Definition of immediate reward function
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-signs-new-contract-with-cepiec-china-for-distribution-of-open-access-books-20210319",title:"IntechOpen Signs New Contract with CEPIEC, China for Distribution of Open Access Books"},{slug:"150-million-downloads-and-counting-20210316",title:"150 Million Downloads and Counting"},{slug:"intechopen-secures-indefinite-content-preservation-with-clockss-20210309",title:"IntechOpen Secures Indefinite Content Preservation with CLOCKSS"},{slug:"intechopen-expands-to-all-global-amazon-channels-with-full-catalog-of-books-20210308",title:"IntechOpen Expands to All Global Amazon Channels with Full Catalog of Books"},{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"}]},book:{item:{type:"book",id:"1998",leadTitle:null,fullTitle:"Water Quality Monitoring and Assessment",title:"Water Quality",subtitle:"Monitoring and Assessment",reviewType:"peer-reviewed",abstract:"The book attempts to covers the main fields of water quality issues presenting case studies in various countries concerning the physicochemical characteristics of surface and groundwaters and possible pollution sources as well as methods and tools for the evaluation of water quality status. 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Its complicated working mechanism includes physical change and chemical reaction of material, procedure of combustion, thermal transmission among gaseous fluid, solid material fluid and the liner. The automation problem of such processes remains unsolved because of the following inherent complexities. A rotary kiln is a typical distributed parameter system with correlative temperature distribution of gaseous phase and solid phase along its axis direction. Limited by device rotation and technical design, sensors and actuators can be installed only at the kiln head and kiln tail, and lumped parameter control strategies are employed to deal with distributed parameter problems. Thus the rotary kiln process is a multivariable nonlinear system with strong coupling, large lag and uncertain disturbances. Moreover, the key controlled variable of burning zone temperature is measured with serious disturbances. Most of rotary kilns are still under manual control with human operator observing the burning status. As a result, the product quality is hard to be kept consistent and energy consumption remains high, kiln liner is easy to wear out, the kiln running rate and yield is low.
\n\t\t\tAlthough several advanced control strategies including fuzzy control (Holmblad & Østergaard, 1995), intelligent control (Jarvensivu et al., 2001a; Jarvensivu et al., 2001b) and predictive control (Zanovello & Budman, 1999) have been introduced into process control of rotary kiln, all these researches focused on stabilizing some key controlled variables but are valid only for cases that boundary conditions do not change frequently. As a matter of fact, the boundary conditions of a rotary kiln often change. For example, the material load, water content and components of the raw material slurry vary frequently and severely. Moreover, the offline analysis data of components of raw material slurry reach the operator with large time delay. Thus conventional control strategy cannot reach automatic control and keep the product quality consistent. To deal with the complexity of operation conditions, the authors have proposed an intelligent control system based on human-machine interaction for an alumina rotary kiln in (Zhou et al., 2004; Zhou et al., 2006), in which human intervention function was design so that, if the operation condition changed largely, the human operator observing burning status can intervene the control actions when the system is in the automatic control mode to enhance the adaptability of the control system.
\n\t\t\tThis chapter develops a supervisory control approach for burning zone temperature based on Q-learning, in which the signals of human intervention are viewed as the reinforcement learning signals. Section 2 makes brief descriptions of process and supervisory control system architecture. Section 3 discusses the detailed methodology of Q-learning-based supervisory control approach. The implementation and industrial applications are shown in Section 4. Finally, Section 5 draws the conclusion.
\n\t\tThe alumina rotary kiln process is described as follows. Raw material slurry is sprayed into the rotary kiln from upper end (the kiln tail). At the lower end (the kiln head), the coal powders from the coal injector and the primary air from the air blower are mixed into bi-phase fuel flow, which is sprayed into the kiln head hood and combusts with the secondary air, which comes from the cooler. The heated gas was brought to the kiln tail by the induced draft fan, while the material moves to the kiln head via the rotation of the kiln and its self weight, in counter direction with the gas. After the material passes through the drying zone, pre-heating zone, decomposing zone, burning zone and cooling zone in sequence, soluble sodium aluminate is generated in the clinker, which is the product of the kiln process. This process aims to reach high digesting rate of alumina in the following digestion procedure.
\n\t\t\tSchematic diagram of the alumina rotary kiln.
The control problem of quality index of kiln production is how to keep the liter weight of clinker being qualified under fluctuated boundary conditions and operating conditions. The liter weight of clinker is hard to measure online and cannot be controlled directly. This paper employs the following strategy to deal with this problem. Some online measurable technologic parameters with closed relations to the final quality index are chosen and controlled into certain ranges governed by technical requirement so that the quality index control is realized indirectly.
\n\t\t\tIn the sintering process, the normal range of sintering temperature
where [
From above analysis, one may conclude that there are two key issues about the control problem of quality index of kiln production. One is how to keep the kiln temperature distribution satisfing technical requirement under fluctuated boundary conditions and operating conditions, i.e. how to keep burning zone temperature, kiln tail temperature and residual oxygen content in combustion gas in their technical required ranges. The other is how to adjust the setpoint range of burning zone temperature so that the liter weight of clinker may be kept qualified under fluctuated boundary conditions and operating conditions.
