Search results for: forest farming

Authors: Songul Cinaroglu

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Background: Random Forest is an efficient, multi-class machine learning method using for classification, regression and other tasks. This method is operating by constructing each tree using different bootstrap sample of the data. Determining the number of trees in random forests is an open question in the literature for studies about improving classification performance of random forests. Aim: The aim of this study is to analyze whether there is an optimal number of trees in Random Forests and how performance of Random Forests differ according to increase in number of trees using sample health data sets in R programme. Method: In this study we analyzed the performance of Random Forests as the number of trees grows and doubling the number of trees at every iteration using “random forest” package in R programme. For determining minimum and optimal number of trees we performed Mc Nemar test and Area Under ROC Curve respectively. Results: At the end of the analysis it was found that as the number of trees grows, it does not always means that the performance of the forest is better than forests which have fever trees. In other words larger number of trees only increases computational costs but not increases performance results. Conclusion: Despite general practice in using random forests is to generate large number of trees for having high performance results, this study shows that increasing number of trees doesn’t always improves performance. Future studies can compare different kinds of data sets and different performance measures to test whether Random Forest performance results change as number of trees increase or not.

Keywords: classification methods, decision trees, number of trees, random forest

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Authors: Mohammad Saeri, Noor Rizkiyah, Kambang Vetrani Asie, Titin Apung Atikah

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The study aims to assess efficient specific-location soybean farming technology assembly by assisting the farmers in applying the suggested technology. Superimposed trial was conducted to know NPK fertilizer effect toward soybean growth and yield and soybean improved variety test for the dissemination of improved variety. The assessment was conducted at the farmers group of Sumber Rejeki, Kepulungan Village, Gempol Sub-district, Pasuruan Regency as the soybean central at Pasuruan area. The number of farmers involved in the study was 38 people with 25 ha soybean area. This study was held from July to October 2012.  The recommended technology package agreed at the socialization time and used in this research were: using Argomulyo variety seeds of 40 kg/ha, planting by drilling, planting by distance of 40x10 cm, deciding the seeds amount of 2-3 seeds per hole, and giving fertilization based on recommendation of East Java AIAT of 50 kg Urea, 100 kg SP-36 and 50 kg KCl.  Farmers around the research location were used as control group. Assessment on soybean farming system was considered effective because it could increase the production up to 38%. The farming analysis showed that the result collaborator farmers gained were positively higher than non-collaborator farmers with RC ratio of 2.03 and 1.54, respectively. Argomulyo variety has the prospect to be developed due to the high yield of about 2 tons/ha and the larger seeds. The NPK fertilization test at the soybean plants showed that the fertilization had minor effect on the yield.

Keywords: farming system, soybean, variety, location specific

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Authors: Adebayo John Julius, Emmanuel Imoagene

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Mitigating global warming through reducing emission from deforestation and degradation (REDD) has been given increasing attentions in government-to-government negotiations while discussions among decision-makers have been going on, it is important to learn about the perception of local people in relation to REDD because the implementation will affect their lives. A survey was conducted using questionnaires to examine the livelihood and forest dependency of the local people in the vicinity of Onigambari and Ido area. Respondents’ income from forest activities and forest resources are collected. Participation in tourism related activities among the household members was also investigated to measure the potential of this “eco-friendly” income generation activity in the local communities. There was a general indication of reducing slash-and-burn activities with distance from the park and involvement in tourism-related job. Most of the local people were willing to accept compensation as alternative for slash-and-burn activities. The compensation preferred is in various form of development and different level of forest and environmental activities

Keywords: livelihood, emission, deforestation, degradation, local people, southwest Nigeria

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Authors: M. Rose Ntsewa, P. E. Dlamini, V. E. Mbanjwa, R. Chauke

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Land-use change from undisturbed forest to tea plantation may lead to accumulation or loss of soil organic carbon (SOC). So far, the factors controlling the vertical distribution of SOC under the long-term establishment of tea plantation remain poorly understood, especially in oxisols. In this study, we quantified the vertical distribution of SOC under tea plantation compared to adjacent undisturbed forest Oxisols sited at different topographic positions and also determined controlling edaphic factors. SOC was greater in the 30-year-old tea plantation compared to undisturbed forest oxisols and declined with depth across all topographic positions. Most of the SOC was found in the downslope position due to erosion and deposition. In the topsoil, SOC was positively correlated with heavy metals; manganese (r=0.62-0.83; P<0.05) and copper (r=0.45-0.69), effective cation exchange capacity (ECEC) (r=0.72) and mean weight diameter (MWD) (r=0.72-0.73), while in the subsoil SOC was positively correlated with copper (r=0.89-0.92) and zinc (r=0.86), ECEC (r=0.56-0.69) and MWD (r=0.48). These relationships suggest that SOC in the tea plantation, oxisols is chemically stabilized via complexation with heavy metals, and physically stabilized by soil aggregates.

