Top PDF Vegetation Assessment of Peat Swamp Forest Using Remote Sensing

Vegetation Assessment of Peat Swamp Forest Using Remote Sensing

Vegetation Assessment of Peat Swamp Forest Using Remote Sensing

Department of Crop Science, Faculty of Agriculture and Food Sciences, University Putra Malaysia Bintulu Campus, 97007 Bintulu, Sarawak, Malaysia Abstract: Problem statement: Peat covers 1.6 million ha (13%) of the 12.4 million ha land area of Sarawak and some of peat swamp forests have been logged. The objective of this study was to assess the impact of logging operation on peat swamp forest in this area. Approach: The study used a remote sensing technique to assess vegetation cover in a peat swamp forest areas in Sarawak as result of logging practice and land clearing activities for oil palm plantation. Vegetation Index was used to assess impact of timber harvesting system and land clearing activities on remaining peat swamp forest in two sites which were logged previously and the possible relationship of change in hydrology. Results: The timber harvesting system was a combination of rail system for log transportation and excavator crawler for log skidding. Drainage work was probably carried out prior to logging activities which was followed up by land preparation for the establishment of the oil palm plantations. There was a general decrease in the level of greenness from 2002-2007. Between the two sites, the level of greenness was relatively lower in the West Site. The high green level of both sites was reduced remarkably in 2007 especially for the West Site and this corresponded to increase in the percentage of medium green level. The changed in the level of greenness in the remnant peat swamp forest could suggest that soil and other conditions such as vegetation structure and floristic composition are unfavorable for the expected rate of forest regeneration. Conclusion: The remnant logged peat swamp forest of the area declined due to a poor state of growth as shown by the dramatically decrease in the level of greenness. The peat swamp forest types strongly related to the hydrological conditions and the associated flow of nutrients and mineral elements. The surrounding hydrology was presumed to have influence the physical and chemical characteristics of the peat.
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A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia

A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia

The loss of huge areas of peat swamp forest in Southeast Asia and the resulting negative environmental effects, both local and global, have led to an increasing interest in peat restoration in the region. Satellite remote sensing offers the potential to provide up-to-date information on peat swamp forest loss across large areas, and support spatially explicit conservation and restoration planning. Fusion of optical and radar remote sensing data may be particularly valuable in this context, as most peat swamp forests are in areas with high cloud cover, which limits the use of optical data. Radar data can “see through” cloud, but experience so far has shown that it doesn’t discriminate well between certain types of land cover. Various approaches to fusion exist, but there is little information on how they compare. To assess this untapped potential, we compare three different classification methods with Sentinel-1 and Sentinel-2 images to map the remnant distribution of peat swamp forest in the area surrounding Sungai Buluh Protection Forest, Sumatra, Indonesia. Results show that data fusion increases overall accuracy in one of the three methods, compared to the use of optical data only. When data fusion was used with the pixel-based classification using the original pixel values, overall accuracy increased by a small, but statistically significant amount. Data fusion was not beneficial in the case of object-based classification or pixel- based classification using principal components. This indicates optical data are still the main source of information for land cover mapping in the region. Based on our findings, we provide methodological recommendations to help those involved in peatland restoration capitalise on the potential of big data.
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A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia

A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia

This study applies data fusion and machine learning techniques to map peat swamp forest loss, and as such continues the work towards automated regional level mapping in Southeast Asia (Miettinen et al. 2017). The main barrier to scaling up the methods described in this paper is the technique used to create the composite opti- cal scene, as the cloud probability raster in the Sentinel-2 Level 2A products was not good enough to be used as a cloud mask. Multi-temporal approaches to cloud detec- tion are being developed to mask cloud (Hagolle et al. 2010; Mateo-Garc ıa et al. 2018) and more work needs to be done to test their functionality in areas with very high cloud cover, adapt them for use with Sentinel-2 data and make the algorithms more accessible to a wider commu- nity of users. Traditionally, low computational power has limited those working in smaller institutions and NGOs, making it hard to map large areas and work with meth- ods that rely on time-series analysis. However, this has changed thanks to the availability of services such as the cloud-based platform Google Earth Engine (Gorelick et al. 2017) and the free virtual machines provided by the European Space Agency’s Research and User Support Ser- vice (RUS 2018). There is currently a growing interest in the potential of big data (Liu et al. 2018), which in a remote sensing context refers to the recent increase in the volume and variety of remote sensing data available, as well as the increase in processing velocity (Chi et al.
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Detection of fire impact and vegetation recovery over tropical peat swamp forest by satellite data and ground-based NDVI instrument

