scholarly journals Clasificación de usos y cubiertas del suelo y análisis de cambios en los alrededores de la Reserva Ecológica Manglares Churute (Ecuador) mediante una serie de imágenes Sentinel-1

2020 ◽  
pp. 131
Author(s):  
D.A. Vélez-Alvarado ◽  
J. Álvarez-Mozos

<p class="p1">Management practices adopted in protected natural areas often ignore the relevance of the territory surrounding the actual protected land (buffer area). These areas can be the source of impacts that threaten the protected ecosystems. This paper reports a case study where a time series of Sentinel-1 imagery was used to classify the land-use/land-cover and to evaluate its change between 2015 and 2018 in the buffer area around the Manglares Churute Ecological Reserve (REMCh) in Ecuador. Sentinel-1 scenes were processed and ground-truth data were collected consisting of samples of the main land-use/land-cover classes in the region. Then, a Random Forests (RF) classification algorithm was built and optimized, following a five-fold cross validation scheme using the training dataset (70% of the ground truth). The remaining 30% was used for validation, achieving an Overall Accuracy of 84%, a Kappa coefficient of 0.8 and successful class performance metrics for the main crops and land use classes. Results were poorer for heterogeneous and minor classes, nevertheless the performance of the classification was deemed sufficient for the targeted change analysis. Between 2015 and 2018, an increase in the area covered by intensive land uses was evidenced, such as shrimp farms and sugarcane, which replaced traditional crops (mainly rice and banana). Even though such changes only affected the land area around the natural reserve, they might affect its water quality due to the use of fertilizers and pesticides that easily. Therefore, it is recommended that these buffer areas around natural protected areas be taken into account when designing adequate environmental protection measures and polices.</p>

Author(s):  
A. G. Koppad ◽  
B. S. Janagoudar

The study was conducted in Uttara Kannada districts during the year 2012&amp;ndash;2014. The study area lies between 13.92&amp;deg;&amp;thinsp;N to 15.52&amp;deg;&amp;thinsp;N latitude and 74.08&amp;deg;&amp;thinsp;E to 75.09&amp;deg;&amp;thinsp;E longitude with an area of 10,215&amp;thinsp;km<sup>2</sup>. The Indian satellite IRS P6 LISS-III imageries were used to classify the land use land cover classes with ground truth data collected with GPS through supervised classification in ERDAS software. The land use and land cover classes identified were dense forest, horticulture plantation, sparse forest, forest plantation, open land and agriculture land. The dense forest covered an area of 63.32&amp;thinsp;% (6468.70&amp;thinsp;sq&amp;thinsp;km) followed by agriculture 12.88&amp;thinsp;% (1315.31&amp;thinsp;sq.&amp;thinsp;km), sparse forest 10.59&amp;thinsp;% (1081.37&amp;thinsp;sq.&amp;thinsp;km), open land 6.09&amp;thinsp;% (622.37&amp;thinsp;sq.&amp;thinsp;km), horticulture plantation and least was forest plantation (1.07&amp;thinsp;%). Settlement, stony land and water body together cover about 4.26 percent of the area. The study indicated that the aspect and altitude influenced the forest types and vegetation pattern. The NDVI map was prepared which indicated that healthy vegetation is represented by high NDVI values between 0.1 and 1. The non-vegetated features such as water bodies, settlement, and stony land indicated less than 0.1 values. The decrease in forest area in some places was due to anthropogenic activities. The thematic map of land use land cover classes was prepared using Arc GIS Software.


Author(s):  
A. G. Koppad ◽  
B. S. Janagoudar

The study was conducted in Uttara Kannada districts during the year 2012&amp;ndash;2014. The study area lies between 13.92&amp;deg;&amp;thinsp;N to 15.52&amp;deg;&amp;thinsp;N latitude and 74.08&amp;deg;&amp;thinsp;E to 75.09&amp;deg;&amp;thinsp;E longitude with an area of 10,215&amp;thinsp;km<sup>2</sup>. The Indian satellite IRS P6 LISS-III imageries were used to classify the land use land cover classes with ground truth data collected with GPS through supervised classification in ERDAS software. The land use and land cover classes identified were dense forest, horticulture plantation, sparse forest, forest plantation, open land and agriculture land. The dense forest covered an area of 63.32&amp;thinsp;% (6468.70&amp;thinsp;sq&amp;thinsp;km) followed by agriculture 12.88&amp;thinsp;% (1315.31&amp;thinsp;sq.&amp;thinsp;km), sparse forest 10.59&amp;thinsp;% (1081.37&amp;thinsp;sq.&amp;thinsp;km), open land 6.09&amp;thinsp;% (622.37&amp;thinsp;sq.&amp;thinsp;km), horticulture plantation and least was forest plantation (1.07&amp;thinsp;%). Settlement, stony land and water body together cover about 4.26 percent of the area. The study indicated that the aspect and altitude influenced the forest types and vegetation pattern. The NDVI map was prepared which indicated that healthy vegetation is represented by high NDVI values between 0.1 and 1. The non- vegetated features such as water bodies, settlement, and stony land indicated less than 0.1 values. The decrease in forest area in some places was due to anthropogenic activities. The thematic map of land use land cover classes was prepared using Arc GIS Software.


Author(s):  
M. Moniruzzam ◽  
A. Roy ◽  
C. M. Bhatt ◽  
A. Gupta ◽  
N. T. T. An ◽  
...  

