Measurements Of Vegetation Cover Research Articles

Modelling land condition to augment Land Degradation Neutrality assessments - The succulent Karoo biome of South Africa as a case study

Land degradation is a threat to ecosystems and to already vulnerable people who live in dryland environments. The United Nations Convention to Combat Desertification (UNCCD) has been tasked with addressing the global challenges posed by land degradation. Although the complexities around land degradation have more recently been acknowledged, spatial modelling of this phenomenon remains a global challenge. In this study, measures of productivity derived from Landsat 8 and Sentinel-2A data have been integrated with local climate, land use, and socio-economic data to provide a more meaningful regional conceptual framework for land degradation. We create a novel method to map land condition utilising a set of 26 biological, topographic, climatic and land use covariates. The model results can be viewed in isolation as a measure of vegetation cover but are more useful when interpreted within the context of the environmental covariates. This can be accomplished through the Succulent Karoo Land Condition Inspector Google Earth Engine application. The data are intended to assist conservation planners in providing spatial data related to the current condition of the land in the biome, as well as to highlight areas where biodiversity is potentially at greater threat and where restoration initiatives or climate change adaptation are most needed.

Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage

BackgroundFractional vegetation coverage (FVC) is a crucial parameter in determining vegetation structure. Automatic measurement of FVC using digital images captured by mobile smart devices is a potential direction for future research on field survey methods in plant ecology, and this algorithm is crucial for accurate FVC measurement. However, there is a lack of insight into the influence of illumination on the accuracy of FVC measurements. Therefore, the main objective of this research is to assess the adaptiveness and performance of different algorithms under varying light conditions for FVC measurements and to deepen our understanding of the influence of illumination on FVC measurement.Methods and resultsBased on a literature survey, we selected four algorithms that have been reported to have high accuracy in automatic FVC measurements. The first algorithm (Fun01) identifies green plants based on the combination of R/G, B/G, and ExG (R, G, and B are the actual pixel digital numbers from the images based on each RGB channel, ExG is the abbreviation of the Excess Green index), the second algorithm (Fun02) is a decision tree that uses color properties to discriminate plants from the background, the third algorithm (Fun03) uses ExG-ExR (ExR is the abbreviation of the Excess Red index) to recognize plants in the image, and the fourth algorithm (Fun04) uses ExG and O{text{tsu}} to separate the plants from the background. Otsu is an algorithm used to determine a threshold to transform the image into a binary image for the vegetation and background. We measured the FVC of several surveyed quadrats using these four algorithms under three scenarios, namely overcast sky, solar forenoon, and solar noon. FVC values obtained using the Photoshop-assisted manual identification method were used as a reference to assess the accuracy of the four algorithms selected. Results indicate that under the overcast sky scenario, Fun01 was more accurate than the other algorithms and the MAPE (mean absolute percentage error), BIAS, relBIAS (relative BIAS), RMSE (root mean square error), and relRMSE (relative RMSE) are 8.68%, 1.3, 3.97, 3.13, and 12.33%, respectively. Under the scenario of the solar forenoon, Fun02 (decision tree) was more accurate than other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 22.70%, − 2.86, − 7.70, 5.00, and 41.23%. Under the solar noon scenario, Fun02 was also more accurate than the other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 20.60%, − 6.39, − 20.67, 7.30, and 24.49%, respectively.ConclusionsGiven that each algorithm has its own optimal application scenario, among the four algorithms selected, Fun01 (the combination of R/G, B/G, and ExG) can be recommended for measuring FVC on cloudy days. Fun02 (decision tree) is more suitable for measuring the FVC on sunny days. However, it considerably underestimates the FVC in most cases. We expect the findings of this study to serve as a useful reference for automatic vegetation cover measurements.

