scholarly journals Study of Forest Cover Change Dynamics between 2000 and 2015 in the Ikongo District of Madagascar Using Multi-Temporal Landsat Satellite Images

2021 ◽  
Vol 10 (03) ◽  
pp. 78-91
Author(s):  
Aimé Richard Hajalalaina ◽  
Arisetra Razafinamaro ◽  
Nicolas Ratolotriniaina
Keyword(s):  
Satellite Images ◽  
Forest Cover ◽  
Forest Cover Change ◽  
Change Dynamics ◽  
Multi Temporal ◽  
Landsat Satellite

Forest cover change patterns in Myanmar (Burma) 1990–2000

2005 ◽  
Vol 32 (4) ◽  
pp. 356-364 ◽  
Author(s):  
PETER LEIMGRUBER ◽  
DANIEL S. KELLY ◽  
MARC K. STEININGER ◽  
JAKE BRUNNER ◽  
THOMAS MÜLLER ◽  
...  
Keyword(s):  
Forest Cover ◽  
Mangrove Forests ◽  
Forest Cover Change ◽  
Charcoal Production ◽  
Important Species ◽  
Agricultural Conversion ◽  
Deforestation Rates ◽  
Commercial Logging ◽  
Closed Canopy ◽  
Landsat Satellite

Myanmar is one of the most forested countries in mainland South-east Asia. These forests support a large number of important species and endemics and have great value for global efforts in biodiversity conservation. Landsat satellite imagery from the 1990s and 2000s was used to develop a countrywide forest map and estimate deforestation. The country has retained much of its forest cover, but forests have declined by 0.3% annually. Deforestation varied considerably among administrative units, with central and more populated states and divisions showing the highest losses. Ten deforestation hotspots had annual deforestation rates well above the countrywide average. Major reasons for forest losses in these hotspots stemmed from increased agricultural conversion, fuelwood consumption, charcoal production, commercial logging and plantation development. While Myanmar continues to be a stronghold for closed canopy forests, several areas have been experiencing serious deforestation. Most notable are the mangrove forests in the Ayeyarwady delta region and the remaining dry forests at the northern edge of the central dry zone.

scholarly journals Multi‐temporal Forest Cover Change and Forest Density Trend Detection in a Mediterranean Environment

2015 ◽  
Vol 28 (4) ◽  
pp. 1188-1198 ◽  
Author(s):  
Elias Symeonakis ◽  
Peter A. Caccetta ◽  
Jeremy F. Wallace ◽  
Eva Arnau‐Rosalen ◽  
Adolfo Calvo‐Cases ◽  
...  
Keyword(s):  
Forest Cover ◽  
Trend Detection ◽  
Mediterranean Environment ◽  
Forest Cover Change ◽  
Forest Density ◽  
Multi Temporal

scholarly journals Pemetaan Mangrove Kepulauan Lease, Provinsi Maluku Menggunakan Data Multi-Temporal Dan Multi-Sensor Citra Satelit Landsat

2018 ◽  
Vol 3 (1) ◽  
pp. 19
Author(s):  
Sam Wouthuyzen ◽  
Fasmi Ahmad
Keyword(s):  
Cloud Cover ◽  
Supervised Classification ◽  
Satellite Images ◽  
Satellite Image ◽  
Multi Temporal ◽  
The Mean ◽  
Landsat 5 Tm ◽  
Landsat Satellite ◽  
The Cost ◽  
Near Future

<strong>Mangrove Mapping of The Lease Islands, Maluku Province Using Multi-Temporal And Multi-Sensor Of Landsat Satellite Images.</strong> Mangrove mapping in the Lease Islands, Maluku Province has been done, but using only a single date satellite image. Therefore, it is difficult to know the dynamics of their changes.  The aim of this study is to map mangroves every 5 year (1985-2015) using multi-sensors (MSS, TM, ETM+ and OLI) of Landsat and field data. Supervised classification using maximum likelihood was used for classifying mangrove and other habitats, and counting their areas. Results showed that mangrove in the Saparua and Nusalaut Islands, consisted of 22 and 13 species, respectively, with the longest distribution along the cost line of Tuhaha Bay due to freshwater supplay from the surrounding river, while the rest are grown in the hardy reef flat substrates. The mean overall acurracies of the maps was good enough (74.7%), except for one Landsat-5 TM and Landat-8 OLI because of the influences of cloud cover or haze.  During 30 years, the areas of mangrove are relatively stable since they are protected by local wisdom called "Kewang". The highest bias of 11.4% that made the areas of mangrove increase or decrease was not due to the utilization or conversion of mangrove, but mainly due to the influences of cloud cover/haze and the geometric differences among Landsat sensors. In the near future, the OBIA method should be try, because it seems to be able to produce mangrove maps with better accuracy.

scholarly journals DETECTION OF ACID SLUDGE CONTAMINATED AREA BASED ON NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) VALUE

2017 ◽  
Vol 11 (1) ◽  
pp. 21
Author(s):  
Nanik Suryo Haryani ◽  
Sayidah Sulma ◽  
Junita Monika Pasaribu
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Dry Season ◽  
Wet Season ◽  
Multi Temporal ◽  
Acid Sludge ◽  
Landsat Satellite ◽  
Solid Form

The solid form of oil heavy metal waste is  known as acid sludge. The aim of this research is to exercise the correlation between acid sludge concentration in soil and NDVI value, and further studying the Normalized Difference Vegetation Index (NDVI) anomaly by multi-temporal Landsat satellite images. The implemented method is NDVI.  In this research, NDVI is analyzed using the  remote sensing data  on dry season and wet season.  Between 1997 to 2012, NDVI value in dry season  is around – 0.007 (July 2001) to 0.386 (May 1997), meanwhile in wet season  NDVI value is around – 0.005 (November 2006) to 0.381 (December 1995).  The high NDVI value shows the leaf health or  thickness, where the low NDVI indicates the vegetation stress and rareness which can be concluded as the evidence of contamination. The rehabilitation has been executed in the acid sludge contaminated location, where the high value of NDVI indicates the successfull land rehabilitation effort.

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