Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data

We discriminated different successional forest stages, forest degradation, and land use classes in the Tapajós National Forest (TNF), located in the Central Brazilian Amazon. We used full polarimetric images from ALOS/PALSAR-2 that have not yet been tested for land use and land cover (LULC) classification, neither for forest degradation classification in the TNF. Our specific objectives were: (1) to test the potential of ALOS/PALSAR-2 full polarimetric images to discriminate LULC classes and forest degradation; (2) to determine the optimum subset of attributes to be used in LULC classification and forest degradation studies; and (3) to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) supervised classifications to discriminate LULC classes and forest degradation. PALSAR-2 images from 2015 and 2016 were processed to generate Radar Vegetation Index, Canopy Structure Index, Volume Scattering Index, Biomass Index, and Cloude–Pottier, van Zyl, Freeman–Durden, and Yamaguchi polarimetric decompositions. To determine the optimum subset, we used principal component analysis in order to select the best attributes to discriminate the LULC classes and forest degradation, which were classified by RF. Based on the variable importance score, we selected the four first attributes for 2015, alpha, anisotropy, volumetric scattering, and double-bounce, and for 2016, entropy, anisotropy, surface scattering, and biomass index, subsequently classified by SVM. Individual backscattering indexes and polarimetric decompositions were also considered in both RF and SVM classifiers. Yamaguchi decomposition performed by RF presented the best results, with an overall accuracy (OA) of 76.9% and 83.3%, and Kappa index of 0.70 and 0.80 for 2015 and 2016, respectively. The optimum subset classified by RF showed an OA of 75.4% and 79.9%, and Kappa index of 0.68 and 0.76 for 2015 and 2016, respectively. RF exhibited superior performance in relation to SVM in both years. Polarimetric attributes exhibited an adequate capability to discriminate forest degradation and classes of different ecological succession from the ones with less vegetation cover.

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ANALYSIS OF THE TARGET DECOMPOSITION TECHNIQUE ATTRIBUTES AND POLARIMETRIC RATIOS TO DISCRIMINATE LAND USE AND LAND COVER CLASSES OF THE TAPAJÓS REGION

Boletim de Ciências Geodésicas ◽

10.1590/s1982-21702019000100002 ◽

2019 ◽

Vol 25 (1) ◽

Cited By ~ 1

Author(s):

Natalia Cristina Wiederkehr ◽

Fábio Furlan Gama ◽

José Cláudio Mura ◽

João Roberto dos Santos ◽

Polyanna da Conceição Bispo ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Forest Degradation ◽

Kappa Index ◽

Degradation Index ◽

Alos Palsar ◽

Iterated Conditional Modes ◽

Forest Region ◽

Complex Scenes ◽

Level 1

Abstract This study aims to analyze the capability of the target decomposition techniques and the polarimetric ratios applied to the ALOS/PALSAR-2 satellite polarimetric images to discriminate the land use and land cover classes in the Tapajós National Forest region, Pará State. Three full polarimetric ALOS/PALSAR-2, level 1 single look complex scenes were selected to generate the coherence and the covariance matrices to derive the Cloude-Pottier and the Freeman-Durden target decomposition attributes. From the radiometrically calibrated PALSAR-2 images, we generated the backscatter coefficients, the cross polarized ratio (RC; HV/HH), the parallel polarized ratio (RP; VV/HH) and the Radar Forest Degradation Index (RFDI). The images resulting from these polarimetric attributes were processed by the Maximum Likelihood (MAXVER) classifier coupled with the Iterated Conditional Modes (ICM) contextual algorithm. We found that the classifications derived from the target decomposition attributes, mainly from the Cloude-Pottier technique, with a Kappa index of 0.75, presented a significant higher performance than those derived from the RC ratio, RP ratio, and RFDI.

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SVM-Based Geospatial Prediction of Soil Erosion Under Static and Dynamic Conditioning Factors

MATEC Web of Conferences ◽

10.1051/matecconf/201820304004 ◽

2018 ◽

Vol 203 ◽

pp. 04004

Author(s):

Muhammad Raza Ul Mustafa ◽

Abdulkadir Taofeeq Sholagberu ◽

Khamaruzaman Wan Yusof ◽

Ahmad Mustafa Hashim ◽

Muhammad Waris Ali Khan ◽

...

