(2020) 20:65
Watanabe et al. BMC Ecol
https://doi.org/10.1186/s12898-020-00331-5

RESEARCH ARTICLE

BMC Ecology
Open Access

Identifying the vegetation type in Google
Earth images using a convolutional neuralnetwork: a case study for Japanese bamboo
forests
Shuntaro Watanabe1,3* , Kazuaki Sumi2 and Takeshi Ise1

Abstract
Background: Classifying and mapping vegetation are crucial tasks in environmental science and natural resource
management. However, these tasks are difficult because conventional methods such as field surveys are highly laborintensive. Identification of target objects from visual data using computer techniques is one of the most promisingtechniques to reduce the costs and labor for vegetation mapping. Although deep learning and convolutional neuralnetworks (CNNs) have become a new solution for image recognition and classification recently, in general, detectionof ambiguous objects such as vegetation is still difficult. In this study, we investigated the effectiveness of adopting
the chopped picture method, a recently described protocol for CNNs, and evaluated the efficiency of CNN for plantcommunity detection from Google Earth images.
Results: We selected bamboo forests as the target and obtained Google Earth images from three regions in Japan.
By applying CNN, the best trained model correctly detected over 90% of the targets. Our results showed that theidentification accuracy of CNN is higher than that of conventional machine learning methods.
Conclusions: Our results demonstrated that CNN and the chopped picture method are potentially powerful tools forhigh-accuracy automated detection and mapping of vegetation.
Keywords: Convolutional neural network, Vegetation mapping, Google earth imagery
Background
Classifying and mapping vegetation are essential tasks
in environmental science research and natural resource
management [1]. Conventional methods (e.g., field surveys, manual interpretation of aerial photographs),
however, are not effective for acquiring vegetation data
because they are labor-intensive and often economicallyexpensive. Remote sensing technology offers a practical and economical means to acquire information on*Correspondence: watanabe@sci.kagoshima‑u.ac.jp
1
Field Science Education and Research Center (FSERC), Kyoto University,
Kitashirakawaoiwake‑cho, Sakyo‑ku, Kyoto 606‑8502, Japan
Full list of author information is available at the end of the article

vegetation cover, especially over large areas [2]. Becauseof its ability to perform systematic observations at various scales, remote sensing can potentially enable classification and mapping of vegetation at high temporal
resolutions.
Detection of discriminating visual features is one of themost important steps in almost all computer vision problems, including in the field of remote sensing. Because
conventional methods such as support vector machines[3] require hand-designed, time-consuming feature
extraction, substantial efforts have been dedicated toward
the development of methods for the automatic extractionof features. Recently, deep learning has become a new
solution for image recognition and classification because© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) andthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material
in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material
is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds thepermitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat​iveco​
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Watanabe et al. BMC Ecol(2020) 20:65

this new method does not require the manual extractionof features.
Deep learning [4, 5] is a type of machine learning technique that uses algorithms inspired by the structure andfunction of the human brain, called artificial neural networks. Deep learning involves the learning of featuresand classifiers simultaneously, and uses training data to
categorize image content without a priori specification ofimage features. Among all deep learning-based networks,
the convolutional neural network (CNN) is the mostpopular for learning visual features in computer visionapplications, including remote sensing. Recent research
has shown that CNN is effective for diverse applications[4–7]. Given its success, CNN has been used intensively
in several distinct tasks in various academic and industrial fields, including plant science. Recent research has
shown that the CNN can successfully detect plant diseases and accurately classify plant specimens in an herbarium [8–10].
CNN is a promising technology in the field of remotesensing as well [11, 12]. In recent years, CNNs havestarted to be used for scene tagging and object detection in remote sensing images. Most previous research
employed the supervised learning method and suggested that CNNs can accurately detect or classify theobjects in remote sensing images. However, the numberof studies that use CNNs for detecting or classifying vegetation in remote sensing images is still limited. Li et al.
[13] successfully detected oil palm trees with an accuracy exceeding 96% using CNNs. Recently, Guirado et al.
[14] used CNNs to detect a wild shrub (Ziziphus lotus)
that has a wide range of shapes and sizes, from Google
Earth images. They demonstrated that CNNs can successfully detect Ziziphus lotus and provide better results
than conventional object detection methods. However,the detection targets in the existing works are vegetation
distributed in a patch form; therefore, detection or classification of vegetation that has ambiguous and amorphousshapes, such as clonal plants, is still challenging. Semantic segmentation [15] is a possible solution to address
this challenge; nevertheless, the application of semantic
segmentation for vegetation classification is still limited
because this approach requires enormous amounts oflabeled data based on pixelwise reference maps [16].
Recently, Ise et al. [17] developed an alternative
method (chopped picture method) to conveniently classify ambiguous and amorphous objects. This method
dissects the images into numerous small squares andefficiently produces the training images. By using thismethod, Ise et al. [17] correctly classified three moss
species and “non-moss” objects in test images with anaccuracy of 95%. However, this method has been applied
only to high-resolution images, and its applicability toPage 2 of 14

