Simulation Pattern of Forest Cover Change in Onigambari Reserve, Ibadan, Oyo State, Nigeria using spectral vegetation index and Markov chain Techniques

2021 ◽  
Author(s):  
Anthony Tobore ◽  
Khadijat Alabi ◽  
Ganiyu Oyerinde ◽  
Bolarinwa Senjobi
Keyword(s):  
Markov Chain ◽  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Sustainable Forest Management ◽  
Decision Makers ◽  
Landsat 8 ◽  
Forest Cover Change ◽  
Sparse Vegetation ◽  
Landsat 7

Abstract Forest cover change (FCC) varies from region to region and is thus considered as one of the drivers of climate change. This study identified the pattern of the FCC for the years 2010 and 2020 using spectral vegetation index and Markov chain Techniques. The Markov chain (MC) and Cellular automata (CA) methods were utilized to simulate the forest cover map for the year 2030. The spectral vegetation index of Landsat 7 Enhanced thematic mapper plus (ETM+) and Landsat 8 Operational land images (OLI) were used to assess the forest cover loss for the year 2010 and 2020 focusing on the Normalized difference vegetation index, (NDVI), Green normalized difference vegetation index (GNDVI) and Difference vegetation index (DVI). Based on the validation result, the accuracy of the forest cover simulation model is more than 75 percent (%). The simulation result shows that if the current deforestation and encroachment continues, the forest cover will continue to be endangered and thus leading to a decrease in dense forest, plantation, and sparse vegetation by 20.9%, 16.1%, and 20% respectively. This study will be helpful for planners and decision-makers in ensuring sustainable forest management.

scholarly journals Influence of the use and the land cover of the catchment in the water quality of the semiarid tropical reservoirs

2018 ◽  
Vol 7 (7) ◽  
pp. 389
Author(s):  
Herika Cavalcante ◽  
Patrícia Silva Cruz ◽  
Leandro Gomes Viana ◽  
Daniely De Lucena Silva ◽  
José Etham De Lucena Barbosa
Keyword(s):  
Water Quality ◽  
Supervised Classification ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Dissolved Phosphorus ◽  
Sparse Vegetation ◽  
Tropical Reservoirs

The aim of this study was to evaluate some parameters of water quality of semiarid reservoirs under different uses and occupation of the catchment’s soil. For this, the reservoirs Acauã and Boqueirão, belonging to the Paraíba do Norte river watershed and Middle and Upper course sub catchments, respectively, were studied. For this, water samples were collected in August, September and October 2016. From these samples, total and dissolved phosphorus, nitrate, nitrite, ammonia, chlorophyll, dissolved and suspended solids were analyzed. In addition, images of the Landsat 8 satellite were acquired for the calculation of the Normalized Difference Vegetation Index (NDVI), and for the supervised classification of the use and occupation of the sub catchments. Thus, it was observed that, in general, the Acauã reservoir presented values of phosphorus and nitrogen and solids larger than the Boqueirão reservoir, due to the greater urban area, even though it had a smaller total area of the basin. Both reservoirs presented low vegetation rates and high areas of sparse vegetation and exposed soil, increasing the propensity to soil erosion and the transport of nutrients from the basin to the reservoirs, making water quality worse or impossible.

Remote Sensing

2015 ◽  
Author(s):  
Frank D. Eckardt
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Laser Pulses ◽  
Global Scale ◽  
Dynamic Topography ◽  
Forest Cover Change ◽  
Atmospheric Remote Sensing