Keyword(s):

Soil Erosion ◽

Watershed Management ◽

Land Use Planning ◽

Land Surface ◽

Vegetation Index ◽

Support Vector ◽

Susceptibility Map ◽

Rainfall Erosivity ◽

Kappa Index ◽

Conditioning Factors

Land degradation caused by soil erosion remains an important global issue due to its adverse consequences on food security and environment. Geospatial prediction of erosion through susceptibility analysis is very crucial to sustainable watershed management. Previous susceptibility studies devoid of some crucial conditioning factors (CFs) termed dynamic CFs whose impacts on the accuracy have not been investigated. Thus, this study evaluates erosion susceptibility under the influence of both non-redundant static and dynamic CFs using support vector machine (SVM), remote sensing and GIS. The CFs considered include drainage density, lineament density, length-slope and soil erodibility as non-redundant static factors, and land surface temperature, soil moisture index, vegetation index and rainfall erosivity as the dynamic factors. The study implements four kernel tricks of SVM with sequential minimal optimization algorithm as a classifier for soil erosion susceptibility modeling. Using area under the curve (AUC) and Cohen’s kappa index (k) as the validation criteria, the results showed that polynomial function had the highest performance followed by linear and radial basis function. However, sigmoid SVM underperformed having the lowest AUC and k values coupled with higher classification errors. The CFs’ weights were implemented for the development of soil erosion susceptibility map. The map would assist planners and decision makers in optimal land-use planning, prevention of soil erosion and its related hazards leading to sustainable watershed management.

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An Object Detection Method Based on Independent Local Features

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2006.p0744 ◽

2006 ◽

Vol 18 (6) ◽

pp. 744-750

Author(s):

Ryouta Nakano ◽

◽

Kazuhiro Hotta ◽

Haruhisa Takahashi

Keyword(s):

Object Detection ◽

Detection Method ◽

Principal Component ◽

Component Analysis ◽

Local Features ◽

Local Feature ◽

Superior Performance ◽

Support Vector ◽

Car Detection ◽

Training Samples

This paper presents an object detection method using independent local feature extractor. Since objects are composed of a combination of characteristic parts, a good object detector could be developed if local parts specialized for a detection target are derived automatically from training samples. To do this, we use Independent Component Analysis (ICA) which decomposes a signal into independent elementary signals. We then used the basis vectors derived by ICA as independent local feature extractors specialized for a detection target. These feature extractors are applied to a candidate area, and their outputs are used in classification. However, the number of dimension of extracted independent local features is very high. To reduce the extracted independent local features efficiently, we use Higher-order Local AutoCorrelation (HLAC) features to extract the information that relates neighboring features. This may be more effective for object detection than simple independent local features. To classify detection targets and non-targets, we use a Support Vector Machine (SVM). The proposed method is applied to a car detection problem. Superior performance is obtained by comparison with Principal Component Analysis (PCA).

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Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes

Remote Sensing ◽

10.3390/rs11010037 ◽

2018 ◽

Vol 11 (1) ◽

pp. 37 ◽

Cited By ~ 15

Author(s):

Julien Denize ◽

Laurence Hubert-Moy ◽

Julie Betbeder ◽

Samuel Corgne ◽

Jacques Baudry ◽

...

Keyword(s):

Land Use ◽

Time Series ◽

Vegetation Cover ◽

Temporal Dynamics ◽

Agricultural Practices ◽

Agricultural Landscapes ◽

Support Vector ◽

Kappa Index ◽

Spatio Temporal ◽

Agricultural Areas

Monitoring vegetation cover during winter is a major environmental and scientific issue in agricultural areas. From an environmental viewpoint, the presence and type of vegetation cover in winter influences the transport of pollutants to water resources. From a methodological viewpoint, characterizing spatio-temporal dynamics of land cover and land use at the field scale is challenging due to the diversity of farming strategies and practices in winter. The objective of this study was to evaluate the respective advantages of Sentinel optical and SAR time-series to identify land use in winter. To this end, Sentinel-1 and -2 time-series were classified using Support Vector Machine and Random Forest algorithms in a 130 km² agricultural area. From the classification, the Sentinel-2 time-series identified winter land use more accurately (overall accuracy (OA) = 75%, Kappa index = 0.70) than that of Sentinel-1 (OA = 70%, Kappa = 0.66) but a combination of the Sentinel-1 and -2 time-series was the most accurate (OA = 81%, Kappa = 0.77). Our study outlines the effectiveness of Sentinel-1 and -2 for identify land use in winter, which can help to change agricultural practices.