low-resolution images, such as remote sensing images,
has not yet been investigated.
In this study, we investigated the efficiency of adopting a deep learning model and the chopped picture
method for computer-based vegetation detection from
Google Earth images. We used bamboo forests as thetarget for vegetation detection. In recent years, bamboo
has become invasive in Japan. The bamboo species moso
(Phyllostachys edulis) and madake (Phyllostachys reticulata) are the two main types of exotic bamboo. Since the1970s, the bamboo industry in Japan has declined as aresult of cheaper bamboo imports and heavy labor costs[18]. Consequently, many bamboo plantations were left
unmanaged, eventually leading to the invasion of adjacent native vegetation [19–21].
In this study, we specifically addressed the following research questions: (1) how does the resolution ofimages affect the accuracy of detection; (2) how does the
chopping size of training images affect the accuracy ofdetection; and (3) can a model that was trained for one
geographical location work well for a different location?

Materials and methods
Target area and image acquisition

In this study, we chose three regions (Sanyo-Onoda, Ide,and Isumi) in Japan to perform the analyses (Fig. 1). We
used Google Earth as the source of imagery. Google Earthimages have various data sources, ranging from mediumresolution Landsat images to high-resolution QuickBird
satellite images. Herein, we used high-resolution QuickBird satellite images (spatial resolution: 0.65 m/pixel).
From a given sampling location, we obtained the images
at zoom levels of 1/500 (spatial resolution: ~ 0.13 m/
pixel), 1/1000 (spatial resolution: ~ 0.26 m/pixel), and1/2500 (spatial resolution: ~ 0.65 m/pixel). Each study site
was imaged from Google Earth images during October
2014 (Sanyo-Onoda), May 2017 (Ide), and January 2017
(Isumi).
Methods and background concepts for the neuralnetworks

In this study, we employed CNNs (Fig. 2), which are a
special type of feedforward neural networks that consistof several convolutional layers and pooling layers.
A feedforward neural network is an artificial neuralnetwork wherein connections between the nodes do notform a cycle. These networks, which perform modelingsimilar to the neuron activity in the brain, are generally
presented as systems of interconnected processing units(artificial neurons) that can compute values from inputs,
resulting in an output that may be used on further units.
Artificial neurons are basically processing units that compute some operations over several input variables andWatanabe et al. BMC Ecol(2020) 20:65

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Fig. 1 Target regions of this research. This figure was generated using data Global Map Japan version 2.2 (Geospatial Information Authority ofJapan)

Fully-connected layer

Convolutional layer

ReLU

Pooling layer

Convolutional layer

Pooling layer

Convolutional layer

ReLU

Pooling layer

Output layer

Input images

Convolutional layer

ReLU

Pooling layer
ReLU

Softmax function

2 - 1000 convolution and pooling layer

Extract the feature of images
Fig. 2 Schematic of the convolutional neural networks

usually have one output calculated through the activation function. Typically, an artificial neuron has a weightwi that represents the degree of connection between

