scholarly journals Mapping Forest Type and Tree Species on a Regional Scale Using Multi-Temporal Sentinel-2 Data

2019 ◽  
Vol 11 (8) ◽  
pp. 929 ◽  
Author(s):  
Agata Hościło ◽  
Aneta Lewandowska
Keyword(s):  
Tree Species ◽  
Forest Cover ◽  
Forest Type ◽  
Regional Scale ◽  
Large Area ◽  
Mountain Area ◽  
Topographic Information ◽  
Multi Temporal ◽  
Sentinel 2

There are a limited number of studies addressing the forest status, its extent, location, type and composition over a larger area at the regional or national levels. The dense time series and a wide swath of Sentinel-2 data are a good basis for forest mapping and tree species identification over a large area. This study presents the results of the classification of the forest/non-forest cover, forest type (broadleaf and coniferous) and the identification of eight tree species (beech, oak, alder, birch, spruce, pine, fir, and larch) using the multi-temporal Sentinel-2 data in combination with topographic information. The study was conducted over the large mountain area located in southern Poland. The Random Forest classifier was used to first derive a forest/non-forest map. Second, the forest was classified into broadleaf and coniferous. Finally, the tree species classification was carried out following two approaches: (i) Non-stratified, where all species were classified together within the forest mask and (ii) stratified, where the broadleaf and coniferous tree species were classified separately within the forest type masks. The overall accuracy for the forest/non-forest cover reached 98.3% and declined slightly to 94.8% for the classification of the forest type. The use of the topographic information did not increase the accuracy of either result. The role of the topographic variables increased significantly in the process of tree species delineation. By combining the topographic information (in particular, digital elevation model) with the multi-temporal Sentinel-2 data, the classification of eight tree species improved from 75.6% to 81.7% (approach 1). A further increase in accuracy to 89.5% for broadleaf and 82% for coniferous species was observed following the stratified approach number 2. The highest overall accuracy (above 85%) was obtained for beech, oak, birch, alder, and larch. The study confirmed the potential of the multi-temporal Sentinel-2 data for accurate delineation of the forest cover, forest type, and tree species at the regional scale.

scholarly journals Forest Type and Tree Species Classification of Nemoral Forests With Sentinel-1 and 2 Time Series Data

2021 ◽  
Author(s):  
Kristian Skau Bjerreskov ◽  
Thomas Nord-Larsen ◽  
Rasmus Fensholt
Keyword(s):  
Tree Species ◽  
Forest Cover ◽  
Forest Type ◽  
Error Rates ◽  
Sensor Data ◽  
Series Data ◽  
Forest Types ◽  
Multi Temporal ◽  
Species Groups

Mapping forest extent and forest cover classification are important for the assessment of forest resources in socio-economic as well as ecological terms. Novel developments in the availability of remotely sensed data, computational resources, and advances in areas of statistical learning have enabled fusion of multi-sensor data, often yielding superior classification results. Most former studies of nemoral forests fusing multi-sensor and multi-temporal data have been limited in spatial extent and typically to a simple classification of landscapes into major land cover classes. We hypothesize that multi-temporal, multi-censor data will have a specific strength in further classification of nemoral forest landscapes owing to the distinct seasonal patterns of the phenology of broadleaves. This study aimed to classify the Danish landscape into forest/non-forest and further into forest types (broadleaved/coniferous) and species groups, using a cloud-based approach based on multi-temporal Sentinel 1 and 2 data and machine learning (random forest) trained with National Forest Inventory (NFI) data. Mapping of non-forest and forest resulted in producer accuracies of 99% and 90 %, respectively. The mapping of forest types (broadleaf and conifer) within the forested area resulted in producer accuracies of 95% for conifer and 96% for broadleaf forest. Tree species groups were classified with producer accuracies ranging 34-74%. Species groups with coniferous species were the least confused whereas the broadleaf groups, especially Oak, had higher error rates. The results are applied in Danish National accounting of greenhouse gas emissions from forests, resource assessment and assessment of forest biodiversity potentials.

scholarly journals Machine Learning for Tree Species Classification Using Sentinel-2 Spectral Information, Crown Texture, and Environmental Variables

