scholarly journals GIS and Remote Sensing Analysis of the Impact of Land use Land Cover Change on Forest Degradation: Evidence from the Central Part of Taraba State, Nigeria

2021 ◽  
pp. 27-39
Author(s):  
Umar Jauro Abba ◽  
Adewuyi Taiye ◽  
Yusuf Mohammed Bakoji ◽  
Bashir Babanyaya Mohammed ◽  
Adamu Auwal Umar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Local Governments ◽  
Early Stage ◽  
Central Zone ◽  
Google Earth ◽  
Forest Environment ◽  
Gis And Remote Sensing ◽  
Bare Land ◽  
The Impact

Forest is a fundamental, significant, and valuable component of a sustainable environment. Ecosystem services, biodiversity development, and economic growth in any nation depend on the proficient use of forests and their resources. However, deforestation has remained the single most important environmental phenomenon threatening the existence of the forest environment in Nigeria. This study was carried out to assess the exploitation of forestland in the central zone of Taraba state using GIS and remote sensing techniques. The satellite imageries used are Lands at imageries of 2006, 2012, and 2018. Ground Control points (GCPs) were obtained from Google earth to validate the coordinates of the classified imageries. The result obtained from 2006 classification showed that thick forest occupied the total of 1685448.99 ha equivalent to 80.38% and was the highest land cover suffering a decline in the area amounting to 694696 ha which equals to 33.13% in 2018. The pattern of land cover changes at the early stage was restricted to dissection and perforation in 2006. A remarkable expansion of bare land patches accompanied by total attrition of thick forest was identified due North in Bali local government area as compared to Gashaka and Kurmi local governments that have fragmented and little shrinking pattern of changes from 6.87% in 2006 to 37.65% in 2018. This shows that; as bare land increases, thick forests keep on decreasing within thirteen (13) years. It was recommended that increased reforestation efforts, sensitization and periodical campaigns against deforestation, and redesign of the existing forestry laws by the state government to curtail incessant incidents of deforestation in the study area be undertaken.

scholarly journals APPLICATION OF GIS AND REMOTE SENSING TO MONITOR THE IMPACT OF DEVELOPMENT ACTIVITIES ON THE COASTAL ZONE OF JAZAN CITY ON THE RED SEA, SAUDI ARABIA

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 45-50 ◽  
Author(s):  
A. G. Al-Zubieri ◽  
R. A. Bantan ◽  
R. Abdalla ◽  
S. Antoni ◽  
T. A. Al-Dubai ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Coastal Zone ◽  
Red Sea ◽  
Coastal Area ◽  
Google Earth ◽  
Gis And Remote Sensing ◽  
Wide Range ◽  
The Impact ◽  
The City

<p><strong>Abstract.</strong> Jazan city is a fast-growing coastal city in the southern part of Saudi Arabia, Red Sea. Recently, it has encountered quick industrial development activities. To monitor these activities, the changes in coastal zone morphology explore over the last 30 years (1987&amp;ndash;2017) using GIS and remote sensing techniques. Four satellite images (TM and ETM) acquired during these intervals were performed. Furthermore, a development and growth of the city were created based on direct digitizing from Google Earth Pro to identify the extension and expansion of the area of study during this period. The magnitudes of erosion, deposition, and landfilling at differential scales through the period of study were determined using photo-interpretation on the changes of surface area and the extension of the city landward. The results illustrated remarkable changes and shifting of shoreline seaward along the coast and extending of dwelling zone in the city. Erosion and accretion take place mostly in the earlier interval (1987&amp;ndash;2000) in some parts of the coast followed by landfilling occurring in the northern and middle parts of the coastal area in the interval (2000&amp;ndash;2013). However, the magnitudes were different from interval to interval. The relative changes were 14.33, 58.56, and 27.11&amp;thinsp;% at the periods from 1987&amp;ndash;2000, 2000&amp;ndash;2013, and 2013&amp;ndash;2017, respectively. However, dwelling zone extended dramatically from 23.31&amp;thinsp;km<sup>2</sup> in 1987, to 25.32&amp;thinsp;km<sup>2</sup> in 2000, 63.37&amp;thinsp;km<sup>2</sup> in 2013, and to reach 67.90&amp;thinsp;km<sup>2</sup> in 2017. These changes probably attributed to human activities in the coastal area due to construct a new economic city in the northern part during the period between 2003 and 2013 along with different socio-economic activities. The tidal flat in front of the city is shrunk due to this landfilling. This landfilling has been destructed a wide range of mangrove ecozones and possibly impacted the biotics in the area.</p>