Detection of fire impact and vegetation recovery over tropical peat swamp forest by satellite data and ground-based NDVI instrument

Remote sensing is a powerful tool to monitor the earth surface in different spectral bands of the visible, infrared, and radar frequencies. Changes of the areas of interest can be detected easily over time. Satellite imagery provides a viable source of data from which updated land cover information can be extracted to inventory and monitor changes in vegetation cover (Roberts et al. 1997, Boehm and Siegert 2001). Some researchers have reported that post-fire satellite-based surveys have confirmed the Kalimantan Island was most severely affected by the 1997–1998 fires, with fire activity split into two intense burning periods, separated by the 1997–1998 monsoon rains that commenced in November 1997 (Siegert and Hoffmann 2000). Page et al. (2002) estimated that 0.19–0.23 Gt of carbon were released to the atmosphere through peat combustion, with a further 0.05 Gt released from burning of its overlying vegetation in the Kalimantan Island. Using quick look imagery from the SPOT satellite, the total area of forest cleared or damaged directly by the fires has been estimated at 30,600 km 2 . In East Kalimantan, 5.2 + 0.3 million hectares, including 2.6 million hectares of forest, was burned with varying degrees of damage (Siegert and Hoffmann 2000, Siegert et al. 2001).
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Assessment of coastal vegetation degradation using remote sensing in False Bay, South Africa

Assessment of coastal vegetation degradation using remote sensing in False Bay, South Africa

confusing due to interim effects of environmental dynamics such as, clearing of alien invasive vegetation and frequent bush fires. The study results revealed that images taken in the dry, summer season better detected invasive alien vegetation. The three image derivatives (brightness, compactness and standard deviation for NIR) adapted from Lück-Vogel, O’Farrell & Roberts (2013) however, did not yield satisfactory regression results for deriving a classification for the coastal environment. This is because of transformed features such as, buildings, play-grounds and agricultural land-uses were masked out in the study. However, the spectral bands important for vegetation assessment, namely the RED, NIR1 and NIR2 worked appropriately and yielded good results in the DTC. The CoCT habitat condition data layer was found to be too coarse for this study thus resulting in low regression results which were not used further for the DTC. This was because to the habitat condition polygons included several types of intactness or degradation.
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Greenhouse gas emissions resulting from conversion of peat swamp forest to oil palm plantation.

Greenhouse gas emissions resulting from conversion of peat swamp forest to oil palm plantation.

We thank the Royal Geographical Society (with IBG) for funding this project through the Ralph Brown expedition award (to PA), with additional support from the University of Nottingham (RBEA0414RGS112250). Hannah Cooper was supported by the STARs DTP (NE-M009106-1). We are very grateful to the Selangor State Forestry Department for granting forest reserve access and in providing field ranger support (including Mr. Mohd Rosli B. Md Kadim (Rosli), Mr. Sabaruddin Mohd Shahid (Din) and the rest of the team at Raja Musa and Sungai Karang Forestry Offices) as a follow-up to the 2013 Biodiversity Expedition organised in association with Malaysia Nature Society. We also thank staff at the Global Environment Centre, Selangor for support and PKPS Agricultural Develop- ment Corporation for granting site access. Finally, we thank Centre for Remote Imaging, Sensing and Processing at the National University of Singapore for sharing the dataset used for Fig. 1 . We declare that we have no con flict of interests.
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Assessment of Net Primary Productivity (NPP) of Forest Over Nainital District Using Remote Sensing

Assessment of Net Primary Productivity (NPP) of Forest Over Nainital District Using Remote Sensing

Therefore, it is necessary to apply alternative methods of NPP estimation to replace or supplement traditional approaches in collecting ecological data. The development of remote sensing has by far enhanced the ability to study and understand ecosystems with improved accuracy (Lu, 2006). Remote sensing provides a valuable opportunity to improving the estimation of NPP at landscape and regional scales in a cost effective, efficient and accurate approach (Running et al., 1999; Running, 2000) at high temporal and spatial scales (Tucker et al., 2001). As a result, various NPP estimation models that use remote sensing data have been developed (Goetz et al., 2000). Recently, the Landsat-8 meteorological approach was developed (Running, 2001) to provide a consistent, continuous estimate of photosynthetic production (Running et al., 1999). Remote sensing offers a consistent and readily updated source of information for the quantification, monitoring and verification of aboveground NPP and carbon sinks from regional to global scales (Curran et al., 1994), so remote sensing data may provide a useful means for measuring carbon stocks in forests (Jiang et al., 1999). A promising advance technology in remote measurements, e.g. scanning lidar (a pulsed laser), new type of sensor that explicitly measures canopy height, the VCL mission (the Vegetation Canopy Lidar Mission-VCL), improve the ability to successfully measure forests carbon (Means et al., 1999; Lefskyet al., 1999).
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ASSESSMENT OF INCREASING THREAT OF FOREST FIRES IN UTTARAKHAND, USING REMOTE SENSING AND GIS TECHNIQUES