Classification of images

artificial neurons, some input variables xi , and a threshold vector b. Mathematically, the total input and output
of artificial neurons can be described as follows:Watanabe et al. BMC Ecolu=(2020) 20:65

Page 4 of 14

wi xi

(1)

i

z = f (u + b) = fi

wi xi + b(2)where u, z , x , w , and brepresent the total input, output,
input variables, weights, and bias, respectively. f (•)
denotes an activation function: a nonlinear function suchas a sigmoid, hyperbolic, or rectified linear function. We
employed a rectified linear function as the activation
function, and this function is referred to as the Rectified
Linear Unit (ReLU). ReLU can be defined as follows:u(u > 0)f (u) = max{0, u} =(3)0(u ≤ 0)A CNN consists of a convolutional layer and a poolinglayer. The convolutional layer plays a role in capturing
the features from the images. In this process, a fixed-size
filter runs over the images and extracts the patterns of
shades of colors in the images. After each convolutional
layer, there are pooling layers that are created to reducethe variance of features; this is accomplished by computing some operations of a particular feature over a regionof the image.
The pooling layer has two functions. The first function is to reduce the position sensitivity of the featurethat is extracted at the convolution layer so that the output amount of the pooling layer does not change evenwhen the position of the feature amount extracted by
the convolution layer is shifted within the image. The
second function is to enlarge the receptive field for thefollowing convolutional layers. Two operations may berealized on the pooling layers, namely, max and average operations, in which the maximum and mean values
are selected over the feature region, respectively. This
process ensures that the same results can be obtainedeven when image features have small translations or
rotations, and this is crucial for object classificationand detection. Thus, the pooling layer is responsible
for sampling the output of the convolutional layer andpreserving the spatial location of the image, as well as
selecting the most useful features for the next layers.
After several convolutional and pooling layers, there
are fully connected layers, which take all neurons in theprevious layer and connect them to every single neuron
in its layer.
Finally, following all the convolution, pooling, andfully connected layers, a classifier layer may be used
to calculate the class probability of each image. We
employed the softmax function in this layer. The softmax function calculates the probabilities of each target

class over all possible target classes and is written as
follows:euk
yk = softmaxk (u1 , u2 , · · · , uK ) = K u
j
j=1 e

(4)

where k represents the number of output units and u represents input variables.
To evaluate the performance of the network, a loss
function needs to be defined. The loss function evaluates the effectiveness of the network in modeling thetraining dataset. The objective of training is to minimize the error of the loss function. The cross entropy ofthe softmax function is defined as follows:

E=−

K
N 


tn,k logyk(5)

n=1 k=1

where t denotes the vector for the training data, K
represents the possible class, and N represents the total
number of instances.
Approach

A schematic of our approach is shown in Fig. 3. We prepared the training data by using the chopped picture
method [17]. First, in this method, we collected images
that were (a) nearly 100% covered by bamboo and (b) notcovered by bamboo. Next, we “chopped” these images into
small squares with 50% overlap both vertically and horizontally. Finally, we used the chopped images as training
images. Details about the size and number of training
images for each study site are presented in Table 1.
We created a model for image classification from a
CNN for bamboo forest detection. As opposed to theconventional approaches for training classifiers withhand-designed feature extraction, the CNN learns thefeature hierarchy from pixels to classifiers and trains
layers jointly. We used the final layer of the CNN model
to detect the bamboo coverage from Google Earthimages. First, we randomly shuffled all images to avoid
overlapping of the training data and validation data.
Then, we used 75% of the obtained images as training
data and the remaining 25% as validation data.
We used the LeNet network [22], which is a classicaldeep learning model, because this study uses small-size
images as the training and validation data. This network is constructed with two convolutional layers, twopooling layers, and a fully connected layer. The networkarchitecture is discussed in Table 2. The model parameters implemented in this study included the number oftraining epochs (30), learning rate (0.01), train batch size(64), and validation batch size (32).

