scholarly journals Forest type mapping using object-based classification method in Kapilvastu district, Nepal

Banko Janakari â—½  
2016 â—½  
Vol 26 (1) â—½  
pp. 38-44
Author(s):  
A. K. Chaudhary â—½  
A. K. Acharya â—½  
S. Khanal
Keyword(s):  
Nearest Neighbor â—½  
Forest Type â—½  
Accuracy Assessment â—½  
Regression Tree â—½  
Classification And Regression Tree â—½  
Systematic Sampling â—½  
Landsat 8 â—½  
Forest Types â—½  
Shorea Robusta â—½  
Object Based

In the recent years, object-based image analysis (OBIA) approach has emerged with an attempt to overcome limitations inherited in conventional pixel-based approaches. OBIA was performed using Landsat 8 image to map the forest types in Kapilvastu district of Nepal. Systematic sampling design was adopted to establish sample points in the field, and 70% samples were used for classification and 30% samples for accuracy assessment. Landsat image was pre-processed, and the slope and aspect derived from the ASTER DEM were used as additional predictors for classification. Segmentation was done using eCognition v8.0 with the scale parameter of 20, ratios of 0.1 and 0.9 for shape and color, respectively. Classification and Regression Tree (CART) and nearest neighbor classifier (k-NN) methods were used for object-based classification. The major forest types observed in the district were KS (Acacia catechu/ Dalbergia sissoo), Sal (Shorea robusta) and Tropical Mixed Hardwood. The k-NN classification technique showed higher overall accuracy than the CART method. The classification approach used in this study can also be applied to classify forest types in other districts. Improvement in classification accuracy can be potentially obtained through inclusion of sufficient samples from all classes.Banko JanakariA Journal of Forestry Information for NepalVol. 26, No. 1, Page: 38-44, 2016

scholarly journals Evaluating Efficiency of Sampling Schemes in Tropical Natural Forests: Review and Simulation Experience from Kenya

2019 â—½  
pp. 1-15
Author(s):  
Joseph Hitimana â—½  
James Legilisho Ole Kiyiapi â—½  
Balozi Kirongo Bekuta
Keyword(s):  
Forest Type â—½  
Sampling Error â—½  
Basal Area â—½  
Sampling Technique â—½  
Montane Forest â—½  
Systematic Sampling â—½  
Natural Forests â—½  
Forest Types â—½  
Sampling Schemes â—½  
Plot Size

Forest measurements, especially in natural forests are cumbersome and complex. 100% enumeration is costly and inefficient. This study sought to find out reliable, efficient and cost-effective sampling schemes for use in tropical rain forest (TRF), moist montane forest (MMF) and dry woodland forest (DWF) in Kenya. Forty-eight sampling schemes (each combining sampling intensity (5, 10, 20, 30%), plot size (25, 50, 100, 400 m2) and sampling technique (simple random sampling, systematic sampling along North-South and along East-West orientations) were generated for testing estimates of forest attributes such as regeneration through simulation using R-software. Sampling error and effort were used to measure efficiency of each sampling scheme in relation to actual values. Though forest sites differed in biophysical characteristics, cost of sampling increased with decreasing plot size regardless of the forest type and attribute. Accuracy of inventory increased with decreasing plot size. Plot sizes that captured inherent variability were 5mx5m for regeneration and trees ha-1 across forest types but varied between forest types for basal area. Different sampling schemes were ranked for relative efficiency through simulation techniques, using regeneration as an example. In many instances systematic sampling-based sampling schemes were most effective. Sub-sampling in one-hectare forest unit gave reliable results in TRF (e.g. SSV-5mx5m-30%) and DWF (e.g. SSV-10mx10m-30%) but not in MMF (5mx5m-100%). One-hectare-complete-inventory method was found inevitable for regeneration assessment in montane forest.