A novel vegetation index-based workflow for semi-arid, sparse woody cover mapping

Vegetation cover maps across ecologically-fragile and particularly arid and semi-arid forest ecosystems are prerequisites for their monitoring and management. Direct and field-based measurements of vegetation cover pose serious challenges due to high costs and inaccessibility in harsh terrains, whereas multispectral remote sensing offers objective, spatially-explicit and rapid alternatives. One of the most straightforward tools is the use of broadband vegetation indices (VIs), which are mathematical derivations from multispectral bands that are correlated with various vegetation traits. There are a number of broadband VIs that reach their optimum performance by calibrating their regulatory parameters. We improved the performance of selected VIs for both greenness estimation and land-cover classification across semi-arid woodlands by optimizing their regulatory parameters. We showed this across two separate areas in highly-fragile and sparse vegetation of Zagros mountains of Iran. Regulatory parameters were optimized by multi-objective particle swarm optimization (MOPSO) for Enhanced VI (EVI) and two innovative, more complex broadband indices that use red, blue, and near-infrared multispectral bands. Then, they were applied to estimate greenness and classify vegetation, and were validated by subsets of very high-resolution optical imagery. The results suggest high accuracy of these indicators for estimating and classifying vegetation compared with the commonly-used broadband VIs. Amongst the improved VIs, the one with a more complex combination of spectral bands comparatively returned the best performance, that was 1.34 × and 1.33 × higher in greenness estimation and 1.58 × higher in classification compared with the benchmark NDVI. They also described a higher variance across systematic transects in both regions. In conclusion, both greenness estimation and classification of semi-arid, sparse woodlands were more accurate by optimizing their regulatory parameters.

Benefits of Short-Duration, High-Stocking Rate Opportunistic Grazing on Arid Rangelands During Favorable Conditions

The aim of this study was to assess the impact of short grazing periods with high-stocking density on vegetation during weather conditions favorable to plant growth. Continuous grazing is widely practiced in Tunisian arid rangelands and across most drylands and deserts of the Middle East and North Africa. In the early 1990s, the Tunisian government combined a variety of incentives and restrictions on rangeland grazing practices and initiated a national strategy for rangeland improvement. The strategy emphasizes grazing exclusion for three consecutive years, a practice known locally as gdel. At the end of this period, grazing is allowed with no restrictions or with some guidelines. However, these regulations created discontent among pastoral communities, mainly when local rainfall conditions result in a considerable quantity of green biomass. High stocking-density grazing for a short period would help satisfy pastoralist concerns and achieve the government’s strategic goal. This study was implemented in three arid rangeland types of southern Tunisia that have been under restricted grazing for 2 years. Each area in the study was grazed for short periods in late May (about 7 days) with a flock of 150 head of sheep. Measurements of vegetation cover, forage productivity, density, and species richness were taken before and after grazing. Our results suggest that perennial vegetation is more stable under grazing than annual vegetation. The presence of annual species would enhance rangeland vegetation cover and diversity and at the same time offers an opportunity for livestock to select a high-quality diet rich in protein. Opportunistic grazing, applied to heterogeneous rangelands in late spring and the beginning of the annual temperature increase, encouraged animals to select annual plants due to their higher palatability, higher digestibility, and water content rather than heavily grazing perennial species. Perennial biomass materials that die and do not fall through rapid biological decay tended to decline with increased period of placement, resulting in further oxidation to CO2 which affects photosynthetic performance negatively and may eventually cause plant death. Trampling from high-density stocking enhances the litterfall rates and removes the oxidized plant material. These findings may contribute to strategies for addressing the extreme climatic variations that threaten rangeland and livestock sustainability.

Detection and quantification of broadleaf weeds in turfgrass using close-range multispectral imagery with pixel- and object-based classification

ABSTRACT The current practice used to evaluate broadleaf weed cover in turfgrass is visual assessment, which is time consuming and often leads to inconsistencies among evaluators. In this study, we investigated the effectiveness of constructing Random Forest models (RF), either pixel-, object-based (OBIA) or a combination of both to detect and quantify broadleaf weed cover. High resolution multispectral images were captured of 136 turfgrass plots, seeded with five species of Festuca L. and overseeded with either clover (Trifolium repens L.), daisy (Bellis perennis L.), yarrow (Achillea millefolium L.), or a mixture of all three weeds. Ground measurements of vegetation cover and bare soil were taken with a point quadrat and digital image analysis. Weeds were detected with 99% accuracy by OBIA, followed by the combined approach (98%) and Pixel-based approach (93%). Accuracy at distinguishing among weed species was somewhat lower (89%, 81% and 90%, respectively), with yarrow contributing most to the decrease in accuracy. The predictions based on ground measurements were further compared to field measurements. For both soil and weed classification, models that used shape features (OBIA and combined) resulted in better agreement with field measurements compared to Pixel- based classifications. Our study suggests that broadleaf weed cover comprised of species such as clover and daisy can be accurately quantified with high resolution multispectral images; however, quantifying yarrow cover remains challenging.

