FOREST TREE SPECIES DISTRIBUTION MAPPING USING LANDSAT SATELLITE IMAGERY AND TOPOGRAPHIC VARIABLES WITH THE MAXIMUM ENTROPY METHOD IN MONGOLIA

Forest is a very important ecosystem and natural resource for living things. Based on forest inventories, government is able to make decisions to converse, improve and manage forests in a sustainable way. Field work for forestry investigation is difficult and time consuming, because it needs intensive physical labor and the costs are high, especially surveying in remote mountainous regions. A reliable forest inventory can give us a more accurate and timely information to develop new and efficient approaches of forest management. The remote sensing technology has been recently used for forest investigation at a large scale. To produce an informative forest inventory, forest attributes, including tree species are unavoidably required to be considered. <br><br> In this study the aim is to classify forest tree species in Erdenebulgan County, Huwsgul province in Mongolia, using Maximum Entropy method. The study area is covered by a dense forest which is almost 70% of total territorial extension of Erdenebulgan County and is located in a high mountain region in northern Mongolia. For this study, Landsat satellite imagery and a Digital Elevation Model (DEM) were acquired to perform tree species mapping. The forest tree species inventory map was collected from the Forest Division of the Mongolian Ministry of Nature and Environment as training data and also used as ground truth to perform the accuracy assessment of the tree species classification. Landsat images and DEM were processed for maximum entropy modeling, and this study applied the model with two experiments. The first one is to use Landsat surface reflectance for tree species classification; and the second experiment incorporates terrain variables in addition to the Landsat surface reflectance to perform the tree species classification. All experimental results were compared with the tree species inventory to assess the classification accuracy. Results show that the second one which uses Landsat surface reflectance coupled with terrain variables produced better result, with the higher overall accuracy and kappa coefficient than first experiment. The results indicate that the Maximum Entropy method is an applicable, and to classify tree species using satellite imagery data coupled with terrain information can improve the classification of tree species in the study area.

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FOREST TREE SPECIES DISTRIBUTION MAPPING USING LANDSAT SATELLITE IMAGERY AND TOPOGRAPHIC VARIABLES WITH THE MAXIMUM ENTROPY METHOD IN MONGOLIA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-593-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 593-596 ◽

Cited By ~ 1

Author(s):

Shou Hao Chiang ◽

Miguel Valdez ◽

Chi-Farn Chen

Keyword(s):

Maximum Entropy ◽

Satellite Imagery ◽

Tree Species ◽

Maximum Entropy Method ◽

Forest Tree ◽

Entropy Method ◽

Surface Reflectance ◽

Species Classification ◽

Forest Tree Species ◽

Tree Species Classification

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Tree Species Classification by Integrating Satellite Imagery and Topographic Variables Using Maximum Entropy Method in a Mongolian Forest

Forests ◽

10.3390/f10110961 ◽

2019 ◽

Vol 10 (11) ◽

pp. 961 ◽

Cited By ~ 3

Author(s):

Shou-Hao Chiang ◽

Miguel Valdez

Keyword(s):

Maximum Entropy ◽

Satellite Imagery ◽

Tree Species ◽

Forest Inventory ◽

Forest Tree ◽

Slope Aspect ◽

Surface Reflectance ◽

Forest Tree Species ◽

Topographic Information ◽

Reflectance Data

Forests are an important natural resource that achieve ecological balance by regulating water regimes and promoting soil conservation. Based on forest inventories, the government is able to make decisions to sustainably conserve, improve, and manage forests. Fieldwork for forestry investigation requires intensive physical labor, which is costly and time-consuming, especially for surveys in remote mountainous regions. Remote sensing technology has been recently used for forest investigation on a large scale. An informative forest inventory must include forest attributes, including details of tree species; however, tree species mapping is not always applicable due to the similarity of surface reflectance and texture between tree species. Topographic variables such as elevation, slope, aspect, and curvature are crucial in allocating ecological niches to different species; therefore, this study suggests that integrating topographic information and optical satellite image classification can improve mapping accuracy for tree species. The main purpose of this study is to classify forest tree species in Erdenebulgan County, Huwsgul Province, Mongolia, by integrating Landsat satellite imagery with a Digital Elevation Model (DEM) using a Maximum Entropy algorithm. A forest tree species inventory from the Forest Division of the Mongolian Ministry of Nature and Environment was used as training data and as ground truth to perform the accuracy assessment. In this study, the classification was made using two different experimental approaches. First, classification was done using only Landsat surface reflectance data; and second, topographic variables were integrated with the Landsat surface reflectance data. The integration approach showed a higher overall accuracy and kappa coefficient, indicating that an accurate forest inventory can be achieved by integrating satellite imagery data and other topographic information to enhance the practice of forest management in remote regions.