Watanabe et al. BMC Ecol(2020) 20:65

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Fig. 3 Outline of the approach adopted in this research. This figure was generated using data from Google Earth images (Image data: ©2018 CNES/
Airbus & Digital Globe)

Table 1 Size and number of training images used in eachexperiment
Zoom level

1/500

1/10001/2500Chopping size (pix)

28

56

84

28

14

Sanyo-Onoda

67398

16207

6866

16000

16000

Ide
Isumi

79043

18653

2836

18000

18000

64106

15083

2837

15000

15000

Table 2 Architecture of the network used in this study
Layer name

Filter size

Numberof output
neurons

Convolution 1

5×5

20

5×5

50

Pooling 1
Convolution 2
Pooling 2
Fully connected 1

Evaluation of learning accuracy

The model was validated in each learning epoch byusing the accuracy and loss functions. “Accuracy” indicates the accuracy of the model in classifying the validation images, whereas “loss” represents the inaccuracyof prediction by the model. If model learning is successful, loss (val) is low and accuracy is high. However,
if loss (val) becomes high during learning, this indicatesover fitting.
Evaluation of model performance

To evaluate the performance of the model, we used theconfusion matrix. First, we obtained 20 new images,
which were uniformly covered by bamboo forest or
objects other than bamboo forest, from each study site.
In addition, 20 new images were sampled from multiple locations that were at least 5 km from the locations
where the training images for each study site were sampled from. Second, we re-sized the images by using the
chopped picture method without overlap. Third, we randomly sampled 500 images from the re-sized images.
Fourth, we applied the model to the sampled images and

2×2
2×2

500

Fully connected 2

2

evaluated the classification accuracy. Finally, we categorized the classification results into four groups: true positive (TP), false positive (FP), false negative (FN), and true
negative (TN). Next, we calculated the classification accuracy, recall rate, and precision rate by using the following
equations:Classification accuracy= (TP + TN )/(TP + TN + FP + FN )(6)

Recall rate = TP/(TP + FN )(7)

Precision rate = TP/(TP + FP)(8)

Influence of image resolution on the classification accuracy

To quantify the influence of image resolution on theaccuracy of detection, we constructed a model that correspond to image resolution. We obtained images at a

Watanabe et al. BMC Ecol(2020) 20:65

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zoom level of 1/500 (spatial resolution: ~ 0.13 m/pixel),
1/1000 (spatial resolution: ~ 0.26 m/pixel), and 1/2500(spatial resolution: ~ 0.65 m/pixel) from each study site.
Next, we applied the chopped picture method. To adjust
the spatial extent of each chopped image, we chopped 56,
28, and 14 pixels for the 1/500, 1/1000, and 1/2500 levels,
respectively. After constructing the model, we applied it
to the 500 new images and calculated the classification
accuracy, recall rate, and precision rate.

object-specific texture measures based on grey-level cooccurrence matrix (GLCM) as an image feature for SVM.
To calculate the GLCM texture, we first calculated theluminance value of the image, which is accomplished bydefining the weights for R, G, and B. Then, five GLCM
texture measures of mean, variance, contrast, homogeneity, and dissimilarity were calculated. We employed
RBF kernels for SVM classification and determined theparameters (gamma and cost) by a grid search method.

Influence of chopping grid size on the classification
accuracy

Robustness assessment

To quantify the influence of spatial extent of the chopping
grid on the accuracy of detection, we chopped the 1/500
resolution images (spatial resolution: ~ 0.13 m/pixel)
at each study site for three types of pixel sizes (84, 56,
28). After constructing the model, we applied it to newimages and calculated the classification accuracy, recall
rate, and precision rate. Note that our method does not
perform dense prediction; therefore, the resolution of the
final image will be lower than that of the original image.
We considered this to be acceptable for the purpose ofthis study, because a previous study [23] has reported
that more than 90% of the bamboo forests are distributed
as patches of size greater than 5000 m2 in Japan.
Transferability test