Mapping Impacts of Human Activities from Nighttime Light on Vegetation Cover Changes in Southeast Asia

It is commonly believed that the impacts of human activities have decreased the natural vegetation cover, while some promotion of the vegetation growth has also been found. In this study, negative or positive correlations between human impacts and vegetation cover were tested in the Southeast Asia (SEA) region during 2012–2018. The Visible Infrared Imaging Radiometer Suite—Day/Night Band (VIIRS/DNB) nocturnal data were used as a measure of human activities and the moderate resolution imaging spectroradiometer (MODIS)/normalized difference vegetation index (NDVI) diurnal data were used as a measure of vegetation cover. The temporal segmentation method was introduced to calculate features of two sets of time series with spatial resolution of about 500 m, including the overall trend, maximum trend, start date, and change duration. The regions with large variation in human activities (V-change region) were first extracted by the Gaussian fitted method, and 8.64% of the entire SEA (VIIRS overall trend <−0.2 or >0.4) was set as the target analysis area. According to statistics, the average overall VIIRS trend for the V-change region in SEA was about 2.12, with a slight NDVI increment. The time lag effect was also found between vegetation cover and human impacts change, with an average of 10.26 months. Our results indicated a slight green overall trend in the SEA region over the most recent 7 years. The spatial pattern of our trend analysis results can be useful for vegetation management and regional planning.

A High Spatial Resolution Satellite Remote Sensing Time Series Analysis of Cape Bounty, Melville Island, Nunavut (2004–2018)

Changes in vegetation have been observed in areas of the Arctic due to changing climate. This study examines a normalized difference vegetation index (NDVI) time series (2004–2018) of high spatial resolution satellite data (i.e., IKONOS, WorldView-2, WorldView-3) to determine if vegetation abundance has changed over the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut. Image data were corrected to top-of-atmosphere reflectance and normalized for time series analysis using the pseudo-invariant feature (PIF) method. Percent vegetation cover measurements and indices derived from local climate data (growing degree days base 5 °C; GDD5) were used to contextualize NDVI trends in different vegetation types and within active layer detachments (ALDs). NDVI showed similar patterns within the different vegetation types and across the ALDs. There was no significant change in NDVI nor in GDD5 over time. However, there were statistically significant (p < 0.05) relationships between the GDD5 and NDVI for all vegetation types. Using field measurements with high spatial resolution remote sensing data helps link changes in NDVI with changes to vegetation and earth surface processes. The challenges of integrating high spatial resolution satellite data from different sensors in a time series analysis are also discussed.

UAS and Landsat imagery to determine fuel condition for fire behaviour prediction on spinifex hummock grasslands of arid Australia

ABSTRACTFlammable spinifex grasslands of arid Western Australia cover about 98 million hectares of the state, and large wildfires in this environment threaten biodiversity, life, property and cultural values. Understanding fire behaviour in spinifex grasslands informs prescribed burning and wildfire suppression activities. Unmanned aerial systems (UAS) are aiding in improving fire behaviour prediction by providing comprehensive and accurate measurements of vegetation cover, volume and height, the fuel characteristics of vegetation that influence fire behaviour. Classification of spinifex cover derived from UAS image capture has been compared to field transects. Data from UAS align better with Landsat satellite imagery than fuel cover measures from field transects. A good correlation was found between UAS-derived vegetation cover and Landsat imagery, which means satellite imagery can be used with confidence to estimate and map fuel cover at a range of temporal and spatial scales. UAS have also proven useful in the development of a spectral index describing spinifex cover. The rapid development of affordable UAS instruments and software has enabled the production of point clouds, which provide further vegetation structure information not available from previous image captures. These developments in UAS application together with satellite imagery will enable fire managers to more efficiently and accurately map fuel characteristics at a range of scales, greatly enhancing their ability to forecast fire danger and to predict fire behaviour without having to carry out costly ground-based field measurements.