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Spatial near future modeling of land use and land cover changes in the temperate forests of Mexico

PeerJ ◽

10.7717/peerj.6617 ◽

2019 ◽

Vol 7 ◽

pp. e6617 ◽

Cited By ~ 1

Author(s):

Jesús A. Prieto-Amparán ◽

Federico Villarreal-Guerrero ◽

Martin Martínez-Salvador ◽

Carlos Manjarrez-Domínguez ◽

Griselda Vázquez-Quintero ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Change ◽

Temperate Forests ◽

Secondary Forest ◽

Primary Forest ◽

Support Vector ◽

Land Cover Changes ◽

Land Use Land Cover ◽

Kappa Index

The loss of temperate forests of Mexico has continued in recent decades despite wide recognition of their importance to maintaining biodiversity. This study analyzes land use/land cover change scenarios, using satellite images from the Landsat sensor. Images corresponded to the years 1990, 2005 and 2017. The scenarios were applied for the temperate forests with the aim of getting a better understanding of the patterns in land use/land cover changes. The Support Vector Machine (SVM) multispectral classification technique served to determine the land use/land cover types, which were validated through the Kappa Index. For the simulation of land use/land cover dynamics, a model developed in Dinamica-EGO was used, which uses stochastic models of Markov Chains, Cellular Automata and Weight of Evidences. For the study, a stationary, an optimistic and a pessimistic scenario were proposed. The projections based on the three scenarios were simulated for the year 2050. Five types of land use/land cover were identified and evaluated. They were primary forest, secondary forest, human settlements, areas without vegetation and water bodies. Results from the land use/land cover change analysis show a substantial gain for the secondary forest. The surface area of the primary forest was reduced from 55.8% in 1990 to 37.7% in 2017. Moreover, the three projected scenarios estimate further losses of the surface are for the primary forest, especially under the stationary and pessimistic scenarios. This highlights the importance and probably urgent implementation of conservation and protection measures to preserve these ecosystems and their services. Based on the accuracy obtained and on the models generated, results from these methodologies can serve as a decision tool to contribute to the sustainable management of the natural resources of a region.

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DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions

BMC Bioinformatics ◽

10.1186/s12859-019-3214-6 ◽

2019 ◽

Vol 20 (S19) ◽

Cited By ~ 3

Author(s):

Yi Zheng ◽

Hui Peng ◽

Xiaocai Zhang ◽

Zhixun Zhao ◽

Xiaoying Gao ◽

...

Keyword(s):

Drug Interactions ◽

Large Scale ◽

Dimensional Space ◽

Principal Component ◽

Computational Prediction ◽

Superior Performance ◽

Support Vector ◽

Learning Method ◽

K Nearest Neighbors ◽

Lower Dimensional Space

Abstract Background Drug-drug interactions (DDIs) are a major concern in patients’ medication. It’s unfeasible to identify all potential DDIs using experimental methods which are time-consuming and expensive. Computational methods provide an effective strategy, however, facing challenges due to the lack of experimentally verified negative samples. Results To address this problem, we propose a novel positive-unlabeled learning method named DDI-PULearn for large-scale drug-drug-interaction predictions. DDI-PULearn first generates seeds of reliable negatives via OCSVM (one-class support vector machine) under a high-recall constraint and via the cosine-similarity based KNN (k-nearest neighbors) as well. Then trained with all the labeled positives (i.e., the validated DDIs) and the generated seed negatives, DDI-PULearn employs an iterative SVM to identify a set of entire reliable negatives from the unlabeled samples (i.e., the unobserved DDIs). Following that, DDI-PULearn represents all the labeled positives and the identified negatives as vectors of abundant drug properties by a similarity-based method. Finally, DDI-PULearn transforms these vectors into a lower-dimensional space via PCA (principal component analysis) and utilizes the compressed vectors as input for binary classifications. The performance of DDI-PULearn is evaluated on simulative prediction for 149,878 possible interactions between 548 drugs, comparing with two baseline methods and five state-of-the-art methods. Related experiment results show that the proposed method for the representation of DDIs characterizes them accurately. DDI-PULearn achieves superior performance owing to the identified reliable negatives, outperforming all other methods significantly. In addition, the predicted novel DDIs suggest that DDI-PULearn is capable to identify novel DDIs. Conclusions The results demonstrate that positive-unlabeled learning paves a new way to tackle the problem caused by the lack of experimentally verified negatives in the computational prediction of DDIs.

