DEVELOPMENT OF ECOLOGICAL
MANAGEMENT SYSTEM FOR PLANTED
FOREST BASED ON ELM DEEP LEARNING
ALGORITHM
Zhe Wang*
School of Electronic and Electrical Engineering, Shanghai University of
Engineering Science, shanghai, 201620, China
whospurp@163.com
Reception: 18/11/2022 Acceptance: 07/01/2023 Publication: 27/01/2023
Suggested citation:
W., Zhe (2023). Development of ecological management system for
planted forest based on ELM deep learning algorithm. 3C Empresa.
Investigación y pensamiento crítico, 12(1), 165-184. https://doi.org/
10.17993/3cemp.2023.120151.165-184
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ABSTRACT
Plantations play a central and lever role in maintaining the ecological balance of the
earth, maintaining the overall function of the terrestrial ecosystem, and promoting the
coordinated development of economic society and ecological construction. In order to
strengthen the ecological management of plantation forests and improve the
ecological level of forest region, the C/S framework is taken as the basic structure,
and the programming mode of business model-user interface controller is used, on
J2EE platform. The ecological management system of a planted forest is constructed
by the evaluation module, the principal component comprehensive analysis module of
ecological function value and the demand prediction module of planted forest based
on extreme learning machine and deep learning algorithm, and runs under the support
of windows system, oracle 15G and above database software. The indexes and
factors affecting the ecological function of plantation forests were evaluated and
analyzed, and the final management decision was given by the prediction module.
The results showed that the plant density significantly affected plant biomass, organic
carbon storage, water content and nutrient accumulation, and the comprehensive
evaluation indexes of four ecological functions increased from 32.69, 31.84, 33.71 and
35.46 to 86.18, 89.46, 89.83 and 88.76, respectively. Although the degree of influence
of the system on lemon strip plants, herbaceous plants, surface litter and soil varies, it
still has good feasibility, effectiveness and practicality, and can assist the scientific
ecological management of artificial plantation forests.
KEYWORDS
Extreme learning machine; Deep learning algorithm; Plantation forests; Ecological
management system; Principal component analysis
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PAPER INDEX
ABSTRACT
KEYWORDS
1. INTRODUCTION
2. DEVELOPMENT AND DESIGN OF ECOLOGICAL
MANAGEMENT SYSTEM FOR ARTIFICIAL PLANTATION
FOREST
2.1. Evaluation module design
2.2. Design of principal component comprehensive analysis module
2.3. Design of demand prediction module of planted forest based on
extreme learning machine deep learning algorithm
3. APPLICATION OF THE SYSTEM
3.1. Overview of the study area
3.2. Selection, measurement and calculation of ecological function
evaluation index of planted forest
4. RESULTS AND ANALYSIS
4.1. Plant biomass, organic carbon storage and water content of
artificial plantation forest
4.2. Nutrient accumulation in plantation forests
5. DISCUSSION
6. CONCLUSION
7. DATA AVAILABILITY STATEMENT
REFERENCES
9. CONFLICT OF INTEREST
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1. INTRODUCTION
As the main body of the terrestrial ecosystem, the forest has always been the focus
of debate [1-2]. The function of the forest ecological environment can be direct or
indirect. It can be tangible or intangible [3]. The function of forest ecology refers to the
natural environmental conditions and effects that are formed by the forest ecosystem
and ecological process and maintained by human survival [4].
The management of forest ecology is a systematic and complex project. At present,
soil erosion, biodiversity reduction, wetland degradation, land desertification, and
other problems are still serious, and the important role of forests in maintaining
national ecological security has not been fully played [5-8]. With the development of
industrial civilization and the aggravation of global warming, the ecological functions of
forests, such as soil and water conservation, water conservation, carbon fixation and
oxygen release, air purification and environment beautification, have attracted
people's attention [9-10].