Keywords: oxisols, tea plantation, topography, undisturbed forest

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Authors: Rogelio Omar Corona-Núñez, Alma Mendoza-Ponce

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The Tropical Dry Forest not only is one of the most important tropical ecosystems in terms of area, but also it is one of the most degraded ecosystems. However, little is known about the degradation impacts on carbon stocks, therefore in carbon emissions. There are different studies which explain its deforestation dynamics, but there is still a lack of understanding of how they correlate to carbon losses. Recently different authors have built current biomass maps for the tropics and Mexico. However, it is not clear how well they predict at the local scale, and how they can be used to estimate carbon emissions. This study quantifies the forest net carbon losses by comparing the potential carbon stocks and the different current biomass maps in the Southern Pacific coast in Oaxaca, Mexico. The results show important differences in the current biomass estimates with not a clear agreement. However, by the aggregation of the information, it is possible to infer the general patterns of biomass distribution and it can identify the driving forces of the carbon emissions. This study estimated that currently ~44% of the potential carbon stock estimated for the region is still present. A total of 6,764 GgC has been emitted due to deforestation and degradation of the forest at a rate of above ground biomass loss of 66.4 Mg ha-1. Which, ~62% of the total carbon emissions can be regarded as being due to forest degradation. Most of carbon losses were identified in places suitable for agriculture, close to rural areas and to roads while the lowest losses were accounted in places with high water stress and within the boundaries of the National Protected Area. Moreover, places not suitable for agriculture, but close to the coast showed carbon losses as a result of urban settlements.

Keywords: above ground biomass, deforestation, degradation, driving forces, tropical deciduous forest

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Authors: Chittana Phompila

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The current increased population pushes increasing demands for natural resources and living space. In Laos, urban areas have been expanding rapidly in recent years. The rapid urbanization can have negative impacts on landscapes, including forest and agriculture lands. The primary objective of this research were to map current urban areas in a large city in Savannakhet province, in Laos, 2) to compare changes in urbanization between 1990 and 2018, and 3) to estimate forest and agriculture areas lost due to expansions of urban areas during the last over twenty years within study area. Landsat 8 data was used and existing GIS data was collected including spatial data on rivers, lakes, roads, vegetated areas and other land use/land covers). GIS data was obtained from the government sectors. Object based classification (OBC) approach was applied in ECognition for image processing and analysis of urban area using. Historical data from other Landsat instruments (Landsat 5 and 7) were used to allow us comparing changes in urbanization in 1990, 2000, 2010 and 2018 in this study area. Only three main land cover classes were focused and classified, namely forest, agriculture and urban areas. Change detection approach was applied to illustrate changes in built-up areas in these periods. Our study shows that the overall accuracy of map was 95% assessed, kappa~ 0.8. It is found that that there is an ineffective control over forest and land-use conversions from forests and agriculture to urban areas in many main cities across the province. A large area of agriculture and forest has been decreased due to this conversion. Uncontrolled urban expansion and inappropriate land use planning can lead to creating a pressure in our resource utilisation. As consequence, it can lead to food insecurity and national economic downturn in a long term.

Keywords: urbanisation, forest cover, agriculture areas, Landsat 8 imagery

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Authors: Fawehinmi Olabisi Alaba, O. R. Adeniyi

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Farming households faces lots of disaster which contribute to endemic poverty. Anticipated increases in extreme weather events will exacerbate this. Primary data was administered to farming household using multi-stage random sampling technique. The result of the analysis shows that majority of the respondents (69.9%) are male, have mean household size, years of formal education and age of 5±1.14, 6±3.41, and 51.06±10.43 respectively. The major (48.9%) type of disaster experienced is flooding. Major coping mechanism adopted is sourcing for support from family and friends. Age, education, experience, access to extension agent, and mitigation control method contribute significantly to vulnerability to disaster. The major adaptation method (62.3%) is construction of drainage. The study revealed that the coping mechanisms employed may become less effective as increasingly fragile livelihood systems struggle to withstand disaster shocks. Thus there is need for training of the farmers on measures to adapt to mitigate the shock from disasters.

Keywords: adaptation, disasters, flooding, vulnerability

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Authors: Abdelaziz Moujane, Abedelali Boulli, Abdellah Ouigmane

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The present work focuses on the assessment of forestlandscape changes in the region of ZaouitAhansal, usingmultitemporal satellite images at high spatial resolution.Severalremotesensingmethodswereappliednamely: The supervised classification algorithm and NDVI whichwerecombined in a GIS environment to quantify the extent and change in density of forest stands (holmoak, juniper, thya, Aleppo pine, crops, and others).The resultsobtainedshowedthat the forest of ZaouitAhansal has undergonesignificantdegradationresulting in a decrease in the area of juniper, cedar, and zeenoak, as well as an increase in the area of baresoil and agricultural land. The remotesensing data providedsatisfactoryresults for identifying and quantifying changes in forestcover. In addition, thisstudycould serve as a reference for the development of management strategies and restoration programs.

