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2021 | Buch

Spatial Modeling in Forest Resources Management

Rural Livelihood and Sustainable Development

herausgegeben von: Dr. Pravat Kumar Shit, Prof. Hamid Reza Pourghasemi, Dr. Pulakesh Das, Dr. Gouri Sankar Bhunia

Verlag: Springer International Publishing

Buchreihe: Environmental Science and Engineering


Über dieses Buch

This book demonstrates the measurement, monitoring, mapping, and modeling of forest resources. It explores state-of-the-art techniques based on open-source software & R statistical programming and modeling specifically, with a focus on the recent trends in data mining/machine learning techniques and robust modeling in forest resources.

Discusses major topics such as forest health assessment, estimating forest biomass & carbon stock, land use forest cover (LUFC), dynamic vegetation modeling (DVM) approaches, forest-based rural livelihood, habitat suitability analysis, biodiversity and ecology, and biodiversity, the book presents novel advances and applications of RS-GIS and R in a precise and clear manner.

By offering insights into various concepts and their importance for real-world applications, it equips researchers, professionals, and policy-makers with the knowledge and skills to tackle a wide range of issues related to geographic data, including those with scientific, societal, and environmental implications.



Forest Resources Measurement, Monitoring and Mapping

Chapter 1. Forest Management with Advance Geoscience: Future Prospects
The creation and implementation, involving key stakeholders, of context-specific forest management practices plays a significant role in the achievements of sustainable forest management. A number of site-growth modelling studies have been funded in recent years with the goal of developing quantitative relations between the site Index and specific biophysical indicators. With considerable time period, the role of forests in meeting the requirements for minor resources and ecological services has been recognized beyond the mere supply of forest. Present chapter describes advance geoscience application in forest management and also suggesting present research work to be adopted in future forest management plan. Counter-measures and recommendations were suggested on different forest management aspects, including developing consolidated structured data sets, designing top-ranking model monitoring and analysis and creating a multi-scenario decision support network. Finally, we proposed the main field of research in forestry research by incorporating and developing the participatory method, crowd sourcing, crisis mapping models and simulation systems and by linking data integration framework of geospatial technology, evaluation system and decision support system, to enhance forestry management by systematically and efficiently.
Gouri Sankar Bhunia, Pravat Kumar Shit
Chapter 2. Estimation of Net Primary Productivity: An Introduction to Different Approaches
The net primary productivity (NPP) is defined as the net carbon gain by plants in natural and agricultural ecosystems, which is computed by subtracting the autotrophic respiration from the gross photosynthetic carbon uptake by the ecosystems. It acts as the indicators of carbon sequestration, ecosystem health, and agricultural yield which are important in the context of climate change, its impact and mitigation, and food security. The NPP can be estimated in multiple ways including the direct and indirect measurements and modelling. The various direct NPP measurements are ground-based in situ observations of ecosystem-atmosphere carbon flux such as the micrometeorological flux-gradient method, eddy covariance, flux chamber measurements etc. The indirect measurements of NPP include the satellite-derived NPP estimates which are computed from the directly measured spectral reflectances, using different biophysical relations such as the light use efficiency model etc. However the accuracy of these products varies geospatially and largely depends on the retrieval of input parameters and representativeness of underlying model parameterization. There are two major modelling approaches to estimate the NPP namely bottom-up and top-down estimates. The bottom-up models compute the NPP from the directly recorded variables such as temperature, precipitation, radiation, wind, atmospheric CO2 concentration etc. using the biome-specific functional relations due to which these are also known as the process-based models. The top-down or inverse models use the matrix inversion method to predict the sources and sinks of CO2 emission in a region from the directly measured concentrations by the surface stations and/or satellites and thus the NPP of that region. The NPP estimates from measurements and models are used to calculate the carbon budgets at different scales from ecosystem-level to global scale. However significant uncertainties exist in such estimates due to insufficient surface measurements, under-representation of several regions and ecosystems, imperfect boundary conditions and parameterizations in models. While the direct measurements provide more accurate estimates of NPP, these require to be carried over for long duration using multiple different instruments which are prone to errors and data-loss whereas the models can provide large-scale estimates of NPP but need to be validated against realistic in situ measurements across an wide array of ecosystems. The aforementioned aspects of NPP estimation are discussed in detail in the present chapter.
Pramit Kumar Deb Burman
Chapter 3. Assessing Forest Health using Geographical Information System Based Analytical Hierarchy Process: Evidences from Southern West Bengal, India
