Early understory succession following catastrophic wind damage in a deciduous forest

1999 ◽  
Vol 29 (12) ◽  
pp. 1997-2002 ◽  
Author(s):  
Jeff P Castelli ◽  
Brenda B Casper ◽  
Jon J Sullivan ◽  
Roger Earl Latham
Keyword(s):  
Soil Moisture ◽  
Vegetation Cover ◽  
Environmental Variables ◽  
Growth Form ◽  
Deciduous Forest ◽  
Wind Damage ◽  
Forest Understory ◽  
Light Soil ◽  
Major Nutrients ◽  
Understory Vegetation Cover

Early succession was followed in a 2.5-ha gap created by a severe wind storm in a 5.5-ha fragment of eastern North American deciduous forest. Understory vegetation cover by species, light, soil moisture, and levels of several major nutrients were measured in 1 × 2 m census plots 3 years prior to the disturbance. Coincidentally, the storm felled 50-55% of the trees over a portion of these plots. Vegetation cover by species was again measured in all plots 3 years following the disturbance. Species were grouped by growth form, and group cover values used to examine changes in the composition of the vegetation and to determine whether these changes were correlated with any measured predisturbance environmental variables. Given the size of the gap, shade-intolerant tree species were expected to increase but did not, most likely because of repression by the shrub layer. The main response to the disturbance appeared to occur through reorganization of existing vegetation. The value of predisturbance species cover data and limitations of our sample sizes are discussed.

scholarly journals Vegetation change (1988–2010) in Camdeboo National Park (South Africa), using fixed-point photo monitoring: The role of herbivory and climate

Koedoe ◽  
2013 ◽  
Vol 55 (1) ◽  
Author(s):  
Mmoto L. Masubelele ◽  
Michael T. Hoffman ◽  
William Bond ◽  
Peter Burdett
Keyword(s):  
South Africa ◽  
Fixed Point ◽  
National Parks ◽  
Vegetation Cover ◽  
National Park ◽  
Vegetation Change ◽  
Growth Form ◽  
Annual Rainfall ◽  
Management Interventions

Fixed-point photo monitoring supplemented by animal census data and climate monitoring potential has never been explored as a long-term monitoring tool for studying vegetation change in the arid and semi-arid national parks of South Africa. The long-term (1988–2010), fixed-point monitoring dataset developed for the Camdeboo National Park, therefore, provides an important opportunity to do this. Using a quantitative estimate of the change in vegetation and growth form cover in 1152 fixed-point photographs, as well as series of step-point vegetation surveys at each photo monitoring site, this study documented the extent of vegetation change in the park in response to key climate drivers, such as rainfall, as well as land use drivers such as herbivory by indigenous ungulates. We demonstrated the varied response of vegetation cover within three main growth forms (grasses, dwarf shrubs [< 1 m] and tall shrubs [> 1 m]) in three different vegetation units and landforms (slopes, plains, rivers) within the Camdeboo National Park since 1988. Sites within Albany Thicket and Dwarf Shrublands showed the least change in vegetation cover, whilst Azonal vegetation and Grassy Dwarf Shrublands were more dynamic. Abiotic factors such as drought and flooding, total annual rainfall and rainfall seasonality appeared to have the greatest influence on growth form cover as assessed from the fixed-point photographs. Herbivory appeared not to have had a noticeable impact on the vegetation of the Camdeboo National Park as far as could be determined from the rather coarse approach used in this analysis and herbivore densities remained relatively low over the study duration.Conservation implications: We provided an historical assessment of the pattern of vegetation and climatic trends that can help evaluate many of South African National Parks’ biodiversity monitoring programmes, especially relating to habitat change. It will help arid parks in assessing the trajectories of vegetation in response to herbivory, climate and management interventions.

scholarly journals An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

2019 ◽  
Author(s):  
Bouchra Ait Hssaine ◽  
Olivier Merlin ◽  
Jamal Ezzahar ◽  
Nitu Ojha ◽  
Salah Er-raki ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Energy Balance ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Wide Range ◽  
The Mean

Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1 km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014–2018). The field was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015–2016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated αPT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved αPT remains at a mostly constant value (∼ 0.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62 W/m2 for S1, S2, S3 and B1 respectively.

scholarly journals Towards an integrated soil moisture drought monitor for East Africa

2012 ◽  
Vol 9 (4) ◽  
pp. 4587-4631 ◽  
Author(s):  
W. B. Anderson ◽  
B. F. Zaitchik ◽  
C. R. Hain ◽  
M. C. Anderson ◽  
M. T. Yilmaz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
East Africa ◽  
Remote Monitoring ◽  
Vegetation Cover ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Surface Model ◽  
Monitoring Methods ◽  
Data Scarcity