Keywords: remote sensing, GIS, satellite image, NDVI, deforestation, zaouit ahansal

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Authors: Loubna Khalile, Hicham Lahlaoi, Hassan Rhinane, A. Kaoukaya, S. Fal

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Green spaces is an essential element, they contribute to improving the quality of lives of the towns around them. They are a place of relaxation, walk and rest a playground for sport and youths. According to United Nations Organization Forests cover 31% of the land. In Morocco in 2013 that cover 12.65 % of the total land area, still, a small proportion compared to the natural needs of forests as a green lung of our planet. The Benslimane Forest is a large green area It belongs to Chaouia-Ouardigha Region and Greater Casablanca Region, it is located geographically between Casablanca is considered the economic and business Capital of Morocco and Rabat the national political capital, with an area of 12261.80 Hectares. The essential problem usually encountered in suburban forests, is visitation and tourism pressure it is anthropogenic actions, as well as other ecological and environmental factors. In recent decades, Morocco has experienced a drought year that has influenced the forest with increasing human pressure and every day it suffers heavy losses, as well as over-exploitation. The Moroccan forest ecosystems are weak with intense ecological variation, domanial and imposed usage rights granted to the population; forests are experiencing a significant deterioration due to forgetfulness and immoderate use of forest resources which can influence the destruction of animal habitats, vegetation, water cycle and climate. The purpose of this study is to make a model of the degree of degradation of the forest and know the causes for prevention by using remote sensing and geographic information systems by introducing climate and ancillary data. Analytic hierarchy process was used to find out the degree of influence and the weight of each parameter, in this case, it is found that anthropogenic activities have a fairly significant impact has thus influenced the climate.

Keywords: analytic hierarchy process, degradation, forest, geographic information system

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Authors: Ayuko Itsuki, Sachiyo Aburatani

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The seasonal (January, April, July and October) and monthly soil respiration rate and the monthly litter fall amounts were examined in the laurel-leaved (B_B-1) and Cryptomeria japonica (B_B-2 and PW) forests in the Kasugayama Hill Primeval Forest (Nara, Japan). The change of the seasonal soil respiration rate corresponded to that of the soil temperature. The soil respiration rate was higher in October when fresh organic matter was supplied in the forest floor than in April in spite of the same temperature. The seasonal soil respiration rate of B_B-1 was higher than that of B_B-2, which corresponded to more numbers of bacteria and fungi counted by the dilution plate method and by the direct count method by microscopy in B_B-1 than that of B_B-2. The seasonal soil respiration rate of B_B-2 was higher than that of PW, which corresponded to more microbial biomass by the direct count method by microscopy in B_B-2 than that of PW. The correlation coefficient with the seasonal soil respiration and the soil temperature was higher than that of the monthly soil respiration. The soil respiration carbon was more than the litter fall carbon. It was suggested that the soil respiration included in the carbon dioxide which was emitted by the plant root and soil animal, or that the litter fall supplied to the forest floor included in animal and plant litter.

Keywords: field soil respiration rate, forest soil, litter fall, mineralization rate

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Authors: Bojana Horvat, Barbara Karleusa, Goran Volf, Nevenka Ozanic, Ivica Kisic

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Forest fire is a major threat in many regions in Croatia, especially in coastal areas. Although they are often caused by natural processes, the most common cause is the human factor, intentional or unintentional. Forest fires drastically transform landscapes and influence natural processes. The main goal of the presented research is to analyse and quantify the impact of the forest fire on hydrological processes and propose the model that best describes changes in hydrological patterns in the analysed catchments. Keeping in mind the spatial component of the processes, geospatial analysis is performed to gain better insight into the spatial variability of the hydrological response to disastrous events. In that respect, two catchments that experienced severe forest fire were delineated, and various hydrological and meteorological data were collected both attribute and spatial. The major drawback is certainly the lack of hydrological data, common in small torrential karstic streams; hence modelling results should be validated with the data collected in the catchment that has similar characteristics and established hydrological monitoring. The event chosen for the modelling is the forest fire that occurred in July 2019 and burned nearly 10% of the analysed area. Surface (land use/land cover) conditions before and after the event were derived from the two Sentinel-2 images. The mapping of the burnt area is based on a comparison of the Normalized Burn Index (NBR) computed from both images. To estimate and compare hydrological behaviour before and after the event, curve number (CN) values are assigned to the land use/land cover classes derived from the satellite images. Hydrological modelling resulted in surface runoff generation and hence prediction of hydrological responses in the catchments to a forest fire event. The research was supported by the Croatian Science Foundation through the project 'Influence of Open Fires on Water and Soil Quality' (IP-2018-01-1645).