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TESTING THE POTENTIAL OF VEGETATION INDICES FOR LAND USE/COVER CLASSIFICATION USING HIGH RESOLUTION DATA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w4-279-2017 ◽

2017 ◽

Vol IV-4/W4 ◽

pp. 279-283 ◽

Cited By ~ 3

Author(s):

A. Karakacan Kuzucu ◽

F. Bektas Balcik

Keyword(s):

Land Use ◽

Support Vector Machine ◽

Maximum Likelihood ◽

Rural Areas ◽

Vegetation Index ◽

Vegetation Indices ◽

Support Vector ◽

Kappa Statistics ◽

Suitability Analysis ◽

Agricultural Field

Accurate and reliable land use/land cover (LULC) information obtained by remote sensing technology is necessary in many applications such as environmental monitoring, agricultural management, urban planning, hydrological applications, soil management, vegetation condition study and suitability analysis. But this information still remains a challenge especially in heterogeneous landscapes covering urban and rural areas due to spectrally similar LULC features. In parallel with technological developments, supplementary data such as satellite-derived spectral indices have begun to be used as additional bands in classification to produce data with high accuracy. The aim of this research is to test the potential of spectral vegetation indices combination with supervised classification methods and to extract reliable LULC information from SPOT 7 multispectral imagery. The Normalized Difference Vegetation Index (NDVI), the Ratio Vegetation Index (RATIO), the Soil Adjusted Vegetation Index (SAVI) were the three vegetation indices used in this study. The classical maximum likelihood classifier (MLC) and support vector machine (SVM) algorithm were applied to classify SPOT 7 image. Catalca is selected region located in the north west of the Istanbul in Turkey, which has complex landscape covering artificial surface, forest and natural area, agricultural field, quarry/mining area, pasture/scrubland and water body. Accuracy assessment of all classified images was performed through overall accuracy and kappa coefficient. The results indicated that the incorporation of these three different vegetation indices decrease the classification accuracy for the MLC and SVM classification. In addition, the maximum likelihood classification slightly outperformed the support vector machine classification approach in both overall accuracy and kappa statistics.

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Land Use Cover changes in the western escarpment of Rift Valley in the Gamo Zone, Southern Ethiopia

10.1101/2021.09.08.459379 ◽

2021 ◽

Author(s):

Temesgen Dingamo ◽

Serekebirhan Takele ◽

Sebsebe Demissew ◽

Zerihun Woldu

Keyword(s):

Land Use ◽

Rift Valley ◽

Vegetation Index ◽

Driving Forces ◽

Accuracy Assessment ◽

Forest Degradation ◽

Southern Ethiopia ◽

Forest Biodiversity ◽

Charcoal Production ◽

Lulc Change

AbstractLULC changes are caused by natural and human alterations of the landscape that could largely affect forest biodiversity and the environment. The aim of the study was to analyzed LULC change dynamics in the western escarpment of the rift valley of the Gamo Zone, Southern Ethiopia. Digital satellite images downloaded from USGS were analyzed using ERDAS Imagine (14) and Arc GIS 10.2 software and supervised image classification was used to generate LULC classification, accuracy assessment and Normalized Difference Vegetation Index (NDVI). Drivers of LULC change were identified and analyzed. Four land classes were identified such as forest, farmland, settlement and water-wetland. Settlement and farmlands have increased by 7.83% and 5.88%, respectively. On the other hand, both forest and water bodies and wetland decreased by aerial coverage of 11.03% and 2.68%, respectively. The overall accuracy of the study area was 92.86%, 94.22% and 94.3% with a kappa value of 0.902, 0.92 and 0.922, respectively. NDVI values ranged between -0.42 to 0.73. Agricultural expansion (31.4%), expansion of settlement (25.7%) and Fuelwood collection and Charcoal production (22.9%) were the main driving forces that jeopardize forest biodiversity of the study area. Integrated land use and policy to protect biodiversity loss, forest degradation and climate changes are deemed necessary.