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The Effect of Topographic Correction on Forest Tree Species Classification Accuracy

Remote Sensing ◽

10.3390/rs12050787 ◽

2020 ◽

Vol 12 (5) ◽

pp. 787

Author(s):

Chao Dong ◽

Gengxing Zhao ◽

Yan Meng ◽

Baihong Li ◽

Bo Peng

Keyword(s):

Remote Sensing ◽

Tree Species ◽

Forest Tree ◽

Species Classification ◽

Illumination Condition ◽

Topographic Correction ◽

Forest Tree Species ◽

Tree Species Classification ◽

Inconsistency Rate ◽

Rotation Model

Topographic correction can reduce the influences of topographic factors and improve the accuracy of forest tree species classification when using remote-sensing data to investigate forest resources. In this study, the Mount Taishan forest farm is the research area. Based on Landsat 8 OLI data and field survey subcompartment data, four topographic correction models (cosine model, C model, solar-canopy-sensor (SCS)+C model and empirical rotation model) were used on the Google Earth Engine (GEE) platform to carry out algorithmic data correction. Then, the tree species in the study area were classified by the random forest method. Combined with the tree species classification process, the topographic correction effects were analyzed, and the effects, advantages and disadvantages of each correction model were evaluated. The results showed that the SCS+C model and empirical rotation model were the best models in terms of visual effect, reducing the band standard deviation and adjusting the reflectance distribution. When we used the SCS+C model to correct the remote-sensing image, the total accuracy increased by 4% when using the full-coverage training areas to classify tree species and by nearly 13% when using the shadowless training area. In the illumination condition interval of 0.4–0.6, the inconsistency rate decreased significantly; however, the inconsistency rate increased with increasing illumination condition values. Topographic correction can enhance reflectance information in shaded areas and can significantly improve the image quality. Topographic correction can be used as a pretreatment method for forest species classification when the study area’s dominant tree species are in a low light intensity area.

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Spectral and Texture Features Combined for Forest Tree species Classification with Airborne Hyperspectral Imagery

Journal of the Indian Society of Remote Sensing ◽

10.1007/s12524-014-0392-6 ◽

2014 ◽

Vol 43 (1) ◽

pp. 101-107 ◽

Cited By ~ 20

Author(s):

Yuanyong Dian ◽

Zengyuan Li ◽

Yong Pang

Keyword(s):

Tree Species ◽

Hyperspectral Imagery ◽

Texture Features ◽

Forest Tree ◽

Species Classification ◽

Forest Tree Species ◽

Tree Species Classification

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FUSION OF LIDAR AND HYPERSPECTRAL DATA FOR SEMANTIC SEGMENTATION OF FOREST TREE SPECIES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-487-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 487-494

Author(s):

E. Tusa ◽

J. M. Monnet ◽

J. B. Barré ◽

M. Dalla Mura ◽

J. Chanussot

Keyword(s):

Tree Species ◽

Semantic Segmentation ◽

Forest Tree ◽

Feature Reduction ◽

Lidar Data ◽

Topographic Effects ◽

Species Classification ◽

Forest Tree Species ◽

Tree Species Classification ◽

Feature Descriptors

Abstract. Hyperspectral images (HI) and Light Detection and Ranging (LiDAR) provide high resolution radiometric and geometric information for monitoring forests at individual tree crown (ITC) level. It has many important applications for sustainable forest management, biodiversity assessment and healthy ecosystem preservation. However, the integration of different remote sensing modalities is a challenging task for tree species classification due to different artifacts such as the lighting variability, the topographic effects and the atmospheric conditions of the data acquisition. The characterization of ITC can benefit from the extraction and selection of robust feature descriptors that solve these issues. This paper aims to investigate the integration of feature descriptors from HI and LiDAR by using the intra-set and inter-set feature importance for the semantic segmentation of forest tree species. A fusion methodology is proposed between high-density LiDAR data – (20 pulses m−2) and VNIR HI – (160 bands and 0.80 m spatial resolution) acquired on French temperate forests along an altitude gradient. The proposed scheme has three inputs: the field inventory information, the HI and the LiDAR data. Our approach can be described in nine stages: polygon projection, non-overlapping pixel selection, vegetation and shadow removal, LiDAR feature extraction, height mask, robust PCA (rPCA), feature reduction and classification. The overall accuracy of tree species classification at pixel-level was 68.9% by using random forest (RF) classifier. Our approach showed that 74.0% of trees were correctly assigned overall, by having conifer species such as Norway Spruce (Picea abies) with a producer’s accuracy of 97.4%.