\n\t\t\tGeneral structure of the supervisory control system for rotary kiln process.
This paper has constructed a supervisory control system consisting of a supervisory level and a process control level, whose general structure is shown in Fig. 2. The final target of this supervisory control system is to keep the production quality index, i.e. the clinker unit weight, being acceptable even if the boundary conditions changed. The related process control strategies in process control level include, 1) a hybrid intelligent temperature controller was designed, which coordinated the coal feeding
The main part of the supervisory level is an intelligent setting model of
The intelligent setting model of burning zone temperature consists of a pre-setting model of burning zone temperature, a compensation model and a setting selector mechanism. The pre-setting model is to give the upper and lower limits of setpoint range of burning zone temperature, denoted by \n\t\t\t\t
\n\t\t\t\t
As a matter of fact, the main problem we are facing is that the components of raw material slurry often change due to unstable raw material mixing process and the offline analysis data reach to the operator with large time delay so that the operator or the pre-setting model cannot directly adjust the setpoint of
To deal with such a problem, a compensation model and a setting selector are appended. When the offline analysis data of components of raw material slurry are known and input into the system, i.e. the \n\t\t\t\t
\n\t\t\t\t
Reinforcement learning is learning with a critic instead of a teacher. The only feedback provided by the critic is a scalar signal
Q-learning (Watkins & Dayan, 1992) is a reinforcement learning method where the learner builds incrementally a Q-function which attempts to estimate the discounted future rewards for taking actions from given states. The output of the Q-function for state \n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
where
\n\t\t\t\twhere \n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
In this section, we may design an online self-learning system based on reinforcement learning to gradually establish the optimal policy of setpoint adjustment of
For the learning system, the environment includes the rotary kiln process, the temperature controller and the operator. The environment provides current states and reinforcement payoffs to the learning system. The learning system produces the compensated upper and lower limits of setpoint range of \n\t\t\t\t\t
Schematic diagram of setpoint adjustment approach for
In a Markov decision process (MDP), only the sequential nature of the decision process is relevant, not the amount of time that passes between decision stages. A generalization of this is the semi-Markov decision process (SMDP) in which the amount of time between one decision and the next is a random variable. For the learning process, we define \n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
The design of the learning system concerns the following key issues:
\n\t\t\t\tConstruction of the environment perception state set;
\n\t\t\t\tDetermination of the action set;
\n\t\t\t\tDetermination of the immediate reward function;
\n\t\t\t\tDetermination of the learning algorithm.
\n\t\t\tWhen components of raw material slurry fluctuate and related offline analysis data are unavailable, we hope that the learning system can estimate the changes of the components of raw material slurry through the percepted information about the environment state. From this idea, some related variables are selected from online measurable variables of the kiln process based on human experience, with which the state vector \n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
where\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
Since the state space \n\t\t\t\t\t
By identifying one partition per possible feature vector, the feature extraction mapping \n\t\t\t\t\t
where \n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
Each feature function maps the state space \n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
Because the compensation model for the setpoint of burning zone temperature needs only to be applicable for the normal kiln operating conditions, the design of state set needs certain filtration in the feature space\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
The learning system aims to deduce the proper or best actions of setpoint adjustment of \n\t\t\t\t\t
\n\t\t\t\t\t
During \n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
r | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t0.4 | \n\t\t\t\t\t\t\t0.4 | \n\t\t\t\t\t\t\t0.4 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t-0.2 | \n\t\t\t\t\t\t\t0.2 | \n\t\t\t\t\t\t\t-0.4 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t-0.2 | \n\t\t\t\t\t\t\t-0.4 | \n\t\t\t\t\t\t\t0.2 | \n\t\t\t\t\t\t
Definition of immediate reward function
where \n\t\t\t\t\t
\n\t\t\t\t\t
and \n\t\t\t\t\t
\n\t\t\t\t\t
The immediate reward function
During\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
The whole learning algorithm of the learning system under learning mode is summarized as follows:
\n\t\t\t\tStep 1: If it is in initialization, then the Q value table of state-action pairs is initialized according to expert experience, otherwise goto step 2 directly;
\n\t\t\t\tStep 2: During\n\t\t\t\t\t
\n\t\t\t\t\t
Step 3: Search in the Q table to make state matching, if unsuccessful then goto step 2 to make state judgement again, if successful then go ahead;
\n\t\t\t\tStep 4: The action selector chooses an amendment of setpoint of \n\t\t\t\t\t
Step 5: During\n\t\t\t\t\t
Step 6: When the current \n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
Step 7: Judge if the learning should be finished. When all evaluation values of state-action pairs in the Q table do not change obviously, it means that the Q-function have converged, and the compensation model is well trained.
\n\t\t\t\tThe problem of Q table initialization: there is no explicit tutor signal in reinforcement learning, the learning procedure is carried out through constant interaction with environment to get the reward signals. Usually, less information from environment will results low learning efficiency of reinforcement learning. In this paper different initial evaluation values are given for different actions under same state based on expert experience so that the convergence of the algorithm has been speedup, and online learning efficiency has been enhanced.