Keywords: Croatia, forest fire, geospatial analysis, hydrological response

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Authors: M. E. Hammond, R. Pokorny

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Not much research has gone into the dynamics of natural regeneration of trees species in tropical forest regions. This study seeks to investigate the impact of gap sizes and light distribution in forest floors on the regeneration of Celtis mildbraedii (CEM), Nesogordonia papaverine (NES) and Terminalia superba (TES). These are selected economically important tree species with different shade tolerance attributes. The spatial distribution patterns and the potential regeneration competition index (RCI) among species using height to diameter ratio (HDR) have been assessed. Gap sizes ranging between 287 – 971 m² were selected at the Bia Tano forest reserve, a tropical moist semi-deciduous forest in Ghana. Four (4) transects in the cardinal directions were constructed from the center of each gap. Along each transect, ten 1 m² sampling zones at 2 m spacing were established. Then, three gap microsites (labeled ecozones I, II, III) were delineated within these sampling zones based on the varying temporal light distribution on the forest floor. Data on height (H), root collar diameter (RCD) and regeneration census were gathered from each of the ten sampling zones. CEM and NES seedlings (≤ 50 cm) and saplings (≥ 51 cm) were present in all ecozones of the large gaps. Seedlings of TES were observed in all ecozones of large and small gaps. Regression analysis showed a significant negative linear relationship between independent RCD and H growth variables on dependent HDR index in ecozones II and III of both large and small gaps. There was a correlation between RCD and H in both large and small gaps. A strong regeneration competition was observed among species in ecozone II in large (df 2, F=3.6, p=0.035) and small (df 2, F=17.9, p=0.000) gaps. These results contribute to the understanding of the natural regeneration of different species with regards to light regimes in forest floors.

Keywords: Celtis mildbraedii, ecozones, gaps, Nesogordonia papaverifera, regeneration, Terminalia superba

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Authors: Sher Ali

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Organic agriculture has gained significant momentum globally as a sustainable and environmentally friendly farming practice. In Pakistan, amidst growing concerns about food security, environmental degradation, and health issues related to conventional farming methods, the adoption of organic agriculture presents a promising pathway for agricultural development. This abstract aims to provide an overview of the status, opportunities, challenges, and future directions of organic agriculture in Pakistan. It delves into the current state of organic farming practices, including the extent of adoption, key crops cultivated, and the regulatory framework governing organic certification. Furthermore, the abstract discusses the unique opportunities that Pakistan offers for organic agriculture, such as its diverse agro-climatic zones, rich biodiversity, and traditional farming knowledge. It highlights successful initiatives and case studies that showcase the potential of organic farming to improve rural livelihoods, enhance food security, and promote sustainable agricultural practices. However, the abstract also addresses the challenges hindering the widespread adoption of organic agriculture in Pakistan, ranging from limited awareness and technical know-how among farmers to inadequate infrastructure and market linkages. It emphasizes the need for supportive policies, capacity-building programs, and investment in research and extension services to overcome these challenges and promote the growth of the organic agriculture sector. Lastly, the abstract outlines future directions and recommendations for advancing organic agriculture in Pakistan, including strategies for scaling up production, strengthening certification mechanisms, and fostering collaboration among stakeholders. By shedding light on the opportunities, challenges, and potential of organic agriculture in Pakistan, this abstract aims to contribute to the discourse on sustainable farming practices at the upcoming Agro Conference in the USA. It invites participants to engage in dialogue, share experiences, and explore avenues for collaboration toward promoting organic agriculture for a healthier, more resilient food system.

Keywords: agriculture, challenges, organic, Pakistan

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Authors: Pavel A. Strizhak, Geniy V. Kuznetsov, Ivan S. Voytkov, Dmitri V. Antonov

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This paper presents the results of experiments carried out at a specialized test site for establishing macroscopic patterns of heat and mass transfer processes at localizing model combustion sources of ground forest fires with the use of barrier lines in the form of a wetted lay of material in front of the zone of flame burning and thermal decomposition. The experiments were performed using needles, leaves, twigs, and mixtures thereof. The dimensions of the model combustion source and the ranges of heat release correspond well to the real conditions of ground forest fires. The main attention is paid to the complex analysis of the effect of dispersion of water aerosol (concentration and size of droplets) used to form the barrier line. It is shown that effective conditions for localization and subsequent suppression of flame combustion and thermal decomposition of forest fuel can be achieved by creating a group of barrier lines with different wetting width and depth of the material. Relative indicators of the effectiveness of one and combined barrier lines were established, taking into account all the main characteristics of the processes of suppressing burning and thermal decomposition of forest combustible materials. We performed the prediction of the necessary and sufficient parameters of barrier lines (water volume, width, and depth of the wetted lay of the material, specific irrigation density) for combustion sources with different dimensions, corresponding to the real fire extinguishing practice.