Abstract. Drought in East Africa is a recurring phenomenon with significant humanitarian impacts. Given the steep climatic gradients, topographic contrasts, general data scarcity, and, in places, political instability that characterize the region, there is a need for spatially distributed, remotely derived monitoring systems to inform national and international drought response. At the same time, the very diversity and data scarcity that necessitate remote monitoring also make it difficult to evaluate the reliability of these systems. Here we apply a suite of remote monitoring techniques to characterize the temporal and spatial evolution of the 2010–2011 Horn of Africa drought. Diverse satellite observations allow for evaluation of meteorological, agricultural, and hydrological aspects of drought, each of which is of interest to different stakeholders. Focusing on soil moisture, we apply triple collocation analysis (TCA) to three independent methods for estimating soil moisture anomalies to characterize relative error between products and to provide a basis for objective data merging. The three soil moisture methods evaluated include microwave remote sensing using the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) sensor, thermal remote sensing using the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm, and physically-based land surface modeling using the Noah land surface model. It was found that the three soil moisture monitoring methods yield similar drought anomaly estimates in areas characterized by extremely low or by moderate vegetation cover, particularly during the below-average 2011 long rainy season. Systematic discrepancies were found, however, in regions of moderately low vegetation cover and high vegetation cover, especially during the failed 2010 short rains. The merged, TCA-weighted soil moisture composite product takes advantage of the relative strengths of each method, as judged by the consistency of anomaly estimates across independent methods. This approach holds potential as a remote soil moisture-based drought monitoring system that is robust across the diverse climatic and ecological zones of East Africa.

scholarly journals Microarthropod population dynamics in a subtropical deciduous forest of the Eastern-Ghat hill range in India in response to variation in edaphic factors

2020 ◽  
Author(s):  
Pratik Acharya ◽  
Suryasikha Samal ◽  
C.S.K. Mishra
Keyword(s):  
Soil Moisture ◽  
Deciduous Forest ◽  
Principal Component ◽  
Vital Role ◽  
Population Variation ◽  
Edaphic Factors ◽  
Eastern India ◽  
Wet Season ◽  
Nutrient Pool ◽  
Soil Microarthropods

Abstract Background: Soil microarthropods are considered as major groups of soil fauna which facilitate the decomposition of organics in soil. In forests, the sustenance of nutrient pool is dependent on the density and diversity of these animals. Edaphic factors of habitat play vital role in species distribution of any region. Any changes in population structure of microarthropod may affect the ecosystem adversely. This study reports the seasonal variation of microarthropod population of the orders Collembola, Acari and Hymenoptera in five sampling zones, degraded (DF), dense mixed (DMF), open mixed (OMF), bamboo (BF) and wet land (WL) in a subtropical deciduous forest (Chandaka-Dampara) of Eastern India. Results: Seven species of Collembola and four species each of Acari and Hymenoptera were identified. Ecological indices did not show noticeable species diversity in different sampling zones of the forest. Heatmap analysis indicated high relative abundance of Collembola in WL irrespective of season. The abundance of Acari was high in OMF and DF, Hymenoptera in DMF and OMF for dry and wet season respectively. Wet season indicated significantly higher microarthropod population irrespective of species. The correlation colour matrix and principal component analysis (PCA) showed significant positive correlation of arthropod population with soil moisture and organic carbon. Significant population variation in the animal population were observed between dry and wet seasons. Conclusion: The forest floor was dominated by Collembola order of microarthropod species irrespective of sampling zone and season. Soil moisture and carbon contents in different seasons were found to be most sensitive growth regulators of microarthropod populations In Chandaka forest of Eastern India.

scholarly journals GIS modelling for locating the risk zone of soil erosionin a deciduous forest

2013 ◽  
Vol 59 (No. 2) ◽  
pp. 87-91 ◽  
Author(s):  
M. Nasiri
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Water Conservation ◽  
Vegetation Cover ◽  
Deciduous Forest ◽  
Pairwise Comparison ◽  
Geographical Information ◽  
Conservation Areas ◽  
Erosion Risk ◽  
Highest Weight