Vegetation plays an important role in sustaining the ecological biota and maintaining the equilibrium of environment. Thus, assessing vegetation status includes analyzing ecological dynamism, enough soil nutrients and vegetation health. Nearly 24% area of the India is under forest providing a range of resources to local communities. Bankura district has substantial forest cover comprising three divisions i.e., north Bankura division, south Bankura division and Panchet division. Nearly 1463.56 km2 territorial extent of the district comes under forest jurisdiction constituting 21.27% of the total geographical area of the district. Per capita availability of forest in this district is 0.046 ha which is lower than the other districts of south western districts of West Bengal. Therefore, it is essential to analyze the health of vegetation in this region. Present study aims to analyze the forest health using different indices namely Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Greenness Index (GI), Shadow Index (SI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Built-up Index (NDBI) Perpendicular Vegetation Index (PVI), and Normalized Difference Moisture Index (NDMI) during 1990 and 2019 using Landsat 5 TM (1990) and Landsat 8 data (2019) under the model of Analytical Hierarchy Process (AHP). Results revealed that forest health was largely affected during the study period due to land transformation and disturbances created by anthropogenic activities. Findings of the study based on this 29-year spatio-temporal vegetation dynamics will ameliorate the local stake holders for managing and maintaining the health of vegetation in the study area.
Shyamal Dutta, Sufia Rehman, Mehebub Sahana, Haroon Sajjad
Chapter 4. Ecological Determinants of Woody Plant Species Richness in the Indian Himalayan Forest
The ecological importance of woody plant species richness is well known. The role of abiotic ecological determinants on structuring the vegetation has been well studied. The present study evaluated the independent and integrated strength of the abiotic and biotic determinants in explaining species richness of woody plants in the Indian Himalayan forest. The primary field inventory data was collected using nested quadrat method (tree species at 10 × 10 m2, shrub species at 5 × 5 m2, and herb species at 1 × 1 m2 quadrats) for different life forms and for the abundance estimation within each 1 km transect. Each transect was laid in a 6.3 × 6.3 km2 grid on the study site. The biotic determinants included diameter at breast height (d.b.h.) and tree height, whereas the abiotic determinants were temperature, precipitation, soil moisture, relative humidity and elevation. A total of 302 woody plant species (233 genera and 53 families) were recorded from the field inventory. The woody plant species richness was found to range from 1 to 54 per ha at transect level. Structural Equation Model (SEM) evaluated different combinations of ecological determinants for woody plant species richness. The abiotic or biotic determinants were non-significant if considered independently; however, the integration of both resulted in a significant relation with woody plant species richness. The best combination of ecological determinants include density d.b.h. ≥ 2.5 cm, tree height, relative humidity, and elevation (R2 = 0.53). Overall, the integration of biotic and abiotic determinants better explained woody plant species richness in the Indian Himalayan forest.
Mahanand Swapna, Tamang Deeke Doma, Sikder Arunima, Gudasalamani Ravikanth, Muneeswaran Mariappan, Ganesan Rengaian
Chapter 5. Multivariate Analysis of Soil-Vegetation Interaction and Species Diversity in a Natural Environment of Rhus coriaria L. (Case Study: Bideskan Habitat, Southern Khorasan, Iran)
To manage rangeland ecosystems, the first step is to determine effective factors on species distribution and diversity. The prediction models of species distribution determine the most effective factors for any plant species and examine behavior of the species interacted with environmental variables and also accompanying species. In this work, to study ecological characteristics and to determine the most important environmental factors affecting the Sumac (Rhus coriaria L.) species, its range was mapped using a randomly-systematic approach to take 30 plots of 10 m2. The soil samples were taken from a depth of 0 to 30 cm. The evenness and richness indices were computed based on species frequency in each plot and each community, i.e. witness and Rhus coriaria L. The independent samples t-test, Principal Component Analysis (PCA) and Canonical Correspondence Analysis (CCA) were employed for comparing natural Sumac habitat with control area (without the presence of Rhus coriaria L.). According to the Shannon-Weiner diversity index, Sumac habitat was more diverse and based on the evenness index of 0.717, it showed more uniform distribution compared to control area. The student’s t-test of independent samples in two areas demonstrated a significant higher amount (between 30 and 140%) of electrical conductivity, saturated electrical conductivity, potassium, organic matter in Sumac habitat, as compared with control area. Finally, the relationship analysis between soil factors and vegetation using the multivariate techniques of PCA and CCA showed that the soil characteristics, saturation moisture percentage, electrical conductivity, nitrogen, organic matter, lime, potassium, silt and acidity had the most impact on separation of two regions and distribution of Sumac species.
Graphical Abstract
Sh. Ghollasimod, H. Memarian, J. Shamshiri
Chapter 6. Comparative Assessment of Forest Deterioration through Remotely Sensed Indices—A Case Study in Korba District (Chhattisgarh, India)
In various studies, such as meteorology, agriculture and ecology, quantitative estimation of biophysical variables is very important, and thus information about the spatial and temporal distribution of these variables are highly useful. Remote sensing is meanwhile regarded as an important source of knowledge in broad areas for the estimation of fractional vegetation coverage. In the remote sensing, estimation of vegetation characteristics using spectral indices have become very common today, but soil and rocks reflections are also much more than the reflection in these areas of sparse vegetation, which makes it difficult to distinguish plant signals. In this analysis, a variety of spectral indices have been considered to estimate biophysical vegetation parameters to boost vegetation signal in remotely-sensed data and provide an estimated measurement of living green vegetation using Landsat4,5 Thematic Mapper (TM) and Landsat8 Operational Land Imager (OLI) sensor data. To identify the best vegetation index for sparsely vegetated semi-arid and arid region of Chhattisgarh state using four vegetation indexes; Normalized Difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI). The TNDVI indices showed the best fractional vegetation cover to estimate the highest precision.