Discriminating and monitoring rangeland condition classes with MODIS NDVI and EVI indices in Iranian arid and semi-arid lands

ABSTRACTMonitoring is essential for appropriate rangeland management. The present study aimed to examine the potential of moderate resolution imaging spectroradiometer (MODIS) satellite imagery in rangeland condition assessment and monitoring within and across vegetation types in the arid and semi-arid rangelands of central Iran. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were calculated from MODIS Aqua/Terra Level 1B data (related to 2003–2013). The obtained values were compared with vegetation cover measurements and rangeland condition classes at 110 sampling sites using linear regression, one-way analysis of variance (ANOVA), independent-samples t-tests, and Tukey’s pairwise comparisons. The results showed that two indices made stronger predictions of vegetation cover within a vegetation type (R2 > 0.87, P < 0.001) than across vegetation types (R2 > 0.51, P < 0.001). Both NDVI and EVI worked well across vegetation types (P ≤ 0.001) in predicting rangeland condition classes (poor, fair, and good), but their performance varied between vegetation types. The NDVI classified about 73, 19, and 7.5% of the rangelands in poor, fair, and good condition classes, respectively. The good performance of MODIS NDVI index at different landscapes indicates that this index has high potential in detecting vegetation cover and discriminating different condition classes, therefore, it can be used to aid field- based techniques in rangeland condition assessment and monitoring.

How green is your garden?: Urban form and socio-demographic factors influence yard vegetation, visitation, and ecosystem service benefits

Private yards provide city residents with access to ecosystem services that can be realized through passive (vegetation availability) and active (time spent in yards: frequency and duration) means. However, urban densification is leading to smaller yards with less vegetation. Here, we examine how urban form and socio-demographic factors affect the potential ecosystem service benefits people can gain via passive (e.g. climate regulation) and active (e.g. recreation) pathways. Two measures of vegetation cover (0.15–2m, >2m) are used as a proxy for passive ecosystem service benefits, and two measures of yard use (use frequency, total time spent across a week) are used for active ecosystem service benefits. We use survey and GIS data to measure personal and physical predictors that could influence these variables for 520 residents of detached housing in Brisbane, Australia. We found house age and yard size were positively correlated with vegetation cover, and people with a greater nature relatedness and lower socio-economic disadvantage also had greater vegetation cover. Yard size was an important predictor of yard use, as was nature relatedness, householder age, and presence of children in the home. Vegetation cover showed no relationship, indicating that greater cover alone does not promote ecosystem service delivery through the active use pathway. Together our results show that people who have higher nature relatedness may receive greater benefits from their yards via both passive and active means as they have more vegetation available to them in their yards and they interact with this space more frequently and for longer time periods.

Modeling hydrologic responses to land management scenarios for the Chi River Sub-basin Part II, Northeast Thailand

The Chi River Sub-basin Part II, in Northeast (NE) Thailand, experiences many anthropogenic activities due to the agricultural expansion and intensification, forest deterioration, and a high demand for resource utilization. Land use/land cover change (LULCC) associated with land management, agricultural and conservation practices within the basin can significantly affect hydrologic responses. Like other basins in NE Thailand, the Chi River Sub-basin Part II—comprising more than 60 % of agricultural lands—is characterized by severe floods, droughts, and sedimentation, which directly affect human well-being. Therefore, the objective of this study was to investigate the influences of land management and conservation practices on discharge and sediment yield, and then to develop appropriate management solutions, which could provide alternatives to the current watershed management practices. Three management scenarios—current land use with conservation practices, land use planning (LUP) based on Watershed Classification (WSC), and WSC with conservation practices—were modeled with the Soil and Water Assessment Tool (SWAT), and the results were compared to the existing conditions. The findings indicated that LULCC and conservation practices—conserving the remaining forest, reforestation, applying strip cropping to agricultural land, and channel stabilization by plant cover and engineered constructions—had a little effect on discharge, but greatly influenced the sediment yield. Current land use with the addition of conservation practices resulted in a slight decrease in both total discharge and sediment yield. WSC resulted in a small decrease in discharge, but a dramatic increase in sedimentation. Lastly, WSC together with conservation practices resulted in a slight decrease in discharge and a small increase in sedimentation. These results informed that land management that adopts conservation practices has a strong effect on discharge and sediment yield. A decreased in forest cover when applied WSC resulted in a slight decrease in discharge, but much higher sediment yield. When conservation practices were applied, sediment yield was found to be significantly decreased. Therefore, watershed management should consider LUP, vegetation cover measures, and channel improvements to sustain the Chi River Sub-basin Part II.

