Landscape Change in the Steppe of Algeria South-West Using Remote Sensing

2018 ◽  
Vol 18 (1) ◽  
pp. 41-52
Author(s):  
Tayeb Sitayeb ◽  
Ishak Belabbes
Keyword(s):  
Remote Sensing ◽  
Social Systems ◽  
Bare Soil ◽  
Climatic Conditions ◽  
Landscape Dynamics ◽  
Landsat 8 ◽  
Natural Ecosystems ◽  
Stipa Tenacissima ◽  
Steppe Species ◽  
Sparse Vegetation

Abstract Landscape dynamics is the result of interactions between social systems and the environment, these systems evolving significantly over time. climatic conditions and biophysical phenomena are the main factors of landscape dynamics. Also, currently man is responsible for most changes affecting natural ecosystems. The objective of this work is to study the dynamics of a typical landscape of western Algeria in time and space, and to map the distribution of vegetation groups constitute the vegetation cover of this ecosystem. as well as using a method of monitoring the state of a fragile ecosystem by remote sensing to understand the processes of changes in this area. The steppe constitutes a large arid area, with little relief, covered with low and sparse vegetation. it lies between the annual isohyets of 100 to 400 mm, subjected to a very old human exploitation with an activity of extensive breeding of sheep, goats, and camels. Landsat satellite data were used to mapping vegetation groups in the Mecheria Steppe at a scale of 1: 300,000. Then, a comparison was made between the two maps obtained by a classification of Landsat-8 sensor Operational Land Imager (OLI) acquired on March 18, 2014, and Landsat-5 sensor Thematic Mapper (TM) acquired on April 25, 1987. The results obtained show the main changes affecting the natural distribution of steppe species, a strong change in land occupied by the Stipa tenacissima steppe with 65% of change, this steppe is replaced by Thymelaea microphylla, Salsola vermiculata, lygeum spartum and Peganum harmala steppe. an absence from the steppe Artemisia herba-alba that has also been replaced by the same previous steppes species. The groups with Quercus ilex and Juniperus phoenicea are characterized by a strong regression that was lost 60% of its global surface and transformed by steppe to stipa tenacissima and bare soil.

scholarly journals A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 231
Author(s):  
Can Trong Nguyen ◽  
Amnat Chidthaisong ◽  
Phan Kieu Diem ◽  
Lian-Zhi Huo
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Urban Areas ◽  
Near Infrared ◽  
Urban Landscape ◽  
Bare Soil ◽  
Critical Element ◽  
Landsat 8 ◽  
Climate Conditions ◽  
Fallow Period

Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions.

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 Analisis Kerapatan Vegetasi di Kecamatan Pangandaran melalui Citra Landsat 8

2020 ◽  
Vol 4 (1) ◽  
pp. 32-38
Author(s):  
Dwi Yanti ◽  
Indri Megantara ◽  
Muhamad Akbar ◽  
Sabila Meiwanda ◽  
Syauqi Izzul ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Unsupervised Classification ◽  
Landsat 8 ◽  
Vegetation Density ◽  
Sparse Vegetation ◽  
Intended Object ◽  
Density Map ◽  
Validation Tests

ABSTRAKPenginderaan jauh merupakan alat dan teknik untuk mengambil data spasial tanpa menyentuh secara langsung objek yang dituju. Salah satu kegunaan penginderaan jauh adalah mengetahui tingkat kerapatan vegetasi menggunakan metode unsupervised classification K- Means dan perhitungan NDVI. Penelitian ini dilakukan di Kecamatan Pangandaran dan menghasilkan peta kerapatan vegetasi. Hasil klasifikasi kerapatan vegetasi di Kecamatan Pangandaran menghasilkan sebanyak 5 klasifikasi yaitu badan air, vegetasi jarang, cukup rapat, rapat, sangat rapat. Peta kerapatan vegetasi tersebut telah dilakukan uji akurasi dan validasi lapangan dengan akurasi sebesar 25% tingkat akurasi dari hasil interpretasi yang diperoleh menunjukan bahwa peta yang dihasilkan belum memenuhi standar USGS untuk dapat digunakan yaitu sebesar 85%.Kata Kunci: Pengindraan Jauh, NDVI, K-Means- Kerapatan Vegetasi, PangandaranABSTRACTRemote sensing is a tool and technique for retrieving spatial data without touching the intended object. One of the uses of remote sensing is to knowing the level of vegetation density using the unsupervised classification K-Means method and NDVI calculations.This research was conducted in Pandandaran sub-district and produced a map of vegetation density. The results of the classification of vegetation density in the Pangandaran sub-district resulted in as many as 5 classifications namely water bodies, sparse vegetation, fairly dense, dense, very dense. Vegetation density map has been carried out field accuracy and validation tests with an accuracy of 25% The accuracy of the interpretation results obtained shows that the map produced does not meet USGS standards to be used that is equal to 85%

