thematic mapper
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Author(s):  
L. C. Orakwe ◽  
A. E. Ekpo ◽  
C. M. Abraham ◽  
N. Tom-Cyprian

The occurrence of soil loss is a continuous process and occurs spatially across the earth’s surface. The study of soil loss is a necessity for proper understanding of the processes and the rate of soil loss for conservational purpose. Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) image data was acquired for 1986, 2003 and 2020 were used to derive the C factor of the RUSLE model while other factors of the model were kept fixed for the years considering their inability to change easily. The RUSLE model was used to determine the trend of the soil loss on the alluvium geologic formation considering their land use/land cover changes for 1986, 2003 and 2020. The rainfall erosivity of the study area had an average of 8201.45MJmmha-1h-1yr-1. The soil erodibility index of the soils obtained from Alluvium had an average of 0.150tons MJ-1 hmm-1. The slope length and steepness factor of the study area range from 0 to 2.574. the crop cover factor of for 1986 range from 0.52 to 0.87, 2003 range from 0.52 to 0.87 and 2020 range from 0.62 to 0.92. No active field conservation was found out within the study area as described by Wischmeier and Smith. The results obtained show that 1986, 2003 and 2020 had a soil loss of 1966.3, 2167.85 and 3361.14 tonha-1yr-1 respectively. The results show that the study area is experiencing an increased trend of soil loss. This result can serve as guide into understanding the past and current rate of soil loss for soil resource planning and management


2021 ◽  
Vol 13 (23) ◽  
pp. 4785
Author(s):  
Hao Fu ◽  
Wei Zhao ◽  
Qiqi Zhan ◽  
Mengjiao Yang ◽  
Donghong Xiong ◽  
...  

Afforestation is one of the most efficient ways to control land desertification in the middle section of the Yarlung Zangbo River (YZR) valley. However, the lack of a quantitative way to record the planting time of artificial forest (AF) constrains further management for these forests. The long-term archived Landsat images (including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI)) provide a good opportunity to capture the temporal change information about AF plantations. Under the condition that there would be an abrupt increasing trend in the normalized difference vegetation index (NDVI) time-series curve after afforestation, and this characteristic can be thought of as the indicator of the AF planting time. To extract the indicator, an algorithm based on the Google Earth Engine (GEE) for detecting this trend change point (TCP) on the maximum NDVI time series within the growing season (May to September) was proposed. In this algorithm, the time-series NDVI was initially smoothed and segmented into two subspaces. Then, a trend change indicator Sdiff was calculated with the difference between the fitting slopes of the subspaces before and after each target point. A self-adaptive method was applied to the NDVI series to find the right year with the maximum TCP, which is recorded as the AF planting time. Based on the proposed method, the AF planting time of the middle section of the YZR valley from 1988 to 2020 was derived. The detected afforestation temporal information was validated by 222 samples collected from the field survey, with a Pearson correlation coefficient of 0.93 and a root mean squared error (RMSE) of 2.95 years. Meanwhile, the area distribution of the AF planted each year has good temporal consistency with the implementation of the eco-reconstruction project. Overall, the study provides a good way to map AF planting times that is not only helpful for sustainable management of AF areas but also provides a basis for further research on the impact of afforestation on desertification control.


Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1251
Author(s):  
Mawuli Asempah ◽  
Wahib Sahwan ◽  
Brigitta Schütt

The current trends of land use dynamics have revealed a significant transformation of settlement spaces. In the Wa Municipality of Ghana, the changes in land use and land cover are inspired by a plethora of driving forces. In this study, we assessed the geo-physical drivers of settlement expansion under land use dynamics in the Wa Municipality of Ghana. The study employed geospatial and remote sensing tools to map and analyse the spatio-temporal dynamics of the landscape, using Landsat satellite imageries: thematic mapper (TM), enhanced thematic mapper (ETM) and operational land imager (OLI) from 1990 to 2020. The study employed a binomial logistic regression model to statistically assess the geo-physical drivers of settlement expansion. Random forest (RF)–supervised classification based on spatio-temporal analyses generated relatively higher classification accuracies, with overall accuracy ranging from 89.33% to 93.3%. Urban expansion for the last three decades was prominent, as the period from 1990 to 2001 gained 11.44 km2 landmass of settlement, while there was 11.30 km2 gained from 2001 to 2010, and 29.44 km2 gained from 2010 to 2020. Out of the independent variables assessed, the distance to existing settlements, distance to river, and distance to primary, tertiary and unclassified roads were responsible for urban expansion.