2020 ◽  
Vol 12 (12) ◽  
pp. 2049
Author(s):  
Joongbin Lim ◽  
Kyoung-Min Kim ◽  
Eun-Hee Kim ◽  
Ri Jin
Keyword(s):  
Machine Learning ◽  
South Korea ◽  
Tree Species ◽  
North Korea ◽  
Forest Type ◽  
Forest Area ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Forest Types

The most recent forest-type map of the Korean Peninsula was produced in 1910. That of South Korea alone was produced since 1972; however, the forest type information of North Korea, which is an inaccessible region, is not known due to the separation after the Korean War. In this study, we developed a model to classify the five dominant tree species in North Korea (Korean red pine, Korean pine, Japanese larch, needle fir, and Oak) using satellite data and machine-learning techniques. The model was applied to the Gwangneung Forest area in South Korea; the Mt. Baekdu area of China, which borders North Korea; and to Goseong-gun, at the border of South Korea and North Korea, to evaluate the model’s applicability to North Korea. Eighty-three percent accuracy was achieved in the classification of the Gwangneung Forest area. In classifying forest types in the Mt. Baekdu area and Goseong-gun, even higher accuracies of 91% and 90% were achieved, respectively. These results confirm the model’s regional applicability. To expand the model for application to North Korea, a new model was developed by integrating training data from the three study areas. The integrated model’s classification of forest types in Goseong-gun (South Korea) was relatively accurate (80%); thus, the model was utilized to produce a map of the predicted dominant tree species in Goseong-gun (North Korea).

scholarly journals ­ Predicting Pinus monophylla forest cover in the Baja California desert by remote sensing

2018 ◽  
Author(s):  
Jonathan G Escobar-Flores ◽  
Carlos A Lopez-Sanchez ◽  
Sarahi Sandoval ◽  
Marco A Marquez-Linares ◽  
Christian Wehenkel
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Baja California ◽  
Arid Zones ◽  
Climate Variables ◽  
Pinus Monophylla ◽  
Single Leaf ◽  
Sentinel 2A ◽  
Sentinel 2

Background. The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world's only 1-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This subspecies is distributed as a relict in the geographically isolated arid Sierra La Asamblea at elevations of between 1,010 and 1,631 m, with mean annual precipitation levels of between 184 and 288 mm. The aim of this research was i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea, Baja California (Mexico) by using Sentinel-2 images, and ii) to test and describe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that i) Sentinel-2 images can be used to predict the P. monophylla distribution in the study site due to higher resolution (x3) and increased number of bands (x2) relative to Landsat-8 , and ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine where conifers can become established and persist. Methods. An atmospherically corrected a 12-bit Sentinel-2A MSI image with ten spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index. Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multivariate binominal logistical regression and Random Forest classification including cross valuation (10-fold) were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Results. We estimated, using supervised classification of Sentinel-2 satellite images, that P. monophylla covers 6,653 ± 319 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). The ruggedness was the most influential environmental predictor variable and indicated that the probability of P. monophylla occurrence was higher than 50% when the degree of ruggedness was greater than 17.5 m. When average temperature in the warmest month increased from 23.5 to 25.2 °C, the probability of occurrence of P. monophylla decreased. Discussion. The classification accuracy was similar to that reported in other studies using Sentinel-2A MSI images. Ruggedness is known to generate microclimates and provides shade that decreases evapotranspiration from pines in desert environments. Identification of P. monophylla in the Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.

scholarly journals ­ Predicting Pinus monophylla forest cover in the Baja California desert by remote sensing

2018 ◽  
Author(s):  
Jonathan G Escobar-Flores ◽  
Carlos A Lopez-Sanchez ◽  
Sarahi Sandoval ◽  
Marco A Marquez-Linares ◽  
Christian Wehenkel
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Baja California ◽  
Arid Zones ◽  
Climate Variables ◽  
Pinus Monophylla ◽  
Single Leaf ◽  
Sentinel 2A ◽  
Sentinel 2