Soumen Bramha, Gouri Sankar Bhunia, Sant Ram Kamlesh, Pravat Kumar Shit
Chapter 7. Comparison of Sentinel-2 Multispectral Imager (MSI) and Landsat 8 Operational Land Imager (OLI) for Vegetation Monitoring
The availability of the coarse to moderate resolution no-cost remote sensing data and advances in image processing algorithms have exponentially increased the usage of geo-spatial technology in the last few decades. The latest Sentinel-2 Multispectral Imager (MSI) provides the surface reflectance data in VNIR and SWIR ranges since 2015 at higher spatial, temporal, and spectral resolution compared to the Landsat multispectral sensors, which have been providing such data since 1970s. The symmetry in spectral bands, sensor’s spectral response, spatial and radiometric resolution of Sentinel-2 MSI with the Landsat-8 OLI (Operational Land Imager) enables their integrated and complimentary use. In this study, we have compared the surface reflectance and vegetation indices (such as NDVI and EVI) values obtained from MSI and OLI sensors in four homogeneous land use land cover (LULC) features as cropland, agriculture fallow land, dense forest and open forest. The assessment is carried out for pre- and post-monsoon seasons over the Banki sub-division region of Cuttack district, Odisha, India. For all the LULC classes, high similarity is observed in the surface reflectance values in each band except NIR, green and red band. Similarly, for both the vegetation indices derived from Landsat 8 and Sentinel-2 data, high correlation with lower RMSE is observed for all the LULC classes. The correlation (R2) for cropland varied between 0.87 and 0.96, which varied between 0.56 and 0.97 for agriculture fallow, between 0.58 and 0.9 for dense forest, and between 0.68 and 0.87 for open forest. The surface reflectance pattern obtained for different vegetated features are similar for Landsat 8 and Sentinel-2. However, significantly a higher surface reflectance is observed for Landsat 8 in NIR band followed by red and green bands, where the differences are low for blue, SWIR1 and SWIR2 bands. The comparative assessment of indices suggests a higher correlation in values between Landsat 8 and Sentinel-2 for homogeneous features compared to heterogeneous class as forest. Thus, the integrated or complementary use of Landsat 8 and Sentinel-2 for heterogeneous features may induce some biases with a limited accuracy.
Santanu Ghosh, Debabrata Behera, S. Jayakumar, Pulakesh Das
Chapter 8. Comparative Assessments of Forest Cover Change in Some Districts of West Bengal, India using Geospatial Techniques
Forests are one of the most important components on our planet as they regulate a number of natural systems viz. the food chain, the water cycle, the carbon cycle etc. In this study, we have focused on the forest cover of districts that has a very small percentage of area covered by forests. The study has been performed for a time period of 30 years i.e. from 1990 to 2020 and a time series analysis of the changes in forest cover has been done. The forested areas are divided into three type namely very dense forest, moderately dense forest and open forest. Five districts in the state of West Bengal have been selected namely Hooghly, Nadia, Purba and Paschim Bardhhaman (considered together as Burdwan) and Purulia. These districts particularly belong to the South Bengal region and out of them namely Hooghly, Nadia and Bardhhaman are also a part of deltaic region of the Lower Ganga which is known as the Bhagirathi-Hooghly River in West Bengal. These districts are chosen because they are one of the most populous districts both in the state and also in the country but they lack adequate amount of natural vegetation cover, the reasons for which can be cited are the availability of fertile land for agriculture which consequently makes these places one of the most suitable areas for human beings to survive and thrive. In this study a standardised and simple index namely the Normalized Difference Vegetation Index (NDVI) has been used to delineate various kinds of forest and a Land Use Land Cover (LULC) classification has been done to study the present land use condition of these districts. It has been found that the district of Purulia has the maximum forest cover while the district of Hooghly lacks areas with very dense forest. Studies have shown that forest cover conditions has improved for all of these districts since the last decade but most of these improvements has been observed in the open forest category which signifies that social forestry might have been taking place which proves an increasing concern among people and the government in saving the environment.
Mitrajit Chatterjee, Atma Deep Dutta
Chapter 9. Assessment of Forest Health using Remote Sensing—A Case Study of Simlipal National Park, Odisha (India)
Forest ecosystems fulfill the entire ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. In the present study we focused to determine forest health pattern of Simlipal National Park (Odisha, India) based on Remote Sensing and GIS techniques. Multitemporal Landsat 8 operational Land Imager (OLI) data are derived from USGS Earth Explorer Community. Normalized difference vegetation index (NDVI), SARVI (Soil and Atmospherically Resistant Vegetation Index), Modified Chlorophyll Absorption Ratio (MCARI), and Moisture Stress Index (MSI) have been used to create different vegetation indices to estimate forest health. Finally, Weighted overlay analysis is performed on GIS platform to identify the forest health pattern in the national park. NDVI index showed the maximum accuracy for identifying vegetation classes. Results showed in the eastern and central part of the study area having excellent vegetation cover. Good to moderate vegetation cover areas are observed in the south and small pockets in north of the study area. The excellent vegetation coverage area also increases day by day. To exclude the agricultural lands and cloud cover from forest area images from the month of January are selected.
Partha Sarathi Mahato, Kathakali Bandhopadhyay, Gouri Sankar Bhunia