Given the large variations in the visual appearance ofbamboo forest across different cities, it is of interest to
study to what extent a model trained for one geographical location can be applied to a different geographicallocation. As such, we conducted experiments in which
we trained a model for one (or more) city and applied it
to a different set of cities. To test the transferability, weused 10 new 1/500 resolution images, and evaluated theperformance of the model based on the classification
accuracy, recall rate, and precision rate.
Comparison with existing machine learning method

We compared the classification performance of our
method with that of support vector machine (SVM), a
supervised nonparametric classification algorithm. The
SVM classification algorithm is commonly employed
in remote sensing for a range of applications [24]. The
SVM classifier tries to find the optimal hyperplane inn-dimensional classification space with the highest margin between classes.
In this experiment, we provided 56 pixel-chopped
1/500 resolution images for each study site as training
images. Then, we applied the model to newly sampled
500 images and evaluated the classification accuracy,
recall rate, and precision rate. We used RGB value andWe evaluated the classification performance of our
method using different amounts of training data. In thisexperiment, we provided 15000, 1000, and 100 pixelchopped images for each study site as training images.
Then, we evaluated the classification accuracy, recall rate,
and precision rate using 500 images that were randomly
sampled from the images used in model performance
evaluation.

Results
Fluctuation of accuracy and loss during the learning
epochs

The accuracy for classifying the validation data of the
final layer ranged from 94 to 99%, with an average of
97.52%. The loss values for the validation data ranged
from 0.008 to 0.214, with an average of 0.086 (Fig. 4). The
values of accuracy increased and those of loss decreased
following the learning epochs (Fig. 4). These results suggest that all the models were not overfitted to the datasets
and successfully learned the features of chopped pictures.
Influence of image resolution on the classification accuracy

The classification accuracy ranged from 74 to 93%(Fig. 5a). The recall rate and precision rate for bamboo
forest ranged 49% to 90% and 94% to 99%, respectively(Fig. 5b, d), and those for objects other than bamboo forest ranged from 94 to 99% and 66% to 91%, respectively(Fig. 5c, e). The classification accuracy and recall rate
for bamboo forest declined following the decline in theimage resolution (Fig. 5a, b).
Influence of chopping grid size on the classification
accuracy

The classification accuracy ranged from 82 to 93%(Fig. 6a). The recall rate and precision rate for bamboo
forest ranged from 72 to 90% and 89% to 99%, respectively (Fig. 6b, d), and those for objects other thanbamboo forest ranged from 89 to 99% and 75% to 91%,
respectively (Fig. 6c, e). The intermediate-size images (56pixels) exhibited the highest classification accuracy for allsites (Fig. 6a).

Watanabe et al. BMC Ecol(2020) 20:65

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15

20

25

0.8
0

5

10

15
Epocs

1/10001/1000Loss (val)

60

25

30

20

25

30

20

25

30

0.0

40

20

0.8

Epocs

15

20

25

30

0

5

10

15

Epocs

Epocs

1/25001/2500Loss (val)

60
20

0.0

40

0.8

10

0.4

5

80 1000

Accuracy (%)

0.4

30

0.4

10

80 100520

Accuracy (%)

0All
Sanyo-Onoda
Ide
Isumi

0.0

40

All
Sanyo-Onoda
Ide
Isumi

Loss (val)60

80 1001/500

20Accuracy (%)

1/500

0

5

10

15

20

25

30

0

5

Epocs

10

15
Epocs

Fig. 4 Accuracy and loss (val) of the model at each learning epochComparison with SVM

Robustness assessment

Our results show that the CNN classifier outperformed
the SVM classifier (Fig. 7). The classification accuracyof CNN ranged from 86 to 88%, whereas that of SVM
ranged from 74 to 85% (Fig. 7). The recall rate of CNN
ranged from 79 to 92%, whereas that of SVM ranged
from 71 to 91% (Fig. 7). The precision rate of CNN
ranged from 82 to 97%, and that of SVM ranged from 77
to 93% (Fig. 7).