Author(s):  
V. Pompapathi ◽  
Shard Chander ◽  
Ashwin Gujrati ◽  
H.A. Solanki ◽  
R. P. Singh

Turbidity is one of the important water quality parameters, which is required to understand the eco-hydrological process such as a trophic state of water, soil erosion into the river system, mixing of other water sources, runoff, discharge etc. An algorithm has been developed to estimate the turbidity (in NTU: Nephelometric Turbidity Unit) over inland waters using Red band of optical multispectral dataset. Field measurements were carried out over Ukai reservoir for 27-28th March 2018 for pre monsoon and 27-30th September 2018 for post monsoon seasons, sampling sites ranging from turbid to clear water. Where in situ water leaving reflectance and turbidity were measured. Model was derived between in situ measured turbidity and spectral reflectance of Red band of Landsat series of datasets includes Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) data from 1993-2018. The model was applied to derive the turbidity maps of Ukai reservoir for pre-monsoon (March, April and May months) season and post monsoon (September, October and November months) seasons. Overall turbidity was in the range of 1.47-25 NTU during the field data collection for both pre and post monsoon seasons. To investigate the results in detail, the reservoir was divided into three parts, i.e. Down (A), Middle (B) and Up Streams (C). The water was relatively clear in the downstream portion with average turbidity less than 5 NTU over the study period. While maximum turbidity was observed in the upstream portion with values more than 20 NTU. In the middle portion, the turbidity values were fluctuating within the range 4-13 NTU with an average value of 6 NTU. These turbidity maps can be used to determine underwater light attenuation that has importance in ecosystem modelling.


2021 ◽  
Vol 12 (2) ◽  
pp. 213-227
Author(s):  
Md. Jahir Uddin ◽  
Faisal Jahangir Swapnil

Land Surface Temperature (LST) is a key phenomenon in worldwide climate change. The knowledge of surface temperature is important to a range of issues and themes in earth sciences, central to urban climatology, global environmental change, and human-environment interactions. In this study, LST for Kushtia District, Khulna division, Bangladesh, is derived using Arc-GIS software version from the images of Landsat 8 Optical Land Imager (OLI) of 30 m resolution and Thermal Infrared Sensor (TIR) data of 100 m resolution, Landsat-7 Enhanced Thematic Mapper plus (ETM+) with opto-mechanical sensor and Spatial Resolution of 30 m (60 m – thermal, 15-m panchromatic) and Landsat-5 Thematic MAPPER (TM) satellites. A total time span of 20 years, starting from 1998 to 2018 is selected. At every 5 years interval starting from 1998, air temperature, LST, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) have been calculated. Using the equation from Landsat user’s handbook, the digital number of thermal infrared band is converted into spectral radiance. Plank’s Inverse Function is used to obtain the effective at-sensor brightness temperature from the spectral radiance. The surface emissivity based on NDVI classes is used to retrieve the final LST. The study reveals that LST is increasing with the passage of time. Maximum values of LST are found along the North-East and North-West regions of Kushtia district. NDVI is found to have positive correlation with LST. Also, it has been found that NDWI has little influence on LST. The reasons behind the rise and fall of LST in different years are explained from changes in total vegetation coverage and total abundance of water body coverage viewpoint. The spatial distribution figures of air temperature, LST, NDVI and NDWI could be used as a guideline for urban planning, strategies for quality improvement of urban environment and a smart solution to the reduction of LST.