\n\t\t\tThe main task of the learning system is to estimate the variations of the kiln operating conditions continuously, and to adjust the setpoint range of burning zone temperature accordingly. Such adjustments should be made when the burning zone temperature is fairly controlled smooth by the temperature controller. Such a judgment signal is given out from the hybrid intelligent temperature controller. If the temperature control is in the abnormal conditions, the learning procedure must be postponed. In this case the setpoint range of the burning zone temperature is kept constant.
\n\t\t\t\tMoreover, setpoint adjustments should be made when the learning system make accurate judgment about the kiln operating conditions. Because of complexity and fluctuation of kiln operating conditions, accurate judgment for current state usually needs long time, and the time span between two setpoint adjustments cannot be too short, otherwise the calculated immediate reward cannot reflect the real influence of the above adjustment upon the behaviour and performance of the control system. Thus special attention should be paid to selection of \n\t\t\t\t\t
\n\t\t\t\t\t
After long term running, large characteristic changes of components of raw material slurry, coal and kiln device may appear. The previous optimal designed compensation model for the setpoint of burning zone temperature might become invalid under new operating conditions. This needs new optimal design to keep good performance of control system for long term. In this case, the reinforcement learning system should be switched into the learning mode, and above models can be established through new learning to improve the performance, so that the control system has strong adaptability for long term running. This is an important issue drawing the attentions of the enterprise.
\n\t\t\tShanxi Alumina Plant is the largest alumina plant in Asia with megaton production capacity. It has 6 oversize rotary kilns of φ4.5×110m. Its production employs the series parallel technology of Bayer and Sintering Processes. Such a production technology makes components of the raw material of rotary kilns often vary in large range. It is more difficult to keep a stable kiln operation than ordinary rotary kiln.
\n\t\t\tA supervisory control system has been developed in the #4 rotary kiln of Shanxi Alumina Plant based on the proposed structure and the setpoint adjustment approach of burning zone temperature. It is implemented in the I/A Series 51 DCS of Foxboro. The Q-learning-based strategy has been realized in the configuration environment of Fox Draw and ICC of I/A Series 51 DCS. Related parameters are chosen as\n\t\t\t\t
\n\t\t\t\t
The setpoint of burning zone temperature is properly adjusted after learning.
\n\t\t\t\tFig. 4 shows the condition that, after a period of learning, a set of relatively stable strategies of setpoint adjustment has been established so that the setpoint range of
The adjustment actions of the above reinforcement learning system result in satisfactory performance of the kiln temperature controller, with reasonable and acceptable regulation amplitude of coal feeding and regulation rhythm, so that the adaptability for variations of operating conditions has been significantly enhanced and the production quality index, liter weight of clinker, can be kept to reach the technical requirement even if the boundary conditions and operation conditions change. Meanwhile, human interventions become weaker and weaker since the model application has improved the system performance.
\n\t\t\tIn the period of test run, the running rate of supervisory control system has been up to 90%. Negative influences on the heating and operating conditions from human factors have been avoided, rationalization and stability of clinker production has been kept, and operational life span of kiln liner has been prolonged remarkably. The qualification rate of clinker unit weight has been enhanced from 78.67% to 84.77%; production capacity in unit time per kiln has been increased from 52.95t/h to 55t/h with 3.9% increment. The kiln running rate has been elevated up to 1.5%. Through the calculation based on average 10℃ reduction of kiln tail temperature and average 2% decrease of the residual oxygen content in combustion gas, it can be concluded that 1.5% energy consumption has been saved.
\n\t\tIn this chapter, we focus on the discussion about an implementation strategy of how to employ reinforcement learning in control of a typical complex industrial process to enhance control performance and adaptability for the variations of operating conditions of the automatic control system.
\n\t\t\tOperation of large rotary kilns is difficult and relies on experienced human operators observing the burning status, because of their inherent complexities. Thus the problem of human-machine coordination is addressed when we design the rotary kiln control system, and the human intervention and adjustment can be introduced. Except for emergent operation conditions that need urgent human operation for system safety, the fact is observed that human interventions to the automatic control system usually imply human’s discontent to the performance of the control system when the variation of boundary conditions occurs. From this idea, an online reinforcement learning-based supervisory control system is designed, in which the human interventions might be defined as the environmental reward signals. The optimal mapping between rotary kiln operating conditions and adjustment of important controller setpoint parameters can be established gradually. Successful application of this strategy in an alumina rotary kiln has shown that the adaptability and performance of the control system have been improved effectively.
\n\t\t\tFurther research will focus on trying to improve the setting model of the burning zone temperature by introducing the offline analysis data of clinker liter weight to reject the other uncertain disturbances in the quality control of kiln production.