Recently, there have been many reports on forest ecological functions, especially
on water conservation, carbon sequestration and oxygen release. Therefore,
managing forest ecology objectively, dynamically and scientifically can deepen
people's environmental awareness, strengthen the leading position of forestry
construction in the ecological environment of the national economy, and improve the
level of forest ecological environment. It is of great practical significance to accelerate
the integration of the environment into the national economic accounting system and
correctly handle the relationship between social-economic development and
ecological environmental protection [11-13].
Based on the above research background, researchers in related fields are trying
to achieve good research results in forest ecological management. Such as literature
[14] according to Ukraine and the EU regulatory filing modern requirements for
environmental protection and biodiversity conservation and the suggestion, proposing
to overcome the ecological problems of radioactive pollution of forest ecosystems
from the perspective of environmental management, and create a fire prevention and
forest management system based on science, to prevent the personnel and the public
excessive exposure from various sources, Prevention of secondary diffusion of
radionuclides to relatively clean areas by fire is achieved through the use of
hydrodynamic active water extinguishers and the laying of a polyethylene guanidine
based barrier in front of the fire line. Literature [15] used mixed methods to conduct
ecological management of tropical dry forests on 11079 hectares of land in Colombia.
With the participation of 64 experts, the Delphi method was applied to conduct
quantitative research on the ecological situation from 2018 to 2020. The results
showed that all knowledge management practices identified had a certain impact on
ecological management. It also contributes to the generation, transformation, and
mobilization of scientific knowledge on each component of the ecological restoration
process of tropical dry forests. Literature [16] aims at how to realize the maximization
of the value of forest ecological resources to provide theoretical reference to the
sustainable development of the global forest resources, using the theory of ecological
capital is discussed how to define the concept of forest ecological resources
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capitalization, analyzed from two aspects of forest ecological resources capitalization
of inner motivation, and how people change the use of forest resources, the results
show that the realization path of capitalization of forest ecological resources can
provide theoretical reference for maximizing the value of forest ecological resources
and realizing the sustainable development of global forest resources. Literature [17] in
30 provinces as a sample, constructed the index system of forest ecological security
and the efficiency of forest management, with the help of the CCR model, the coupling
coordination model and the spatial panel model, from 2003 to 2017 China's provincial
forest ecological safety and the management efficiency of the forestry coupling
coordination degree and its temporal pattern characteristics and influence factors in
the measurement and analysis, The results show that, in terms of forest ecological
security, the index increases as a whole, and the coupling coordination degree
changes from near uncoordinated to intermediate coordinated. This study can provide
a theoretical reference framework for China's forest ecological management
decisions. According to the consensus on the ecological environment in previous
studies and the characteristics of the study area. Literature [18] established a
quantitative evaluation index system for the comprehensive ecological environment of
forest ecosystem nature reserve based on water, air, soil and biological environment,
constructed a weightless cloud model, and provided a weightless evaluation
mechanism. The results showed that the results of this study can provide theoretical
support for the evaluation of forest ecosystem nature reserves and general evaluation
when the weight is difficult to determine or uncertain. Literature [19] in human forest
botanical garden as the research object, from the regional environmental quality,
development conditions and the scenic area characteristic value of three standard
level selected 23 indicators, to evaluate the ecological tourism development potential,
using the analytic hierarchy process (AHP) to determine the weight of each index, and
using the fuzzy comprehensive evaluation method to three standard level and
evaluate the ecological tourism development potential, The conclusion suggested that
scenic spots should maintain their advantages, enhance tourism characteristics,
strengthen ecological civilization construction, and promote the in-depth development
of ecological tourism. The above research results have established a relatively
comprehensive and applicable forest ecological evaluation index system from different
emphases, which has a certain promoting effect on improving the forest ecological
environment and enhancing the ecological level.