Modeling, Risk Assessment and Vulnerability

Chapter 10. Forest Health Monitoring using Hyperspectral Remote Sensing Techniques
Hyperspectral Remote sensing is a handy tool for forest health monitoring. This study focuses on forest health monitoring using hyperspectral satellite data and validates it with tree spectral data. In the study area, increasing mining and anthropogenic activities within and near forest lands have caused threats to forest health. All these necessitate assessing the forest health in the areas surrounding mines. We have used two methods for the forest health assessment: one is VIs (vegetation indices) based model, and another is tree spectral analysis. The supervised classification (SAM) method was used for forest health classification based on spectral data. The results showed that a healthy forest portion was located in the hilly side of the study area while an unhealthy segment was situated alongside the mines. Hyperion data-based VIs model shows better accuracy than spectral based other methods. Also, it was found that the hyperspectral data based forest health classification gave a higher accuracy than multispectral data. Finally, forest health results were justified by ground tree spectral data. This work provides an effective guideline for forest planning and management.
Narayan Kayet
Chapter 11. Estimating Above Ground Biomass (AGB) and Tree Density using Sentinel-1 Data
Assessment of the forest above ground biomass (AGB) and tree density is essential for various studies related to forest structure, productivity, carbon cycle, atmospheric processes, climate change etc. including forest cover management activities and framing the conservation policies. The freely available C-band Sentinel-1 microwave data allows to estimate forest cover biomass at high spatial resolution; moreover, the Sentinel-2 optical data enables to integrate the biophysical attributes. In the current study, the AGB has been estimated in a Shorea robusta (sal) dominated forest cover in sub-tropical region employing the Sentinel-1 microwave data. The inventory of forest cover attributes has been collected in 40 sample plots, where the in-situ AGB was geo-statistically linked with the satellite observations. Employing the univariate linear regression, it has been observed that the microwave backscatter obtained in the VH band well explained (R2 = 0.63) the variability of the in-situ AGB in comparison to the backscatter received in VV band (R2 = 0.44) and optical data derived EVI image (R2 = 0.45). The predicted biomass map verified with the test data points indicated an accuracy of R2 = 0.45, with low RMSE (±17 tonnes/ha) and slight underestimation (bias = −0.024). However, the accuracy in tree density estimation obtained from the AGB map was observed much higher (R2 = 0.87). The field observed AGB varied between 88.56 tonnes/ha and 170.29 tonnes/ha, where the satellite data derived AGB estimated the range of 44.1 tonnes/ha and 249 tonnes/ha for the entire study area. Majority of the biomass was estimated in the range of 100–200 tonnes/ha, which was contributed by majority of the tree density region varying between 69/ha and 75/ha. However, few patches are observed to have much higher and lower AGB, which could be indicating the highly dense and less dense forest cover regions in the study area, respectively. The uniform AGB map indicates the selected region to be more homogenous forest cover area.
Sambhunath Roy, Sujoy Mudi, Pulakesh Das, Sujit Ghosh, Pravat Kumar Shit, Gouri Shankar Bhunia, John Kim
Chapter 12. Forest Fire Risk Assessment for Effective Geoenvironmental Planning and Management using Geospatial Techniques
Forest are essential natural resources having the role of supporting economic activity, which plays a significant role in regulating the climate and the carbon cycle. Forest ecosystems increasingly threatened by fires caused by a range of natural and anthropogenic factors. Hence, spatial assessment of fire risk is critical to reducing the impacts of wildland fires. In the current research, the evaluation of forest fire risk (FFR) assessment performed by geospatial data of Melgaht Tiger Reserve Forest (MTRS), Maharashtra, India. We have used eleven natural and anthropogenic parameters (slope, altitude, topographic position index (TPI), aspect, rainfall, land surface temperature (LST), air temperature, wind speed, normalized differential vegetation index (NDVI), distance to road and distance to settlement) for FFR assessment based on the Analytic hierarchy process (AHP) and Frequency ratio (FR) models in a GIS framework. The results from AHP and FR models shown similar trends. The AHP model was significantly higher accuracy than the FR model. AHP and FR models based FHR maps were classified into five classes (very low, low, moderate, high, and very high). According to the generated FFR maps, the very high-risk class was found at some forest blocks (Mangtya, Kund, Gudfata, Katharmal, Amyar). The sensitivity analysis showed that some parameters (wind speed, air temperature, LST, slope, altitude, distance to settlement, and distance to the road) were more sensitive to forest fire risk. The FFR results were justified by the forest fire sample points (Forest Survey of India) and burn images (2010–2018). This work will provide a basic guideline for effective geo-environmental planning and management of Melgaht Tiger Reserve Forest.
Narayan Kayet
Chapter 13. Forest Disturbance Analysis of Selected Blocks of Midnapore Subdivision using Digital Remote Sensing Technique
Change is ubiquitous in forest ecosystems. Forests undergo both seasonality ns well as enduring escalation cycles which may vary in long term. These long-term changes are punctuated by habitually interim disturbances from fire, insects, disease, and harvest which strongly alter the state and functioning of the forest. The spatio-temporal information on process-based forest loss as well as change is essential for a wide range of applications. The disturbances over forest cover and resulted changes alter the water and carbon cycles of forest stands as well as bang the habitat and biodiversity of these ecosystems. To effectively understand how forest disturbance impacts forest state and functioning, the disturbance and related effects on forest cover is needed to be quantified at spatial scale where human management and natural strife occur. In this present study, the spatial pattern of forest cover of the four blocks like Garhbeta 1, Garhbeta 2, Garhbeta 3 and Salboni under Midnapore Sadar Sub-division was analysed on temporal scale. The forested area of this region is region is lying under the Midnapore forest division, the total area of which is 50,267.49 ha. Forest is the one of the important natural resources of this area and the important source of rural livelihood as well as ecological sustainability. But it is changing temporally rather is fading and maximum stress is seen onto the dense forest and open forest. So for the restoration practice, forest disturbance indexing as well as identification of disturbed sites and an account on forest regeneration and degeneration are so needful. Therefore, as per the goal of the study a Multicriteria based Forest Disturbance Index and forest fragmentation analysis were deployed. Finally it is so pertinent to mention that the forest covers under the blocks like Garhbeta 2 and Salboni were in a miserable state.
Ratnadeep Ray, Swarnali Biswas, Ahona Bej
Chapter 14. Comparison of AHP and Maxent Model for Assessing Habitat Suitability of Wild Dog (Cuon alpinus) in Pench Tiger Reserve, Madhya Pradesh