Background. The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world's only 1-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This subspecies is distributed as a relict in the geographically isolated arid Sierra La Asamblea at elevations of between 1,010 and 1,631 m, with mean annual precipitation levels of between 184 and 288 mm. The aim of this research was i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea, Baja California (Mexico) by using Sentinel-2 images, and ii) to test and describe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that i) Sentinel-2 images can be used to predict the P. monophylla distribution in the study site due to higher resolution (x3) and increased number of bands (x2) relative to Landsat-8 , and ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine where conifers can become established and persist. Methods. An atmospherically corrected a 12-bit Sentinel-2A MSI image with ten spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index. Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multivariate binominal logistical regression and Random Forest classification including cross valuation (10-fold) were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Results. We estimated, using supervised classification of Sentinel-2 satellite images, that P. monophylla covers 6,653 ± 46 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). The ruggedness was the most influential environmental predictor variable and indicated that the probability of P. monophylla occurrence was higher than 50% when the degree of ruggedness was greater than 17.5 m. When average temperature in the warmest month increased from 23.5 to 25.2 °C, the probability of occurrence of P. monophylla decreased. Discussion. The classification accuracy was similar to that reported in other studies using Sentinel-2A MSI images. Ruggedness is known to generate microclimates and provides shade that decreases evapotranspiration from pines in desert environments. Identification of P. monophylla in the Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.

scholarly journals A Deep Fusion uNet for Mapping Forests at Tree Species Levels with Multi-Temporal High Spatial Resolution Satellite Imagery

2021 ◽  
Vol 13 (18) ◽  
pp. 3613
Author(s):  
Ying Guo ◽  
Zengyuan Li ◽  
Erxue Chen ◽  
Xu Zhang ◽  
Lei Zhao ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Tree Species ◽  
High Spatial Resolution ◽  
Forest Type ◽  
Species Level ◽  
Forest Inventories ◽  
Forest Classification ◽  
Plantation Species ◽  
Multi Temporal ◽  
Artificial Intelligence Technology

It is critical to acquire the information of forest type at the tree species level due to its strong links with various quantitative and qualitative indicators in forest inventories. The efficiency of deep-learning classification models for high spatial resolution (HSR) remote sensing image has been demonstrated with the ongoing development of artificial intelligence technology. However, due to limited statistical separability and complicated circumstances, completely automatic and highly accurate forest type mapping at the tree species level remains a challenge. To deal with the problem, a novel deep fusion uNet model was developed to improve the performance of forest classification refined at the dominant tree species level by combining the beneficial phenological characteristics of the multi-temporal imagery and the powerful features of the deep uNet model. The proposed model was built on a two-branch deep fusion architecture with the deep Res-uNet model functioning as its backbone. Quantitative assessments of China’s Gaofen-2 (GF-2) HSR satellite data revealed that the suggested model delivered a competitive performance in the Wangyedian forest farm, with an overall classification accuracy (OA) of 93.30% and a Kappa coefficient of 0.9229. The studies also yielded good results in the mapping of plantation species such as the Chinese pine and the Larix principis.

scholarly journals Assessing the Potential of Multi-spectral and Multi-temporal Satellite Images for Classification and Mapping of Plant Communities in a Temperate Region

2021 ◽  
Author(s):  
Ram C. Sharma ◽  
Hidetake Hirayama ◽  
Masatsugu Yasuda ◽  
Miki Asai ◽  
Keitarou Hara
Keyword(s):  
Land Cover ◽  
Plant Community ◽  
Plant Communities ◽  
Gradient Boosting ◽  
Temperate Region ◽  
Multi Temporal ◽  
Community Types ◽  
Study Sites ◽  
Sentinel 2

Classification and mapping of plant communities is an essential step for conservation and management of ecosystems and biodiversity. We adopt the Genus-Physiognomy-Ecosystem (GPE) system developed in previous study for satellite-based classification of plant communities. This paper assesses the potential of multi-spectral and multi-temporal images collected by Sentinel-2 satellites. This research was conducted in five representative study sites in a temperate region. It consists of 44 types of plant communities including a few land cover types as well. The plant community types were enumerated in the study sites and ground truth data were prepared with reference to extant vegetation surveys, visual interpretation of high-resolution images, and onsite field observations. We acquired all Sentinel-2 Level-1C product images available for the study sites between 2017-2019 and generated monthly median composite images consisting of ten spectral and twelve spectral-indices. Gradient Boosting Decision Trees (GBDT) classifier was employed as an efficient and distributed gradient boosting technique for the supervised classification of big datasets involved in the research. The cross-validation accuracy in terms of kappa coefficient varied from 87% in Oze site with 41 land cover and plant community types to 95% in Hakkoda site with 19 land cover and plant community types; with average performance of 91% across all sites. In addition, the resulting maps demonstrated a clear distribution of plant community types involved in all sites, highlighting the potential of Sentinel-2 multi-spectral and multi-temporal images with GPE classification system for operational and broad-scale mapping of land cover and plant communities.