Author(s):  
Ogunlade, Simeon. O. Ph.D. ◽  

This research aimed at mapping the spatiotemporal dynamics of the Industrial Layout located in Akure Ondo State Nigeria. The dataset used are the administrative map of Ondo State, Akure Industrial Layout Boundary,various Landsat imageries of 32m resolution which are Thematic Mapper (TM) of 1986 & 1991, Enhanced Thematic Mapper Plus (ETM)+ of 2002, Operational Land Imager / Thermal Infrared Sensor (OLI/TIRS) of 2014, 2017, 2020; and Worldview 3 image 2020 of 1.24m resolution. The Landsat data were used to extract the different Land use/Land cover (LULC) within the study area. GPS receiver and Worldview 3 image were used to obtain the coordinates of the different LULC classes, which aided in the classification of image, and also for accuracy assessment of the classified image. All the Landsat standard data products were processed, to ascertain that they are free of radiometric and geometric errors using the Level 1 Product Generation System (LPGS) and extracted to obtain the landsat image bands. The extracted Landsat images (bands) were used in the processing and calculating the Normalized Difference Vegetation Index (NDVI) and calculation of LULC changes. Evaluation the accuracy of the results produced from the land cover classification was carried out by comparing the results of ground coordinates with the coordinates obtained from a higher resolution image (Worldview 3 image) in order to determine the accuracy of the land cover classification in the study area. The trend of changes of land cover in these areas was assessed and also, the prediction for the future condition both in terms of development was determined based on the results obtained from the initial results. Results from various maps produced and numerical data generated showed that Akure Industrial Layout was mainly dominated by shrub and grass land in 1986 and has in 34 years experienced transformation of 604% in the built environment (18% /year), 119% of Bareland (3.5%/year), and -29% of Grassland (0.9%/year), -66% of Shrub (2%/year). The forecast of the probable spatial extent for the years 2025 and 2030 were estimated to be 175.3Ha and 214.8Ha respectively, which shows there will be a continuous increase in the future development in Akure Industrial layout. The research recommended a proactive action from the government and end-users that will ensure a sustained manageability of the layout.


2021 ◽  
Vol 13 (18) ◽  
pp. 3737
Author(s):  
Bojana Horvat ◽  
Josip Rubinić

One of the most prominent tourist destinations in the Adriatic coast, the city of Opatija, is facing a problem concerning seasonal drinking water shortages. The existing water resources are no longer sufficient, and attention is being given to alternative resources such as the underlying karstic aquifer and several coastal springs in the city itself. However, the water potential of the area still cannot be estimated due to the insufficient hydrological data. The goal of this research was to evaluate the use of thermal infrared (TIR) remote sensing as the source of valuable information that will improve our understanding of the groundwater discharge dynamics. Ten Landsat ETM+ (enhanced thematic mapper plus) and two Landsat TM (thematic mapper) images of the north Adriatic, recorded during 1999–2004 at the same time as the field discharge measurements, were used to derive sea surface temperature (SST) and to analyze freshwater outflows seen as the thermal anomaly in the TIR images. The approach is based on finding the functional relationship between the size of the freshwater thermal signatures and the measured discharge data, and to estimate the water potential of the underlying aquifer. It also involved analyzing the possible connection between the adjusted size of the spring’s thermal signatures and groundwater level fluctuations in the deeper karst hinterland. The proposed methodology resulted in realistic discharge estimates, as well as a good fit between thermal anomalies with measured discharges and the groundwater level. It should be emphasized that the results are site specific and based on a limited data set. However, they confirm that the proposed method can provide additional information on groundwater outflow dynamics and coastal springs’ freshwater quantification.


2021 ◽  
Vol 10 (11) ◽  
pp. e253101119537
Author(s):  
Bárbara Alves Batista ◽  
Washington Luiz Félix Correia Filho ◽  
José Francisco de Oliveira-Júnior ◽  
Dimas de Barros Santiago ◽  
Carla Taciane dos Santos

O crescimento das cidades juntamente com a formação desordenada de grandes metrópoles ao redor do mundo resulta em grandes mudanças no uso e ocupação do solo. Porém, há poucos estudos que relacionem a expansão urbana e seus efeitos nas cidades do Nordeste do Brasil (NEB). Assim, o objetivo deste estudo foi avaliar a expansão urbana em Maceió-Alagoas entre 1985 e 2020 a partir de produtos orbitais Land Surface Temperature (LST) e Normalized Difference Vegetation Index (NDVI), com a finalidade de detectar as mudanças e os seus efeitos ambientais. Para isto foram utilizados produtos orbitais adquiridos dos sistemas-sensores Landsat 5/Thematic Mapper (TM) e 8/Operational Land Imager (OLI). No estudo utilizaram-se quatro imagens para a observação da variação espaço-temporal da urbanização, correspondente aos anos de 1987,1998, 2006 e 2020. Os mapas temáticos de NDVI e LST foram gerados a partir do software de ambiente R. Os resultados obtidos apontaram alterações substanciais no uso e ocupação do solo detectado pelo NDVI, e aumento na LST ao longo dos 35 anos. Tal variabilidade ocorreu nos bairros localizados na porção norte e noroeste da cidade, resultante dos programas de incentivo do Governo Federal na década de 2009, principalmente o Complexo do Benedito Bentes (CBB) com as maiores transformações no uso e ocupação do solo, principalmente o maior aumento na LST entre 7,5-10,0°C. Os efeitos produzidos pela expansão urbana foram atenuados devido as áreas de proteção ambiental.