Human initial afforestation is mainly in order to production needs, as a part of the
agricultural production, with the progress of social productivity, timber demand
increasing, under the natural state of forest resource components are insufficient to
satisfy the human production and life, people began planting plantation, planted forest
ecological level and function is becoming more and more attention [20]. Therefore,
this article is based on C/S architecture and the J2EE platform, in the Struts
framework with the business model - user interface - controller programming model,
under the support of construct artificial planting forest ecological management system,
through the synergy evaluation module, the ecological function value of the principal
component comprehensive analysis module and the depth of the extreme learning
machine learning algorithm of planted forests demand forecast module, Various
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indicators and factors affecting the ecological function of planted forests were
evaluated and analyzed in order to better understand the biodiversity of planted
forests and its regulation approaches and mechanisms on ecological function and to
provide scientific basis and technical means for guiding the ecological management
and sustainable management of planted forests [21].
2. DEVELOPMENT AND DESIGN OF ECOLOGICAL
MANAGEMENT SYSTEM FOR ARTIFICIAL
PLANTATION FOREST
Based on the C/S framework and J2EE platform, the business model -user
interface-controller programming mode of the struts framework is adopted and design
the environmental management system for artificial planting forests as shown in
Figure 1. The system includes an evaluation module, principal component
comprehensive analysis module of ecological function value and planting forest
demand prediction module of extreme learning machine deep learning algorithm. The
three modules work together to evaluate and analyze the indicators and factors that
affect the ecological function of planted forests and provide management and support
for decision-makers. The evaluation unit mainly includes three units: single ecological
function value evaluation of all planted forest species, total ecological function value
evaluation of single planted forest species, and total ecological function value
evaluation of all planted forest species. The system server uses windows system,
oracle 15G or above database, Tomcat6.0.67 or above server, JDK2.0 or above
development package; The client uses IE 9.0 or higher.
Figure 1. architecture diagram of the environmental management system of artificial plantation
forest
Ecological management system
for artificial planting forests
Evaluation module Principal Component
Synthesis Analysis Module
Planting demand forecast
module
Principal
compone
nt
analysis
strategies
Demand
predictio
n model
based on
extreme
learning
machine
deep
learning
algorithm
Evaluatio
n unit of
total
ecologica
l function
value of
all
planted
forest
species
Unit for
rating the
total
ecologica
l function
value of
forest
species in
a single
planted
forest
A single
ecologica
l function
value
evaluatio
n unit for
all
planted
forest
species
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2.1. EVALUATION MODULE DESIGN
The ecological function value evaluation algorithm of the four units of the evaluation
module is described as follows:
(1) Single ecological function value evaluation unit of all planted forest species: this
unit includes the ecological function of water conservation, soil conservation, and
carbon dioxide fixation of planted forest species. The value evaluation formulas of
different ecological functions are as follows:
(1)
Where, is the ecological function value of water conservation of dominant tree
species in artificial plantation forests.
(2)
Where, is the ecological function value of soil conservation of dominant tree
species in artificial plantation forests.
(3)
Where, is the ecological function value of fixed carbon dioxide of dominant
tree species in artificial plantation forests.
(2) Total ecological function value evaluation of monoculture forest species: this unit
contains the total ecological function of different dominant tree species, and the value
evaluation formula is as follows:
(4)
Where, is the corresponding ecological function value of oxygen release of
dominant tree species in the planted forest. is the ecological function value of
biological storage energy of each dominant tree species in the planted forest; is
the ecological function value of biodiversity of each dominant tree species in the
plantation.
(3) Total ecological function value evaluation of all planted forests and species: the
total ecological function value evaluation algorithm of this unit is as follows:
(5)
Evaluation module by running the business model - user interface - controller
programming software, all sorts of forest of planted forest respectively calculated
single, single planting forest ecological function value of total sorts of forest ecological
function value and all sorts of forest of planted forest ecological function value of three
indicators, namely the complete management system of planted forest ecological
function value assessment, the evaluation function of ecological management system
of artificial plantation forest was realized.
