scholarly journals Combining Visual Interpretation and Supervised Classification Technique with Optical Satellite Data for Classifying Tropical Forest Cover

2001 ◽  
Vol 7 (1) ◽  
pp. 39-45
Author(s):  
Muhamad Buce Saleh Wirakartakusumah ◽  
Nobuyuki Abe
Keyword(s):  
Tropical Forest ◽  
Satellite Data ◽  
Supervised Classification ◽  
Forest Cover ◽  
Visual Interpretation ◽  
Classification Technique ◽  
Optical Satellite Data

scholarly journals Pakistan�s Timberline Forest Ecosystem Dynamics vis-a-vis Changing Climate Across Three Provinces (KPK, AJK and GB) Using GIS/RS Techniques

2020 ◽  
Vol 71 (5) ◽  
pp. 86-98
Author(s):  
Tariq Mahmood ◽  
Sarwat Naz Mirza ◽  
Saeed Gulzar ◽  
Mohammad Hanif
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Temporal Dynamics ◽  
Climatic Factors ◽  
Ecosystem Dynamics ◽  
Primary Data ◽  
Climate Change Scenarios ◽  
Visual Interpretation ◽  
Classification Technique ◽  
Anthropogenic Threats

World`s forests have always been under anthropogenic threats leading to instantaneous and sound supervision using satellite-based data collecting capabilities through GIS/RS. Timber line in Pakistan consists of sub-alpine forests and alpine pastures of KPK, AJK and GB. Research findings showed that earth`s climate was changing rapidly than previously assumed, demanding the spatio-temporal dynamics analysis using GIS/RS techniques in prevalent climate change scenarios. Two separate methods were used; 1) Visual Interpretation of Supervised Classification Technique for AJK & GB and 2) object Oriented Classification of Supervised Classification Technique for KPK. The primary data for T (max), T (min) & Ppt. (1980-2013) was taken from PMD, Islamabad. Province wise data showed 15 % change in forest cover area for timber line in both KPK (142780 Ha) & AJK (24990 Ha), followed by GB (39267 Ha) as13 % across 2005-2014. The results calculated that the average upward shift in timberline was highest in KPK (285 m), followed by AJK (233 m) and GB (170m) across 2005-2014.The data also showed change in avg. T (max) was 3.4�, -1.8� and 0.6� C, avg. T (min) was -2�, -1.5� and -2� C while change in total precipitation was 88.5, -3.7 and 75.5 mm for AJK, GB and KPK respectively. The Pearson`s Correlation Co-efficient chart concluded that climatic factors showed a strong and positive correlation among themselves as well as with change in elevation. However, the correlation among climatic factors and change in forest cover area was weak concluding deforestation to be exclusively an anthropogenic phenomena. Change in elevation showed a weak and negative correlation with change in area while all other correlations were non-significant.

Global tropical forest area measurements derived from coarse resolution satellite imagery: a comparison with other approaches

1998 ◽  
Vol 25 (1) ◽  
pp. 37-52 ◽  
Author(s):  
PHILIPPE MAYAUX ◽  
FRÉDÉRIC ACHARD ◽  
JEAN-PAUL MALINGREAU
Keyword(s):  
Tropical Forest ◽  
Satellite Data ◽  
Information Technologies ◽  
Forest Cover ◽  
Forest Area ◽  
Base Line ◽  
High Resolution Data ◽  
Forest Policies ◽  
Forest Distribution ◽  
Definition Of

Definition of appropriate tropical forest policies must be supported by better information about forest distribution. New information technologies make possible the development of advanced systems which can accurately report on tropical forest area issues. The European Commission TREES (Tropical Ecosystem Environment observation by Satellite) project has produced a consistent map of the humid tropical forest cover based on 1 km resolution satellite data. This base-line reference information can be further calibrated using a sample of high-resolution data, in order to produce accurate forest area estimates. There is good general agreement with other pantropical inventories (Food & Agriculture Organization of the United Nations Forest Resources Assessment 90, World Conservation Union Conservation Atlas of Tropical Forests, National Aeronautics & Space Administration [USA] Landsat Pathfinder) using different approaches (compilation of existing data, statistical sampling, exhaustive survey with satellite data). However, for some countries, large differences appear among the assessments. Discrepancies arising from this comparison are here analysed in terms of limitations associated with each approach and they are generally associated with differences in forest definition, data source and processing methodology. According to the different inventories, the total area of closed tropical forest is estimated at 1090–1220 million hectares with the following continental distribution: 185–215 million hectares in Africa, 235–275 million hectares in Asia, and 670–730 million hectares in Latin America. A proposal for improving the current state of forest statistics by combining the contribution of the various methods under review is made.

scholarly journals Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data

2020 ◽  
Vol 12 (24) ◽  
pp. 4080
Author(s):  
Olena Kavats ◽  
Dmitriy Khramov ◽  
Kateryna Sergieieva ◽  
Volodymyr Vasyliev
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Vegetation Index ◽  
Reference Sample ◽  
Optical Data ◽  
Visual Interpretation ◽  
Practical Applications ◽  
Harvest Dates ◽  
Ndvi Time Series ◽  
Optical Satellite Data