\n\t\tForest is a conditional renewable resource which can be regenerated but needs a certain period of time to maintain its sustainable functioning. In India, the forest resources have been found to be depleting at a pace which is much high [1]. Rapid industrialization, urbanization and over-exploitation have resulted not only in decline but also in permanent loss of forest cover to an alarming rate [2]. The major driver behind all these factors is the uncontrolled population growth of humans which leads to the dramatic increase in the demand for wood and forest products. The over-exploitation of forest resources has taken place beyond the sustained yields to fulfil the needs of humans, thus bringing a change in the net forest cover [2, 3]. With the current rate of population explosion, the world population could be expected to increase from 7.6 billion to about 10 billion in the next 30 to 35 years [4]. The growing demand for food can be expected to rise by 50% in the given period, and it is a matter of serious concern. Rational utilization and proper management of the forest resources are the most viable ways to prevent mass destruction of forests and large-scale species extinction. It is necessary to find the links between the growing demands and meeting the demands in a sustainable manner. The scope of future studies must focus on the solution to establish this link by incorporating the principles of forestry, restoration ecology and natural resource economics.
Deforestation occurs when a land initially dominated by naturally occurring tree species is converted to provide certain services in response to the human demand. The UN Food and Agricultural Organization (FAO) has defined deforestation as
The value of forest is simple to understand but sometimes tough to quantify. The various values of the forest have been shown in Figure 1. Forest has a major contribution on the global economy and supports livelihood of the majority of rural populations in the world [7]. The direct uses of forest are most easy to quantify as it is directly related to economic returns. The indirect use and option value although play a major role in defining the valuation of the forest resources are seldom quantified and are being intangible in nature [8]. There is also a no-use value of the forest resources which considers leaving the current forest intact as a heritage for the future generation and for satisfaction and happiness of the current population. But, these eco-centric views alone cannot suffice the support for a change in policy and land use pattern. There are some other important values of forests that are difficult to quantify. One of the major roles that forests play is that it acts as a major carbon sink [9, 10, 11]. Plants utilize carbon dioxide in the process of photosynthesis and store it in the form of carbohydrates, and these carbohydrates reach the soil as dead organic matter and contribute to soil carbon sink. When forests are cleared, less CO2 is absorbed by plants, and atmospheric CO2 concentration increases with the passage of time due to unavailable sink. Also, there is a marked reduction in soil organic carbon with the loss of vegetation cover, thereby affecting the productivity of the ecosystem. Productivity is believed to be an indicator of carbon assimilation capacity, and hence the more productive the forests are, the more carbon it stores [12, 13, 14]. The tropical forests are among the most productive as well as the most threatened ecosystem in present time with the maximum rate of deforestation. Thus, it is imperative to control the rate of deforestation in order to avoid the adversities associated with it.
Different values of forests ecosystems
The value and functions of forests are immense and diverse. Similarly, the impacts of deforestation are global and commune the whole forest community. One of the major constraints in understanding the true impact of desertification on forests is the inadequacy and imprecise form of data.
The effect of large-scale deforestation is global, but it is necessary to assess its impacts on microclimate, regional climate and global climate to form a holistic understanding of mechanism [15]. The association of deforestation with the increased CO2 concentration in the atmosphere and changes in the mass balances and surface energy can result in climate change at the local and global level [16]. The change in land use pattern especially the clearing of forest cover affects both hydrometeorological and global CO2 concentrations leading to more warming as CO2 readily absorbs infrared radiation [17]. Clearing tree cover and vegetation leads to increase in albedo of the region as bare soil reflects more solar radiation than vegetation, which again is a factor for altering regional radiation flux [15, 18]. One of the noticeable changes in regional climates occurs when the cloud formation shifts to higher elevations from lowland plains as a result of deforestation in the later area [19]. In a global scale, deforestation leads to warmer and drier weather due to the synergistic effect of reduced evapotranspiration, increased albedo and CO2 concentration that triggers desertification, loss in biodiversity and melting of polar ice caps, ultimately leading to food insecurity. All these effects are successors of extremes in climate variation that are produced by the large-scale reduction in forest cover [20]. The estimated quantity of CO2 added to the atmosphere due to deforestation in the tropics is roughly two billion tonnes [21]. It is interesting to note that the CO2 emissions due to clearing of forest will almost be equivalent to 25% of what is added to the atmosphere due to anthropogenic emissions [22]. The shift in climate is somehow correlated to reducing forest cover. Further researches can clearly define the mechanisms and pathways by which these shifts are progressing and how they can be mitigated.
The global water cycle depends on the amount and distribution of precipitation for which one of the influencing factors is evapotranspiration [23]. There is a direct effect on drinking water on the basis of quality and quantity, fisheries and aquatic habitats, occurrence of flood and drought, life of dams on account of increase siltation and agriculture due to poor quality irrigation and crop yield [24, 25]. It must be recognized that the protective role of forests is operative and has a major impact on urban water resources [26]. Forests play an important role in maintaining the watersheds [27]. The degraded or degrading watersheds can be recovered by forestation, but once the forest or vegetation cover is lost, the watershed becomes vulnerable to erosion. This erosion leads to siltation in the downstream areas and consequently reduces the depth of river bed increasing the chances of flood. There are two main effects of deforestation that increase the chances of flooding. One is by reducing the tree fountain effect, i.e. interception and moisture uptake by the trees would cease after deforestation reducing the moisture holding capacity of soil that leads to greater runoff and erosion. And other is by the process of soil compaction and poor soil structure that will lead to reduced organic matter content of soil devoid of vegetation cover [28]. There are severe long-term effects on soil as a cause of deforestation. During slash-and-burn or shifting cultivation, an area of forests clears and exposes the bare soil to weather extremes of high solar insolation and heavy rainfall [29]. In the absence of the forest cover and organic matter, soil could not accommodate heavy precipitation, and the fertile layers of soil used to be easily washed away ultimately reduces the long-term productivity. The effect on soil is dependent on the interrelation and synergistic effect of evapotranspiration and infiltration that are directly altered due to loss in vegetation cover [23].