The maps of altitude, geology, vegetation cover and land use were prepared and classified as the main criteria to locate soil and water conservation programs. Analytical Hierarchy Process (AHP) was used to determine the relative priorities of these criteria by pairwise comparison. All the thematic maps were then integrated using the overlay process in Geographical Information System (GIS) and the final map of soil erosion risk was produced. Results indicated that vegetation cover was given the highest weight (0.494). The geology was assigned the second highest weight (0.313), as the main cause of initiation of the erosion of erodible lands. Land-use change has a local influence on soil erosion, so it was assigned the third weight (0.151). Altitude is a low-impact variable for predicting the water and soil conservation areas. &nbsp;

scholarly journals Localized basal area affects soil respiration temperature sensitivity in a coastal deciduous forest

2020 ◽  
Vol 17 (3) ◽  
pp. 771-780 ◽  
Author(s):  
Stephanie C. Pennington ◽  
Nate G. McDowell ◽  
J. Patrick Megonigal ◽  
James C. Stegen ◽  
Ben Bond-Lamberty
Keyword(s):  
Soil Moisture ◽  
Soil Respiration ◽  
Spatial Variability ◽  
Temperature Sensitivity ◽  
Large Scale ◽  
Deciduous Forest ◽  
Basal Area ◽  
Soil Surface ◽  
Study Sites ◽  
Positive Effect

Abstract. Soil respiration (Rs), the flow of CO2 from the soil surface to the atmosphere, is one of the largest carbon fluxes in the terrestrial biosphere. The spatial variability of Rs is both large and poorly understood, limiting our ability to robustly scale it in space. One factor in Rs spatial variability is the autotrophic contribution from plant roots, but it is uncertain how the presence of plants affects the magnitude and temperature sensitivity of Rs. This study used 1 year of Rs measurements to examine the effect of localized basal area on Rs in the growing and dormant seasons, as well as during moisture-limited times, in a temperate, coastal, deciduous forest in eastern Maryland, USA. In a linear mixed-effects model, tree basal area within a 5 m radius (BA5) exerted a significant positive effect on the temperature sensitivity of soil respiration. Soil moisture was the dominant control on Rs during the dry portions of the year, while soil moisture, temperature, and BA5 all exerted significant effects on Rs in wetter periods. Our results suggest that autotrophic respiration is more sensitive to temperature than heterotrophic respiration at these sites, although we did not measure these source fluxes directly, and that soil respiration is highly moisture sensitive, even in a record-rainfall year. The Rs flux magnitudes (0.46–15.0 µmol m−2 s−1) and variability (coefficient of variability 10 %–23 % across plots) observed in this study were comparable to values observed in similar forests. Six Rs observations would be required in order to estimate the mean across all study sites to within 50 %, and 518 would be required in order to estimate it to within 5 %, with 95 % confidence. A better understanding of the spatial interactions between plants and microbes, as well as the strength and speed of above- and belowground coupling, is necessary to link these processes with large-scale soil-to-atmosphere C fluxes.

scholarly journals An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2736 ◽  
Author(s):  
Jueying Bai ◽  
Qian Cui ◽  
Wen Zhang ◽  
Lingkui Meng
Keyword(s):  
Soil Moisture ◽  
Vegetation Cover ◽  
Land Surface ◽  
Feature Space ◽  
Entire Study ◽  
Vegetation Indexes ◽  
Input Variables ◽  
Entire Study Area ◽  
Infrared Data

A method is proposed for the production of downscaled soil moisture active passive (SMAP) soil moisture (SM) data by combining optical/infrared data with synthetic aperture radar (SAR) data based on the random forest (RF) model. The method leverages the sensitivity of active microwaves to surface SM and the triangle/trapezium feature space among vegetation indexes (VIs), land surface temperature (LST), and SM. First, five RF architectures (RF1–RF5) were trained and tested at 9 km. Second, a comparison was performed for RF1–RF5, and were evaluated against in situ SM measurements. Third, two SMAP-Sentinel active–passive SM products were compared at 3 km and 1 km using in situ SM measurements. Fourth, the RF5 model simulations were compared with the SMAP L2_SM_SP product based on the optional algorithm at 3 km and 1 km resolutions. The results showed that the downscaled SM based on the synergistic use of optical/infrared data and the backscatter at vertical–vertical (VV) polarization was feasible in semi-arid areas with relatively low vegetation cover. The RF5 model with backscatter and more parameters from optical/infrared data performed best among the five RF models and was satisfactory at both 3 km and 1 km. Compared with L2_SM_SP, RF5 was more superior at 1 km. The input variables in decreasing order of importance were backscatter, LST, VIs, and topographic factors over the entire study area. The low vegetation cover conditions probably amplified the importance of the backscatter and LST. A sufficient number of VIs can enhance the adaptability of RF models to different vegetation conditions.

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