scholarly journals TOWARDS A REMOTE SENSING BASED ASSESSMENT OF LAND SUSCEPTIBILITY TO DEGRADATION: EXAMINING SEASONAL VARIATION IN LAND USE-LAND COVER FOR MODELLING LAND DEGRADATION IN A SEMI-ARID CONTEXT

2016 ◽  
Vol III-8 ◽  
pp. 137-144 ◽  
Author(s):  
Gofamodimo Mashame ◽  
Felicia Akinyemi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Degradation ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Bare Land ◽  
Level I ◽  
The Impact ◽  
Semi Arid

Land degradation (LD) is among the major environmental and anthropogenic problems driven by land use-land cover (LULC) and climate change worldwide. For example, poor LULC practises such as deforestation, livestock overstocking, overgrazing and arable land use intensification on steep slopes disturbs the soil structure leaving the land susceptible to water erosion, a type of physical land degradation. Land degradation related problems exist in Sub-Saharan African countries such as Botswana which is semi-arid in nature. LULC and LD linkage information is still missing in many semi-arid regions worldwide.Mapping seasonal LULC is therefore very important in understanding LULC and LD linkages. This study assesses the impact of seasonal LULC variation on LD utilizing Remote Sensing (RS) techniques for Palapye region in Central District, Botswana. LULC classes for the dry and rainy seasons were classified using LANDSAT 8 images at Level I according to the Food and Agriculture Organization (FAO) International Organization of Standardization (ISO) code 19144. Level I consists of 10 LULC classes. The seasonal variations in LULC are further related to LD susceptibility in the semi-arid context. The results suggest that about 985 km² (22%) of the study area is susceptible to LD by water, major LULC types affected include: cropland, paved/rocky material, bare land, built-up area, mining area, and water body. Land degradation by water susceptibility due to seasonal land use-land cover variations is highest in the east of the study area where there is high cropland to bare land conversion.

scholarly journals TOWARDS A REMOTE SENSING BASED ASSESSMENT OF LAND SUSCEPTIBILITY TO DEGRADATION: EXAMINING SEASONAL VARIATION IN LAND USE-LAND COVER FOR MODELLING LAND DEGRADATION IN A SEMI-ARID CONTEXT

2016 ◽  
Vol III-8 ◽  
pp. 137-144 ◽  
Author(s):  
Gofamodimo Mashame ◽  
Felicia Akinyemi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Degradation ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Bare Land ◽  
Level I ◽  
The Impact ◽  
Semi Arid

Land degradation (LD) is among the major environmental and anthropogenic problems driven by land use-land cover (LULC) and climate change worldwide. For example, poor LULC practises such as deforestation, livestock overstocking, overgrazing and arable land use intensification on steep slopes disturbs the soil structure leaving the land susceptible to water erosion, a type of physical land degradation. Land degradation related problems exist in Sub-Saharan African countries such as Botswana which is semi-arid in nature. LULC and LD linkage information is still missing in many semi-arid regions worldwide.Mapping seasonal LULC is therefore very important in understanding LULC and LD linkages. This study assesses the impact of seasonal LULC variation on LD utilizing Remote Sensing (RS) techniques for Palapye region in Central District, Botswana. LULC classes for the dry and rainy seasons were classified using LANDSAT 8 images at Level I according to the Food and Agriculture Organization (FAO) International Organization of Standardization (ISO) code 19144. Level I consists of 10 LULC classes. The seasonal variations in LULC are further related to LD susceptibility in the semi-arid context. The results suggest that about 985 km² (22%) of the study area is susceptible to LD by water, major LULC types affected include: cropland, paved/rocky material, bare land, built-up area, mining area, and water body. Land degradation by water susceptibility due to seasonal land use-land cover variations is highest in the east of the study area where there is high cropland to bare land conversion.