Deforestation directly increases erosion and siltation rates. There is an increased risk of flooding in Yangtze River basin in China and the major river basins in East Asia and the Amazonian basin [23, 30]. The slopes and terrains are more vulnerable to such situations. There are formations of shallow gullies which may be accounted for the concentrated flow of runoff that prevails due to long-term erosion. Cultivation and ploughing along the slopes cause rapid erosion in the areas with less vegetation cover [31]. In the Loess Plateau, the slopes of steepness greater than 15° showed shallow gully erosion as a result of cultivation activities [32]. The availability of dead vegetation can reduce the surface runoff from the early season rain and check soil erosion [33]. It is necessary to sustain the forest and vegetation cover to maintain the soil productivity and water quality of both the surface and underground sources.
Forests are very rich in biodiversity and store a vast gene pool, and the majority of species occur in the tropical forests. It consists of two-thirds of all known species and 65% of 10,000 species that are recognized as endangered by the International Union for Conservation of Nature (IUCN) [34]. The biodiversity could be regarded as an important asset that is necessary to conserve for future utilization. The World Health Organization states that the traditional people, almost 80% of them, rely on the local biodiversity for traditional medicines. A loss in the biodiversity may directly affect their health care and well-being [35]. Another most noticeable impact of deforestation is the increasing human-wildlife conflict. The decreasing forest cover is limiting the habitats of many species due to which is forcing them to intrude with human welfare [36]. There are increasing instances of invasion and animal killing. The northern margin of West Bengal, India, forms a significant portion of the Himalaya Biodiversity Hotspot. The area has observed heavy fragmentation in the last decade, and as a result, there was a huge loss in the agricultural crop and both human and elephant life due to conflicts. There are reports of mortality of 20 elephants and 50 persons annually from this area [37]. It is also estimated that if deforestation in the Himalayas continue at the current rate, the dense forest cover (>40% canopy cover) will be restricted to 10% of land area in the Indian Himalayas by 2100. This may lead to a significant loss of 366 endemic plants and 35 endemic vertebrates [38]. The loss in biodiversity is of global concern irrespective of regional and local importance. Conserving the forest and an increase in forest cover certainly find a positive correlation with the increase and sustenance of biodiversity. Conversion of forest land to agricultural fields and other land use could be a threat to major loss in biological diversity in the near future.
Forests contribute to the world economy in terms of timber production and other forest produces. There are different contributions of forest as a means of direct employment in forestry services and other value-added contributions as recreation and aesthetics. The loss of tropical forest cover annually may account for about 45 billion US dollars [39]. The destruction of forest eliminates the sources of economic gain directly obtained and also eliminates the potential gain from the resources that the forest sustains as biodiversity, soil and water. Also, the destruction of forest increases the negative externalities in the form of increasing CO2 concentration, risk of flood and human-wildlife conflict [40]. The proximity of settlements to protected areas further intensifies the chances of human-wildlife conflict (HWC). It is reported that settled households face high risk of HWC due to their close proximity of the Kanha National Park in Central India [41]. Deforestation has its social influence in the form of long-term effect. Development is a serious concern for indigenous community as it certainly leads to a change or shift of their culture and tradition. The cultural and religious aspects of the community are seldom preserved amid infrastructural advancement that leads to land and social conflict [42]. In a study of household survey from rural areas of Madhya Pradesh and Chhattisgarh state of India, it was found that the poorest of the local community gained about 30% of their living from forest produce which was claimed to be even higher than the returns from agriculture. Also, forest provides an option as safety net during the period of crisis in rural areas [43]. Various ecological services provided by the forests have been lost due to deforestation which in turn has immediate effects on the local communities dependent on these services for their daily needs. The inclusive approach for the sustainable management of forest resource is a vital consideration that considers economy as a subset of the society which in itself is a subset of the environment. Such considerations can help sustaining the constantly declining forest cover and its long-term impacts.
The forest cover was found to be increased by ca. 1% for the year 2017 which is 21.54% of the total geographic area when compared to that of forest cover status in 2015 which is a positive sign towards the constant efforts that are being put in to protect the forest (Figure 2). This positive change in the forest cover is mainly attributed to the conservation and management practices that include afforestation activities, participation of local peoples for better protection measures in plantation areas and traditional forest areas, expansion of trees outside forest, etc. Also, with this increase in the forest cover, the country has procured 8th position among the top 10 countries reporting the greatest annual forest area gain. Although there has been an increase in the total forest cover in India, still there are certain regions within the country that has sought a reduction in the same (Figure 3). The main reasons attributed for this reduction are shifting cultivation, rotational felling, other biotic pressures, diversion of forest lands for developmental activities, etc. [44]. A transition in the various forest cover classes has also occurred over the past. The present status of forest cover (%) of India belonging to various forest cover classes has been shown in Figure 4. It has been found that there is a reduction in the moderately dense forest and an increment in the open forest depicting degradation of forest cover to some extent. Changes within the forest cover classes result in decrease in the production capacity, thereby leading to forest degradation [45]. Control and regular check of these activities can help in strengthening the conservatory efforts of forest protection. This is the necessity of the present time to conserve the forests in order to sustain the vital ecosystems and the major carbon sink to combat the effects of global climate change and ultimately maintain the environmental balance.