Geo-forestry of Landslide-affected Areas in a Part of Central Himalaya

1986 â—½  
Vol 13 (4) â—½  
pp. 299-309 â—½  
Author(s):  
A.K. Tiwari â—½  
J.S. Mehta â—½  
O.P. Goel â—½  
J.S. Singh
Keyword(s):  
Land Use â—½  
Forest Type â—½  
Pine Forest â—½  
Aerial Photographs â—½  
Central Himalaya â—½  
Forest Types â—½  
Pinus Roxburghii â—½  
Shorea Robusta â—½  
Crown Cover â—½  
Chir Pine

Black-and-white aerial photographs were used to map the lithology, land-use/forest types, and landslide zones (namely old, active, or potential) in a part of Central Himalaya. The landslide and land-use/forest type maps were simultaneously studied, and the frequency distribution of the landslide zones in different land-uses and forest types was estimated. The correlation between the maps indicated the following: In old landslide-affected sites, agriculture was the predominant land-use, followed by Pinus roxburghii forest (≤ 40% crown cover), scrub vegetation, and wasteland (including grassland). The presence of other forests (e.g. forests dominated by climax species such as Shorea robusta at low elevations and Quercus spp. at higher elevations) indicates a high potentiality of recovery of the ecosystems involved, provided biotic (especially anthropic) factors are not too intensive.The active and potential landslide zones were concentrated along geologically active planes, namely thrusts and faults, and/or in the vicinity of toe-erosion of hill-slopes. These two were dominated by P.roxburghii forest (≤ 40% crown cover). The broadleaf forests showed minimal signs of active and potential landslides, perhaps because of their multistratal character which is conducive to minimizing soil-loss compared with the mostly single-storeyed Chir Pine forest. It is, therefore, suggested that the sites should be maintained under a multistratal broadleaf canopy to conserve the soil. Where, however, the Chir Pine forest is already developed, appropriate silvicultural measures may be taken to increase its crown cover to more than 40%.

scholarly journals Mapping Forest Composition with Landsat Time Series: An Evaluation of Seasonal Composites and Harmonic Regression

Remote Sensing â—½  
10.3390/rs12040610 â—½  
2020 â—½  
Vol 12 (4) â—½  
pp. 610 â—½  
Author(s):  
Bryce Adams â—½  
Louis Iverson â—½  
Stephen Matthews â—½  
Matthew Peters â—½  
Anantha Prasad â—½  
...  
Keyword(s):  
Time Series â—½  
Forest Type â—½  
Vegetation Indices â—½  
Feature Space â—½  
Forest Composition â—½  
Landsat 8 â—½  
Forest Types â—½  
Young Forest â—½  
Thematic Resolution

The Landsat program has long supported pioneering research on the recovery of forest information by remote sensing technologies for several decades, and efforts to improve the thematic resolution and accuracy of forest compositional products remains an area of continued innovation. Recent development and application of Landsat time series analysis offers unique opportunities for quantifying seasonality and trend components among different forest types for developing alternative feature sets for forest vegetation mapping. Within a large forested landscape in Southeastern Ohio, USA, we examined the use of harmonic metrics developed from time series of all available Landsat-8 observations (2013–2019) relative to seasonal image composites, including accompanying spectral components and vegetation indices. A reference dataset among three sources was integrated and used to categorize forest inventory data into seven forest type classes and gradient compositional response. Results showed that the combination of harmonic metrics and topographic variables achieved an accuracy agreement with the reference data of 74.9% relative to seasonal composites (71.6%) and spectral indices (70.3%). Differences in agreement were attributed to improved discrimination of three heterogeneous upland hardwood classes and an early-successional, young forest class, all forest types of primary interest among managers across the region. Variable importance metrics often identified the cosine and sine terms that quantify the seasonality in spectral values in the harmonic feature space, suggesting these aspects best support the characterization of forest types at greater thematic detail than seasonal compositing procedures. This study demonstrates how advanced time series metrics can improve forest type modeling and forest gradient quantifications, thus showcasing a need for continued exploration of such approaches across different forest types.

scholarly journals Weather Forecast through Data Mining

2021 â—½  
pp. 90-95
Author(s):  
Swati Pandey â—½  
Shruti Sharma â—½  
Shubham Kumar â—½  
Kanchan Bhatt â—½  
Dr. Rakesh Kumar Arora
Keyword(s):  
Nearest Neighbor â—½  
Weather Forecasting â—½  
Regression Tree â—½  
Weather Forecast â—½  
Weather Conditions â—½  
Classification And Regression Tree â—½  
Support Vector â—½  
K Nearest Neighbor â—½  
Random Forest Classification â—½  
Forest Classification