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2.2. DESIGN OF PRINCIPAL COMPONENT COMPREHENSIVE
ANALYSIS MODULE
The principal component analysis method [22] was adopted to comprehensively
and systematically understand the comprehensive strength of the ecological function
value of different planted forest types, and many indicators reflecting the ecological
function value characteristics of planted forest types were considered from different
aspects. The program flow chart of the principal component analysis algorithm is
shown in Figure 2. The specific description is as follows:
(1) Data standardization of ecological value evaluation index;
(2) The correlation between ecological value evaluation indicators;
(3) Calculate the covariance matrix of standardized data;
(4) Calculate all the eigenvalues of the covariance matrix, calculate the
eigenvectors, determine the number of principal components according to the
cumulative ratio of eigenvalues;
(5) Calculate principal component load value and factor score coefficient matrix to
determine the principal component expression; To calculate the comprehensive score
is to carry out the principal component score, sort the scores according to the size of
the score value, and output the most important first several ecological value
evaluation indicators.
With the support of Windows system, Oracle 15G or above database,
Tomcat6.0.67 or above server, JDK2.0 or above development package and other
software, the algorithm program of principal component analysis can run according to
the process shown in Figure 2. Principal component scoring is carried out according
to the obtained comprehensive score. Several important evaluation indexes of
ecological value were obtained, and the analysis function of ecological management
system of artificial plantation forest was realized.
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Figure 2. program flow chart of principal component analysis algorithm
2.3. DESIGN OF DEMAND PREDICTION MODULE OF PLANTED
FOREST BASED ON EXTREME LEARNING MACHINE DEEP
LEARNING ALGORITHM
The prediction module is mainly used for quantitative prediction and analysis of the
demand for artificial plantation forests. The change in plantation density is influenced
by the social economy, population, natural environment and other factors [23]. So
choose gross GDP ($one hundred million), at the end of the total population (ten
thousand people), non-agricultural population (ten thousand people), fiscal revenue
(one hundred million yuan), financial expenditure (one hundred million yuan), the first
industry (one hundred million yuan), the second industry (one hundred million yuan),
the third industry (one hundred million yuan), the output value of industrial output
value ($one hundred million), construction (one hundred million yuan), the per capita
net income (yuan), fixed asset investment ($one hundred million), Urban input (100
million yuan), rural input (100 million yuan), annual precipitation (100 million cubic
meters) and other indicators constitute the driving index system of the change of
artificial planting forest density (10 thousand trees/ha). There is no simple linear
relationship between each driving factor and the density of the planted forest, and
there is a correlation in time. Therefore, an extreme learning machine deep learning
Begin
Find the covariance array and the feature root
Computes feature vectors
The cumulative contribution rate is calculated,
and the number of principal components is given
Calculate the principal component score
and arrange it by size to give the result
End
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algorithm is adopted to build the demand time series prediction model with the nearest
neighbor domain theory [24] as the core, as shown in Figure 3. The prediction process
is described as follows:
(1) Extraction of training samples: in order to complete the prediction of planting
forest density at time , samples most similar to the prediction sequence
of should be extracted from all training samples as the recombination samples,
and recombination samples should be used as the input of the local model. The
process of finding the nearest neighbor is the process of measuring the similarity
between the prediction sequence and all the training samples. Through this step,
the best prediction model of planting forest density can be built.
In order to select nearest neighbors of from set as recombination samples,
a method is needed to calculate the similarity of two sets of sequences. Assume that
and are two time series:
(6)
and are difference sequences of two time series and respectively:
(7)
Assuming that is the standardized Euclidean distance between and
[25], is the standardized Euclidean distance between and , we
use the mixed Euclidean distance to calculate the similarity of the two groups of time
series:
(8)
is the mixed Euclidean distance of and . We calculated the mixed
Euclidean distance of each element in the set and , finally obtained mixed
Euclidean distances, and selected elements with the shortest distance as the
extracted training samples.