The classification accuracy of the model trained with
15000 images ranged from 88.4% to 92.9% (Fig. 8). The
recall rate and precision rate for bamboo forest ranged
from 80.4% to 90.4% and 93.9% to 98.8%, respectively(Fig. 8). The classification accuracy of the model trainedwith 1000 images ranged from 86 to 88% (Fig. 8). In
this case, the recall rate and precision rate for bamboo
forest ranged from 79% to 92.4% and 82.6% to 97.0%,

(2020) 20:65

Page 8 of 14b Bamboo forest

100
20
0

0

0

Isumi

Sanyo-Onoda Ide

Isumi

Site

Site

d Bamboo forest

e Objects other than bamboo forest

Sanyo-Onoda Ide

Isumi

Site

1/10001/2500

20

40

60

80

100

1/500

0

0

20

40

60

Precision rate (%)

80

100

60

Recall rate (%)

60
20

40

Recall rate (%)

80

80

100
80
60
40
20

Accuracy (%)

Sanyo-Onoda Ide

Precision rate (%)

c Objects other than bamboo forest

100

a Classification accuracy

40

Watanabe et al. BMC Ecol

Sanyo-Onoda Ide

Isumi

Sanyo-Onoda Ide

Site

Isumi

Site

Fig. 5 Sensitivity of the image scale versus the test accuracy

respectively (Fig. 8). The classification accuracy of themodel trained with 100 images ranged from 76.6% to82.4% (Fig. 8), in which case the recall rate and precision rate for bamboo forest ranged from 71.2% to 88.8%
and 76.4% to 90.1%, respectively (Fig. 8). Note that evenwhen the number of training images was decreased to100, a classification accuracy of greater than 75% wasmaintained.

Transferability and classification performance

In general, the performance of the model was poor when
it was trained with samples from a given city and tested
with samples from a different city (Fig. 9a). When themodel trained with the images of Isumi city was applied
to the other cities, the recall rate was the worst (Fig. 9b).
Conversely, the model trained with the images of Sanyocity had the highest recall rate (Fig. 9b). We noticed that

(2020) 20:65

Page 9 of 14b Bamboo forest

100
20
0

0

0

Isumi

Sanyo-Onoda Ide

Isumi

Site

d Bamboo forest

e Objects other than bamboo forest

Sanyo-Onoda Ide

Isumi

Site

28pix

56pix

84pix

80
60
40
20
0

0

20

40

60

Precision rate (%)

80

100

Site

100

60

Recall rate (%)

60
20

40

Recall rate (%)

80

80

100
80
60
40
20

Accuracy (%)

Sanyo-Onoda Ide

Precision rate (%)

c Objects other than bamboo forest

100

a Classification accuracy

40

Watanabe et al. BMC Ecol

Sanyo-Onoda Ide

Isumi

Sanyo-Onoda Ide

Site

Isumi

Site

Fig. 6 Sensitivity of the pixel size versus the test accuracy

a more diverse set (all) did not yield a better performance
when applied at different locations, compared with themodels trained on individual cities (Fig. 9).

Discussion
In this paper, we demonstrated that the chopped picture
method and CNN could accurately detect bamboo forest in Google Earth imagery. Recent studies [13, 14] haveshown that the use of CNN for processing of color (RGB)
images of the Earth surface yield high-accuracy results in

the recognition of different tree species. However, mostof the existing studies are aimed at identifying tree species at the individual tree level and cannot be applied tothe detection of vegetation with ambiguous and amorphous shapes such as clonal plants. In this study, we
employed the chopped picture method to detect bamboo
forest from Google Earth images, and our results demonstrated good performance even though we employed themost classical CNN (LeNet). Additionally, our method
outperformed SVM (Fig. 7), suggesting that the chopped