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USING SENTINEL 2 TIME SERIES FOR FOREST TREE SPECIES CLASSIFICATION. CASE STUDY: NE OF ROMANIA

19th SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings19th, Science and Technologies in Geology, Exploration And Mining ◽

10.5593/sgem2019v/1.4/s03.054 ◽

2019 ◽

Author(s):

Alexandra-Petronela Stoleriu

Keyword(s):

Time Series ◽

Tree Species ◽

Forest Tree ◽

Species Classification ◽

Forest Tree Species ◽

Tree Species Classification ◽

Sentinel 2

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Tropical Forest Tree Species Classification Using Meter-Scale Image Data

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8517738 ◽

2018 ◽

Author(s):

Matthew Cross ◽

Ted Scambos

Keyword(s):

Tropical Forest ◽

Tree Species ◽

Image Data ◽

Forest Tree ◽

Species Classification ◽

Forest Tree Species ◽

Tree Species Classification

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An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery

Remote Sensing ◽

10.3390/rs13101868 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1868

Author(s):

Martina Deur ◽

Mateo Gašparović ◽

Ivan Balenović

Keyword(s):

Spatial Resolution ◽

Satellite Imagery ◽

Tree Species ◽

Classification Accuracy ◽

New Technologies ◽

Deciduous Forest ◽

Species Classification ◽

Tree Species Classification ◽

Object Based ◽

Very High

Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.

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Tree Species Classification in Mixed Deciduous Forests Using Very High Spatial Resolution Satellite Imagery and Machine Learning Methods

Remote Sensing ◽

10.3390/rs12233926 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3926

Author(s):

Martina Deur ◽

Mateo Gašparović ◽

Ivan Balenović

Keyword(s):

Machine Learning ◽

Satellite Imagery ◽

Tree Species ◽

Learning Algorithms ◽

Spectral Characteristics ◽

Machine Learning Algorithms ◽

Support Vector ◽

Species Classification ◽

Tree Species Classification ◽

Very High

Spatially explicit information on tree species composition is important for both the forest management and conservation sectors. In combination with machine learning algorithms, very high-resolution satellite imagery may provide an effective solution to reduce the need for labor-intensive and time-consuming field-based surveys. In this study, we evaluated the possibility of using multispectral WorldView-3 (WV-3) satellite imagery for the classification of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) in a lowland, mixed deciduous forest in central Croatia. The pixel-based supervised classification was performed using two machine learning algorithms: random forest (RF) and support vector machine (SVM). Additionally, the contribution of gray level cooccurrence matrix (GLCM) texture features from WV-3 imagery in tree species classification was evaluated. Principal component analysis confirmed GLCM variance to be the most significant texture feature. Of the 373 visually interpreted reference polygons, 237 were used as training polygons and 136 were used as validation polygons. The validation results show relatively high overall accuracy (85%) for tree species classification based solely on WV-3 spectral characteristics and the RF classification approach. As expected, an improvement in classification accuracy was achieved by a combination of spectral and textural features. With the additional use of GLCM variance, the overall accuracy improved by 10% and 7% for RF and SVM classification approaches, respectively.

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A New CBAM-P-Net Model for Few-Shot Forest Species Classification Using Airborne Hyperspectral Images

Remote Sensing ◽

10.3390/rs13071269 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1269

Author(s):

Long Chen ◽

Xiaomin Tian ◽

Guoqi Chai ◽

Xiaoli Zhang ◽

Erxue Chen

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

High Precision ◽

Tree Species ◽

Extraction Efficiency ◽

Forest Tree ◽

Hyperspectral Images ◽

Classification Model ◽

Species Classification ◽

Forest Tree Species

High-precision automatic identification and mapping of forest tree species composition is an important content of forest resource survey and monitoring. The airborne hyperspectral image contains rich spectral and spatial information, which provides the possibility of high-precision classification and mapping of forest tree species. Few-shot learning, as an application of deep learning, has become an effective method of image classification. Prototypical networks (P-Net) is a simple and practical deep learning network, which has significant advantages in solving few-shot classification problems. Considering the high band correlation and large data volume associated with airborne hyperspectral images, how to fully extract effective features, filter or reduce redundant features is the key to improving the classification accuracy of P-Net, in order to extract effective features in hyperspectral images and obtain a high-precision forest tree species classification model with limited samples. In this research, we embedded the convolutional block attention module (CBAM) between the convolution blocks of P-Net, the CBAM-P-Net was constructed, and a method to improve the feature extraction efficiency of the P-Net was proposed, although this method makes the network more complex and increases the computational cost to a certain extent. The results show that the combination strategy using Channel First for CBAM greatly improves the feature extraction efficiency of the model. In different sample windows, CBAM-P-Net has an average increase of 1.17% and 0.0129 in testing overall accuracy (OA) and kappa coefficient (Kappa). The optimal classification window is 17×17, the OA reaches 97.28%, and Kappa reaches 0.97, which is an increase of 1.95% and 0.0214 along with just 49 s of training time expended, respectively, compared with P-Net. Therefore, using a suitable sample window and applying the proposed CBAM-P-Net to classify airborne hyperspectral images can achieve high-precision classification and mapping of forest tree species.

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