ASSESSMENT OF INCREASING THREAT OF FOREST FIRES IN UTTARAKHAND, USING REMOTE SENSING AND GIS TECHNIQUES

Forest fires in Uttrakhand have been regular and historic feature. Every year forest fires in Uttrakhand causes great loss to the forest ecosystem, diversity of flora and fauna and economic wealth. Forest fire is one of the major disasters in the forests of Uttarakhand. High temperatures with no atmospheric moisture were one of the important reason for forest fires in Uttrakhand. Remote sensing and GIS technology that has been used to detect forest fires and recent technology can send an early warning to prevent forest fires. Remote sensing has also been effectively used in the study monitoring, detection of forest fire and future planning. Thus the main aim of this study is the causes and where the forest fire takes place by preparing fire events density map on the bases of using Remote Sensing real time forest fire data from EOSDIS NASA and GIS techniques. The various parameters are taken in to account land use from BHUVN, forest type map, relief and slope. Later on to analyze the causes these aspects were compared with fire density map. Topography, slope and types of vegetation play important role in the spread of forest fire. Outcome of the study demonstrate that the proximity to inhabited places, dry climatic conditions and dry Chir pine forest cover are mainly responsible forest fire in the state. These types of studies are helpful to suggest preventive measures in different risk areas of Uttrakhand.
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Assessing water stress of desert vegetation using remote sensing : the case of the Tamarugo forest in the Atacama Desert (Northern Chile)

Assessing water stress of desert vegetation using remote sensing : the case of the Tamarugo forest in the Atacama Desert (Northern Chile)

Assessing water stress of Tamarugo using in situ data and remote sensing Besides spatial and spectral constraints, the assessment of arid vegetation has also temporal constraints. Desert species have developed different adaptations to cope with water scarcity and these adaptations can affect remote sensing based retrievals of biophysical parameters. A good example is the Tamarugo tree in the Atacama Desert (Northern Chile), which adjusts the leaf angle to avoid facing direct solar radiation at the hottest time of the day (midday) (Chávez et al. 2013b). These leaf movements, known as pulvinar movements, are controlled by changes in cellular turgor pressure of a structure located in the base of the leaves and folioles (the pulvinus) (Taiz and Zeiger 2010), and they can have an important impact on canopy spectral reflectance (Chávez et al. 2013b; Moran et al. 1989; Taiz and Zeiger 2010). Furthermore, in a previous study we analyzed the effects of water stress on Tamarugo plants under laboratory conditions by measuring a set of biophysical variables as well as the spectral reflectance response (Chávez et al. 2013b). Using a modeling approach (SLC radiative transfer model (Verhoef and Bach 2007)), we found that the indices to retrieve canopy water content were more affected by leaf movements than those used to retrieve leaf area index (LAI). Good estimators of LAI were the Red-Edge Chlorophyll Index and the NDVI. However, our previous study (Chávez et al. 2013b) was conducted under laboratory conditions and no research has been carried out to test these vegetation indices under field conditions, where the solar angle and intensity are changing during the day.
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Ecological impacts of deforestation and forest degradation in the peat swamp forests of northwestern Borneo

Ecological impacts of deforestation and forest degradation in the peat swamp forests of northwestern Borneo

were similar (~ 0.5), a pixel that was clear-cut in 1999 was shown to be structurally different from another pixel selectively logged in 2009 in terms of regrowth rate and tree height. The former had uniformly short vegetation stature, most of which was dominated by Pandanus andersonii and Nephrolepsis biserrata (Kobayashi 2000), whereas the latter had a few tall, poor quality trees left behind by the loggers. Post logging, vegetation moisture increased, which should be taken as caution due to NDMI’s susceptibility to contamination to shadow from within pixel tall canopy, moisture in the regenerated ferns and the water saturated condition in the soil (Jin and Sader 2005). Tree cover as seen from the CHM did not distinguish old deforestation patches from newly logged ones. Whereas the traditional, wall-to-wall mapping approach often assumed that reduction in forest cover meant deforestation and hence resulted in a one time, binary assessment of forest condition i.e. logged or intact, the use of time series can provide extra information such as the mode of deforestation (clear cut vs. selective logging) and the subsequent rate of recovery.
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Assessment of vegetation dynamics using remote sensing and GIS: A case of Bosomtwe Range Forest Reserve, Ghana