Keywords: forest fire, barrier water lines, pyrolysis front, flame front

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Authors: Gregory Sullivan

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The evolution of global warming has escalated the number and complexity of forest fires around the world. As an example, the United States and Brazil combined generated more than 30,000 forest fires last year. The impact to our environment, structures and individuals is incalculable. The world has learned to try to take this in stride, trying multiple ways to contain fires. Some countries are trying to use cameras in limited areas. There are discussions of using hundreds of low earth orbit satellites and linking them together, and, interfacing them through ground networks. These are all truly noble attempts to defeat the forest fire phenomenon. But there is a better, simpler answer. A bigger piece of the solutions puzzle is to see the fires while they are small, soon after initiation. The approach is to see the fires while they are very small and report their location (latitude and longitude) to local first responders. This is done by placing a sensor at geostationary orbit (GEO: 26,000 miles above the earth). By placing this small satellite in GEO, we can “stare” at the earth, and sense temperature changes. We do not “see” fires, but “measure” temperature changes. This has already been demonstrated on an experimental scale. Fires were seen at close to initiation, and info forwarded to first responders. it were the first to identify the fires 7 out of 8 times. The goal is to have a small independent satellite at GEO orbit focused only on forest fire initiation. Thus, with one small satellite, focused only on forest fire initiation, we hope to greatly decrease the impact to persons, property and the environment.

Keywords: space detection, wildfire early warning, demonstration wildfire detection and action from space, space detection to first responders

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Authors: Lule Basha, Eralda Gjika

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The exchange rate is one of the most important economic variables, especially for a small, open economy such as Albania. Its effect is noticeable in one country's competitiveness, trade and current account, inflation, wages, domestic economic activity, and bank stability. This study investigates the fluctuation of Albania’s exchange rates using monthly average foreign currency, Euro (Eur) to Albanian Lek (ALL) exchange rate with a time span from January 2008 to June 2021, and the macroeconomic factors that have a significant effect on the exchange rate. Initially, the Random Forest Regression algorithm is constructed to understand the impact of economic variables on the behavior of monthly average foreign currencies exchange rates. Then the forecast of macro-economic indicators for 12 months was performed using time series models. The predicted values received are placed in the random forest model in order to obtain the average monthly forecast of the Euro to Albanian Lek (ALL) exchange rate for the period July 2021 to June 2022.

Keywords: exchange rate, random forest, time series, machine learning, prediction

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Authors: Okunola Solomon Olufemi

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The study focused on the contribution of urban agriculture to the livelihood of 107 respondents in Kwara State of Nigeria. The study employed structured questionnaire to collect relevant data for the study while descriptive statistics such as frequency distribution and percentage were employed to analyses the objectives. Most respondents (82.9%), were one time or the other in a married state and they had formal education (80.4%) while many of the lot (58.9%) had superior education ranging from OND to Ph.D level. These farmers were either retired government workers or those trying to argument their family income or trying to be food sufficient. Most of the respondents (77.4%) had a farm size of less than or equal to 3 hectares showing that most the urban farmers were smallholders which might be as a result of stiff completion for land resource. Most of the respondents had a relatively large family size of 6 and above members. Most respondents used family labor (59.8%). Respondents in the study area also made use of the cooperatives and daily contributions for loanable funds, while few respondents utilized the formal sector. .Urban agriculture accounted for 84.4% of the Livelihood outcomes of the respondents. While non-farming activities contributed 17.6%.Most of the respondents (31.8%) participated in non-farming activities to generate extra income while their major constraints were shortage of land both in term of access and tenure (34.1%), limited access to resource and agricultural inputs (29.3%), and prohibitive urban policies and regulation (23.2%).

Keywords: poverty throes, urban agriculture, rigors of farming, non-farming, married state

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Authors: Turuganti Venkateswarlu, Jagadeesh Anmala

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Excessive runoffs from various non-point source land uses, and other point sources are rapidly contaminating the water quality of streams in the Upper Green River watershed, Kentucky, USA. It is essential to maintain the stream water quality as the river basin is one of the major freshwater sources in this province. It is also important to understand the water quality parameters (WQPs) quantitatively and qualitatively along with their important features as stream water is sensitive to climatic events and land-use practices. In this paper, a model was developed for predicting one of the significant WQPs, Fecal Coliform (FC) from precipitation, temperature, urban land use factor (ULUF), agricultural land use factor (ALUF), and forest land-use factor (FLUF) using Random Forest (RF) algorithm. The RF model, a novel ensemble learning algorithm, can even find out advanced feature importance characteristics from the given model inputs for different combinations. This model’s outcomes showed a good correlation between FC and climate events and land use factors (R2 = 0.94) and precipitation and temperature are the primary influencing factors for FC.