scholarly journals Evaluation of the status of land use/land cover change using remote sensing and GIS in Jewha Watershed, Northeastern Ethiopia

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Dereje Gebrie Habte ◽  
Satishkumar Belliethathan ◽  
Tenalem Ayenew
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Catchment Area ◽  
Environmental Conservation ◽  
Land Use Land Cover ◽  
Cultivated Land ◽  
Plantation Forest ◽  
Bare Land

AbstractEvaluation of land use/land cover (LULC) status of watersheds is vital to environmental management. This study was carried out in Jewha watershed, which is found in the upper Awash River basin of central Ethiopia. The total catchment area is 502 km2. All climatic zones of Ethiopia, including lowland arid (‘Kola’), midland semi-arid (‘Woinadega’), humid highland (Dega) and afro alpine (‘Wurch’) can be found in the watershed. The study focused on LULC classification and change detection using GIS and remote sensing techniques by analyzing satellite images. The data preprocessing and post-process was done using multi-temporal spectral satellite data. The images were used to evaluate the temporal trends of the LULC class by considering the years 1984, 1995, 2005 and 2015. Accuracy assessment and change detection of the classification were undertaken by accounting these four years images. The land use types in the study area were categorized into six classes: natural forest, plantation forest, cultivated land, shrub land, grass land and bare land. The result shows the cover classes which has high environmental role such as forest and shrub has decreased dramatically through time with cultivated land increasing during the same period in the watershed. The forest cover in 1984 was about 6.5% of the total catchment area, and it had decreased to 4.2% in 2015. In contrast, cultivated land increased from 38.7% in 1984 to 51% in 2015. Shrub land decreased from 28 to 18% in the same period. Bare land increased due to high gully formation in the catchment. In 1984, it was 1.8% which turned to 0.6% in 1995 then increased in 2015 to 2.7%. Plantation forest was not detected in 1984. In 1995, it covers 1.5% which turned to be the same in 2015. The study clearly demonstrated that there are significant changes of land use and land cover in the catchment. The findings will allow making informed decision which will allow better land use management and environmental conservation interventions.

scholarly journals APLIKASI PENGINDERAAN JAUH DAN SISTEM INFORMASI GEOGRAFI UNTUK PEMETAAN DAERAH BERPOTENSI TSUNAMI DI KABUPATEN KUPANG PROVINSI NUSA TENGGARA TIMUR

2018 ◽  
Vol 3 (2) ◽  
pp. 170-178
Author(s):  
Lidia Agustina Rumaal ◽  
Jehunias L. Tanesib ◽  
Jonshon Tarigan
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Population Density ◽  
Land Cover ◽  
Arrival Time ◽  
Land Cover Map ◽  
The Impact ◽  
Monitoring Area ◽  
Wave Arrival