scholarly journals Índices Espectrais e Temperatura de Superfície em Áreas Queimadas no Parque Estadual do Araguaia em Mato Grosso

2020 ◽  
Vol 13 (2) ◽  
pp. 648
Author(s):  
Juliana Barbosa da Silva Lotufo ◽  
Nadja Gomes Machado ◽  
Luciene De Mello Taques ◽  
Dalila Morgana De Souza Mützenberg ◽  
Névio Lotufo Neto ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Vegetation Index ◽  
Absorption Capacity ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Natural Ecosystems ◽  
Mato Grosso ◽  
White Ash ◽  
Burnt Areas

As queimadas impactam negativamente a biodiversidade dos ecossistemas naturais, alterando os atributos físicos e biológicos e influenciando os fluxos de energia e biogeoquímicos. Sendo assim, o objetivo deste trabalho foi analisar através do sensoriamento remoto o efeito de áreas queimadas sobre os índices espectrais NDVI (Índice de Vegetação por Diferença Normalizada) e NBR (Índice de Queimada Normalizada) e na temperatura de superfície no Parque Estadual do Araguaia (PEA) em Mato Grosso, Brasil. Imagens do satélite de Landsat 8 OLI foram utilizadas para analisar a superfície no período pré-queimada (15/06/2015) e pós-queimada (21/10/2015) no Parque Estadual. Os resultados demonstraram que o NDVI apresentou maiores valores nas áreas com vegetação densa e menores valores em solo exposto, associados à vegetação seca, carbonização ou com completa ausência de vegetação. O NBR apresentou valores negativos nas áreas queimadas, devido ao aumento da refletância após passagem do fogo devido à deposição de cinzas brancas. A temperatura da superfície foi maior no pós-queimada relacionada a uma maior capacidade de absorção da superfície (cor preta das cinzas). Estes resultados são atribuídos aos efeitos combinados de maior exposição do solo, aumento da absorção da radiação pela vegetação carbonizada e redução da evapotranspiração relativa à vegetação verde existente no período pré-queimada.  Spectral Indexes and Surface Temperature on Burnt Areas at Araguaia State Park in Mato Grosso A B S T R A C TFires harm the biodiversity of natural ecosystems, changing physical and biological attributes and influencing energy and biogeochemical flows. Therefore, the objective of this work was to analyze through remote sensing the effect of burnt areas on the NDVI (Normalized Difference Vegetation Index) and NBR (Normalized Burn Index) spectral indexes and on the surface temperature in Araguaia State Park (PEA) in Mato Grosso, Brazil. Satellite images of Landsat 8 OLI were used to analyze the surface in the pre-burned (06/15/2015) and post-burned (10/21/2015) period in the State Park. The results showed that NDVI showed higher values in areas with dense vegetation and lower values in exposed soil, associated with dry vegetation, carbonization or with a complete absence of vegetation. The NBR showed negative values in the burnt areas, due to the increase in reflectance after the passage of the fire due to the deposition of white ash. The surface temperature was higher in the post-firing period due to a greater absorption capacity of the surface (black color of the ashes). These results are attributed to the combined effects of greater soil exposure, increased absorption of radiation by carbonized vegetation and reduced evapotranspiration relative to green vegetation in the pre-burned period.Keywords: Fire; Protected Area; Remote Sensing.

scholarly journals Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China

2020 ◽  
Vol 12 (3) ◽  
pp. 578
Author(s):  
Yuchen Wang ◽  
Yu Zhang ◽  
Nan Ding ◽  
Kai Qin ◽  
Xiaoyan Yang
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Land Surface ◽  
Estimation Method ◽  
Bare Soil ◽  
Urban Surface ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat ◽  
The Impact