Assessment of vegetation dynamics using remote sensing and GIS: A case of Bosomtwe Range Forest Reserve, Ghana

Changing conditions owing to increasing forest fragmentation make land cover and change detection analysis an extremely important consideration for sustainable forest management. This study applied supervised classification using maximum-likelihood algorithm in Quantum GIS to detect land use land cover changes in the Bosomtwe Range Forest Reserve, Ghana from 1991, 2002 and 2017 using Landsat 4 – TM, Landsat 7 – ETM and Sentinel-2 satellite imageries respectively. Based on the results of the study, it is concluded that land use/cover of Bosomtwe Range Forest Reserve have undergone remarkable changes for over the period of 26 years. The current status of forest cover is estimated to be 2995.45 ± 401.86 ha and 2090.03 ± 412.78 ha of closed and opened forest canopy respectively. Conversely, built-up areas (1531.68 ± 487.13 ha) remains virtually high (20%) though it shows a decrease in comparison to the same area in 2002. The land use land cover change map clearly identified probable areas of forest depletion especially in the north eastern and western portions of the reserve. It is recom- mended that potential spatial drivers of change should be identified to generate suitable image for change modelling of the reserve, coupled with earmarking of degraded areas for reforestation projects to improve upon the forest cover.
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Assessment of vegetation dynamics using remote sensing and GIS: A case of Bosomtwe Range Forest Reserve, Ghana

Assessment of vegetation dynamics using remote sensing and GIS: A case of Bosomtwe Range Forest Reserve, Ghana

a b s t r a c t Changing conditions owing to increasing forest fragmentation make land cover and change detection analysis an extremely important consideration for sustainable forest management. This study applied supervised classification using maximum-likelihood algorithm in Quantum GIS to detect land use land cover changes in the Bosomtwe Range Forest Reserve, Ghana from 1991, 2002 and 2017 using Landsat 4 – TM, Landsat 7 – ETM and Sentinel-2 satellite imageries respectively. Based on the results of the study, it is concluded that land use/cover of Bosomtwe Range Forest Reserve have undergone remarkable changes for over the period of 26 years. The current status of forest cover is estimated to be 2995.45 ± 401.86 ha and 2090.03 ± 412.78 ha of closed and opened forest canopy respectively. Conversely, built-up areas (1531.68 ± 487.13 ha) remains virtually high (20%) though it shows a decrease in comparison to the same area in 2002. The land use land cover change map clearly identified probable areas of forest depletion especially in the north eastern and western portions of the reserve. It is recom- mended that potential spatial drivers of change should be identified to generate suitable image for change modelling of the reserve, coupled with earmarking of degraded areas for reforestation projects to improve upon the forest cover.
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Remote sensing and GIS for wetland vegetation study

Remote sensing and GIS for wetland vegetation study

the pioneer and low marsh communities are dominated by Salicornia, Suaeda, sometimes with Puccinellia, and often also combined with Scirpus and Phragmites at some sites, though the areas are not large (Burd, 1989). Along the northern (Scottish) shore of the Solway, the upper marsh zone is rich in sedges (Carex spp.) and rushes (Juncus spp.), and often shows transitions to freshwater and brackish marshes. Sea-purslane (Atriplex protulacoides) saltmarsh is less widespread than on south and east coasts due to the prevalence of grazing, mainly by cattle. The common saltmarsh-grass (Puccinellia maritima) is the first colonist of the mudflats in parts of the Solway, while in the main mid-to-upper marsh the vegetation type is dominated by red fescue (Festuca rubra) saltmarsh (Juncetum gerardii) (Barne, et al., 1996). Burd (1989) has described the saltmarshes of Scotland in considerable detail as part of the NCC survey of British saltmarshes. They are dotted all around the coast, but with only eight of the thirteen recognised NVC lower saltmarsh communities, and six of nine NVC middle saltmarsh communities being found in Scotland (Boorman, 2003).
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Identification and antimicrobial activity of Micromonospora strains from Thai peat swamp forest soils

Identification and antimicrobial activity of Micromonospora strains from Thai peat swamp forest soils