scholarly journals Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon

Forests ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 941
Author(s):  
Adam Waśniewski ◽  
Agata Hościło ◽  
Bogdan Zagajewski ◽  
Dieudonné Moukétou-Tarazewicz
Keyword(s):  
Random Forest ◽  
Satellite Images ◽  
Forest Cover ◽  
Forest Type ◽  
Random Forest Classifier ◽  
Type Model ◽  
Spectral Bands ◽  
Freshwater Swamp ◽  
The Impact ◽  
Sentinel 2

This study is focused on the assessment of the potential of Sentinel-2 satellite images and the Random Forest classifier for mapping forest cover and forest types in northwest Gabon. The main goal was to investigate the impact of various spectral bands collected by the Sentinel-2 satellite, normalized difference vegetation index (NDVI) and digital elevation model (DEM), and their combination on the accuracy of the classification of forest cover and forest type. Within the study area, five classes of forest type were delineated: semi-evergreen moist forest, lowland forest, freshwater swamp forest, mangroves, and disturbed natural forest. The classification was performed using the Random Forest (RF) classifier. The overall accuracy for the forest cover ranged between 92.6% and 98.5%, whereas for forest type, the accuracy was 83.4 to 97.4%. The highest accuracy for forest cover and forest type classifications were obtained using a combination of spectral bands at spatial resolutions of 10 m and 20 m and DEM. In both cases, the use of the NDVI did not increase the classification accuracy. The DEM was shown to be the most important variable in distinguishing the forest type. Among the Sentinel-2 spectral bands, the red-edge followed by the SWIR contributed the most to the accuracy of the forest type classification. Additionally, the Random Forest model for forest cover classification was successfully transferred from one master image to other images. In contrast, the transferability of the forest type model was more complex, because of the heterogeneity of the forest type and environmental conditions across the study area.

scholarly journals Predicting Pinus monophylla forest cover in the Baja California Desert by remote sensing

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4603 ◽  
Author(s):  
Jonathan G. Escobar-Flores ◽  
Carlos A. Lopez-Sanchez ◽  
Sarahi Sandoval ◽  
Marco A. Marquez-Linares ◽  
Christian Wehenkel
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Baja California ◽  
Arid Zones ◽  
Climate Variables ◽  
Pinus Monophylla ◽  
Probability Of Occurrence ◽  
Single Leaf ◽  
Sentinel 2

The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world’s only one-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This tree is distributed as a relict subspecies, at elevations of between 1,010 and 1,631 m in the geographically isolated arid Sierra La Asamblea, an area characterized by mean annual precipitation levels of between 184 and 288 mm. The aim of this research was (i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea by using Sentinel-2 images, and (ii) to test and describe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that (i) Sentinel-2 images can be used to predict the P. monophylla distribution in the study site due to the finer resolution (×3) and greater number of bands (×2) relative to Landsat-8 data, which is publically available free of charge and has been demonstrated to be useful for estimating forest cover, and (ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine the sites where conifers can become established and persist. An atmospherically corrected a 12-bit Sentinel-2A MSI image with 10 spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index (NDVI). Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multiple linear binominal logistical regression and Random Forest classification including cross validation were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Using supervised classification of Sentinel-2 satellite images, we estimated that P. monophylla covers 6,653 ± 319 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed most to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). Ruggedness was the most influential environmental predictor variable, indicating that the probability of occurrence of P. monophylla was greater than 50% when the degree of ruggedness terrain ruggedness index was greater than 17.5 m. The probability of occurrence of the species decreased when the mean temperature in the warmest month increased from 23.5 to 25.2 °C. Ruggedness is known to create microclimates and provides shade that minimizes evapotranspiration from pines in desert environments. Identification of the P. monophylla stands in Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.

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