(2) Prediction model derivation of extreme learning machine deep learning
algorithm: the deep learning algorithm of extreme learning machine integrates the
idea of self-coding [26] and encodes the output by minimizing reconstruction error so
that the output can approach the original input infinitely. This structure provides an
abstract representation of the input and thus captures the deep features of the original
input. Figure 3 describes the prediction modeling process of the algorithm for the
output planting forest density at time . The extracted training sample is
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taken as the input of the network. It is assumed that the network is composed of the
hidden layer of layer, and
represents the weight parameters to
be learned in the network.
Figure 3. architecture diagram of a prediction model based on extreme learning machine deep
learning algorithm
Each layer in the network can be decoupled as an independent extreme learning
machine [27], and the target output of each extreme learning machine is equal to the
input of the extreme learning machine. In this way, the low-dimensional representation
of the input data can be obtained, that is, the hidden layer output of the extreme
learning machine, and this output can be used as the input of the next extreme
learning machine. The output weight
of the extreme learning machine is calculated
by the following formula:
(9)
Where, is the rule item;
is the output matrix of the hidden layer of an extreme
learning machine.
Then the weight parameter is calculated by the following formula:
(10)
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Finally, the feature expression is obtained, that is, the output of the hidden layer of
layer , as the hidden layer of an independent extreme learning machine, and the
output weight of the extreme learning machine is obtained by similar calculation.
The weight parameter of the network is calculated by the following formula:
(11)
According to the obtained network weight parameter , the
prediction model architecture of extreme learning machine deep learning algorithm is
defined. After the training sample data is input into the input layer of the prediction
model, layer by layer calculation is carried out according to the determined network
structure, and of planted forest density can be calculated, which provides decision
basis for ecological management of planted forest.
3. APPLICATION OF THE SYSTEM
3.1. OVERVIEW OF THE STUDY AREA
An artificial plantation forest area with an area of about 2,000 square kilometers
and a planting density of 9.64 million trees per hectare in 2017 was selected as the
research target. The research area is located at 110°13 '~111°35' east longitude and
36°46 '~37 °52' north latitude, 983-1684m altitude and mountainous area. It is a
typical hilly and gully region with complex terrain and closed traffic. The main type of
soil is the yellow soil developed from the parent material of loess, with fine particles
and deep soft soil, which is conducive to farming. The main vegetation types include
agricultural land crops, grasslands, and shrubland. The main grassland species are
alfalfa, long miscanthus, thyme, ice grass, iron pole, pig hair down, Chinese
asparagus herb, two cracks, and so on. Shrub species mainly include Salix
psammophila, peach, apricot, caragana korshinskii. Caragana korshinskii is widely
planted in this region because of its drought resistance, cold resistance, sand
resistance and good soil and water conservation. There are large typical experimental
and demonstration areas of the caragana korshinskii plantation. The system was put
into use in 2018. According to the prediction model of extreme learning machine deep
learning algorithm, the reasonable planting density of artificial planting forest in this
region is 23.24 million trees/ha. Caragana korshinskii will be planted in 2021.
3.2. SELECTION, MEASUREMENT AND CALCULATION OF
ECOLOGICAL FUNCTION EVALUATION INDEX OF
PLANTED FOREST
In order to verify the ecological management effect of the system on the region, the
ecological function of the forest region is evaluated from four aspects [28-31]: plant
biomass, organic carbon storage, nutrient accumulation, and water retention capacity.
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The evaluation system and the measurement method of secondary indicators are
shown in Table 1. The first-level indicators include (1) plant biomass: caragana
korshinskii and herbaceous aboveground biomass and litter biomass; (2) organic
carbon storage: the aboveground and ground litter of caragana korshinskii and
herbaceous plants and soil organic carbon storage; (3) total nitrogen and total
phosphorus reserves of caragana korshinskii plants and herbaceous plants
aboveground and litters; soil nutrient index: soil total nitrogen, nitrate nitrogen,
ammonium nitrogen, and available phosphorus contents; (4) water content of
caragana korshinskii plants and herbaceous plants aboveground, litter and soil.