Peat swamp forest is a very special type of the evergreen forests that occurs in fresh-water marshy land (Posa et al., 2011) and was an interested source for soil sampling because it was different from the normal soil. Nowadays, Micromonospora strains from tropical peat swamp forests are still limited in isolation and the research on taxonomy and antibiotic production of them have so far received little attention. Thus, the attempt to sampling the soil samples in the unique sources is focused. In this study, Micromonospora strains isolated from peat swamp forest soils in the southern areas of Thailand were characterized and identified by both classical and molecular techniques, and the investigation of antimicrobial activity is also performed.
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Research on the Optimal Vegetation Cover for Remote Sensing Assessment of Soil Erosion Risk Using the Temporal Matching Relationship between Rainfall and Vegetation

Research on the Optimal Vegetation Cover for Remote Sensing Assessment of Soil Erosion Risk Using the Temporal Matching Relationship between Rainfall and Vegetation

Soil erosion is the combined result of natural factors and human factors, and has become one of the most significant environmental problems worldwide, threat- ening agricultural productivity and food security (DeGraffenried & Shepherd 2009; Zhang et al., 2010; Verachtert et al., 2011; Zhao et al., 2018). Efficient in- tervention to control soil erosion requires assessment models for predicting the spatial location and intensity of degradation (Cohen et al., 2005; Zhang et al., 2012). Among the factors affecting soil erosion (rainfall, soil erodibility, vegeta- tion cover, topographic factors, land cover, etc.), rainfall and vegetation cover are most active factors. Rainfall is the main driving factor (Zhang & Fu, 2003). Raindrop splashing and separating soil particles, and runoff eroding and trans- porting lead to soil erosion. However, not all rainfall events can cause soil ero- sion, and only those being able to generate sufficient runoff to transport sedi- ment are erosive rainfall (Xie et al., 2002; Liu et al., 2007). Hancock (2009) demonstrated that annual sediment output was variable and non-linear with in- creased rainfall amount and intensity. Vegetation is a positive factor in prevent- ing soil erosion, and is one of the most active factors (Zhan & Wang, 2001). In- appropriate land use and destruction of vegetation will lead to increasing soil erosion (Huang et al., 2004). Vegetation can improve the ability of soil resistance to erosion, such as lowering rainfall kinetic energy by vegetation stem, slowing runoff velocity by plant stems and litter, increasing ability of soil resistance to erosion by plant roots (Zhang et al., 2000). Vegetation weaken raindrops hitting the ground, increase ground roughness and disperse the raindrops power in the canopy; carious vegetation also increase soil organic matter content and further improve the physical and chemical properties of soil (Gilly & Risse, 2000; Zhang & Liang, 1996). However, vegetation obviously changes over time, and vegeta- tion cover will also be significantly different in different seasons, thus the ability preventing soil erosion varies during the year.
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From peat swamp forest to oil palm plantations: The stability of tropical peatland carbon

From peat swamp forest to oil palm plantations: The stability of tropical peatland carbon

TpkS2, which corresponds to the temperature at which hydrocarbon compound release in maximised (Disnar, 1994; Espitalié et al., 1985), showed an increase with land conversion towards oil palm plantations. The lower TpkS2 values in forest and drained sites (403 and 416 °C respectively) are characteristic of the thermal breakdown of more labile polysaccharides and lignins. In contrast, values over 420°C, as measured in recently planted and mature oil palm sites (425 and 441°C respectively), are typical of increasingly immature humic substances (Disnar et al., 2003). TOC was high and showed little variation between forest, drained and recently planted sites (ranging from 45 % to 47 %), however TOC was significantly lower in the mature oil palm sites with 37 % (Table 1) (F 3,32 = 2.92, p<0.05, Table 2).
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Remote sensing and vegetation mapping in South Africa

Remote sensing and vegetation mapping in South Africa

The current awareness by planners and land- managers of the im portant role that vegetation plays in maintaining a stable environment has created a demand for vegetation mapping and monitoring at various scales. The majority of these surveys can be more efficiently and accurately done with the aid of remotely sensed products. However, although air photographs have been widely used as support products in vegetation studies in this country, there have been few published studies on the comparative effectiveness of different products for specific purposes, such as that by Jarman (1977) in the eastern Orange Free State, who compared the accuracy of air photo interpretation from panchro­ matic, colour and infra red false colour air photography. Most workers have had one film or remotely sensed product available to them or, at best, have been in a position to order a particular product because of prior knowledge of its effec­ tiveness. This paper attempts to bring together
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Conversion of peat swamp forest to oil palm cultivation reduces the diversity and abundance of macrofungi