Habitat suitability is an illustrious mission to conserve the ecosystems and its components. Human wildlife conflicts have increased tremendously due to habitat fragmentation; reason being human encroachment for their own use or for commercial purpose like forest products and unsustainable development programs leading to major decline in Biodiversity. Pench Tiger Reserve (PTR), Madhya Pradesh has great capacity to nurture young Wild dogs (Cuon alpinus) because it supports lots of biodiversity especially herbivores which will be great availability of food and have great tree cover on the landscape. The motive of this study is to find out the landscape which will suitable for Wild dog (Cuon alpinus) at PTR, MP using presence SDM (Species Distribution Model) at 900 m grain Size using Maxent, Ecological Niche Factor analysis and Bioclim and compare with AHP (Analytical hierarchical process). Maxent gives contributions of every factor in percentage also gives responses curve of every factor separately and also give permutation importance of every factor. Results show that the model with accuracy more than 0.90 AUC value. Bioclimatic variable 17 and LULC contributes the moderately high in the model. For AHP only need experts, who fill the Saaty table on the basis of environmental variables importance to wild dog (Cuon alpinus). Calculate or predict the habitat suitability of wild dog (Cuon alpinus) by experts rating to every environmental variable (comparison of every variables with respect to each other) from 1 to 9 or inverse like 1/9. Maxent shows that out of the about 750 km2 areas, more than 150 km2 found to be highly suitable at 900 m grain size SDM. This shows PTR, MP has the great potential to nurture or sustain Wild dog (Cuon alpinus) because wild dog (Cuon alpinus) need larger area to walk, they walk daily around 20–25 km to find their food and PTR, MP is immensely rich in large size herbivore animals like Chital (Axis axis). Strict implementation of laws is required this can be achieved by government agencies like forest department and others along with that awareness can be spread among the local residents of the PTR, MP so the wildlife and their habitat can be conserve by the conservationists and wildlife experts.
Dhruv Jain, G. Areendran, Krishna Raj, Varun Dutta Gupta, Mehebub Sahana
Chapter 15. Assessment of Forest Cover Dynamics using Forest Canopy Density Model in Sali River Basin: A Spill Channel of Damodar River
In a spatio-temporal scale, changing conditions of forest land cover and its detection study is an important concern for sustainable forest management. Nowadays, the forest canopy density (FCD) model has been used for the analysis and management of forest resources through identifying the forest gap areas where afforestation should be started immediately. The present study applied FCD model to detect changes in forest land cover in Sali River basin between the years 2000 and 2018. Moreover, the vegetation indices like Bareness Index (BI), Greenness Vegetation Index (GVI), Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI), and Shadow Index (SI) along with weighted overlay analysis have been used to prepare FCD map of the Sali river basin. It has been noticed from FCD map that south and north-eastern part of the study area covered with high canopy density in comparison with north and north-western region in the year 2000. Whereas, in the year of 2018, high FCD has been found in the middle portion of the southern region and the rest of the area varies from low to medium FCD.
Asish Saha, Manoranjan Ghosh, Subodh Chandra Pal, Indrajit Chowdhuri, Rabin Chakrabortty, Paramita Roy, Biswajit Das, Sadhan Malik
Chapter 16. Estimation of Aboveground Stand Carbon using Landsat 8 OLI Satellite Image: A Case Study from Turkey
Accurate and consistent measurement of carbon stocks and flows in forest ecosystems has recently gained global importance. This study aims to estimate the aboveground stand carbon (AGSC) using Landsat 8 OLI satellite image in pure Crimean pine stands and to compare the results of various modeling techniques. In this context, a total of 108 sample plots were firstly taken in a case study forest area. The AGSC of each sample area was calculated using a species-specific carbon equation developed for the case study area. The band values, vegetation indices, and texture values for each sample plot were also obtained from Landsat 8 OLI satellite image. The relationships between the AGSC and the band values, vegetation indices, and texture values were investigated by multivariate linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) models. The results demonstrated that the ANN models with Bayesian regularization are better than the MLR and SVM models to estimate the AGSCin pure Crimean pine stands. Also, the band values showed better predictive performance in explaining the variation in AGSCthan vegetation indices and texture values.
Alkan Günlü, Sedat Keleş, İlker Ercanli, Muammer Şenyurt
Chapter 17. Spatio-temporal Variation of Evapotranspiration Derived from Multi-temporal Landsat Datasets using FAO-56 Penman-Monteith Method
Reference evapotranspiration (ET0) is the representation of real-time crop-specific measurement of evapotranspiration and could be used for measuring the available water for agriculture. Accurate estimation of reference evapotranspiration (ET0) is required for irrigation management and water resource allocation. Satellite remote sensing provides an opportunity to estimate its quantity and map the spatio-temporal distribution of evapotranspiration in an efficient way. There are several methods developed for estimating ET0 but most of them are mainly based on daily meteorological data provided by weather station networks. This paper aims to estimate the monthly reference evapotranspiration (ET0) by the FAO-56 Penman-Monteith method using the remote sensing data (LANDSAT 8-OLI and LANDSAT 7-ETM+) of 2014, 2015, 2016 and weather data (Maximum and minimum temperature, Dew point temperature, wind speed, relative humidity) over the Dwarakeswar river basin. Input parameters required for this model are emissivity, land surface temperature (LST), net radiation, soil heat flux (G), air temperature, actual and saturation vapor pressure and wind speed. This study indicates that evapotranspiration variation in this area is closely related to crop growth. Evapotranspiration values were found low (66–120 mm/month) when paddy fields were empty and the fields were covered by very sparse vegetation. Whereas, the estimated values were high (120–180 mm/month) in cropping season and in monsoon, when vegetation cover was dense. Furthermore, the evapotranspiration estimation results were analyzed and validated with MODIS data which shows a good agreement between them.
Tanushree Basu Roy, Dipanwita Dutta, Abhisek Chakrabarty
Chapter 18. Monitoring and Prediction of Dynamics in Sundarban Forest using CA–Markov Chain Model
Mangrove ecosystems play an important functional role in providing coastal protection, carbon sequestration, coastal habitat, climate change resilience and socioeconomic services for coastal communities. The present research study investigates spatiotemporal variation, forest health status and predicts the land cover changes in Sundarban mangrove forests of India using multi-temporal satellite images and cellular automata and Markov Chain model. The results revealed that mangrove forest extent has decreased by 3.14% from 1994 and 2019. The image classification resulted in overall accuracy of 74% in 1994, 81% in 2004, 78% in 2014 and 84.5% in 2019 respectively. The satellite-based vegetation indices were analysed for assessing the health of the forests. The findings of present study indicate deteriorating health of the forest and observed significant vegetation stresses over the western to central part of the study region due to anthropogenic activities. The CA Markov model predicted that the extent of mangrove forests could possibly decline from 2011.60 km2 to 1939.24 km2by the period 2029. The results of the present study could foster better decisions, precise mitigation and sustainable development strategies for the region.
Sarmistha Halder, Kaberi Samanta, Sandipan Das