Weather Forecasting is the attempt to predict the weather conditions based on parameters such as temperature, wind, humidity and rainfall. These parameters will be considered for experimental analysis to give the desired results. Data used in this project has been collected from various government institution sites. The algorithm used to predict weather includes Neural Networks(NN), Random Forest, Classification and Regression tree (C &RT), Support Vector Machine, K-nearest neighbor. The correlation analysis of the parameters will help in predicting the future values. This web based application we will have its own chat bot where user can directly communicate about their query related to Weather Forecast and can have experience of two-way communication.

scholarly journals MAPPING AND MODELLING ABOVEGROUND WOODY BIOMASS AND CARBON STOCK IN SAL (<i>SHOREA ROBUSTA</i> GAERTN. F.) FORESTS OF DOON VALLEY USING GEOSPATIAL TECHNIQUES

2018 â—½  
Vol XLII-5 â—½  
pp. 877-882
Author(s):  
S. Purohit â—½  
S. P. Aggarwal â—½  
N. R. Patel
Keyword(s):  
Forest Management â—½  
Forest Type â—½  
Woody Biomass â—½  
Exponential Model â—½  
Measured Data â—½  
Landsat 8 â—½  
Prediction Errors â—½  
Forest Management Planning â—½  
Doon Valley â—½  
Shorea Robusta

<p><strong>Abstract.</strong> Information on the quantitative and qualitative distribution of forest biomass is helpful for effective forest management. Besides its quantitative use, Biomass plays a twin role by acting as a carbon source and sinks but its long-term carbon-storing ability is of considerable importance which is helpful in lessening global warming and climate change impacts. The present study was done for mapping aboveground woody biomass (Bole) (AGWB) of <i>Shorea robusta</i> (Gaertn.f.) forests in Doon valley by establishing relationships between field measured data, satellite data derived variables and geostatistical techniques. Landsat 8 Operational Land Imager (OLI) data was used in preparing the forest homogeneity map (forest type and density). 55 sampling plots of 0.1<span class="thinspace"></span>ha were laid across the Doon Valley using stratified random sampling. Correlations were established between Landsat 8 OLI derived variables and field measured data and were evaluated. Field measured biomass has got the maximum correlation with NDVI (0.7553) and it was further used for carrying out multivariate kriging (Cok) for biomass prediction map. Prediction errors for the AGWB were lowest for exponential model with RMSE<span class="thinspace"></span>=<span class="thinspace"></span>66.445<span class="thinspace"></span>Mg/ha, Average Standard Error<span class="thinspace"></span>=<span class="thinspace"></span>71.07694<span class="thinspace"></span>Mg/ha and RMSS<span class="thinspace"></span>=<span class="thinspace"></span>0.95097. Carbon is calculated as 47% of the biomass value.AGWB was ranged from 163.381 to 750.025 Mg/ha and Carbon from 76.789 to 352.512<span class="thinspace"></span>Mg/ha. Cokriging was found as a better alternative as compared to direct radiometric relationships for the spatial distribution of the AGWB of <i>Shorea robusta</i> (Gaertn.f.) forests and this study would be helpful in better forest management planning and research purposes.</p>

scholarly journals Classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four Classification Algorithms

Remote Sensing â—½  
10.3390/rs13173433 â—½  
2021 â—½  
Vol 13 (17) â—½  
pp. 3433
Author(s):  
Masoumeh Aghababaei â—½  
Ataollah Ebrahimi â—½  
Ali Asghar Naghipour â—½  
Esmaeil Asadi â—½  
Jochem Verrelst
Keyword(s):  
Accuracy Assessment â—½  
Classification Tree â—½  
Principal Component â—½  
Classification Algorithms â—½  
Landsat 8 â—½  
Auxiliary Data â—½  
Management Actions â—½  
Object Based â—½  
Significant Difference â—½  
Plant Ecological