Abstrak Telah dilakukan pemetaan daerah rawan tsunami berdasarkan estimasi waktu tiba gelombang dan tutupan lahan di Kabupaten Kupang Provinsi Nusa Tenggara Timur menggunakan aplikasi Penginderaan Jauh dan Sistem Informasi Geografi. Penelitian ini bertujuan untuk mengidentifikasi, memetakan daerah rawan tsunami dan tingkat kerawanannya menurut estimasi waktu tiba gelombang dan tutupan lahan sebagai upaya mitigasi dampak bencana tsunami terhadap kepadatan penduduk. Metode penelitian secara umum dibagi dalam empat tahap utama yaitu pembangunan basis data berupa pembuatan peta tutupan lahan, peta gempa dan peta batimetri. Analisis data kerawanan dari peta tutupan lahan dan etimasi waktu tiba gelombang, penyajian hasil data dalam bentuk tingkat kerawanan masing-masing peta dan analisis hasil penelitian berupa tingkat kerawanan secara kualitatif masing-masing daerah titik pantau menurut peta tutupan lahan maupun estimasi waktu tiba gelombang. Selain itu, dampak kerawanan tsunami diklasifikasikan menurut tingkat kepadatan penduduk untuk kebutuhan mitigasi sebagai berikut Kecamatan Kupang Timur, Kupang Barat, Sulamu, Amfoang Timur, Semau, Semau Selatan, Amfoang Utara, Amfoang Barat Daya, Amfoang Barat Laut dan Fatuleu Barat. Kata kunci : Peta rawan tsunami, Penginderaan Jauh, Sistem Informasi Geografi, Estimasi Waktu Tiba Gelombang  Abstract Mapping of hazard tsunami areas based on estimation of arrival time of wave and land cover in Kupang Regency of East Nusa Tenggara Province using remote sensing application and geographic information system has been done. The  aims of this research are to mapping the hazard tsunami area and tsunami vulnerability level in Kupang Regency East Nusa Tenggara according to the estimated arrival time of the wave and land cover as an effort to mitigate the impact of the tsunami disaster on population density. These generally devided into four main phase namely development of database in the form of land cover map , seismic maps and bathymetry maps, data analysis of research results in the form of qualitative vulnerability of each monitoring area according to land cover map and estimated wave arrival time. Presentation of data results in the form of vulnerability level of each map and analysis and results analysis of research the form of vulnerability level of each map and analysis of research results in the form of qualitative vulnerability of each monitoring area according to land cover map and estimated wave arrival time. And then, the impact of tsunami vulnerability is classified according to population density levels for mitigation needs as follows Kupang Timur, Kupang Barat, Sulamu, Amfoang Timur, Semau, Semau Selatan, Amfoang Utara, Amfoang Barat Daya, Amfoang Barat Laut and Fatuleu Barat. Keywords: Tsunami Hazard Map, Remote Sensing, Geographic Information System, Estimated Time of arrival Wave

scholarly journals Assessment of Land Use and Vegetative Cover in Kano Metropolis (from 1975-2015) Employing GIS and Remote Sensing Technology

2020 ◽  
Vol 27 (2) ◽  
pp. 1-7
Author(s):  
M. Haruna ◽  
M.K. Ibrahim ◽  
U.M. Shaibu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Agricultural Land ◽  
Landsat 8 ◽  
Vegetative Cover ◽  
Gis And Remote Sensing ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Agricultural Planning ◽  
Bare Land

This study applied GIS and remote sensing technology to assess agricultural land use and vegetative cover in Kano Metropolis. It specifically examined the intensity of land use for agricultural and non agricultural purpose from 1975 – 2015. Images (1975, 1995 and 2015), landsat MSS/TM, landsat 8, scene of path 188 and 052 were downloaded for the study. Bonds for these imported scenes were processed using ENVI 5.0 version. The result indicated five classified features-settlement, farmland, water body, vegetation and bare land. The finding revealed an increase in settlement, vegetation and bare land between 1995 and 2015, however, farmland decreased in 2015. Indicatively, higher percentage of land use for non agricultural purposes was observed in recent time. Conclusively, there is need to accord surveying the rightful place and priority in agricultural planning and development if Nigeria is to be self food sufficient. Keywords: Geographic Information System, Agriculture, Remote sensing, Land use, Land cover

scholarly journals Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change

2020 ◽  
Vol 12 (4) ◽  
pp. 688 ◽  
Author(s):  
Jacky Lee ◽  
Jeffrey A. Cardille ◽  
Michael T. Coe
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Spatial Resolution ◽  
Processing Speed ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Wide Audience ◽  
Data Set ◽  
Mato Grosso

Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems.

