Using historical records, aerial photography and dendroecological methods to determine vegetation changes in a grassy woodland since European settlement

2007 ◽  
Vol 55 (1) ◽  
pp. 1 ◽  
Author(s):  
Julia A. Franco ◽  
John W. Morgan
Keyword(s):  
Soil Moisture ◽  
Aerial Photography ◽  
Large Scale ◽  
Soil Disturbance ◽  
Vegetation Changes ◽  
Historical Records ◽  
Understorey Vegetation ◽  
European Settlement ◽  
Native Shrubs ◽  
Open Woodland

By using historical records, aerial photography and dendroecological methods, we assessed the vegetation changes that have occurred in a grassy-woodland landscape at Inverleigh, Victoria, since 1850. Land managers have perceived that encroachment by native shrubs such as Acacia paradoxa DC. has occurred in woodlands in the area after their reservation for conservation following a long period of stock grazing, but data are needed to place these recent changes in context. The vegetation has passed through three management phases since early European settlement and these have contributed to the present vegetation patterns. The area was (1) initially set aside as a timber reserve at the time of European settlement and was periodically grazed by stock. (2) Logging, plantation forestry and stock grazing regimes caused large-scale disturbances to the understorey vegetation during the early 1900s and continued to the 1980s. In the 1970s, disturbances caused by recreational activities intensified the vegetation modification. During this time, the vegetation changed from an open woodland to denser, shrubbier woodland. Most soil disturbances ceased when (3) the area was declared a flora reserve in 1988. Evidence suggests that with the cessation of these disturbances, populations of the native shrub Acacia paradoxa increased dramatically, reducing the tree-gap area significantly. The major increase occurred from 1974 to 2002 when the area of tree gap declined by 38%. Age-class analyses suggested that most (>80%) of the A. paradoxa population is less than 25 years old, but plants may be able to live beyond 60 years. Logistic regression modelling suggested that distance to closest track influences present-day A. paradoxa distribution, as does soil moisture. This suggests that the soil disturbance from grading tracks and vehicle movements may be facilitating both the spread and initial establishment of A. paradoxa, particularly on soils of higher soil-moisture holding capacity. Strategies for future woodland management must consider how the current vegetation dynamics reflect past land-use history, and land managers must choose appropriate goals for biodiversity conservation in the light of these changes.

scholarly journals The Impact of Observed Vegetation Changes on Land–Atmosphere Feedbacks During Drought

2014 ◽  
Vol 15 (2) ◽  
pp. 759-776 ◽  
Author(s):  
X. H. Meng ◽  
J. P. Evans ◽  
M. F. McCabe
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Vegetation Cover ◽  
Large Scale ◽  
Vegetation Changes ◽  
Positive Role ◽  
Vegetation Fraction ◽  
Southeast Australia ◽  
Precipitation Changes ◽  
The Impact

Abstract Moderate Resolution Imaging Spectroradiometer (MODIS)-derived vegetation fraction data were used to update the boundary conditions of the advanced research Weather Research and Forecasting (WRF) Model to assess the influence of realistic vegetation cover on climate simulations in southeast Australia for the period 2000–08. Results show that modeled air temperature was improved when MODIS data were incorporated, while precipitation changes little with only a small decrease in the bias. Air temperature changes in different seasons reflect the variability of vegetation cover well, while precipitation changes have a more complicated relationship to changes in vegetation fraction. Both MODIS and climatology-based simulation experiments capture the overall precipitation changes, indicating that precipitation is dominated by the large-scale circulation, with local vegetation changes contributing variations around these. Simulated feedbacks between vegetation fraction, soil moisture, and drought over southeast Australia were also investigated. Results indicate that vegetation fraction changes lag precipitation reductions by 6–8 months in nonarid regions. With the onset of the 2002 drought, a potential fast physical mechanism was found to play a positive role in the soil moisture–precipitation feedback, while a slow biological mechanism provides a negative feedback in the soil moisture–precipitation interaction on a longer time scale. That is, in the short term, a reduction in soil moisture leads to a reduction in the convective potential and, hence, precipitation, further reducing the soil moisture. If low levels of soil moisture persist long enough, reductions in vegetation cover and vigor occur, reducing the evapotranspiration and thus reducing the soil moisture decreases and dampening the fast physical feedback. Importantly, it was observed that these feedbacks are both space and time dependent.