This article on remote sensing or earth observation focuses on mapping and monitoring systems that produce global-scale data sets which are easily accessible to the wider public. It makes particular reference to low-earth-orbiting remote sensing platforms and sensors and associated image archives such as provided by the Landsat and Moderate-Resolution Imaging Spectroradiometer (MODIS) programs. It also draws attention to handheld space photography, synthetic aperture radar (SAR), and the high-spatial-resolution capability obtained from the commercial remote sensing sector. This entry examines applications that are of global interest and are facilitated through image and data portals. Particular emphasis is placed on products such as the normalized difference vegetation index, real-time fire mapping, forest cover change, geomorphology, and global elevation data as well as actual true- and false-color imagery. All of these can be readily imported as shape or raster files into a Geographic Information System (GIS). Key papers dealing with the global monitoring of the biosphere, dynamic topography, and gravity are being cited. Special emphasis is placed on current capabilities in monitoring recent and ongoing changes in the tropics as well as Arctic and Antarctic environment. Numerous remote sensing systems capture the state and dynamics of rainforests, ice caps, glaciers, and shelf and sea ice, some of which are available in near-real-time trend analysis. Not all sensors produce images; some measure passive microwaves, send laser pulses, or detect small fluctuations in gravitational attraction. Nevertheless, all instruments measure changes in earth’s surface state, indicative of seasonal cycles and long-term trends as well as human impact. This article also makes reference to historic developments, social benefits, and ethical considerations in remote sensing as well as the modern role of aerial photography and airborne platforms. Most people will never get to see a satellite or its instruments, they might not even get to see the available data or imagery, but these systems are directly informing the masses or indirectly shaping the perception of a changing and dynamic world. Future revisions to this article will consider oceanographic and atmospheric remote sensing capabilities.

scholarly journals Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity

2016 ◽  
Vol 185 ◽  
pp. 57-70 ◽  
Author(s):  
D.P. Roy ◽  
V. Kovalskyy ◽  
H.K. Zhang ◽  
E.F. Vermote ◽  
L. Yan ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Landsat 7

scholarly journals Modelling and Delineation of Hydrocarbon Micro - Seepage Prone Zones on Soil and Sediment in Ugwueme, South Eastern Nigeria with Soil Adjustment Vegetation Index (SAVI)

2020 ◽  
pp. 13-33
Author(s):  
Mfoniso Asuquo Enoh ◽  
Uzoma Chinenye Okeke ◽  
Needam Yiinu Barinua
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Bare Soil ◽  
Landsat 8 ◽  
Landsat Images ◽  
Landsat 7 ◽  
Soil And Sediment

Remote Sensing is an excellent tool in monitoring, mapping and interpreting areas, associated with hydrocarbon micro-seepage. An important technique in remote sensing known as the Soil Adjusted Vegetation Index (SAVI), adopted in many studies is often used to minimize the effect of brightness reflectance in the Normalized Difference Vegetation Index (NDVI), related with soil in areas of spare vegetation cover, and mostly in areas of arid and semi–arid regions. The study aim at analyzing the effect of hydrocarbon micro – seepage on soil and sediments in Ugwueme, Southern Eastern Nigeria, with SAVI image classification method. To achieve this aim, three cloud free Landsat images, of Landsat 7 TM 1996 and ETM+ 2006 and Landsat 8 OLI 2016 were utilized to produce different SAVI image classification maps for the study.  The SAVI image classification analysis for the study showed three classes viz Low class cover, Moderate class cover and high class cover.  The category of high SAVI density classification was observed to increase progressive from 31.95% in 1996 to 34.92% in 2006 and then to 36.77% in 2016. Moderately SAVI density classification reduced from 40.53% in 1996 to 38.77% in 2006 and then to 36.96% in 2016 while Low SAVI density classification decrease progressive from 27.51% in 1996 to 26.31% in 2006 and then increased to 28.26% in 2016. The SAVI model is categorized into three classes viz increase, decrease and unchanged. The un – changed category increased from 12.32km2 (15.06%) in 1996 to 17.17 km2 (20.96%) in 2006 and then decelerate to 13.50 km2 (16.51%) in 2016.  The decrease category changed from 39.89km2 (48.78%) in 1996 to 40.45 km2 (49.45%) in 2006 and to 51.52 km2 (63.0%) in 2016 while the increase category changed from 29.57km2 (36.16%) in 1996 to 24.18 km2 (29.58%) in 2006 and to 16.75 km2 (20.49%) in 2016. Image differencing, cross tabulation and overlay operations were some of the techniques performed in the study, to ascertain the effect of hydrocarbon micro - seepage.  The Markov chain analysis was adopted to model and predict the effect of the hydrocarbon micro - seepage for the study for 2030.  The study expound that the SAVI is an effective technique in remote sensing to identify, map and model the effect of hydrocarbon micro - seepage on soil and sediment particularly in areas characterized with low vegetation cover and bare soil cover.