Informal trail creation: hiking, trail running, and mountain bicycling in shortgrass prairie

ABSTRACTThis study considers initial impacts on vegetation cover caused by mountain bicycling, trail running, and hiking in a shortgrass prairie environment. Vegetation cover measurements were taken at multiple intervals following experimental recreational use on three uphill and three downhill trail segments. All three activities caused statistically significant increases in bare ground cover between the first baseline measurement and post-treatment sampling one year later. Short-term effects were more variable: walking and bicycling caused statistically significant increases in bare ground, but running did not. The study suggests that impacts to vegetation differ not just between uses, but also within a single type of recreational activity depending upon site-specific characteristics, and that the timing of use and recovery are important factors in informal trail creation. The rapid creation of trail impacts also has management implications, especially as recreational pressures increase and recreationists seek more challenging terrain and opportunities off-trail. This research suggests that the dynamics of trail formation from running deserve further attention and likely differ from hiking or mountain biking impacts.

Quantifying livestock effects on bunchgrass vegetation with Landsat ETM+ data across a single growing season

ABSTRACTGrassland systems provide important habitat for native biodiversity and forage for livestock, with livestock grazing playing an important role influencing sustainable ecosystem function. Traditional field techniques to monitor the effects of grazing on vegetation are costly and limited to small spatial scales. Remote sensing has the potential to provide quantitative and repeatable monitoring data across large spatial and temporal scales for more informed grazing management. To investigate the ability of vegetation metrics derived from remotely sensed imagery to detect the effect of cattle grazing on bunchgrass grassland vegetation across a growing season, we sampled 32 sites across four prescribed stocking rates on a section of Pacific Northwest bunchgrass prairie in northeastern Oregon. We collected vegetation data on vertical structure, biomass, and cover at three different time periods: June, August, and October 2012 to understand the potential to measure vegetation at different phenological stages across a growing season. We acquired remotely sensed Landsat Enhanced Thematic Mapper Plus (ETM+) data closest in date to three field sampling bouts. We correlated the field vegetation metrics to Landsat spectral bands, 14 commonly used vegetation indices, and the tasselled cap wetness, brightness, and greenness transformations. To increase the explanatory value of the satellite-derived data, full, stepwise, and best-subset multiple regression models were fit to each of the vegetation metrics at the three different times of the year. Predicted vegetation metrics were then mapped across the study area. Field-based results indicated that as the stocking rate increased, the mean vegetation amounts of vertical structure, cover, and biomass decreased. The multiple regression models using common vegetation indices had the ability to discern different levels of grazing across the study area, but different spectral indices proved to be the best predictors of vegetation metrics for differing phenological windows. Field measures of vegetation cover yielded the highest correlations to remotely sensed data across all sampling periods. Our results from this analysis can be used to improve grassland monitoring by providing multiple measures of vegetation amounts across a growing season that better align with land management decision making.

Effects of Special Protection Area Designation on Soil Properties and Vegetation Coverage of Degraded Trails