Ecosistemas ◽  
2021 ◽  
Vol 30 (2) ◽  
pp. 1-11
Author(s):  
Gabriel Alarcon Aguirre ◽  
Rembrandt R. Canahuire Robles ◽  
Felipe M. Guevara Duarez ◽  
Liset Rodríguez Achata ◽  
Luis E. Gallegos Chacón ◽  
...  

: La Amazonia occidental, puntualmente la región de Madre de Dios, es conocida como la capital de la biodiversidad del Perú y es reconocida mundialmente como uno de los lugares con mayor biodiversidad de la Tierra. Sin embargo, se ha visto amenazada por un grave problema de pérdida de bosques. Las principales amenazas ambientales se deben a una mala gestión del territorio que ocasionan la concentración de tierras, expansión agrícola, ganadería, minería de oro y la explotación económica descontrolada. El presente estudio analiza la dinámica de pérdida de bosques y los cambios de uso de suelo entre 1999-2018. Para la cuantificación de la pérdida de bosque se utilizaron técnicas de sensoramiento remoto, imágenes Landsat 5 Thematic Mapper (TM) y 8 Operational Land Imagery (OLI). Las imágenes fueron procesadas utilizando una clasificación supervisada denominada Neural Net. La metodología incluye procedimientos de validación utilizando puntos de verificación de campo e imágenes de teledetección de media y alta resolución de diferentes sensores (SPOT-5, PlanetScope, WorldView y Drone). Los resultados mostraron una pérdida de bosque durante 1999-2018 de 1698.63 km2 , con una tasa anual de -0.21% y una pérdida promedio de 59.28 km2 /año. Para los cambios de bosques a otros usos de la tierra, encontramos la conversión 841.41 km2 durante 2014-2018. Nuestros resultados indican que la agricultura es la mayor responsable del avance de la deforestación (72.90%), mientras que la minería de oro tiene una mayor incidencia en los sectores focalizados.


2021 ◽  
Vol 4 ◽  
Author(s):  
G. N. Tanjina Hasnat

Forest cover change is an important criterion as it affects the environmental balance whereas land surface temperature is a significant parameter within the earth climate system. Spatio-temporal change of forest cover can be detected and land surface temperature can be retrieved by applying remote sensing technology. The present study aimed to capture the impact of forest cover change on land surface temperature in Dudpukuria-Dhopachari Wildlife Sanctuary (DDWS), Bangladesh, using multi-spectral and multi-temporal satellite data. To avoid the biasness in the calculation, leaf flash time was targeted for collecting Landsat images from United States Geological Survey (USGS) Earth Explorer and, based on availability, images were collected purposively which ones had closer time period:1990 (March 5, 1990), 2000 (February 5, 2000), 2010 (February 24, 2010) and 2020 (March 23, 2020). Unsupervised classification was applied over the images Landsat 4–5 Thematic Mapper (TM), 7 Enhanced Thematic Mapper Plus (ETM+), and 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data for detecting forest cover change. To retrieve the land surface temperature, Mono Window Algorithm (MWA) method was applied over similar images. Maximum forest degradation was observed in 2010 and the change found was 17% as compared to 1990. After 2010, the forest started to flourish. Land surface temperature dramatically changes over the time period. The highest land surface temperature in the forested area was observed in 2020 (32.2°C) and it was changed 7.7°C from that of the 1990 (24.5°C). In every 10 years, almost 2.3°C–3.0°C temperature change was detected. In the first three decades, a reverse relationship was observed between land surface temperature and forest cover; however, in the last decade, land surface temperature was found to increase with the increase of forest cover. Thus, the results of the study revealed that land surface temperature may not be relevant with the local forest cover change directly. It can be estimated from the results that local forest cover change may have limited impact on local temperature rather than global forest cover change, whereas global warming could play a vital role in changing land surface temperature locally as well as globally.


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