Forest cover status in India for the last 30 years (1987 to 2017).
MODIS-based land use/land cover map of India for the year (a) 2001 and (b) 2017.
Forest cover (%) of India for the year 2017 (Source: [
Forests are an invaluable resource that is being subjected to so many threats. In order to protect our forests, it is very much necessary to understand the reasons behind their destruction. Differentiating the agents and causes of deforestation will enable in understanding the same [36]. Several disturbances within the forest directly or indirectly contribute in destruction of the forest. This can be interpreted from the results obtained while surveying in different forest patches in India.
Arunachal Pradesh is one of the states that has more than 75% forest cover and has the maximum very dense forest cover type [44]. The state is highly diverse in terms of climate as well as forest cover with tropical, sub-tropical, temperate and alpine forests having higher NDVI (≥7.0) [46]. However, the pressure on forest resources is consistently increasing with the rise in population, development activities, large number of wood-based industries and unsustainable land use practices resulting in their degradation [47]. This has also resulted in decrease in the forest cover of the state [44]. Several disturbances were being observed during the field survey in the forests of Arunachal Pradesh during 2007 to 2010. The major disturbances that were found include lopping, cut stumps, litter collection, soil removal, grazing, fire, NTFP collection and fuelwood collection (Figure 5). Of these, fuelwood collection was found to be the most recurrent activity followed by grazing. Generally, fuelwood collection has not been considered as the major cause of deforestation but leads to the same in certain regions with reduced forest area such as in the Philippines, Thailand and parts of Central America [36]. Forest fire has also been observed as an occasional event in certain parts of Arunachal Pradesh. Fires are generally used as a tool in clearing the forest for shifting cultivation which is one of the major agricultural practices performed in the state. Fires when used responsibly act as a valuable tool in managing forest and agriculture, but when abused, it can lead to deforestation [48, 49]. Other disturbances that can be an indicator of deforestation include NTFP collection and presence of cut stumps in certain forests. Forests of the state are highly diverse in endemic as well as nonendemic species, which need intensive monitoring and management to conserve the species-rich ecosystems from ever increasing anthropogenic pressure and changing climatic conditions [50].
Major disturbances in the forests of Arunachal Pradesh.
Madhya Pradesh is among one of the states of India which is endowed with rich and diverse forests and comprises the largest forest cover in the country [44]. This is mainly because of the efforts that the state has put in to conserve and harness this invaluable resource through innovative measures like community participation and decentralization (MP) [51]. Even after these continuous efforts, there are certain regions within the state where the occurrence of several types of disturbances in the forest has been found. The common disturbances that were being observed during the field survey (2017–2019) include fire, grazing, fuelwood collection, forage removal, litter collection, NTFP collection, lopping, thatch collection, root collection, soil removal, etc. (Figure 6). Among all these fire has been found as the major recurrent type of disturbance in the forests of Madhya Pradesh. Other major disturbances were grazing followed by fuelwood collection. Since every type of deforestation is not intentional but some which are the results of amalgamation of anthropogenic and natural factors like wildfires and subsequent overgrazing can prevent the growth of young trees [52] and thus eventually degrade the quality and productivity of the forest.
Major disturbances in the forests of Madhya Pradesh.
The name of the state ‘Jharkhand’ itself connotes ‘area of land covered with forests’ and has been exhibiting a unique relation with forests since time immemorial [53]. During the forest cover assessment [44], a net increase of 29 sq. km in the forest cover has been observed in the state which is mainly because of the plantation and conservation efforts within recorded forest areas. Although, there was an increment of 314 sq. km in the forest cover within the recorded forest areas, because of the felling of trees outside the forests area, its effect on forest cover has been offset. Also, several types of disturbances can be seen within the forest areas during the field survey (2016–2018), and the major disturbances were fuelwood collection, grazing, forage removal, lopping, cut stumps, thatch collection, root collection, soil removal, litter collection and NTFPs collection (Figure 7). Most of these disturbances were occasional in nature. Although these disturbances are not that recurrent, a regular check is necessary in order to prevent the forests from degrading and in achieving a sustainable forest cover. The forest management strategies should focus on the increasing demands of different timber and non-timber forest produce to conserve the plant diversity of the natural forests of the state [54].
Major disturbances in the forests of Jharkhand.