The algorithms for determining sugarcane harvest dates are proposed; the algorithms allow the ability to monitor large areas and are based on the publicly available Synthetic Aperture Radar (SAR) and optical satellite data. Algorithm 1 uses the NDVI (Normalized Difference Vegetation Index) time series derived from Sentinel-2 data. Sharp and continuous decrease in the NDVI values is the main sign of sugarcane harvest. The NDVI time series allows the ability to determine most harvest dates. The best estimates of the sugarcane areas harvested per month have been obtained from March to August 2018 when cloudy pixel percentage is less than 45% of the image area. Algorithm 2 of the harvest monitoring uses the coherence time series derived from Sentinel-1 Single Look Complex (SLC) images and optical satellite data. Low coherence, demonstrating sharp growth upon the harvest completion, corresponds to the harvest period. The NDVI time series trends were used to refine the algorithm. It is supposed that the descending NDVI trend corresponds to harvest. The algorithms were used to identify the harvest dates and calculate the harvested areas of the reference sample of 574 sugarcane parcels with a total area of 3745 ha in the state of São Paulo, Brazil. The harvested areas identified by visual interpretation coincide with the optical-data algorithm (algorithm 1) by 97%; the coincidence with the algorithm based on SAR and optical data (algorithm 2) is 90%. The main practical applications of the algorithms are harvest monitoring and identification of the harvested fields to estimate the harvested area.

Combining Landsat TM/ETM+ and ALOS AVNIR-2 Satellite Data for Tropical Forest Cover Change Detection

2013 ◽  
Vol 6 (1) ◽  
pp. 102-109 ◽  
Author(s):  
Michael Vollmar ◽  
Rastislav Rasi ◽  
René Beuchle ◽  
Dario Simonetti ◽  
Hans-Jürgen Stibig ◽  
...  
Keyword(s):  
Change Detection ◽  
Tropical Forest ◽  
Satellite Data ◽  
Forest Cover ◽  
Landsat Tm ◽  
Forest Cover Change

scholarly journals Combining optical satellite data and airborne laser scanner data for vegetation classification

2011 ◽  
Vol 3 (5) ◽  
pp. 393-401 ◽  
Author(s):  
Karin Nordkvist ◽  
Ann-Helen Granholm ◽  
Johan Holmgren ◽  
Håkan Olsson ◽  
Mats Nilsson
Keyword(s):  
Satellite Data ◽  
Laser Scanner ◽  
Vegetation Classification ◽  
Scanner Data ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Optical Satellite Data

Semi-Supervised Classification and its Application to Filtering IDS False Positives

2013 ◽  
Vol 427-429 ◽  
pp. 2309-2312
Author(s):  
Hai Bin Mei ◽  
Ming Hua Zhang
Keyword(s):  
Supervised Learning ◽  
Supervised Classification ◽  
Classification Performance ◽  
False Positives ◽  
Training Data ◽  
Classification Model ◽  
Classification Technique

Alert classifiers built with the supervised classification technique require large amounts of labeled training alerts. Preparing for such training data is very difficult and expensive. Thus accuracy and feasibility of current classifiers are greatly restricted. This paper employs semi-supervised learning to build alert classification model to reduce the number of needed labeled training alerts. Alert context properties are also introduced to improve the classification performance. Experiments have demonstrated the accuracy and feasibility of our approach.

The forest resource of Labrador: Landsat Thematic Mapper imagery provides a unique and current perspective

1993 ◽  
Vol 69 (6) ◽  
pp. 667-671 ◽  
Author(s):  
John A. Drieman
Keyword(s):  
Forest Cover ◽  
Thematic Mapper ◽  
Landsat Thematic Mapper ◽  
Forest Resource ◽  
Visual Interpretation ◽  
Digital Format ◽  
Current Perspective ◽  
Colour Composite ◽  
Verification Process ◽  
Forest Cover Types

The need for a current, regional perspective of the forest of Labrador was identified. Mapping of forest cover types, peat-lands, recent burns and clearcut disturbances was accomplished through visual interpretation of 1:1,000,000 scale Landsat Thematic mapper colour composite transparencies and the transfer of interpreted polygons to a geographic information system. The mapping and verification process is described in this paper. The end product, a forest resource map, provides the most up-to-date and detailed information on Labrador's forest cover types and disturbances available on a single map. The digital format of the map facilities area summaries, viewing and printing.

scholarly journals Comparison of Qinzhou bay wetland landscape information extraction by three methods

2014 ◽  
Vol XL-4 ◽  
pp. 21-28 ◽  
Author(s):  
X. Chang ◽  
Q. Zhang ◽  
M. Luo ◽  
C. Dong
Keyword(s):  
Coastal Wetlands ◽  
Supervised Classification ◽  
Coastal Wetland ◽  
Object Oriented ◽  
Visual Interpretation ◽  
Wetland Area ◽  
The Third ◽  
Tasseled Cap Transformation ◽  
Qinzhou Bay ◽  
Landscape Information

Wetland ecosystem plays an important role on the environment and sustainable socio-economic development. Based on the TM images in 2010 with a pretreament of Tasseled Cap transformation, three different methods are used to extract the Qinzhou Bay coastal wetlands using Supervised Classification (SC), Decision Trees (DT) and Object -oriented (OO) methods. Firstly coastal wetlands are picked out by artificial visual interpretation as discriminant standard. The result shows that when the same evaluation template used, the accuracy and Kappa coefficient of SC, DT and OO are 92.00 %, 0.8952; 89.00 %, 0.8582; 91.00 %, 0.8848 respectively. The total area of coastal wetland is 218.3 km<sup>2</sup> by artificial visual interpretation, and the extracted wetland area of SC, DT and OO is 219 km<sup>2</sup>, 193.70 km<sup>2</sup>, 217.40 km<sup>2</sup> respectively. The result indicates that SC is in the f irst place, followed by OO approach, and the third DT method when used to extract Qingzhou Bay coastal wetland.

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