As an important energy absorption process in the Earth’s surface energy balance, evapotranspiration (ET) from vegetation and bare soil plays an important role in regulating the environmental temperatures. However, little research has been done to explore the cooling effect of ET on the urban heat island (UHI) due to the lack of appropriate remote-sensing-based estimation models for complex urban surface. Here, we apply the modified remote sensing Penman–Monteith (RS-PM) model (also known as the urban RS-PM model), which has provided a new regional ET estimation method with the better accuracy for the urban complex underlying surface. Focusing on the city of Xuzhou in China, ET and land surface temperature (LST) were inversed by using 10 Landsat 8 images during 2014–2018. The impact of ET on LST was then analyzed and quantified through statistical and spatial analyses. The results indicate that: (1) The alleviating effect of ET on the UHI was stronger during the warmest months of the year (May–October) but not during the colder months (November–March); (2) ET had the most significant alleviating effect on the UHI effect in those regions with the highest ET intensities; and (3) in regions with high ET intensities and their surrounding areas (within a radius of 150 m), variation in ET was a key factor for UHI regulation; a 10 W·m−2 increase in ET equated to 0.56 K decrease in LST. These findings provide a new perspective for the improvement of urban thermal comfort, which can be applied to urban management, planning, and natural design.

scholarly journals Comparing Sentinel-1, Sentinel-2, and Landsat-8 data in the early recognition of irrigated areas in central Italy

2021 ◽  
Vol 52 (4) ◽  
Author(s):  
Lorenzo Vergni ◽  
Alessandra Vinci ◽  
Francesca Todisco ◽  
Francesco Saverio Santaga ◽  
Marco Vizzari
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Early Recognition ◽  
Central Italy ◽  
Climatic Conditions ◽  
Data Sources ◽  
Landsat 8 ◽  
Irrigation Season ◽  
Precipitation Data ◽  
Sentinel 2

This study evaluated the effectiveness of various remote sensing (RS) data (Sentinel-1, Sentinel-2, and Landsat 8) in the early recognition of irrigated areas in a densely cultivated area of central Italy. The study was based on crop data collected on more than 2000 plots in 2016 and 2017, characterized by quite different climatic conditions. The different RS data sources were used both alone and combined and with precipitation to define corresponding random forest (RF) classifiers whose overall accuracy (OA) was assessed by gradually increasing the number of available features from the beginning of the irrigation season. All tested RF classifiers reach stable OAs (OA 0.9) after 7-8 weeks from the start of the irrigation season. The performance of the radar indexes slightly improves when used in combination with precipitation data, but three weeks of features are required to obtain OA above 80%. The optical indices alone (Sentinel-2 and Landsat 8) reach OA ≈85% in the first week of observation. However, they are ineffective in cloudy conditions or when rainfed and irrigated fields have similar vigour. The most effective and robust indices are those based on combined sources (radar, optical, and meteorological), allowing OAs of about 92% and 96% at the beginning and in the middle of the irrigation season, respectively.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

TEMPERATURA DE SUPERFÍCIE E A RELAÇÃO COM O ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) NA MICROBACIA DO RIO DA BATATEIRAS, CRATO-CE

2019 ◽  
Vol 21 (2) ◽  
pp. 1310-1320
Author(s):  
Cícera Celiane Januário da Silva ◽  
Vinicius Ferreira Luna ◽  
Joyce Ferreira Gomes ◽  
Juliana Maria Oliveira Silva
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Temperature Measurement ◽  
Rainy Season ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Low Density ◽  
Difference Map