PROTOTIPE APLIKASI PENYIRAMAN TANAMAN MENGGUNAKAN SENSOR KELEMBABAN TANAH BERBASIS MICRO CONTOLLER ATMEGA 328

2019 ◽  
Vol 5 (1) ◽  
pp. 97-106
Author(s):  
Rudi Budi Agung ◽  
Muhammad Nur ◽  
Didi Sukayadi
Keyword(s):  
Soil Moisture ◽  
Growth And Development ◽  
Large Scale ◽  
Simple Method ◽  
Moisture Sensor ◽  
Sensor Development ◽  
Soil Moisture Sensor ◽  
Application Systems ◽  
Harvesting Systems ◽  
Working Principle

The Indonesian country which is famous for its tropical climate has now experienced a shift in two seasons (dry season and rainy season). This has an impact on cropping and harvesting systems among farmers. In large scale this is very influential considering that farmers in Indonesia are stilldependent on rainfall which results in soil moisture. Some types of plants that are very dependent on soil moisture will greatly require rainfall or water for growth and development. Through this research, researchers tried to make a prototype application for watering plants using ATMEGA328 microcontroller based soil moisture sensor. Development of application systems using the prototype method as a simple method which is the first step and can be developed again for large scale. The working principle of this prototype is simply that when soil moisture reaches a certainthreshold (above 56%) then the system will work by activating the watering system, if it is below 56% the system does not work or in other words soil moisture is considered sufficient for certain plant needs.

Coal mining-disturbed land reclamation laws

2020 ◽  
pp. 15-26
Author(s):  
T. V. Galanina ◽  
M. I. Baumgarten ◽  
T. G. Koroleva
Keyword(s):  
Large Scale ◽  
Land Reclamation ◽  
Mining Industry ◽  
Development Program ◽  
Soil Disturbance ◽  
Open Pit ◽  
Open Pit Mining ◽  
Disturbed Land ◽  
Critical Problems ◽  
Production Output

Large-scale mining disturbs wide areas of land. The development program for the mining industry, with an expected considerable increase in production output, aggravates the problem with even vaster territories exposed to the adverse anthropogenic impact. Recovery of mining-induced ecosystems in the mineral-extracting regions becomes the top priority objective. There are many restoration mechanisms, and they should be used in integration and be highly technologically intensive as the environmental impact is many-sided. This involves pollution of water, generation of much waste and soil disturbance which is the most typical of open pit mining. Scale disturbance of land, withdrawal of farming land, land pollution and littering are critical problems to the solved in the first place. One of the way outs is highquality reclamation. This article reviews the effective rules and regulations on reclamation. The mechanism is proposed for the legal control of disturbed land reclamation on a regional and federal level. Highly technologically intensive recovery of mining-induced landscape will be backed up by the natural environment restoration strategy proposed in the Disturbed Land Reclamation Concept.

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

scholarly journals Innovative method for installing soil moisture probes in a large‐scale undisturbed gravel lysimeter

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Margaret E. Stevenson ◽  
Monika Kumpan ◽  
Franz Feichtinger ◽  
Andreas Scheidl ◽  
Alexander Eder ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Innovative Method

scholarly journals Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

2015 ◽  
Vol 19 (9) ◽  
pp. 3845-3856 ◽  
Author(s):  
F. Todisco ◽  
L. Brocca ◽  
L. F. Termite ◽  
W. Wagner
Keyword(s):  
Soil Moisture ◽  
Soil Loss ◽  
Large Scale ◽  
Central Italy ◽  
Remote Sensing Data ◽  
Loss Estimation ◽  
Balance Model ◽  
Estimation Accuracy ◽  
Soil Losses