Keywords: water quality, land use factors, random forest, fecal coliform

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Authors: Lazlo Fauth, Andreas Ligocki

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For cost-effective use of harvesters, expensive repairs and unplanned downtimes must be reduced as far as possible. The predictive detection of failing systems and the calculation of intelligent service intervals, necessary to avoid these factors, require in-depth knowledge of the machines' behavior. Such know-how needs permanent monitoring of the machine state from different technical perspectives. In this paper, three approaches will be presented as they are currently pursued in the publicly funded project PreForst at Ostfalia University of Applied Sciences. These include the intelligent linking of workshop and service data, sensors on the harvester, and a special online hydraulic oil condition monitoring system. Furthermore the paper shows potentials as well as challenges for the use of these data in the conception of a predictive maintenance system.

Keywords: predictive maintenance, condition monitoring, forest harvesting, forest engineering, oil data, hydraulic data

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Authors: Sakura Yoshii, Masakazu Abe, Akihiro Iijima

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Nitrogen saturation has become one of the serious issues in the field of forest environment. The watershed protection forests located in the downwind hinterland of Tokyo Metropolitan Area are believed to be facing nitrogen saturation. In this study, we carefully focus on the balance of nitrogen between load and runoff. Annual nitrogen load via atmospheric deposition was estimated to 461.1 t-N/year in the upper reaches of the Kanna River. Annual nitrogen runoff to the forested headwater stream of the Kanna River was determined to 184.9 t-N/year, corresponding to 40.1% of the total nitrogen load. Clear seasonal change in NO3-N concentration was still observed. Therefore, watershed protection forest of the Kanna River is most likely to be in Stage-1 on the status of nitrogen saturation.

Keywords: atmospheric deposition, nitrogen accumulation, denitrification, forest ecosystems

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Authors: Wanderson Goncalves e Goncalves, José Alberto Silva de Sá

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Human actions are a threat to the balance and conservation of the Amazon forest. Therefore the environmental monitoring services play an important role as the preservation and maintenance of this environment. This study classified forest types using data from a forest inventory provided by the 'Florestal e da Biodiversidade do Estado do Pará' (IDEFLOR-BIO), located between the municipalities of Santarém, Juruti and Aveiro, in the state of Pará, Brazil, covering an area approximately of 600,000 hectares, Bands 3, 4 and 5 of the TM-Landsat satellite image, and Self - Organizing Maps. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training the neural network. The midpoints of each sample of forest inventory have been linked to images. Later the Digital Numbers of the pixels have been extracted, composing the database that fed the training process and testing of the classifier. The neural network was trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and the number of examples in the training data set was 400, 200 examples for each class (Dbe and Dbe + Abp), and the size of the test data set was 100, with 50 examples for each class (Dbe and Dbe + Abp). Therefore, total mass of data consisted of 500 examples. The classifier was compiled in Orange Data Mining 2.7 Software and was evaluated in terms of the confusion matrix indicators. The results of the classifier were considered satisfactory, and being obtained values of the global accuracy equal to 89% and Kappa coefficient equal to 78% and F1 score equal to 0,88. It evaluated also the efficiency of the classifier by the ROC plot (receiver operating characteristics), obtaining results close to ideal ratings, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon.

Keywords: artificial neural network, computational intelligence, pattern recognition, unsupervised learning

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Authors: Amit Kumar Verma

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The present study deals with fire prediction model in Terai Arc Landscape, one of the most dramatic ecosystems in Asia where large, wide-ranging species such as tiger, rhinos, and elephant will thrive while bringing economic benefits to the local people. Forest fires cause huge economic and ecological losses and release considerable quantities of carbon into the air and is an important factor inflating the global burden of carbon emissions. Forest fire is an important factor of behavioral cum ecological habit of tiger in wild. Post fire changes i.e. micro and macro habitat directly affect the tiger habitat or land. Vulnerability of fire depicts the changes in microhabitat (humus, soil profile, litter, vegetation, grassland ecosystem). Microorganism like spider, annelids, arthropods and other favorable microorganism directly affect by the forest fire and indirectly these entire microorganisms are responsible for the development of tiger (Panthera tigris) habitat. On the other hand, fire brings depletion in prey species and negative movement of tiger from wild to human- dominated areas, which may leads the conflict i.e. dangerous for both tiger & human beings. Early forest fire prediction through mapping the risk zones can help minimize the fire frequency and manage forest fires thereby minimizing losses. Satellite data plays a vital role in identifying and mapping forest fire and recording the frequency with which different vegetation types are affected. Thematic hazard maps have been generated by using IDW technique. A prediction model for fire occurrence is developed for TAL. The fire occurrence records were collected from state forest department from 2000 to 2014. Disciminant function models was used for developing a prediction model for forest fires in TAL, random points for non-occurrence of fire have been generated. Based on the attributes of points of occurrence and non-occurrence, the model developed predicts the fire occurrence. The map of predicted probabilities classified the study area into five classes very high (12.94%), high (23.63%), moderate (25.87%), low(27.46%) and no fire (10.1%) based upon the intensity of hazard. model is able to classify 78.73 percent of points correctly and hence can be used for the purpose with confidence. Overall, also the model works correctly with almost 69% of points. This study exemplifies the usefulness of prediction model of forest fire and offers a more effective way for management of forest fire. Overall, this study depicts the model for conservation of tiger’s natural habitat and forest conservation which is beneficial for the wild and human beings for future prospective.