scholarly journals IDENTIFIKASI PERUBAHAN KERAPATAN VEGETASI KOTA MANADO TAHUN 2001 SAMPAI 2015

2017 ◽  
Vol 19 (1) ◽  
pp. 65
Author(s):  
Nurlita Indah Wahyuni ◽  
Diah Irawati Dwi Arini ◽  
Afandi Ahmad
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Vegetation Density ◽  
Forage Crops ◽  
Area Change ◽  
Gis And Remote Sensing ◽  
Crown Density ◽  
Landsat 7 ◽  
The Impact

<p class="judulabstrakindo">                                                                 ABSTRAK</p><p class="judulabstrakindo">Kebutuhan manusia akan lahan di wilayah perkotaan menyebabkan perubahan fungsi lahan terutama pada area bervegetasi. Penelitian bertujuan untuk mengkaji perubahan kerapatan vegetasi tahun 2001 dan 2015 di Kota Manado serta pengaruhnya terhadap kualitas lingkungan. Penelitian dimulai dengan melakukan pengumpulan data citra satelit Landsat 7 tahun 2001 tanggal akuisisi 14 Februari 2001 dan Landsat 8 tanggal akusisi 25 Maret 2015, data-data pendukung lainnya yaitu peta administrasi kota Manado tahun 2010, peta rupa bumi kota Manado skala 1:50.000. Metode yang digunakan dalam penelitian ini adalah perbandingan nilai normalized difference vegetation index (NDVI) dengan kanal merah (red) dan infra merah dekat (NIR) yang sudah dikonversi ke nilai reflektan. Teknik analisis menggunakan Sistem Informasi Geografis (SIG) dan penginderaan jauh dengan menentukan kerapatan vegetasi dan diklasifikasikan menjadi kelas kerapatan. Hasil penelitian menunjukkan bahwa perbandingan kelas kerapatan antara 2001 dan 2015 sebagai berikut kelas tidak bervegetasi (air dan awan) mengalami peningkatan sebesar 14,29%, kelas tidak rapat (lahan kosong, pemukiman, bangunan, dan industri) mengalami peningkatan sebesar 42,56%, kelas cukup rapat (tegalan dan tumbuhan ternak) mengalami peningkatan sebesar 48,94%, kelas rapat (perkebunan, sawah kering, dan semak belukar) mengalami penurunan sebesar 68,46% dan kelas sangat rapat (hutan lebat) mengalami penurunan sebesar 314,07%. Selama kurun waktu 15 tahun penurunan areal bervegetasi di Kota Manado diperkirakan 10,57%. Perubahan areal bervegetasi di Kota Manado signifikan terjadi karena kegiatan reklamasi pantai menjadi lahan terbangun serta lahan kosong dan perkebunan menjadi perumahan. Dampak yang saat ini mulai dirasakan dengan adanya perubahan areal bervegetasi adalah peningkatan suhu dan polusi udara di wilayah perkotaan.</p><p class="katakunci"><strong>Kata kunci</strong>:Landsat, Normalized Difference Vegetation Index (NDVI), kerapatan, Kota Manado</p><p class="judulabstraking"><strong><em>                                                                           ABSTRACT</em></strong></p><p class="judulabstraking"><em>Human demand on urban land has brought various impacts toward land use, one of them is vegetation area change. This study aims to identify vegetation density change between period 2001 and 2015 in Manado area along with its influence toward environment quality. The data was collected from Landsat 7 imagery with acquisition date on February 14<sup>th</sup> 2001 and Landsat 8 imagery with acquisition date on March 25<sup>th</sup> 2015. Supporting data i.e. administrative map of Manado City in 2010 and basic map of Manado in scale 1:50.000. We compared normalized difference vegetation index (NDVI) between red band and near infra red (NIR) band. Geographic Information System (GIS) and remote sensing techniques were used to determine and classify crown density of vegetation. The result showed that the density class comparison between 2001 and 2015 were: no vegetation (water body and cloud) increased 14,29%, low dense (bareland, residence, buildings and industry) increased 42,56%, moderately dense (garden and forage crops) increased 48,94%, dense (plantation, dry field and shrubs) decreased 68,46% and highly dense (forest) decreased 314,07%. In the period 15 years there was decreasing of vegetation area in Manado city 10,57% approximately. The significance change of Manado City was occurred due to coast reclamation into building area as well as bare land and plantation become residence. The impact of vegetation area change is the increasing of temperature and air pollution in urban area.</em></p><p><strong><em>Keywords</em></strong><em>: Landsat,</em><em> Normalized Difference Vegetation Index (NDVI)</em><em>, </em><em>density, Manado City</em><em></em></p>