Table 1. evaluation system and component determination method
The contents of different components were determined, and the first-order index of
ecological function was measured using average value method[32-33]. Assuming that
the actual measured value of the secondary index of sample plot is , and the
mean value and standard deviation of the secondary index among all sample plots
of the same factor are and respectively, the calculation formula for the score of
the secondary index of sample plot is as follows:
(12)
Thus, the ecological functional comprehensive index of each level index can be
deduced:
(13)
Where, is the number of all second-level indicators contained in plot .
4. RESULTS AND ANALYSIS
Primary indicators secondary indicators Determination method
biomass Aboveground biomass Model estimation method
Aboveground and litter biomass Weighing method
Organic carbon storage Aboveground and surface litter and soil
organic carbon storage
The external heating method
of potassium dichromate
oxidation
Accumulation of nutrients
Total nitrogen storage in aboveground and
litter
Concentrated sulfuric acid -
hydrogen peroxide digestion
Total phosphorus storage in aboveground
and litter
Vanadium molybdenum
yellow colorimetry
Contents of total nitrogen, nitrate nitrogen
and ammonium nitrogen in soil
Semi-trace Kjeldahl nitrogen
determination
Soil available phosphorus content
Sodium bicarbonate extraction
- molybdenum antimony
resistance colorimetric method
Water retention Water content of aboveground and litter Weighing method
Soil moisture content
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4.1. PLANT BIOMASS, ORGANIC CARBON STORAGE AND
WATER CONTENT OF ARTIFICIAL PLANTATION FOREST
The aboveground biomass of caragana korshinskii, herbaceous plants and litters
were selected as the parameters to evaluate the plant biomass of the planted forest.
Figure 4 shows that plant biomass is significantly affected by planting density of
planted forests. Before the application of ecological management system, the
aboveground biomass and litter biomass of caragana korshinskii and herbage were
only 8.19t/hm2, 6.47t/hm2 and 5.84t/hm2
, respectively. After applying the system for
ecological management, through comprehensive evaluation of the ecological function
of the sample land, they are 10.67t/hm2, 2.51t/hm2, 1.34t/hm2, 12.55t/hm2,
respectively. After the application of ecological management system, organic carbon
storage increased significantly, reaching 30.43t/hm2
in 2021, the aboveground
biomass and litter biomass of herbage increased, and the aboveground biomass and
litter biomass of herbage increased, reaching 40.64t/hm2, 8.76t/hm2 and 7.66t/hm2
in
2021, respectively. The aboveground organic carbon storage of caragana korshinskii
and herbage, litter organic carbon storage and soil organic carbon storage were
selected as parameters to evaluate the ecological function of organic carbon storage.
Caragana korshinskii, herbage, litter and soil organic carbon storage were 7.19t/hm2,
6.48t/hm2, 26.71t/hm2, respectively. Water retention is an important ecological function
of planted forests. In this study, four indexes of caragana korshinskii plants,
herbaceous plants, ground litters and soil water content were selected as parameters
to evaluate the water retention capacity of caragana korshinskii plantations. As shown
in figure 4, you can see that plantation planting density of caragana korshinskii and
plant litter moisture content is less affected, there was no significant difference before
and after the application in the system, but the herbs and soil moisture content in
2017 and 2021 significant difference under different planting density and planting
density increase, herbaceous plants and soil water content is increased greatly, in
2021, they will reach 62.58% and 59.43% respectively. To sum up, the environmental
management system is based on the results of the evaluation of the single ecological
function value of all planted forests, the total ecological function value of all planted
forests and the total ecological function value of all planted forests. By reasonably
adjusting the planting density in the forest region, the coverage rate of surface
vegetation was expanded, and the ecological function indexes were improved. The
comprehensive indexes of plant biomass, organic carbon storage and water content
of the planted forest increased from 32.69, 31.84 and 35.46 in 2017, respectively.