Rural Livelihood and Sustainable Management

Chapter 19. Improving Potential Biodiversity and Human Footprint in Nothofagus Forests of Southern Patagonia through the Spatial Prioritization of their Conservation Values
The role of biodiversity in natural forests is critical as a regulator of ecosystem function, productivity, and provision of ecosystem services. The objective was to analyse the conservation value of Nothofagus forests in Southern Patagonia (Santa Cruz and Tierra del Fuego provinces), Argentina, through integration of maps of potential biodiversity (MPB) and human footprint (HFM), which can help to improve the natural reserve designs through the spatial prioritization of their conservation values. To achieve the, first we determine that the different forest types presented different species assemblage with specific ecological niche requirements which justify differential conservation or management strategies. We used understory plants as proxy, and we selected indicator species of the understory plants for the following analyses. With these species we produce the MPB, and we found that the occurrence of MPB differ from the pattern of HFM according to the different forest types. After that, we identify woodland patches with special values of MPB and low HFM according to the different forest types, and analyzed if the distribution of MPB of the different forest types changed across the current natural protected reserve network, private and public lands. Finally, with these outputs, we propose new methodologies to enhance the current natural reserve network effectiveness. These outputs can be used as a tool to determine new strategies for management and conservation at landscape level in Southern Patagonia.
Yamina Micaela Rosas, Pablo L. Peri, Josela Carrasco, María Vanessa Lencinas, Anna M. Pidgeon, Natalia Politi, Sebastián Martinuzzi, Guillermo Martínez Pastur
Chapter 20. The Role of Local Communities in Sustainable Land and Forest Management
Conservation, protection, and proper utilization of forests play an important role in environmental sustainability of the globe. The ultimate goal of sustainable forest management is to create a balanced and appropriate solution for human well-being and preservation of forest ecosystems. However, one of the prominent obstacles to achieve this goal is the gap existing between governmental development aims and the perspectives of local people and communities. Forest conservation requires an integrated management that works in partnership with local communities. Local and community-based forest management is a multi-dimensional approach to sustainable forest management in which different stakeholders with different interests play a part in achieving a common goal. However, little research has been done in this area. In this regard, the main purpose of this chapter was to examine the role of participation of local community in sustainable land and forest management. This purpose fulfilled through six steps. In the first step, the role of community participation in sustainable forest management and its typology were explained. I the second and third steps, the barriers and drivers of local communities’ participation were introduced, respectively. In the fourth step, techniques for participation of local communities in forest management were analyzed. In the fifth step, some practical experiences related to the participation of local communities in forest management were highlighted. In the sixtieth or final step, some social principles were introduced for agricultural system and interventions aiming at sustainable management of forests and lands.
Latif Haji, Naser Valizadeh, Dariush Hayati
Chapter 21. Non-Timber Forest Products Based Household Industries and Rural Economy—A Case Study of Jaypur Block in Bankura District, West Bengal (India)
Among the 22 blocks of the Bankura district, the Jaypur block occupies first position (5.81%, 2011) in the household industry sector which is also higher than district average (4.18%, 2011) and as well as the national average (3.81%, 2011). In this block, Sal leaf, Khat Bel and Churung Khati are the most economically important products among the available Non-Timber Forest Products (NTFPs) and these are primarily used as raw materials in women dominated cottage industries for making of Sal plate and beads chain. The rural people of the Jaypur block have collected NTFPs such as Sal leaf and seed, Khat Bel, Churung Khati, Mushroom, fruits of Kendu and Mahul etc. from the nearest reserve forest area for income generation and fulfilling the demand of their household food. The main objective of this study is to explore the role of NTFPs based household industries in rural economy at micro-level. To fulfil this objective, primary data are mainly used and these are collected through purposive sampling method with the help of questionnaire survey, participatory observation and focused group discussion. The study reveals that women from the Scheduled Caste and Muslim communities are dominantly engaged within these industries with traditional tools, primitive technology and middle man based marketing system and contributed in household maintenance by sharing their income to remove the poverty and sustain their livelihood. Hence, implementation of micro-level planning with the co-ordination of local people and the administration would be effective to increase the economic return to the villagers in general and rural women in particular.
Debmita Nandi, Sumana Sarkar
Chapter 22. Forest Ecosystem Services and Biodiversity