Watanabe et al. BMC Ecol(2020) 20:65

Page 10 of 14

b Ide

60
20

40

rate (%)

60
40
0

0

20

rate (%)

80 10080 100a Sanyo-onoda

accuracy

recallprec

accuracy

SiterecallprecisionSitec Isumi

40

60

SVM

0

20

rate (%)

80 100

CNN

accuracy

recallprecision

Site
Fig. 7 Comparison of classification performance between CNN and SVM

picture method and CNN would be powerful methods
for high-accuracy image-based bamboo forest detection
and vegetation mapping (Fig. 10).
Influence of image resolution on the classification accuracy

Our results indicate that the image resolution strongly
affects the identification accuracy (Fig. 5). As the resolution rate decreased, the performance of the model also
declined.
Specifically, in the 1/2500 imagery, the recall rate forbamboo forest of Sanyo-Onoda and Isumi city declined
to 59% and 68%, respectively (Fig. 5b). In contrast, theprecision rate for bamboo forest increased as the resolution decreased (Fig. 5d); this result indicates that as theresolution decreases, the model overlooks many bamboo forests. Thus, when the image resolution is low,

it is difficult to learn the features of the object, and ourapproach is critically dependent on high-resolution data,with even the 0.65 m spatial resolution data being insufficient for producing the high-quality results. This result
also suggests that in the deep learning model, misidentification due to false negatives is more likely to occur thanmisidentification due to false positives as the image resolution decreases.
Influence of chopping grid size on classification accuracy

Our results indicate that the chopping grid size also
affects the performance of the model. The classification accuracy was the highest at the medium pixel size
(56 × 56 pixels; Fig. 6a). In contrast to the effects ofimage resolution, the recall rate and precision rate forbamboo forest was also the highest at medium pixelWatanabe et al. BMC Ecol(2020) 20:65

Page 11 of 14

b Ide

60
20

40

rate (%)

60
40

0

0

20

rate (%)

80

80

100

100

a Sanyo-onoda

accuracy

recallprecisionaccuracy

SiterecallprecisionSitec Isumi
1000

100

60
40
0

20

rate (%)

80

100

15000

accuracy

recallprecision

Site

Fig. 8 Evaluation of classification performance of our model with different amounts of training images

size, except for the recall rate for Ide city (Fig. 6b, d).
This result indicates that if the grid size is inappropriate, both false positives and false negatives will
increase.
Increasing the chopping grid size results in an
increase in the number of chopped pictures, in whichbamboo and objects other than bamboo are mixed. In
this study, because we evaluated the performance of themodel by using images that were uniformly covered bybamboo forest or objects other than bamboo forest, theinfluence of imagery consisting of mixed objects on theclassification accuracy could not be evaluated. We will
evaluate the classification accuracy for such images in
our future research.

Transferability among the models

The results of the transferability tests show that transferability was generally poor, and they suggest that the
spatial extent of acquisition of the training data strongly
influences the classification accuracy (Fig. 8). The modeltrained by Sanyo-Onoda city images yielded high recall
rates for the images acquired at all study sites; however,the precision rate was lower than that of the other models (Fig. 8b, c). This means that the model trained by
Sanyo-Onoda city images tends to produce false positive errors. Interestingly, transferability was not found to
be related to the distance among the study sites (Fig. 8).
This result indicates that the classification accuracy acrossthe model reflects the conditions at the local scale, suchWatanabe et al. BMC Ecol(2020) 20:65

Page 12 of 14

no
da

73.5

97.9

72.4

55.1

87

87.3

80.2

26.6

87.4

47.8

98

47

10.2

85.6

79.4

65.2

da

um
i

Trained model

no
Sa

ny

o-

O

i
um
Is

Id

e

O
Sa

ny

o-

um
Is

Id

e

i

no

da

um
i

88

Sa
ny
oO

91.8

98.6

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Trained modelb Recall rate

Al
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79

93.6

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um

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Fig. 9 Transferability of the models trained for one location, but applied at anotheras the weather conditions at the time when the image was
acquired. Additionally, even when we applied a modelthat learned from all training images, the performance ofthe model was not as good as when the training data wereobtained from the same city. The same tendencies havebeen reported in studies that classified land use by using
deep learning [25]. This suggests that increasing the quantity of training data could cause a decrease in the identification accuracy, and it may be difficult to construct anidentification model that is applicable to a broad area.