Increasing to 86.18, 89.46 and 88.76 in 2021, there is an obvious synergistic effect
between planting density and ecological function in the forest region, and artificial
planting forest has a better ability to provide and maintain multiple ecological functions
simultaneously.
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Figure 4. schematic diagram of ecological function change of plantation forest
4.2. NUTRIENT ACCUMULATION IN PLANTATION FORESTS
Total nitrogen and total phosphorus reserves of caragana korshinskii and
herbaceous plants, total nitrogen and total phosphorus reserves of litters, and soil
nutrient content indexes (soil total nitrogen, available phosphorus, nitrate-nitrogen,
ammonium nitrogen) were selected as the parameters to evaluate the ecological
function of nutrient accumulation in planting areas. Figure 5 (a) shows that the
planting density of caragana korshinskii significantly affected the plant total nitrogen
storage of the planted forest. The total nitrogen storage of caragana korshinskii was
significantly higher than that of herbaceous plants and litters. Before the system was
put into use, the planting density was small, and the total nitrogen reserves of
caragana korshinskii, herbaria korshinskii and litters were low, only 135.46kg/hm2,
11.13kg/hm2 and 8.54kg/hm2
. However, after the system was put into use, the total
nitrogen reserves of three kinds of plants were greatly increased. In 2021, the
ecological function of total nitrogen accumulation was 763.08kg/hm2, 25.66kg/hm2
and 51.73kg/hm2
, respectively. The ecological function of caragana korshinskii was
the most obvious before and after application of the system. Compared with total
nitrogen, the total phosphorus reserves of plants in the planted forests with different
planting densities changed more gently, but the functional degree of plants was still
that the total phosphorus reserves of caragana korshinskii were significantly higher
than that of herbaceous plants and litters. Before the use of systematic management
of forest ecology, the total phosphorus reserves of the three are 8.46kg/hm2, 1.23kg/
hm2 and 0.64kg/hm2
, respectively. After management, total phosphorus reserves
reach 36.16kg/hm2, 1.43kg/hm2 and 2.22kg/hm2
, respectively. There was no
significant difference in total phosphorus storage of herbaceous plants and litters
under different planting densities.
It can be seen from Figure 5 (b) that with the increase in plantation density, soil total
nitrogen content also showed a corresponding upward trend, but the increase was not
2017 2021
0
10
20
30
40
50
60
70
Plant biomass
t/hm
2
2017 2021
Moisture content
%
2017 2021
Organic carbon storage
t/hm
2
Caragana microphylla
Herbal
Litter
Soil
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obvious, only increased by 0.08g/kg. Therefore, the application of an ecological
management system had no significant effect on soil total nitrogen content. Before the
application of the system, the contents of soil available phosphorus, nitrate-nitrogen, and
ammonium nitrogen content were only 1.03mg/kg, 3.11mg/kg and 0.37mg/kg. When the
system gave a reasonable planting density and implemented it, the contents of the three
nutrients in the soil were as high as 13.56mg/kg, 18.38mg/kg and 10.84mg/kg,
respectively. This is because, with the support of a principal component analysis strategy
and extreme learning machine deep learning algorithm, the management system
reasonably increases the number of caragana korshinskii plants per unit area, and makes
the biomass of caragana korshinskii and litters at a high level. Therefore, it directly
promotes the increase of total nitrogen and total phosphorus reserves of caragana
korshinskii plants and litters. In addition, by increasing the surface vegetation coverage
and improving the input of soil resources, the nutrient contents of soil such as nitrogen and
phosphorus were increased, and the nutrient accumulation ecological functional
composite index of the cultivated forest increased from 33.71 in 2017 to 89.83 in 2021.