Ecosystems, and the biodiversity and services they support, are intrinsically dependent on climate. During the twentieth century, climate change has had documented impacts on ecological systems, and impacts are expected to increase as climate change continues and perhaps even accelerates. This technical input to the National Climate Assessment synthesizes our scientific understanding of the way climate change is affecting biodiversity, ecosystems, ecosystem services, and what strategies might be employed to decrease current and future risks. In developing countries, the landscape encompassing agricultural land is vital for maintaining biodiversity and providing ecosystem services. The consequences of biomass shot on woody species richness and composition were analysed in forests underneath communal and government management. Interviews on forest use and perception of forest condition and system service delivery were conducted in farmer households bordering the forests. At the same time, the importance of forest ecosystem services has been progressively recognized. Though some initiatives aimed toward protective each biodiversity and ecosystem services are emerging, information gaps still exist regarding their relationships and potential trade-offs in forests. Considerably additional woody species were found within the community-managed forests. Species richness was negatively correlative with walking distance from the closest village and increasing levels of anthropogenic disturbance. There is increasing proof that diversity contributes to forest ecosystem functioning and also the provision of ecosystem services. Here, we are including some of the ecosystem services such as biomass production, production of atmospheric oxygen, soil formation and retention, nutrient cycling, water cycling, and provisioning of habitat. Community forests were typically less degraded than government managed forests, giving support to common pool resource management. Woody vegetation depicted an important supply of fuelwood, timber, fodder, edibles, aromatic and healthful plants. Employing a multidisciplinary framework to analyse system integrity and system service delivery enabled a finer understanding of those complicated agroecological systems, giving support to evidence-based management and conservation coming up with for the long run. Planting mixed-species forests ought to tend additional thought as they're probably to supply a wider range of ecosystem services at intervals the forest and for adjacent land uses.
Afaq Majid Wani, Gyanaranjan Sahoo
Chapter 23. Transformation of Forested Landscape in Bengal Duars: A Geospatial Approach
The Bengal Duars, a landscape of foothill ecology in Eastern Himalaya asherb of rich biodiversity with unique physiography and climate. This landscape is now tremendously under threat disrupting by natural as well as anthropogenic activities. The recent phase of transformation of forest cover caused by illegal felling, encroachment, mining, quarrying activities, further enhancing flood and its associated vulnerability in such landscape. We assess the level of transformation of an area under deforestation, reforestation within forest boundary by using geospatial technology. Landsat imageries of two different periods has been used to find out zonal transformation of different land cover. The study also reveal that the rate of deforestation is more than rate of reforestation and major transformation has observed from dense forest to open forest within 20 years (1990–2010). The recent conversion and disturbances are highlighted through high resolution overview and field observation.
Koyel Sam, Namita Chakma
Chapter 24. Forest-Based Climate Change Social Interventions: Towards a Theoretical Framework
The main purpose of this chapter was to develop a framework for forest-based climate change social interventions which was fulfilled using a multi-stage process. There is no doubt that forests are important to humans, plants, animals, and the planet as a whole. In other words, the ongoing deforestation process and land degradation caused by human activities and climate changes are considered as major challenges for sustainable development around the world. Despite the improvements achieved, there are still many problems with the sustainable protection, conservation, and management of forests in different areas. This necessity has been acknowledged by the need for government interventions at all levels. In the first step of this study, the importance of forest was highlighted in terms of Sustainable Development Goals (SDGs). In the second step, the planned/preventive forest-based climate change social intervention introduced as an effective way to reduce deforestation under climate change. In the third step, some enabling and constraining factors were proposed for successful implementation of forest-based climate change social interventions. In the fourth step, different types of the uses of forest-based climate change social interventions were critically analyzed. In the fifth stage, a typology were introduced for forest-based climate change social interventions. Finally, in light of the results of previous steps, a practical framework for forest-based social interventions under climate change was developed. In general the results of this chapter showed that in all types of forest-based climate change social interventions, the most important constraining factors include structural, political, organizational, economic, executive, collaborative, network building, and follow-up barriers. In addition, enabling factors of these interventions consist flexible designing, institutional analysis, long-term intervention, risk assessment, prioritizing local knowledge, site-specific intervention, socio-cultural forestry, non-profit incentives, social learning, and participation. The framework presented in this study can provide useful insights for forest ecosystem managers, policy-makers, decision-makers, and practitioners who are directly involved in the process of designing and implementing social interventions.
Naser Valizadeh, Sahra Mohammadi-Mehr, Dariush Hayati
Chapter 25. Conversion of Land Use Land Cover and Its Impact on Ecosystem Services in a Tropical Forest
The tropical forest ecosystem provides society-wide range of ecosystem services, but increasing human activity changes the land use/land cover dynamics (LULC) with significant change of the ecosystem services values. The present study is considered to evaluate and monitoring of LULC change along with the ecosystem services of a tropical forest region in Jhargram block West Bengal in India during 1972–2019. LANDSAT satellite imageries (1972, 1987, 1992, 2002, 2012, and 2019) have been analyzed to identify the LULC change and to determine the ecosystem service values using the value transfer method. Based on the Maximum likelihood algorithm image classification techniques using Arc-GIS software v.10.3, the study area is divided into eight land use land cover (LULC) categories like forest, cultivated land, grassland, water bodies, settlement/built-up land, barren land, and sand. We found that forest land continuously decreased by 42.3% between 1972 and 2019 and cultivated land is also decreased by 31.6% from 1972 to 2019. Settlement and Barren land are increased progressively 1209.6%, 394.3% respectively between 1972 and 2019 respectively. The total ecosystem services in Jhargram block have been calculated as $30.52, $29.69, $27.03, $34.1, $29.2, and $21 million in the year of 1972, 1987, 1992, 2002, 2012 and 2019 respectively. The total ecosystem services have reduced by 31.2% due to deforestation. Variations can also be seen in the value of individual ecosystem service functions viz. food production, raw material, erosion control, nutrient cycling, recreation, and climate regulation, which are important contributions in total ecosystem services. The sensitivity analysis indicates that our estimated ecosystem service value for our present study is reasonable and robustness concerning the value coefficient. This information will be helpful for sustainable land use management strategies and policy-making processes in the tropical forest region.
