetation was from 1990 with 980.68 km2, and 1420.35km2 in 2003 to 2072.98km2 in 2016. Whereas, the annual water coverage was about 185.95km2 in 1990 then dropped to 68.27km2 in 2003, and rose to 180.23 km2 in 2016. The water coverage increasing was on the account of barren lands areas, which were significantly decreased. These collected data can be used to deliver accurate information of the values of vegetation,water, wetlands and drylands sustainability of resources which can be used to make plans to increase tourism and protected areas by using barren lands which cannot be reclaimed for agriculture, and cultivate a new renewable energy can be set up  as solar power stations.  

scholarly journals Cambios en la cobertura del suelo en el municipio de Nechí: Una aproximación al impacto ambiental de la minería, 1986-2010

2017 ◽  
Vol 26 (45) ◽  
Author(s):  
Michael Ezequiel Gómez-Rodríguez ◽  
Francisco José Molina-Pérez ◽  
Diana María Agudelo-Echavarría ◽  
Julio Eduardo Cañón-Barriga ◽  
Fabio De Jesús Vélez-Macías
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Bare Soil ◽  
Water Bodies ◽  
Actual State ◽  
Landsat Images ◽  
Mining History ◽  
The Difference

The municipality of Nechí (Antioquia, Colombia) has a long mining history associated with the extraction of gold. This paper evaluates the evolution of land cover changes caused by this mining activity over 24 years. The spatial analysis was based on the Normalized Difference Vegetation Index (NDVI) of three LANDSAT images (1986, 1996 and 2010). The difference in NDVI values between 1986 and 2010 were used to determine the actual state of vegetation, the direction of change (improvement, stability or deterioration), and the area associated with each soil cover. Polygons for different types of coverage (forest, pasture, bare soil, and water bodies) were extracted from each satellite image to quantify the changes and develop land cover maps for each year. Results show that almost 124.8 km² of forest have been lost during the analyzed period. By contrast, water bodies gained an area of 66.3 km². Both results may be related to the type of gold exploitation in the region.

scholarly journals IDENTIFIKASI PERUBAHAN TUTUPAN DAN PENGGUNAAN LAHAN SEBAGAI DASAR PENENTUAN STRATEGI PENGELOLAAN KPHP WAY TERUSAN

2017 ◽  
Vol 13 (3) ◽  
pp. 208
Author(s):  
Tri Santoso ◽  
Melya Riniarti ◽  
Indra Gumay Febryano
Keyword(s):  
Land Cover ◽  
Supervised Classification ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Economic Value ◽  
Market Demand ◽  
Landsat Images ◽  
Economic Interests ◽  
Literature Searches ◽  
Landsat Satellite

Encroachment on forest areas in Indonesia occurs due to various factors mainly related to tenure issues and economic interests. That encroachment occurred in all regions of Indonesia with vary in intensity and amount. Register 47 Way Terusan which has been designated as a KPHP model Way Terusan also being occupied by squatters since the 1990s. The communities within and around the KPHP Way Terusan area has highly dependency on forest resources. The data collection is done in several ways, namely: interviews, literature searches, downloads Landsat satellite imagery and field verification activities. Landsat images Scene: Path 123 and Row 063 for the year 1994, 1999, 2004, 2009 and 2014. Data analysis was conducted using NDVI (Normalized Difference Vegetation Index) and supervised classification. The results of the analysis of land cover in 1994 until 2014 shows the intensity of dynamics of land cover change in the region KPHP Way Terusan. Land cover changes caused as a result of choice of the type of vegetation that has higher economic value. In 2014, the use of cassava cultivation was the highest (55.24%) because of its high economic value, convenient cultivation and market demand. Partnership with agroforestry pattern most likely applied as management strategy policies to accommodate the interests of various stakeholders in KPHP Way Terusan.

scholarly journals Integrating Surface-Based Temperature and Vegetation Abundance Estimates into Land Cover Classifications for Conservation Efforts in Savanna Landscapes

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3456 ◽  
Author(s):  
Herrero ◽  
Southworth ◽  
Bunting ◽  
Kohlhaas ◽  
Child
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Support Vector ◽  
Wet Season ◽  
Rule Based ◽  
Modis Ndvi ◽  
In Situ Data ◽  
Over Time

Southern African savannas are an important dryland ecosystem, as they account for up to 54% of the landscape, support a rich variety of biodiversity, and are areas of key landscape change. This paper aims to address the challenges of studying this highly gradient landscape with a grass–shrub–tree continuum. This study takes place in South Luangwa National Park (SLNP) in eastern Zambia. Discretely classifying land cover in savannas is notoriously difficult because vegetation species and structural groups may be very similar, giving off nearly indistinguishable spectral signatures. A support vector machine classification was tested and it produced an accuracy of only 34.48%. Therefore, we took a novel continuous approach in evaluating this change by coupling in situ data with Landsat-level normalized difference vegetation index data (NDVI, as a proxy for vegetation abundance) and blackbody surface temperature (BBST) data into a rule-based classification for November 2015 (wet season) that was 79.31% accurate. The resultant rule-based classification was used to extract mean Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI values by season over time from 2000 to 2016. This showed a distinct separation between each of the classes consistently over time, with woodland having the highest NDVI, followed by shrubland and then grassland, but an overall decrease in NDVI over time in all three classes. These changes may be due to a combination of precipitation, herbivory, fire, and humans. This study highlights the usefulness of a continuous time-series-based approach, which specifically integrates surface temperature and vegetation abundance-based NDVI data into a study of land cover and vegetation health for savanna landscapes, which will be useful for park managers and conservationists globally.