(a) plant and litter nutrient content
(b) soil nutrient content
Figure 5. schematic diagram of nutrient accumulation
2017 2021
10
20
30
40
50
140
800
Total nitrogen reserves
kg/hm
2
Caragana microphylla
Herbal
Litter
2017 2021
Total phosphorus reserves
kg/hm
2
2017 2021
0
4
8
12
16
20
Total nitrogen g/kg
Rapid available phosphorus mg/kg
Nitrate nitrogen mg/kg
Ammonium nitrogen mg/kg
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5. DISCUSSION
This study focuses on the study of the environmental management system of
artificial planting forests. The data input into the system, through the function
evaluation and prediction, helps to achieve the best combination of forest species
selection, planting area and ecological environmental benefits. The next step for
further in-depth research is as follows:
(1) Combined with 3S technology, global positioning system is used for real-time
positioning, remote sensing for data collection and update, geographic information
system for spatial analysis and comprehensive processing, so as to rapidly update
ecological related data and reduce the cost of manpower and material resources;
(2) For the model base of this study, optimization model and early warning model
need to be added in the future. From the perspective of ecological economics, it is
expected to think about how to arrange the species structure of planted forest and
analyze the optimization between its related functions and generated services.
Forewarning of the loss of ecological function of plantation forest;
(3) For the system, there is currently a lack of expert experience and practical
knowledge, which needs to be enriched and strengthened in the future. In addition,
artificial intelligence technologies such as neural networks, support vector machines
and time series analysis can also be used to acquire knowledge in the system.
6. CONCLUSION
Afforestation is one of the main ways of ecological restoration and land
reclamation. Whether the restoration mode is suitable or not needs long-term
observation to see its restoration effect. The ecological health of the planted forest
also varies with the recovery time. Therefore, this paper takes C/S as the basic
framework and J2EE as the development platform. With the support of principal
component analysis method and extreme learning machine deep learning algorithm,
the Struts framework and business model-user-interface controller programming
mode are adopted in this paper. An environmental management system consisting of
an evaluation module, principal component comprehensive analysis module and
planting demand prediction module has been developed. Through carrying out
experimental research activities on the effect of ecological management of forest
region by the system in the study area, the following three conclusions have been
obtained:
(1) Single system USES all sorts of the forest of planted forest ecological function
value evaluation, the single planting Lin total sorts of forest ecological function value
evaluation, all sorts of the forest of planted forest ecological function value evaluation
three units constructing evaluation module, to provide better practice ecological
management database and decision basis, and planted forest density and obvious
synergistic effect between different ecological functions.
(2) Using principal component analysis comprehensive system understanding of
different sorts of the forest of planted forest ecological function value of
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comprehensive strength, help considering from different sides reflects growing forest
category characteristics of many types of ecological function value indicators, the
artificial planting forests can have better ability to provide and maintain multiple
ecological functions at the same time.
(3) Based on the extreme learning machine deep learning algorithm, the demand
prediction module of planted forest designed by extreme learning machine takes the
nearest neighbor domain theory as the core, making the output infinitely close to the
original input, and giving the reasonable planting density of planted forest in this
region as 23.24 million trees/ha. The biomass, organic carbon storage, nutrient
accumulation and water content in the study area were increased to 57.06t/hm2,
70.81t/hm2, 924.38kg/hm2 and 216.03% from 20.5t/hm2, 27.07t/hm2, 171.21kg/hm2
and 130.35%. The corresponding ecological functional composite index increased
from 32.69, 31.84, 33.71, 35.46 to 86.18, 89.46, 89.83, 88.76, respectively.
7. DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/
supplementary material, further inquiries can be directed to the corresponding author.
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9. CONFLICT OF INTEREST
The authors declare that the research was conducted in the absence of any
commercial or financial relationships that could be construed as a potential conflict of
interest.
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