Soumen Bisui, Sambhunath Roy, Debashish Sengupta, Gouri Sankar Bhunia, Pravat Kumar Shit
Chapter 26. From Genesis to Awaited Success of Joint Forest Management in India
In most of the developing countries of the world including India local people are the chief users and guardians of the different types of ecosystems, and they make the vast majority of daily environmental decisions with their land use and investment choices. They have used their traditional knowledge, since days unknown, to manage natural resources, conserve ecosystems, and adapt to environmental changes. Community management is always based on variety of reasons like resource enhancement, religious and cultural purposes, and many other needs. In India the onset of community management in a large scale during 1990s was mostly for the purpose of resource enhancement, livelihood and biodiversity conservation. But the movement to protect forests did not get its deserved share. Empowerment of people to manage forest resources remained as a far cry. Thereafter, there was no further development in the process of community management as it was envisaged. In the present time, in view of increased need of carbon sequestration, sustainable forest management has beecome a need. But this can not be compromised with the subsistence need of the forest dependent people. Therefore, a new generation community management needs to be deviced to sustain our forest resources for the need of the forest dwellers in the one hand and global need of carbon sink in the other.
T. K. Mishra, Sudipta Kumar Maiti, Saikat Banerjee, S. K. Banerjee
Chapter 27. Google Earth Engine and Its Application in Forest Sciences
In order to support sustainable forest management, it is essential to estimate the extent and change of forest cover and to evaluate the environmental and socio-economic impacts of forest dynamics. It is challenging, however, to calculate forest area on a large scale using traditional statistical survey methods. Access to satellite images make it feasible to monitor the Earth’s forest at different spatial and temporal resolutions. The Google Earth Engine (GEE) is a cloud computing platform, which provides data analysis toolkits to access and to handle remote sensing datasets easily and freely. GEE has been used to analyze environmental changes with the emphasis of forest monitoring. Through GEE’s platform, the user can monitor forest cover by investigating satellite images in different spatial and temporal resolutions with acceptable accuracy. Moreover, this platform’s impressive satellite image archives, coupled with sophisticated in-built processing and analyzing toolkits, immensely help remote sensing-based studies. Hence, a systematic review has been conducted here to survey those studies that have employed this platform for forest monitoring. According to the analysis, when it comes to forest monitoring, the GEE’s platform has been mainly used for two objectives, namely, classification and change detection. Random forest has been identified as the most popular classification method and spectral index difference has been the most efficient method for forest change detection while considering GEE limitation for image preprocessing. Overall, the survey’s result revealed how applying this platform for forest monitoring is trending.
Mojtaba Naghdyzadegan Jahromi, Maryam Naghdizadegan Jahromi, Babak Zolghadr-Asli, Hamid Reza Pourghasemi, Seyed Kazem Alavipanah
Chapter 28. Free-Open Access Geospatial Data and Tools for Forest Resources Management
This chapter presents a description of the various data and techniques used by open-source geospatial foundations (OSGeo) to gather geo specific knowledge and forestry ecosystem services. Remote sensing plays a key role in the estimation of forest parameters and in the detection and reconfiguration of forest cover. The new management of the forest strengthens geospatial tools, approaches and inventions. Crowdsourcing modes range from the collection of data passively passed on to large groups on the web to the active participation of the crowd in the production of data via special mobile apps and web platforms. We searched the Google Scholar and the Science Web for peer-reviewed articles using a combination of the terminology crowd sourcing, social media, volunteered geographical knowledge and landscape and perception, interest and ecosystem resources in forestry to provide an overview of current crowding for the collection of earth observation. For this analysis, the application focus studies either describe active open access geospatial data or research projects using actively crowded geo-information. System-focused research—the second group—offered the framework for addressing participants’ participation in relevant forestry applications. The use of open-source geospatial data and software to collect real-time, locational data to explain forestry application and forestry management is also promising. Actionary open source geospatial technologies and data face a number of challenges: the involvement of adequate participants and sample representatives in order to ensure the utility of the outcome from an approach to public policy—e.g. through access to technology and/or a specific interest in nature—must be discussed more specifically.
Gouri Sankar Bhunia, Pravat Kumar Shit, Debashish Sengupta
Spatial Modeling in Forest Resources Management
herausgegeben von
Dr. Pravat Kumar Shit
Prof. Hamid Reza Pourghasemi
Dr. Pulakesh Das
Dr. Gouri Sankar Bhunia
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