Forest management and protection by the local communities is an age-old practice in India which can be traced back to the protective nature of the Bishnoi Community of Rajasthan towards the local forest and animals as the black buck. The idea of community-based forest management emerged in an administrative level in the 1970s and 1980s. The declaration of the Government of India in June 1990 marked the establishment of Joint Forest Communities in different India states as per the National Forest Policy of 1988. The Earth Summit of 1992 provided with a clear objective of Sustainable Forest Management to which India responded in a positive way. From an increase in the forest cover, non-timber forest product (NTFP) to conservation of native flora and fauna, a whole new realm of forest management strategy by the collaboration of forest departments and local communities aided in decelerating the degradation of natural forest in India [55]. The Participatory Forest Management (PFM) is equivalent to an informal contract in which the local communities are allowed to consume a portion of harvest and NTFP if they protect and conserve it for 5–10 years. In India there is no legal authority of the local community on the forest resource where as in other countries as in Nepal the Community Forest User Groups (CFUG) are registered under their Forest Act, 1993 [56]. It is the positive effort of the local communities of India that the area under Joint Forest Management increased from 22,017,583 ha to 2,144,000 ha in March 2006 with 106,482 recognized Joint Forest Management committees countrywide [57].
Deforestation is a major environmental challenge which has been persistent from the past, and the situation is more worsened at present. Therefore, there is an urgent need to focus on the mitigative measures in order to prevent the distressing effects of deforestation in the near future. In order to alleviate the problem of deforestation, the strategies should be based on the underlying causes of the same. Also, the strategies for mitigating the problem of deforestation require its effective implementation that needs the recognition of the roles of national, state and municipal governments along with the pro-active role of the civil society and private society [36]. The continuous increase in the human population especially in the developing countries has resulted in enhanced pressure on the forests for human settlements and other land use practices. A reduction in the growth rate of human population plays a crucial role in reducing the practice of deforestation [36]. Alternatives to slash-and-burn agriculture can be adopted as a strategy to mitigate the deforestation by boosting the agricultural and forestry productions for the shifting cultivators ([58, 59]). The sustainable alternatives for slash-and-burn cultivation will assist the poor farmers in leading a better life without destroying additional forests [60]. Public policies and laws with greater security, accessibility to the minimal inputs required to maintain or enhance food production and an opportunity to the cultivators to market their products will aid in the sustainability of shifting cultivation systems [61].
Another strategy that can be applied to control the rate of deforestation is through the adoption of sustainable agroforestry, sustainable logging, agro-pastoral production systems, etc. [62]. The sustainable forest management practices can be promoted only if it is ecologically, economically and socially sustainable [36]. Agroforestry has been considered as one of the methods to curb deforestation which in turn aid in reduction of CO2 emissions and mitigation of climate change effect [63, 64]. The adoption of agroforestry practices has resulted in an increase in the income of agroforestry adopters as compared to that of non-agroforestry adopters and has also contributed towards improving soil fertility, reducing deforestation and conserving soil and water [65]. The provision of protected areas is one of the key steps towards an attempt to reduce deforestation which is generally motivated through biodiversity conservation [34]. Also, the global endeavours to reduce tropical deforestation are dependent heavily on the establishment of protected areas. It has been found that protection reduced deforestation as approximately 10% of the protected forests would have been deforested if they would have not been protected [66]. Tropical protected areas reduced deforestation which was liable for around one-tenth of total anthropogenic carbon emissions, thus playing a significant role in mitigating the effects of climate change and protecting biodiversity and ecosystem services [67]. Similarly, a total loss of 15.4% in the unprotected mangrove cover was compensated by the 15.7% rise in the protected mangrove cover (protected by government as per Ramsar Convention) which resulted in a net increase of 13.3% in mangrove cover across India in the last 25 years [68]. Implementation of forest certification can be among one of the strategies to control deforestation around the world. Forest certification is a process through which the producers identify their products in the marketplace and receive greater market accessibility and higher prices for their products by fulfilling certain stringent sustainable forestry standards [69]. Certification has played an important role in protecting Penten forests from 1986 to 2007, and the certified forests experience 20 times less deforestation than non-certified areas [70]. Thus, certification of forest can play a major role in controlling deforestation since the timber certification was found to be negatively related with deforestation, i.e. the increase in the certification process has resulted in a declination in the deforestation rate [71].
The Government of India has been continuously putting significant efforts in terms of conserving the forest resources. They have not only taken measures to conserve the present forest cover but also have initiated several measures to increase the forests and tree cover of the country. Both the national and state governments are jointly responsible for the sustainable management of the forest resources in India. Various steps, rules and laws have been brought and implemented in order to conserve the forest cover of India. Some of these rules and laws are [72]:
The Indian Forest Act, 1927: The act is an amalgamation of laws relating to forests, the transit of forest produce and the duty leviable on timber and other forest produces. It defines the procedures for declaring an area of a reserved forest, a protected forest or a village forest by the state government. With the amendment in the Act in 2012, it also prohibited the fresh clearances in forests and setting fire in a reserved forest.
Forest Conservation Act, 1980 (with an amendment in 1988): The main purpose of the proposition of this act was to conserve the forests and to look into the matters connected therewith or ancillary or incidental thereto. With the implementation of this act, a prior approval of the Central Government is required for any sort of diversion of forest areas for the non-forestry purposes.
[73]: The establishment of the National Forest Policy was also among one of the steps taken by the Government of India in order to ensure compensatory afforestation, essential environmental safeguards, sustainable utilization, maintenance, restoration and enhancement of forest areas.