Synthetic sampling for spatio-temporal land cover mapping with machine learning and the Google Earth Engine in Andalusia, Spain

2020 ◽  
Author(s):  
Laura Bindereif ◽  
Tobias Rentschler ◽  
Martin Batelheim ◽  
Marta Díaz-Zorita Bonilla ◽  
Philipp Gries ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Cover ◽  
Classification Accuracy ◽  
Sampling Methods ◽  
Google Earth ◽  
Land Cover Mapping ◽  
Machine Learning Applications ◽  
Google Earth Engine ◽  
Spatio Temporal

&lt;p&gt;Land cover information plays an essential role for resource development, environmental monitoring and protection. Amongst other natural resources, soils and soil properties are strongly affected by land cover and land cover change, which can lead to soil degradation. Remote sensing techniques are very suitable for spatio-temporal mapping of land cover mapping and change detection. With remote sensing programs vast data archives were established. Machine learning applications provide appropriate algorithms to analyse such amounts of data efficiently and with accurate results. However, machine learning methods require specific sampling techniques and are usually made for balanced datasets with an even training sample frequency. Though, most real-world datasets are imbalanced and methods to reduce the imbalance of datasets with synthetic sampling are required. Synthetic sampling methods increase the number of samples in the minority class and/or decrease the number in the majority class to achieve higher model accuracy. The Synthetic Minority Over-Sampling Technique (SMOTE) is a method to generate synthetic samples and balance the dataset used in many machine learning applications. In the middle Guadalquivir basin, Andalusia, Spain, we used random forests with Landsat images from 1984 to 2018 as covariates to map the land cover change with the Google Earth Engine. The sampling design was based on stratified random sampling according to the CORINE land cover classification of 2012. The land cover classes in our study were arable land, permanent crops (plantations), pastures/grassland, forest and shrub. Artificial surfaces and water bodies were excluded from modelling. However, the number of the 130 training samples was imbalanced. The classes pasture (7&amp;#160;samples) and shrub (13&amp;#160;samples) show a lower number than the other classes (48, 47 and 16&amp;#160;samples). This led to misclassifications and negatively affected the classification accuracy. Therefore, we applied SMOTE to increase the number of samples and the classification accuracy of the model. Preliminary results are promising and show an increase of the classification accuracy, especially the accuracy of the previously underrepresented classes pasture and shrub. This corresponds to the results of studies with other objectives which also see the use of synthetic sampling methods as an improvement for the performance of classification frameworks.&lt;/p&gt;

Assessing and monitoring the vulnerability to drought and climate anomalies of Mediterranenan oak forests by using NDVI

2020 ◽  
Author(s):  
Maria Castellaneta ◽  
Angelo Rita ◽  
J. Julio Camarero ◽  
Michele Colangelo ◽  
Angelo Nolè ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Tree Mortality ◽  
Vegetation Index ◽  
Google Earth ◽  
Oak Forests ◽  
Summer Drought ◽  
Area Index ◽  
Ndvi Time Series ◽  
The Impact

&lt;p&gt;Several die-off episodes related to heat weaves and drought spells have evidenced the high vulnerability of Mediterranean oak forests. These events consisted in the loss in tree vitality and manifested as growths decline, elevated crown transparency (defoliation) and rising tree mortality rate. In this context, the changes in vegetation productivity and canopy greenness may represent valuable proxies to analyze how extreme climatic events trigger forest die-off. Such changes in vegetation status may be analyzed using remote-sensing data, specifically multi-temporal spectral information. For instance, the Normalized Difference Vegetation Index (NDVI) measures changes in vegetation greenness and is a proxy of changes in leaf area index (LAI), forest aboveground biomass and productivity. In this study, we analyzed the temporal patterns of vegetation in three Mediterranean oak forests showing recent die-off in response to the 2017 severe summer drought. For this purpose, we used an open-source platform (Google Earth Engine) to extract collections of MODIS NDVI time-series from 2000 to 2019. The analysis of both NDVI trends and anomalies were used to infer differential patterns of vegetation phenology among sites comparing plots where most trees were declining and showed high defoliation (test) versus plots were most trees were considered healthy (ctrl) and showed low or no defoliation. Here we discuss: i) the likely offset in NDVI time-series between test- versus ctrl- sites; and ii) the impact of summer droughts &amp;#160;on NDVI.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt;: climate change, forest vulnerability, time series, remote sensing.&lt;/p&gt;

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