Keywords: fire prediction model, forest fire hazard, GIS, landsat, MODIS, TAL

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Authors: W. A. D. P. Wanigasundera, P. C. B. Alahakoon

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Kandy district in Sri Lanka has small scale and rain-fed paddy farming, and highly vulnerable to climate change. In this study, the status of climate change was assessed using meteorological data and compared with the perceptions of paddy farming community. Factors affecting the adaptation to the climate smart farming were also assessed. Meteorological data for 33 years were collected and the changes over time compared with the perceptions of farmers. The temperature, rainfall and number of rainy days have increased in both locations. The onset of rains also has shifted. The perceptions of the majority of the farmers were in line with the actual changes. The knowledge and attitudes about the causes of climate change and adaptation were medium and related to level of adoption. Formulating effective communication strategies, and a collaborative approach involving state, private sector, civil society to make Sri Lankan agriculture ‘climate-smart’ is urgently needed.

Keywords: adaptation of climate-smart technology, climate change, perception, rain-fed paddy

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Authors: Chetna Khandelwal

Abstract:

This research aims to study the contentions that Farm Animal Sanctuaries pose to human-animal relationships in modernity, which have developed as a result of globalisation of the meat industry and advancements in technology. The sociological history of human-animal relationships in farming is contextualised in order to set a foundation for the follow-up examination of challenges to existing human-(farm)animal relationships by Farm Animal Sanctuaries. The methodology was influenced by relativism, and the method involved three semi-structured small-group interviews, conducted at locations of sanctuaries. The sample was chosen through purposive sampling and varied by location and size of the sanctuary. Data collected were transcribed and qualitatively coded to generate themes. Findings revealed that sanctuary contentions to established human-animal relationships by factory farming could be divided into 4 broad categories – Revealing horrors of factory farming (involving uncovering power relations in agribusiness); transforming relationships with animals (including letting them emotionally heal in accordance with their individual personalities and treating them as partial-pets); educating the public regarding welfare conditions in factory farms as well as animal sentience through practical experience or positive imagery of farm animals, and addressing retaliation made by agribusiness in the form of technologies or discursive strategies. Hence, this research concludes that The human-animal relationship in current times has been characterised by – (ideological and physical) distance from farm animals, commodification due to increased chasing of profits over welfare and exploitation using technological advancements, creating unequal power dynamics that rid animals of any agency. Challenges to this relationship can be influenced by local populations around the sanctuary but not so dependent upon the size of it. This research can benefit from further academic exploration into farm animal sanctuaries and their role in feminist animal rights activism to enrich the ongoing fight against intensive farming.

Keywords: animal rights, factory farming, farm animal sanctuaries, human-animal relationships

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Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

Abstract:

Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

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Authors: Kettawa Boonprakarn, Jedsarid Sangkaphan, Bejapornd Deekhuntod, Nuntharat Suriyo

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This study aimed to investigate the existence of field corn networks on the Thailand-Burma border under the patron-client contract farming system. The data of this qualitative study were collected through in-depth interviews with nine key informants. The results of the study revealed that the existence of the field corn networks was associated with the relationship where farmers had to share their crops with protectors in the areas under the influence of the KNU (Karen National Union) and the DKBA (Democratic Karen Buddhist Army) or Burmese soldiers. A Mae Liang, the person who starts a network has a connection with a Thaokae, Luk Rai Hua Chai or the head of a group of farmers, and farmers. They are under the patron-client system with trust and loyalty that enable the head of the group and the farmers in the Burma border side to remain under the same Mae Liang even though the business has been passed down to later generations.