O objetivo do presente trabalho é fazer uma comparação entre a temperatura de superfície e o Índice de Vegetação por Diferença Normalizada (NDVI) na microbacia do rio da Batateiras/Crato-CE em dois períodos do ano de 2017, um chuvoso (abril) e um seco (setembro) como também analisar o mapa de diferença de temperatura nesses dois referidos períodos. Foram utilizadas imagens de satélite LANDSAT 8 (banda 10) para mensuração de temperatura e a banda 4 e 5 para geração do NDVI. As análises demonstram que no mês de abril a temperatura da superfície variou aproximadamente entre 23.2ºC e 31.06ºC, enquanto no mês correspondente a setembro, os valores variaram de 25°C e 40.5°C, sendo que as maiores temperaturas foram encontradas em locais com baixa densidade de vegetação, de acordo com a carta de NDVI desses dois meses. A maior diferença de temperatura desses dois meses foi de 14.2°C indicando que ocorre um aumento da temperatura proporcionado pelo período que corresponde a um dos mais secos da região, diferentemente de abril que está no período de chuvas e tem uma maior umidade, presença de vegetação e corpos d’água que amenizam a temperatura.Palavras-chave: Sensoriamento Remoto; Vegetação; Microbacia.                                                                                  ABSTRACTThe objective of the present work is to compare the surface temperature and the Normalized Difference Vegetation Index (NDVI) in the Batateiras / Crato-CE river basin in two periods of 2017, one rainy (April) and one (September) and to analyze the temperature difference map in these two periods. LANDSAT 8 (band 10) satellite images were used for temperature measurement and band 4 and 5 for NDVI generation. The analyzes show that in April the surface temperature varied approximately between 23.2ºC and 31.06ºC, while in the month corresponding to September, the values ranged from 25ºC and 40.5ºC, and the highest temperatures were found in locations with low density of vegetation, according to the NDVI letter of these two months. The highest difference in temperature for these two months was 14.2 ° C, indicating that there is an increase in temperature provided by the period that corresponds to one of the driest in the region, unlike April that is in the rainy season and has a higher humidity, presence of vegetation and water bodies that soften the temperature.Key-words: Remote sensing; Vegetation; Microbasin.RESUMENEl objetivo del presente trabajo es hacer una comparación entre la temperatura de la superficie y el Índice de Vegetación de Diferencia Normalizada (NDVI) en la cuenca Batateiras / Crato-CE en dos períodos de 2017, uno lluvioso (abril) y uno (Septiembre), así como analizar el mapa de diferencia de temperatura en estos dos períodos. Las imágenes de satélite LANDSAT 8 (banda 10) se utilizaron para la medición de temperatura y las bandas 4 y 5 para la generación de NDVI. Los análisis muestran que en abril la temperatura de la superficie varió aproximadamente entre 23.2ºC y 31.06ºC, mientras que en el mes correspondiente a septiembre, los valores oscilaron entre 25 ° C y 40.5 ° C, y las temperaturas más altas se encontraron en lugares con baja densidad de vegetación, según el gráfico NDVI de estos dos meses. La mayor diferencia de temperatura de estos dos meses fue de 14.2 ° C, lo que indica que hay un aumento en la temperatura proporcionada por el período que corresponde a uno de los más secos de la región, a diferencia de abril que está en la temporada de lluvias y tiene una mayor humedad, presencia de vegetación y cuerpos de agua que suavizan la temperatura.Palabras clave: Detección remota; vegetación; Cuenca.

Impact of LULC Changes on LST in Rajshahi District of Bangladesh: A Remote Sensing Approach

2020 ◽  
Vol 3 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Abdulla Al Kafy ◽  
Abdullah Al-Faisal ◽  
Mohammad Mahmudul Hasan ◽  
Md. Soumik Sikdar ◽  
Mohammad Hasib Hasan Khan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Agricultural Land ◽  
Global Climate ◽  
Urban Expansion ◽  
Water Bodies ◽  
Landsat 8 ◽  
Lulc Changes ◽  
The Impact

Urbanization has been contributing more in global climate warming, with more than 50% of the population living in cities. Rapid population growth and change in land use / land cover (LULC) are closely linked. The transformation of LULC due to rapid urban expansion significantly affects the functions of biodiversity and ecosystems, as well as local and regional climates. Improper planning and uncontrolled management of LULC changes profoundly contribute to the rise of urban land surface temperature (LST). This study evaluates the impact of LULC changes on LST for 1997, 2007 and 2017 in the Rajshahi district (Bangladesh) using multi-temporal and multi-spectral Landsat 8 OLI and Landsat 5 TM satellite data sets. The analysis of LULC changes exposed a remarkable increase in the built-up areas and a significant decrease in the vegetation and agricultural land. The built-up area was increased almost double in last 20 years in the study area. The distribution of changes in LST shows that built-up areas recorded the highest temperature followed by bare land, vegetation and agricultural land and water bodies. The LULC-LST profiles also revealed the highest temperature in built-up areas and the lowest temperature in water bodies. In the last 20 years, LST was increased about 13ºC. The study demonstrates decrease in vegetation cover and increase in non-evaporating surfaces with significantly increases the surface temperature in the study area. Remote-sensing techniques were found one of the suitable techniques for rapid analysis of urban expansions and to identify the impact of urbanization on LST.

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