scholarly journals Analysis of vegetation dynamics using the normalized difference vegetation index (NDVI) at the archipelago of Fernando de Noronha, Pernambuco, Brazil

2020 ◽  
pp. 885-901
Author(s):  
Kardelan Arteiro da Silva ◽  
Soraya Giovanetti El-Deir ◽  
José Jorge Monteiro Júnior ◽  
João Paulo de Oliveira Santos ◽  
Emanuel Araújo Silva
Keyword(s):  
State Government ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Management Plan ◽  
Landsat 8 ◽  
Conservation Policies ◽  
Management Plans ◽  
Landsat 7 ◽  
Fernando De Noronha

Island environments have specific biotic and abiotic characteristics, as fragility, limitation of natural resources, geographic isolation, and fragmentation are determining factors that directly affect these areas. Thus, it is relevant to understand the natural evolution of the landscape in the islands, considering the anthropic actions and climate changes in the transformation of vegetation cover, as a means of time series and study of satellite images. This paper aims to analyze the dynamics of the landscape (changes in vegetation cover) of the Fernando de Noronha Archipelago concerning urban development, and other anthropic activities that occurred between 1999 and 2018, through remote sensing images, to establish comparisons with the Island Management Plans that were elaborated in the years of 2005 and 2017. Also, this study intends to raise elements to assist in the spatial management of the Archipelago and to establish Public Conservation Policies for Fernando de Noronha and other island areas. Images from Landsat 7 and Landsat 8 were obtained for scenes from 1999 and 2017, respectively. These images were preprocessed and analyzed in Quantum GIS 2.18 software. And applied the NDVI calculation. It was also used the database found in the sustainable management plan of the archipelago provided by the state government of Pernambuco. With these data, it was possible to diagnose a vegetative growth on the island of about 45.36% in 17 years corroborating with the changes found in the data coming from the island's management plan. However, there are no changes in the phytosociological diversity of the island, this cause is pointed out to the invading and ruderals species of the island that are established and propagate.

scholarly journals Analysis on Thermal Islands Effectors in Ramadi City, Iraq Using Multi-temporal Landsat Images

2020 ◽  
Vol 1 (135) ◽  
pp. 67-78
Author(s):  
Ismael Abbas Hurat
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Landsat Images ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Landsat 7 ◽  
Multi Temporal

This paper analyzes the effects of urban density, vegetation cover, and water body on thermal islands measured by land surface temperature in Al Anbar province, Iraq using multi-temporal Landsat images. Images from Landsat 7 ETM and Landsat 8 OLI for the years 2000, 2014, and 2018 were collected, pre-processed, and anal yzed. The results suggested that the strongest correlation was found between the Normalized Difference Built-up Index (NDBI) and the surface temperature. The correlation between the Normalized Difference Vegetation Index (NDVI) and the surface temperature was slightly weaker compared to that of NDBI. However, the weakest correlation was found between the Normalized Difference Water Index (NDWI) and the temperature. The results obtained in this research may help the decision makers to take actions to reduce the effects of thermal islands by looking at the details in the produced maps and the analyzed values of these spectral indices.

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