<p><strong>Abstract.</strong> Urbanization has given a massive pace in Land Use Land Cover (LULC) changes in rapidly growing cities like Khulna, i.e. the third largest city of Bangladesh. Such impacting changes have taken place in over-decadal scale. It is important because detailed analysis with regularly monitoring will be fruitful to drag the attention of decision maker and urban planner for sustainable development and to overcome the problem of urban sprawl. In this present study, changes in LULC as an impact of urbanization, have been investigated for years 1997, 2002, 2007, 2012 and 2017; using three generation of Landsat data in geographic information system (GIS) domain which has the height competence in recent time. Initially, LULC have categorised into Built-up, Vegetation, Vacant Land, and Waterbody with the help of supervised classification technique. Field work had been carried out for acquiring training dataset and validation. The accuracy has been achieved more than 85% for the changes assessed. Analysis has an outlet with increase in built-up area by 27.92% in year 1997 to 2017 and continued respectively in each successive interval of half a decade at the given years. On the other side waterbody and vacant land decreased correspondingly. Bound to mention, instead to having largest temporal durability, the moderate spatial resolution of Landsat data has a limitation for such urban studies. These changes are responsible by both of natural or anthropogenic factors. Such study will provide a better way out of optimization of land-use to prepare detail area plan (DAP) of Khulna City Corporation (KCC) and Khulna development authority (KDA).</p>


2019 ◽  
Author(s):  
Abreham Berta Aneseyee

Abstract Background: Information on soil loss and sediment export is essential to identify hotspots of soil erosion for conservation interventions in a given watershed. This study aims at investigating the dynamic of soil loss and sediment export associated with land use/land cover change and identifies soil loss hotspot areas in Winike watershed of Omo-gibe basin of Ethiopia. Spatial data collected from satellite images, topographic maps, meteorological and soil data were analyzed. Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) of sediment delivery ratio (SDR) model was used based on analysis of land use/land cover maps and RUSLE factors. Result: The results showed that total soil loss increased from 774.86 thousand tons in 1988 to 951.21 thousand tons in 2018 while the corresponding sediment export increased by 3.85 thousand tons in the same period. These were subsequently investigated in each land-use type. Cultivated fields generated the highest soil erosion rate, which increased by 10.02 t/ha/year in 1988 to 43.48 t/ha/year in 2018. This corresponds with the expansion of the cultivated area that increased from 44.95 thousand ha in 1988 to 59.79 thousand ha in 2018. This is logical as the correlation between soil loss and sediment delivery and expansion of cultivated area is highly significant (p<0.01). Sub-watershed six (SW-6) generated the highest soil loss (62.77 t/ha/year) and sediment export 16.69 t/ha/year, followed by Sub-watershed ten (SW-10) that are situated in the upland plateau. Conversely, the lower reaches of the watershed are under dense vegetation cover and experiencing less erosion. Conclusion: Overall, the changes in land use/land cover affect significantly the soil erosion and sediment export dynamism. This research is used to identify an area to prioritize the watershed for immediate management practices. Thus, land use policy measures need to be enforced to protect the hydropower generation dams at downstream and the ecosystem at the watershed.


Author(s):  
Ujjwala Khare ◽  
Prajakta Thakur

<p>The expansion of urban areas is common in metropolitan cities in India. Pune also has experienced rapid growth in the fringe areas of the city. This is mainly on account of the development of the Information Technology (IT) Parks. These IT Parks have been established in different parts of Pune city. They include Hinjewadi, Kharadi, Talwade and others like the IT parks in Magarpatta area. The IT part at Talwade is located to close to Pune Nashik Highway has had an impact on the villages located around it. The surrounding area includes the villages of Talwade, Chikhli, Nighoje, Mahalunge, Khalumbre and Sudumbre.</p> <p>The changes in the land use that have occurred in areas surrounding Talwade IT parks during the last three decades have been studied by analyzing the LANDSAT images of different time periods. The satellite images of the 1992, 2001 and 2011 were analyzed to detect the temporal changes in the land use and land cover.</p> <p>This paper attempts to study the changes in land use / land cover which has taken place in these villages in the last two decades. Such a study can be done effectively with the help of remote sensing and GIS techniques. The tertiary sector has experienced a rapid growth especially during the last decade near the IT Park. The occupation structure of these villages is also related to the changes due to the development of the IT Park.</p> <p>The land use of study area has been analysed using the ground truth applied to the satellite images at decadal interval. Using the digital image processing techniques, the satellite images were then classified and land use / land cover maps were derived. The results show that the area under built-up land has increased by around 14 per cent in the last 20 years. On the contrary, the land under agriculture, barren, pasture has decreased significantly.</p>


2020 ◽  
Vol 12 (1) ◽  
pp. 9-12
Author(s):  
Arjun G. Koppad ◽  
Syeda Sarfin ◽  
Anup Kumar Das

The study has been conducted for land use and land cover classification by using SAR data. The study included examining of ALOS 2 PALSAR L- band quad pol (HH, HV, VH and VV) SAR data for LULC classification. The SAR data was pre-processed first which included multilook, radiometric calibration, geometric correction, speckle filtering, SAR Polarimetry and decomposition. For land use land cover classification of ALOS-2-PALSAR data sets, the supervised Random forest classifier was used. Training samples were selected with the help of ground truth data. The area was classified under 7 different classes such as dense forest, moderate dense forest, scrub/sparse forest, plantation, agriculture, water body, and settlements. Among them the highest area was covered by dense forest (108647ha) followed by horticulture plantation (57822 ha) and scrub/Sparse forest (49238 ha) and lowest area was covered by moderate dense forest (11589 ha).   Accuracy assessment was performed after classification. The overall accuracy of SAR data was 80.36% and Kappa Coefficient was 0.76.  Based on SAR backscatter reflectance such as single, double, and volumetric scattering mechanism different land use classes were identified.


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