Plant Ecological Unit’s (PEUs) are the abstraction of vegetation communities that occur on a site which similarly respond to management actions and natural disturbances. Identification and monitoring of PEUs in a heterogeneous landscape is the most difficult task in medium resolution satellite images datasets. The main objective of this study is to compare pixel-based classification versus object-based classification for accurately classifying PEUs with four selected different algorithms across heterogeneous rangelands in Central Zagros, Iran. We used images of Landsat-8 OLI that were pan-sharpened to 15 m to classify four PEU classes based on a random dataset collected in the field (40%). In the first stage, we applied the following classification algorithms to distinguish PEUs: Minimum Distance (MD), Maximum Likelihood Classification (MLC), Neural Network-Multi Layer Perceptron (NN-MLP) and Classification Tree Analysis (CTA) for pixel based method and object based method. Then, by using the most accurate classification approach, in the second stage auxiliary data (Principal Component Analysis (PCA)) was incorporated to improve the accuracy of the PEUs classification process. At the end, test data (60%) were used for accuracy assessment of the resulting maps. Object-based maps clearly outperformed pixel-based maps, especially with CTA, NN-MLP and MD algorithms with overall accuracies of 86%, 72% and 59%, respectively. The MLC algorithm did not reveal any significant difference between the object-based and pixel-based analyses. Finally, complementing PCA auxiliary bands to the CTA algorithms offered the most successful PEUs classification strategy, with the highest overall accuracy (89%). The results clearly underpin the importance of object-based classification with the CTA classifier together with PCA auxiliary data to optimize identification of PEU classes.

scholarly journals MAPPING SPATIAL ACCURACY OF FOREST TYPE CLASSIFICATION IN JAXA’s HIGH-RESOLUTION LAND USE AND LAND COVER MAP

2019 â—½  
Vol IV-3/W1 â—½  
pp. 57-63
Author(s):  
N. Tsutsumida â—½  
S. Nagai â—½  
P. Rodríguez-Veiga â—½  
J. Katagi â—½  
K. Nasahara â—½  
...  
Keyword(s):  
Land Use â—½  
Land Cover â—½  
Forest Type â—½  
Accuracy Assessment â—½  
Spatial Accuracy â—½  
Forest Types â—½  
Broadleaf Forest â—½  
Northern Edge â—½  
Accuracy Measures â—½  
Type Classification

<p><strong>Abstract.</strong> Accuracy assessment of forest type maps is essential to evaluate the classification of forest ecosystems quantitatively. However, map users do not understand in which regions those forest types are well classified from conventional static accuracy measures. Hence, the objective of this study is to unveil spatial heterogeneities of accuracies of forest type classification in a map. Four forest types (deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), and evergreen needleleaf forest (ENF)) found in the JAXA’s land use / cover map of Japan were assessed by a volunteered Site-based dataset for Assessment of Changing LAnd cover by JAXA (SACLAJ). A geographically weighted (GW) correspondence matrix was applied to them to calculate the degree of overall agreements of forest type classes (forest overall accuracy), and the degree of accuracy for each forest class (forest user’s and producer’s accuracies) in a spatially varying way. This study compared spatial surfaces of these measures with static ones of them. The results show that the forest overall accuracy of the forest map tends to be relatively more accurate in the central Japan, while less in the Kansai and Chubu regions and the northern edge of Hokkaido. Static forest user’s accuracy measures for DBF, DNF, and ENF are better than forest producer’s accuracy ones, while the GW approach tells us such characteristics vary spatially and some areas have opposite trends. This kind of spatial accuracy assessment provides a more informative description of the accuracy than the simple use of conventional accuracy measures.</p>

Predicting the Recurrence of Child Maltreatment: A Classification and Regression Tree Analysis

PsycEXTRA Dataset â—½  
2007 â—½  
Author(s):  
Eve Sledjeski â—½  
Lisa Dierker â—½  
Rebecca Brigham â—½  
Eileen Breslin
Keyword(s):  
Child Maltreatment â—½  
Regression Tree â—½  
Classification And Regression Tree â—½  
Tree Analysis â—½  
Regression Tree Analysis â—½  
Classification And Regression
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