Conversion of peat swamp forest to oil palm cultivation reduces the diversity and abundance of macrofungi

Peat swamp forest Smallholding a b s t r a c t Deforestation of tropical peat swamp forests is rapidly taking place across Southeast Asia to make way for agricultural expansion. Within forest ecosystems, macrofungi play a vital role, including wood decomposition and nutrient cycles. To reveal the effects of defores- tation and land cover conversion on macrofungi in Southeast Asian tropical forests we assessed the relationship between environmental variables such as air temperature, relative air humidity, soil pH, soil moisture, canopy cover, canopy closure, habitat type (i.e., peat swamp forest, large-scale plantation, monoculture smallholding, and polyculture smallholding) and available substrata with macrofungal species richness and abundance. We sample macrofungi across four habitats on Peninsula Malaysia including peat swamp forest, large-scale plantations, monoculture smallholding and polyculture smallholding. We found that substrate richness had a positive effect on macrofungal morphospecies richness, while soil pH and air temperature had a negative effect. For macrofungal abun- dance, canopy closure and soil moisture had negative effects, whereas substrate richness and relative air humidity had positive effects. Our data showed considerable variation in functional group responses to environmental variables. The abundance of wood-inhabiting fungi was driven primarily by substrate richness, while relative air humidity, soil moisture, and habitat type play minor roles. The abundance of terricolous saprotrophic fungi was determined principally by habitat type, substrate richness, and relative air humidity. Macrofungal community structure was mainly in fluenced by substrate richness, followed by microclimates and soil characteristics. Our results can provides critical ecological data to support conservation stakeholders conserve macrofungi in natural and agricultural peatlands. Our study suggests that the expansion of oil palm monocultures, to the detri- ment of peat swamp forests, is likely to have negative effects on macrofungal biodiversity and further agricultural expansion should be prevented.
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Hyperspectral Remote Sensing of Vegetation and Agricultural Crops

Hyperspectral Remote Sensing of Vegetation and Agricultural Crops

et al. is invaluable. They have implemented an innovative spectral matching technique (SMT; also see Thenkabail et al., 2007 for concept of SMTs) approach that automatically determines and labels crop types by matching the class spectral signatures with the ideal or reference spectral libraries developed using hyperspectral Hyperion data. The Z-score distance SMT that they use accounts for the variation of pixel spectra by measuring the distance between the class spectra and the reference spectra in units of standard deviation. They used this SMT method to identify and label 11 agricultural crops classified using Landsat TM data. Their study established that the accuracies of their automated SMT provided: A. 6 to 11 percent greater accuracies relative to ISODATA classification followed by manual identification of classes, and B. 12 percent greater than the spectral angle mapper (SAM) followed by manual identification of classes. However, the accuracies were 2 to 12% lower than the Maximum Likelihood Classification followed by manual class identification. Their method can be used to automatically identify and label classes classified using multispectral or hyperspectral data. The automated SMT by Parshakov et al. is novel and clearly demonstrates pathway to identify and label crop types and other land use classes automatically saving time and removing user bias. However, there will be complexities of applying automated SMTs over large and complex areas. Nevertheless, by building adequate and accurate ideal or reference spectral libraries of agricultural crops, vegetation categories, and other land use classes as well as by further development of automated SMTs for specific regions of the world, automated class labeling proposed and demonstrated by Parshakov et al. will become accurate, unbiased, rapid, and widely implementable.
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Remote sensing of fractional cover of vegetation and exposed bedrock for karst rocky desertification assessment

Remote sensing of fractional cover of vegetation and exposed bedrock for karst rocky desertification assessment

results showed that the Hyperion estimated fractional covers of PV had good correlation with the field surveyed fractional covers and the R 2 (coefficient of determination) and RMSE (root mean square error) for PV was 0.91 and 0.05, respectively; while for exposed bedrock was not so good, 0.53 and 0.11, respectively. It demonstrated that hyperspectral imagery was able to directly estimate the key ecological indicators of karst rocky desertification, which was in a heterogeneous landscape. As for the ASTER imagery, the results were not so accurate. It showed that multispectral imagery could not be used to effectively estimate the fractional cover of PV and exposed bedrock. Our study indicates that it could use hyperspectral imagery to directly and effectively estimate the fractional cover of PV and exposed bedrock for karst rocky desertification assessment in a heterogeneous landscape of karst ecosystem. © 2011 Published by Elsevier Ltd.
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