This study was conducted to evaluate how the special protection area designations of degraded trails effect on the soil and vegetation recovery of degraded trails. The study areas were established on an opened trail and a 16-year closed trail, which was prohibited to enter after the designation as a special protection area for recovery, at Bukhansan National Park. Soil core sampling and measurements of vegetation cover were performed to compare soil and vegetation properties of the trails. Soil bulk density increased and soil water total nitrogen decreased on the opened trail, while no significant differences were found on bulk density, soil water, total nitrogen, acidity, and organic matter on the closed trail. On the opened trail, vegetation cover was seemed to be degraded, because vegetation litter cover ratio was low and barren rock cover ratio was high. On the closed trail, litter rock barren cover ratio of the closed trail was recovered, but only limited recovery was found on vegetation cover by applying environmental damage condition rating class. In conclusion, the closed trail was recovered by designation of special protection area, while difference in recovery progress of soil and vegetation was found. Therefore, designation of special protection area of degraded area should be based on scientific basis of recovery characteristics of the area. In order to improve the effectiveness of special protection area system, further specific standards for special protection area designation and management would be needed, considering ecological and social importance of target areas.

Using Landsat Vegetation Indices to Estimate Impervious Surface Fractions for European Cities

Impervious surfaces (IS) are a key indicator of environmental quality, and mapping of urban IS is important for a wide range of applications including hydrological modelling, water management, urban and environmental planning and urban climate studies. This paper addresses the accuracy and applicability of vegetation indices (VI), from Landsat imagery, to estimate IS fractions for European cities. The accuracy of three different measures of vegetation cover is examined for eight urban areas at different locations in Europe. The Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) are converted to IS fractions using a regression modelling approach. Also, NDVI is used to estimate fractional vegetation cover (FR), and consequently IS fractions. All three indices provide fairly accurate estimates (MAEs ≈ 10%, MBE’s &lt; 2%) of sub-pixel imperviousness, and are found to be applicable for cities with dissimilar climatic and vegetative conditions. The VI/IS relationship across cities is examined by quantifying the MAEs and MBEs between all combinations of models and urban areas. Also, regional regression models are developed by compiling data from multiple cities to examine the potential for developing and applying a single regression model to estimate IS fractions for numerous urban areas without reducing the accuracy considerably. Our findings indicate that the models can be applied broadly for multiple urban areas, and that the accuracy is reduced only marginally by applying the regional models. SAVI is identified as a superior index for the development of regional quantification models. The findings of this study highlight that IS fractions, and spatiotemporal changes herein, can be mapped by use of simple regression models based on VIs from remote sensors, and that the method presented enables simple, accurate and resource efficient quantification of IS.

Water balance at plot scale for soil moisture estimation using vegetation parameters

The experiment presented and discussed in this paper attempts to understand the relationships between vegetation cover measurements and soil moisture estimations, to improve soil moisture calculations based on the FAO56 water balance, also known as the Kc-ET0 (crop coefficient-reference evapotranspiration) approach. The experiment provided a detailed dataset of in situ measurements, such as soil moisture, vegetation parameters, and spectral and meteorological measurements. The experiment was performed in two 40m2 plots of barley and grass. The study primarily addressed the relationships among vegetation canopy parameters. The results showed that the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Vegetation Water Content (VWC), and Fraction of Vegetation Cover (FVC) were strongly correlated. Based on these relationships, different approaches for calculating the basal crop coefficient (Kcb) were tested, and their influences on the water balance calculation were evaluated. The water content of the soil layer (θ) was calculated in the water balance as a residual of the daily calculation. Actual Evapotranspiration (AET) was also estimated. The results were validated by comparing the field soil moisture values against the soil moisture calculated through FAO56. The results of this comparison were satisfactory for barley (determination coefficient, R2=0.73 and root mean square error, RMSE=0.084cm3cm−3) and grass (R2=0.77, RMSE=0.058cm3cm−3). After testing other formulations of the Kcb using the LAI and FVC, the results showed that an indirect estimation of the Kcb based on a growing parameter, such as the LAI, NDVI or FVC, was adequate for estimating the soil moisture through FAO56. The different approaches used to estimate the Kcb had little impact on the estimate of AET (the differences were smaller than 3mm in the total annual AET for both crops). However, the root depth and the upper limit of potential water content (field capacity) were crucial in the soil moisture and AET estimations. If the field capacity of soils, determined in the laboratory, was forced to match the upper limit of the observed soil moisture in the field, a much better estimate was obtained (R2=0.92 and RMSE=0.035cm3cm−3 for barley and R2=0.91 and 0.023cm3cm−3 for grass); otherwise, the AET increased by 50%.