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Classification of Soybean Pubescence from Multispectral Aerial Imagery

Plant Phenomics ◽

10.34133/2021/9806201 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Robert W. Bruce ◽

Istvan Rajcan ◽

John Sulik

Keyword(s):

Plant Breeding ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Accurate Determination ◽

Principal Component ◽

Training Data ◽

Aerial Imagery ◽

Aerial Images ◽

Support Vector

The accurate determination of soybean pubescence is essential for plant breeding programs and cultivar registration. Currently, soybean pubescence is classified visually, which is a labor-intensive and time-consuming activity. Additionally, the three classes of phenotypes (tawny, light tawny, and gray) may be difficult to visually distinguish, especially the light tawny class where misclassification with tawny frequently occurs. The objectives of this study were to solve both the throughput and accuracy issues in the plant breeding workflow, develop a set of indices for distinguishing pubescence classes, and test a machine learning (ML) classification approach. A principal component analysis (PCA) on hyperspectral soybean plot data identified clusters related to pubescence classes, while a Jeffries-Matusita distance analysis indicated that all bands were important for pubescence class separability. Aerial images from 2018, 2019, and 2020 were analyzed in this study. A 60-plot test (2019) of genotypes with known pubescence was used as reference data, while whole-field images from 2018, 2019, and 2020 were used to examine the broad applicability of the classification methodology. Two indices, a red/blue ratio and blue normalized difference vegetation index (blue NDVI), were effective at differentiating tawny and gray pubescence types in high-resolution imagery. A ML approach using a support vector machine (SVM) radial basis function (RBF) classifier was able to differentiate the gray and tawny types (83.1% accuracy and kappa=0.740 on a pixel basis) on images where reference training data was present. The tested indices and ML model did not generalize across years to imagery that did not contain the reference training panel, indicating limitations of using aerial imagery for pubescence classification in some environmental conditions. High-throughput classification of gray and tawny pubescence types is possible using aerial imagery, but light tawny soybeans remain difficult to classify and may require training data from each field season.

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Benefits of Combining ALOS/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau

Remote Sensing ◽

10.3390/rs13020229 ◽

2021 ◽

Vol 13 (2) ◽

pp. 229

Author(s):

Jessica da Silva Costa ◽

Veraldo Liesenberg ◽

Marcos Benedito Schimalski ◽

Raquel Valério de Sousa ◽

Leonardo Josoé Biffi ◽

...

Keyword(s):

Land Cover ◽

Classification Accuracy ◽

Wind Farms ◽

Support Vector ◽

Kappa Index ◽

Alos Palsar ◽

Santa Catarina ◽

Natural Grasslands ◽

Sar Data ◽

Sentinel 2A

The Santa Catarina Southern Plateau is located in Southern Brazil and is a region that has gained considerable attention due to the rapid conversion of the typical landscape of natural grasslands and wetlands into agriculture, reforestation, pasture, and more recently, wind farms. This study’s main goal was to characterize the polarimetric attributes of the experimental quad-polarization acquisition mode of the Advanced Land Observing Satellite/ Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR-2) for mapping seven land cover classes. The polarimetric attributes were evaluated alone and combined with SENTINEL-2A using a supervised classification method based on the Support Vector Machine (SVM) algorithm. The results showed that the intensity backscattering alone reached an overall classification accuracy of 37.48% and a Kappa index of 0.26. Interestingly, the addition of polarimetric features increased to 71.35% and 0.66, respectively. It shows that the use of polarimetric decomposition features was relatively efficient in discriminating land cover classes. SENTINEL-2A data alone performed better and achieved a weighted overall accuracy and Kappa index of 85.56% and 0.82. This increase was also significant for the Z-test. However, the addition of ALOS/PALSAR-2 derived features to SENTINEL-2A slightly improved accuracy and was marginally significant at a 95% confidence level only when all features were considered. Possible implications for that performance are the accumulated precipitation prior to SAR data acquisition, which coincides with the rainy season period. The experimental quad-polarization mode of ALOS/PALSAR- 2 shall be evaluated in the near future over different seasonal conditions to confirm results. Alternatively, further studies are then suggested by focusing on additional features derived from SAR data such as texture and interferometric coherence to increase classification accuracy. These measures would be an interesting data source for monitoring specific land cover classes such as the threatened grasslands and wetlands during periods of frequent cloud coverage. Future investigations could also address multitemporal approaches employing either single or multifrequency SAR.

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