Keywords: existence, field-corn networks, patron-client system, contract farming

Procedia PDF Downloads 270

Authors: Jang-Hwan Jo

Abstract:

The supply management of ecosystem services of local forests is an essential issue as it is linked to the ecological welfare of local residents. This study aims to estimate the supply, trade-offs, and synergies of local forest regulating ecosystem services using a land cover classification map (LCCM) and a forest types map (FTM). Rigorous literature reviews and Expert Delphi analysis were conducted using the detailed variables of 1:5,000 LCCM and FTM. Land-use scoring method and Getis-Ord Gi* Analysis were utilized on detailed variables to propose a method for estimating supply, trade-offs, and synergies of the local forest regulating ecosystem services. The analysis revealed that the rank order (1st to 5th) of supply of regulating ecosystem services was Erosion prevention, Air quality regulation, Heat island mitigation, Water quality regulation, and Carbon storage. When analyzing the correlation between defined services of the entire city, almost all services showed a synergistic effect. However, when analyzing locally, trade-off effects (Heat island mitigation – Air quality regulation, Water quality regulation – Air quality regulation) appeared in the eastern and northwestern forest areas. This suggests the need to consider not only the synergy and trade-offs of the entire forest between specific ecosystem services but also the synergy and trade-offs of local areas in managing the regulating ecosystem services of local forests. The study result can provide primary data for the stakeholders to determine the initial conditions of the planning stage when discussing the establishment of policies related to the adjustment of the supply of regulating ecosystem services of the forests with limited access. Moreover, the study result can also help refine the estimation of the supply of the regulating ecosystem services with the availability of other forms of data.

Keywords: ecosystem service, getis ord gi* analysis, land use scoring method, regional forest, regulating service, synergies, trade-offs

Procedia PDF Downloads 88

Authors: Thanh Noi Phan, Martin Kappas, Jan Degener

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The currently launched Sentinel 2 (S2) satellite (June, 2015) bring a great potential and opportunities for land use/cover map applications, due to its fine spatial resolution multispectral as well as high temporal resolutions. So far, there are handful studies using S2 real data for land cover classification. Especially in northern Vietnam, to our best knowledge, there exist no studies using S2 data for land cover map application. The aim of this study is to provide the preliminary result of land cover classification using Sentinel -2 data with a rising state – of – art classifier, Random Forest. A case study with heterogeneous land use/cover in the eastern of Hanoi Capital – Vietnam was chosen for this study. All 10 spectral bands of 10 and 20 m pixel size of S2 images were used, the 10 m bands were resampled to 20 m. Among several classified algorithms, supervised Random Forest classifier (RF) was applied because it was reported as one of the most accuracy methods of satellite image classification. The results showed that the red-edge and shortwave infrared (SWIR) bands play an important role in land cover classified results. A very high overall accuracy above 90% of classification results was achieved.

Keywords: classify algorithm, classification, land cover, random forest, sentinel 2, Vietnam

Procedia PDF Downloads 388

Authors: Uttam Kumar Sahoo, K. Lalhmingsangi, J. H. Lalremruati

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Non-timber forest resources particularly the high-value, low volume NTFPs has drawn interest as an activity all over the world during the past three decades that could raise standards of living for the rural folks while being compatible with forest conservation. This is particularly true for the people living in and around or fringes of protected areas. However, the economics that plays between resources’ stock and its utilization by the humans is yet to be validated and evaluated logistically. A study was therefore designed to understand the linkages between resource (especially NTFPs) availability and their utilization, existing threats to this biodiversity conservation and the role of NTFPs within the livelihood systems of those households that are most directly involved in creating conservation threats. About 25% of the households were sampled from the two sites ‘resource-rich’ and ‘resource poor’ area of Dampa Tiger Reserve (Western boundary). Our preliminary findings suggest that the collection of relatively high-volume and low value NTFPs such as fuelwood, fodder has caused degradation of forest resources while the low-volume and high-value NTFPs such as wild edible mushrooms, vegetables, other specialty food products, inputs to crafts, medicinal plants have resulted into species promotion/conservation through their domestication in traditional agroforestry systems including home gardens and/or collateral protection of the Tiger Reserve. It is thus suggested that proper assessment of these biodiversities, their direct and indirect valuation, market and non-market profits etc be carried out in greater details which would result in prescribing effective management plans around the park.

Keywords: household economy, livelihood strategies, non-timber forest products, species conservation

Procedia PDF Downloads 190

Authors: Sadixa Sanjel, Ahmed Sadek, Naseef Mansoor, Zelalem Denekew

Abstract:

Collegiate ice hockey (NCAA) sports analytics is different from the national level hockey (NHL). We apply and compare multiple machine learning models such as Linear Regression, Random Forest, and Neural Networks to predict the win ratio for a team based on their statistics. Data exploration helps determine which statistics are most useful in increasing the win ratio, which would be beneficial to coaches and team managers. We ran experiments to select the best model and chose Random Forest as the best performing. We conclude with how to bridge the gap between the college and national levels of sports analytics and the use of machine learning to enhance team performance despite not having a lot of metrics or budget for automatic tracking.

Keywords: NCAA, NHL, sports analytics, random forest, regression, neural networks, game predictions

Procedia PDF Downloads 114