Post-fire salvage logging reduces carbon sequestration in Mediterranean coniferous forest

Post-fire salvage logging is a common silvicultural practice around the world, with the potential to alter the regenerative capacity of an ecosystem and thus its role as a source or a sink of carbon. However, there is no information on the effect of burnt wood management on the net ecosystem carbon balance. Here, we examine for the first time the effect of post-fire burnt wood management on the net ecosystem carbon balance by comparing the carbon exchange of two treatments in a burnt Mediterranean coniferous forest treated by salvage logging (SL, felling and removing the logs and masticating the woody debris) and Non-Intervention (NI, all trees left standing) using eddy covariance measurements. Using different partitioning approaches, we analyze the evolution of photosynthesis and respiration processes together with measurements of vegetation cover and soil respiration and humidity to interpret the differences in the measured fluxes and underlying processes. Results show that SL enhanced CO 2 emissions of this burnt pine forest by more than 120 g C m −2 compared to the NI treatment for the period June–December 2009. Although soil respiration was around 30% higher in NI during growing season, this was more than offset by photosynthesis, as corroborated by increases in vegetation cover and evapotranspiration. Since SL is counterproductive to climate-change and Kyoto protocol objectives of optimal C sequestration by terrestrial ecosystems, less aggressive burnt wood management policies should be considered.

Countryside vegetation provides supplementary habitat at the landscape scale for woodland birds in farm mosaics

Agricultural environments have a critical role in the global conservation of biodiversity, but the persistence of forest and woodland-dependent species in these systems is often limited by insufficient habitat. Modified or semi-natural ‘countryside’ (matrix) vegetation is used by many species, but its value at the landscape scale is rarely tested. Do such habitats simply provide additional resources for populations sustained by remnant native vegetation in the landscape, or can they enhance populations over and above that sustained by natural vegetation cover? We surveyed woodland-dependent birds in all types of landscape element in 27 farmland mosaics (100 ha each) in south-eastern Australia. Four measures of wooded vegetation cover were quantified: native vegetation only; and combinations of native vegetation plus scattered trees and/or tree plantations. We used an information-theoretic approach to compare the responses of 30 species to each measure of vegetation cover. Woodland birds were well represented in agricultural mosaics (65% of the regional species-pool); however, almost half were recorded only in mosaics with >20% native vegetation cover. The incidence of 23 species was positively related to measures of wooded cover, indicating increased occurrence in mosaics with a greater cover of wooded vegetation. For 12 species, scattered trees and plantations provided supplementary habitat that enhanced their population status at the landscape scale, beyond that sustained by native vegetation cover. While native vegetation has a critical role for conservation in agricultural environments, careful management of wooded countryside elements (such as scattered trees, tree plantations) offers additional benefits to the woodland-dependent avifauna.

Hierarchical object-based classification of ultra-high-resolution digital mapping camera (DMC) imagery for rangeland mapping and assessment

Ultra-high-resolution digital aerial imagery has great potential to complement or replace ground measurements of vegetation cover for rangeland monitoring and assessment. This research investigated object-based image analysis (OBIA) techniques for classifying vegetation in southwestern USA arid rangelands with 4 cm resolution digital aerial imagery. We obtained high r-square values for the regressions relating ground- to image-based measures of percent cover (r-square values: 0.82–0.92). OBIA enabled us to automate the classification process and demonstrated potential for quantifying fine-scale land cover attributes with ultra-high-resolution imagery. This approach exhibits promise for nationwide application for monitoring grazing lands.

A Comparison of Cover Pole With Standard Vegetation Monitoring Methods

ABSTRACT The ability of resource managers to make informed decisions regarding wildlife habitat could be improved with the use of existing data sets and the use of cost‐effective, standardized methods to simultaneously quantify vertical and horizontal cover. We characterized vegetation structure of 3 semiarid plant communities to compare cover pole measurements, standard measurements of vegetation cover, composition, height, and the proportion of the soil surface exposed by large intercanopy gaps. We propose that a more versatile and interpretable description of wildlife habitat can be generated using a line‐point intercept method together with measurements of vegetation height and the proportion of the soil surface exposed by large intercanopy gaps.