scholarly journals APPLICATION OF REMOTE SENSING AND GIS FOR ASSESSING LAND COVER RESOURCES VARIABILITY IN THE SELOUS GAME RESERVE, TANZANIA

2017 ◽  
Vol 1 (2) ◽  
pp. 74
Author(s):  
Phillip W. Mambo ◽  
John E. Makunga
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing And Gis ◽  
Vegetation Density ◽  
Landsat Images ◽  
Gis And Remote Sensing ◽  
Game Reserve ◽  
Year 2000

Purpose: The study was conducted in Selous Game Reserve, with intention of developing GIS and Remote Sensing based wildlife management system in the protected area.Methodology: All habitats were digitised using ArcGIS9.3 in which five scenes of Landsat TM and ETM+ digital images were acquired during dry seasons of the year 2000 and 2010. Band 3 and 4 of the Landsat images were used for calculation of normalized difference vegetation index (NDVI) for determination of vegetation spatial distributionResults: The NDVI maps of year 2000 to 2010 revealed the vegetation density depletion from 0.72 (obtained in 0.46─0.72 value interval and covering 46.5% pixel area) in 2000 as compared to 0.56 ( found in 0.38─0.56 value interval and covering 8.04% pixel area) in 2010 NDVI maps.Unique contribution to theory, practice and policy: It was recommended that there was a necessity to integrate applications of remote sensing and GIS techniques for the assessment and monitoring of the natural land cover variability to detect fragmentation and loss of wildlife species.

scholarly journals The Use of Land Cover Indices for Rapid Surface Urban Heat Island Detection from Multi-Temporal Landsat Imageries

2021 ◽  
Vol 10 (6) ◽  
pp. 416
Author(s):  
Nagihan Aslan ◽  
Dilek Koc-San
Keyword(s):  
Land Cover ◽  
Urban Heat Island ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Heat Island ◽  
Landsat Images ◽  
Multi Temporal ◽  
Urban Heat ◽  
Surface Urban Heat Island

The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. Bursa, which is the fourth largest metropolitan city in Turkey, was selected as the study area, and Landsat multi-temporal images of the summer season were used. Firstly, the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI) and index-based built-up index (IBI) were created using the bands of Landsat images, and LULC classes were determined by applying automatic thresholding. The LST values were calculated using thermal images and SUHI effects were determined. The results show that NDVI, SAVI, MNDWI and IBI indices can be used effectively for the determination of the urban, vegetation and water LULC classes for SUHI studies, with overall classification accuracies between 89.60% and 95.90% for the used images. According to the obtained results, generally the LST values increased for almost all land cover areas between the years 2002 and 2020. The SUHI magnitudes were computed by using two methods, and it was found that there was an important increase in the 18-year time period.

scholarly journals Analysis of Land Cover Changing and Vegetation Index at Kuranji Watershed in Padang, West Sumatera, Indonesia

2019 ◽  
Vol 4 (4) ◽  
pp. 286-290
Author(s):  
Rifky Putera ◽  
Junaidi Junaidi ◽  
Ahmad Junaidi
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Density ◽  
Landsat Images ◽  
Sensing Technology ◽  
One Year ◽  
Dense Class ◽  
Topographic Condition

Various activities around Kuranji watershed included the land conversioncan be impacted to topographic condition and also contributed to altering the vegetation density. Remote sensing technology is an effective methodfor land cover mapping. The objectives of the present study were to analyze the changing of land cover and classifying the vegetation density index in the upstream Kuranji Watershed. This study was conducted at Kuranji Watershed in Padang, West Sumatera Province. Two Landsat images representing the changing of the watershed area during 2017 and 2018 as well as obtaining the classification of vegetation density during corresponding years.Landsat 8 OLI images were classified using a supervised classification technique, then computed the vegetation index using the Normalized Difference Vegetation Index (NDVI). The result showed that the extension of forest area, settlement area and paddy field (283.92; 35.06; and 27 Ha, respectively) and decline of mix dryland agriculture, shrub and garden area (93.68; 277.43; and 190.95 Ha respectively). Decreasing of dense vegetation found at lower dense class (6.47 Ha) and highest dense class (5535.35 Ha). Therefore, the increasing area found at the cloud, dense and higher dense class (93.17; 5525.1; and 109.94 Ha, respectively). So, it is highlighted that changing land cover and vegetation index happen during the only one-year period.

scholarly journals An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification

2020 ◽  
Vol 12 (23) ◽  
pp. 3880
Author(s):  
Chiman Kwan ◽  
David Gribben ◽  
Bulent Ayhan ◽  
Jiang Li ◽  
Sergio Bernabe ◽  
...  
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Land Cover Classification ◽  
Support Vector ◽  
Growth Monitoring ◽  
Land Cover Types ◽  
Imaging Detection ◽  
Better Than

Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).

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