Wildlife Protection Act, 1972: The wildlife protection act was enacted basically to protect wild animals, birds and plants and for matters connected therewith or ancillary or incidental thereto with a view to ensure the ecological and environmental security of the country.
The Scheduled Tribes and Other Traditional Forest Dwellers (Recognition of Forest Rights) Act, 2006: The act was framed with the purpose to recognize and vest the forest rights and occupation in forest land in forest dwelling scheduled tribes and other traditional forest dwellers who have been residing in such forests for generations but whose rights could not be recorded. These recognized rights of the forest dwelling scheduled tribes and other traditional forest dwellers include the responsibilities and authority for sustainable use, conservation of biodiversity and maintenance of ecological balance which in turn aid in strengthening the conservation regime of the forests while ensuring livelihood and food security of the forest dwellings scheduled tribes and other traditional forest dwellers.
Apart from these laws, the Government of India has also established Forest Survey of India (FSI), an organization under the Ministry of Environment, Forest and Climate Change, whose primary work is to gather and evaluate the country’s forest wealth through a nationwide survey to measure forest areas [74]. This in turn aids in determining the factors and drivers behind the loss or gain in forest cover of any particular region in India. Another council, i.e. the Compensatory Afforestation Fund Management and Planning Authority (CAMPA), was established by the Government of India in 2009 as a National Advisory Council under the chairmanship of the Union Minister of Environment, Forest and Climate Change for the monitoring, technical assistance and evaluation of compensatory afforestation activities. This was particularly meant to promote afforestation and regeneration activities as a way of compensating for forest land diverted to non-forest uses [75]. Certain scheme such as Integrated Forest Protection Scheme (IFPS) was being formulated by the government to save the forests from fire. The scheme was designed by combining the forest fire protection and management technique along with forest conservation. Various other missions and programmes such as the National Mission for a Green India (NMGI) and National Afforestation Programme (NAP) were also being launched by the Government of India where the main aim of NMGI was to improve the quality of five million hectares of degraded forests and to bring another five million hectares of non-forest areas under forest cover through social and farm forestry. On the other hand, the NAP was launched with the objective to develop the forest resources with people’s participation, with a focus on improving the livelihood of the forest-fringe communities, especially the poor [75].
The Ministry of Environment, Forest and Climate Change has been optimistic in strengthening the role of women in conservation of forest at local community levels since long. The National Forest Policy [73], for the first time, acknowledged the necessity of including woman members in forestry schemes. The Joint Forest Management Policy of 1990 mandated woman representatives not less than 40% in general body and 50% in executive body of the local forestry institutions like the JFM committee. Later in 2002, the Biodiversity Authority of India reframing the local biodiversity management committee structure mandated the reservation of one-third of its members as women. Thus, this understanding of the role of women in the local-level conservation measures and implementation of related rules has aided in improving the management of forest in rural regions of the country [76].
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\n\nOut of all of the publishing options available to researchers, why choose to contribute your research to an IntechOpen Edited Volume? The reasons are simple. IntechOpen has worked exceptionally hard over the past years to fine tune the Open Access book publishing process and we continue to work hard to deliver the best for all of our contributors. The quality of published content is of utmost importance to us, followed closely by speed, and of course, availability and accessibility. To view current Open Access book projects that are Open for Submissions visit us here.
\n\nQUALITY CONTENT
\n\nOver the years we have learned what is important. What makes a difference to the researchers that work with us, what they value. Something that is very high not only on their lists, but our own, is the quality of the published content.
\n\nOur books contain scientific content written by two Nobel Prize winners, two Breakthrough Prize winners and 73 authors who are in the top 1% Most Cited.
\n\nWith regular submission for coverage in the single most important database, the Book Citation Index in the Web of Science™ Core Collection (BKCI), and no rejected submissions to date, over 43% of all Open Access books indexed in the BKCI are IntechOpen published books.
\n\nIn addition to BKCI, IntechOpen covers a number of important discipline specific databases as well, such as Thomson Reuters’ BIOSIS Previews.
\n\nACCESS
\n\nThe need for up to date information available at the click of a mouse is one thing that sets IntechOpen apart. By developing our own technologies in order to streamline the publishing process, we are able to minimize the amount of time from initial submission of a manuscript to its final publication date, without compromising the rigor of the editorial and peer review process. This means that the research published stays relevant, and in this fast paced world, this is very important.
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\n\nThe utilization of CC licenses allow researchers to retain copyright to their work. Researchers are free to use, adapt and share all content they publish with us. You will never have to pay permission fees to reuse a part of an experiment that you worked so hard to complete and are free to build upon your own research and the research of others. The Edited Volume helps bring together research from all over the world and compiles that research into one book - accessible for all. The research presented in chapter one can inspire the author of chapter three to take his or her research to the next level. It is about sharing ideas, insights and knowledge.
\n\nCan collaboration be inspired by a publishing format? At IntechOpen, the answer is yes. The way the research is published, the way it is accessed, it’s all part of our mission to help academics make a greater impact by giving readers free access to all published work.
\n\nOur Open Access book collection includes:
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