Watanabe et al. BMC Ecol(2020) 20:65

Page 13 of 14

images or inaccurate information due to local weather
conditions [28]. A possible way to overcome such difficulties is to use higher resolution satellites such as WorldView–4. It is expected that the classification accuracyof our approach will be further improved by using evendeeper neural networks and images obtained by higher
resolution satellites such as WorldView–4 in the future.
Abbreviations
CNN: Convolutional neural network; SVM: Support vector machine.
Acknowledgements
We are very grateful to anonymous reviewer for constructive comments.
Authors’ contributions
SW designed the research based on discussion with TI. SW and KS conducted
research. SW and TI wrote the manuscript. All authors read and approved thefinal manuscript.

Fig. 10 Example of applying the model to the wide area of Ide city.
The left image is the original Google Earth image, and the right image
shows the results of bamboo forest detection. Bamboo forests arehighlighted by red, and objects other than bamboo are highlighted
by green. This figure was generated using data from Google Earthimage (Image data: ©2018 CNES/Airbus & Digital Globe)

Conclusion
We demonstrated that the deep learning model presented herein can detect bamboo forest from Google
Earth images accurately. Our results suggested that the
CNN technique and the chopped picture method would
be powerful tools for high-accuracy image-based vegetation mapping, and exhibit great potential for reducing
the efforts and costs required for vegetation mapping aswell as improving the current status of monitoring the
distribution of bamboo. Recently, bamboo expansion has
become an important problem in Japan because of its
invasiveness [18]. Although some studies have analyzed
the bamboo forest distribution probability on a national
scale [26, 27], monitoring of bamboo expansion is stillchallenging because it is labor-intensive. Nonetheless,our method has a certain degree of robustness in reducing the amount of training data (see Fig. 8). Therefore,
we conclude that our approach could potentially lead tothe creation of a semi or even fully automated system formonitoring bamboo expansion. Our results also suggestthat the identification accuracy depends on the image
resolution and chopping grid size; especially, the spatial
resolution of training data strongly affects the model
performance. Although satellite-based remote sensing
has been widely studied and applied, it still has problems
such as insufficient information due to low-resolution

Funding
This work was supported by JST PRESTO, Japan (Grant No. JPMJPR15O1), JSPS
KAKENHI (Grant no. JP18H03357) and the Re:connect Program of the Nippon
Foundation—Kyoto University Joint Project.
Availability of data and materials
This study was a re-analysis of existing data that are publicly available. Data on
the Google Earth images openly available at https​://earth​.googl​e.com/web/.
The Caffe deep learning framework can be accessed at http://caffe​.berke​
leyvi​sion.org/. The glcm packages and e1071 package [29] in R were used for
calculating the GLCM texture and SVM implementation. These packages canbe accessed at https​://cran.r-proje​ct.org/web/packa​ges/glcm/index​.html andhttps​://cran.r-proje​ct.org/web/packa​ges/e1071​/index​.html
Ethics approval and consent to participate
This study included no data or analysis requiring special consent to conduct
or to publish.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Field Science Education and Research Center (FSERC), Kyoto University,
Kitashirakawaoiwake‑cho, Sakyo‑ku, Kyoto 606‑8502, Japan. 2 Graduate Schoolof Agriculture, Kyoto University, Kitashirakawaoiwake‑cho, Sakyo‑ku, Kyoto
606‑8502, Japan. 3 Graduate School of Science and Engineering, Kagoshima
University, 1‑21‑40 Korimoto, Kagoshima 890‑0065, Japan.
Received: 17 July 2019 Accepted: 19 November 2020

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