Abstract. The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008–2013. The results showed that including soil moisture observations in the event rainfall–runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ~ 0.35 and a root mean square error (RMSE) of ~ 2.8 Mg ha−1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

Silvicultural treatments, microclimatic conditions and seedling response in Southern Interior clearcuts

1998 ◽  
Vol 78 (1) ◽  
pp. 115-126 ◽  
Author(s):  
R. L. Fleming ◽  
T. A. Black ◽  
R. S. Adams ◽  
R. J. Stathers
Keyword(s):  
Soil Moisture ◽  
Seedling Establishment ◽  
Initial Period ◽  
Mineral Soil ◽  
Soil Disturbance ◽  
Near Surface ◽  
Lower Elevation ◽  
Organic Horizons ◽  
Soil Temperatures ◽  
Microclimatic Conditions

Post-harvest levels of soil disturbance and vegetation regrowth strongly influence microclimate conditions, and this has important implications for seedling establishment. We examined the effects of blading (scalping), soil loosening (ripping) and vegetation control (herbicide), as well as no soil disturbance, on growing season microclimates and 3-yr seedling response on two grass-dominated clearcuts at different elevations in the Southern Interior of British Columbia. Warmer soil temperatures were obtained by removing surface organic horizons. Ripping produced somewhat higher soil temperatures than scalping at the drier, lower-elevation site, but slightly reduced soil temperatures at the wetter, higher-elevation site. Near-surface air temperatures were more extreme (higher daily maximums and lower daily minimums) over the control than over exposed mineral soil. Root zone soil moisture deficits largely reflected transpiration by competing vegetation; vegetation removal was effective in improving soil moisture availability at the lower elevation site, but unnecessary from this perspective at the higher elevation site. The exposed mineral surfaces self-mulched and conserved soil moisture after an initial period of high evaporation. Ripping and scalping resulted in somewhat lower near-surface available soil water storage capacities. Seedling establishment on both clearcuts was better following treatments which removed vegetation and surface organic horizons and thus enhanced microclimatic conditions, despite reducing nutrient supply. Such treatments may, however, compromise subsequent stand development through negative impacts on site nutrition. Temporal changes in the relative importance of different physical (microclimate) and chemical (soil nutrition) properties to soil processes and plant growth need to be considered when evaluating site productivity. Key words: Microclimate, soil temperature, air temperature, soil moisture, clearcut, seedling establishment

scholarly journals Historical climate controls soil respiration responses to current soil moisture

2017 ◽  
Vol 114 (24) ◽  
pp. 6322-6327 ◽  
Author(s):  
Christine V. Hawkes ◽  
Bonnie G. Waring ◽  
Jennifer D. Rocca ◽  
Stephanie N. Kivlin
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Soil Respiration ◽  
Carbon Cycling ◽  
Large Scale ◽  
Microbial Respiration ◽  
Rainfall Gradient ◽  
Soil Microbial ◽  
Historical Climate ◽  
Moisture Dependence

Ecosystem carbon losses from soil microbial respiration are a key component of global carbon cycling, resulting in the transfer of 40–70 Pg carbon from soil to the atmosphere each year. Because these microbial processes can feed back to climate change, understanding respiration responses to environmental factors is necessary for improved projections. We focus on respiration responses to soil moisture, which remain unresolved in ecosystem models. A common assumption of large-scale models is that soil microorganisms respond to moisture in the same way, regardless of location or climate. Here, we show that soil respiration is constrained by historical climate. We find that historical rainfall controls both the moisture dependence and sensitivity of respiration. Moisture sensitivity, defined as the slope of respiration vs. moisture, increased fourfold across a 480-mm rainfall gradient, resulting in twofold greater carbon loss on average in historically wetter soils compared with historically drier soils. The respiration–moisture relationship was resistant to environmental change in field common gardens and field rainfall manipulations, supporting a persistent effect of historical climate on microbial respiration. Based on these results, predicting future carbon cycling with climate change will require an understanding of the spatial variation and temporal lags in microbial responses created by historical rainfall.

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