The vegetation large-scale mapping of hydromorphic ecosystems

2013 ◽  
pp. 125-135
Author(s):  
N. A. Volkova
Keyword(s):  
Soil Moisture ◽  
Plant Communities ◽  
Large Scale ◽  
Steppe Zone ◽  
Rostov Region

In order to study processes taking place in secondary overmoisturised ecosystems large-scale mapping of vegetation of the ecosystems was realized. The research was taken in the steppe zone in the Rostov region. 6 yearly maps (1997–2003) of plant communities were completed at the key plot. Plant communities distribution and dynamics mostly depend on soil moisturizing and salinization. Three main types of plant successions were distinguished: 1 – fluctuation changes of plant communities effected by soil moisture degree; 2 – plant successions influenced by intermittent overmoisturization; 3 – plant successions under antropogenic influence.

scholarly journals Vegetation cover of riparian and coastal zones of forest swamps in the Slobozhansky National Nature Park (structural features and direction of transformation)

2019 ◽  
Keyword(s):  
Plant Communities ◽  
Vegetation Cover ◽  
Large Scale ◽  
Calluna Vulgaris ◽  
Steppe Zone ◽  
Structural Features ◽  
Forest Steppe ◽  
Coastal Zones ◽  
Gis Applications ◽  
Nature Park

This article is devoted to study the characteristics of ecological-cenotic structure and directions of vegetation cover transformations in riparian and coastal zones of forest swamps within the forest-steppe zone (north-western part of Kharkiv Oblast, Ukraine). The survey has been conducted in 2013–2018 in the territory of the Slobozhansky National Natural Park. Plant communities were surveyed both in phanerophyte and grass biotopes types, having different genesis and degree of disturbance (from terrestrial to aquatic, from native to artificially created forest plantations). A number of regionally rare plant species were identified in their composition (Dryopteris carthusiana (Vill.) H.P.Fuchs, Majanthemum bifolium (L.) F.W.Schmidt, Calluna vulgaris (L.) Hull, Equisetum sylvaticum L., Potentilla erecta (L.) Raeusch., Rubus saxatilis L., Eriophorum angustifolium Honck., E. vaginatum L., Drosera rotundifolia L., Nymphaea candida C.Presl, Utricularia vulgaris L.) and U. minor L. – a species listed in the Red Book of Ukraine. Route and stationary techniques of field surveys were used for clarification the general features of horizontal vegetation structural organization, as well as for studying the effect of environmental factors on state and stability of plant communities. Usage of mobile GIS applications in geodata collection and their further processing in ArcMap project allowed us to develop a large-scale geobotanical map (1:1500) as an area of permanent botanical survey (1.9 ha). According to the results of phytoindication, indicators of 7 environmental edaphotop regimes in 25 plant communities studied (objects of mapping and further monitoring) have been identified. Based on the geobotanical map, integration of data on the intensity of vegetation transformation have been conducted. It was found that plant communities ІV (the highest) and III degrees of transformation cover an area more than 0.6 hectares (one third of the total plot area). These communities were occurred mainly in phanerophyte-type biotopes (aspen, willow, birch, and, partly, pine), which have been affected by pyrogenic and biogenic factors. The GIS-project created as a result of the study serves as a geo-information base that can be further improved and used to solve other applied problems.

Transformation of the composition and horizontal structure of steppe communities in East Mongolia under anthropogenic influence and its reflection on large-scale maps

1995 ◽  
pp. 22-41
Author(s):  
V. N. Khramtsov ◽  
P. P. Dmitriev
Keyword(s):  
Plant Communities ◽  
Vegetation Cover ◽  
Large Scale ◽  
Steppe Zone ◽  
State Farm ◽  
Actual Structure ◽  
Horizontal Structure ◽  
Zonal Vegetation ◽  
Animal Populations ◽  
Fallow Lands

In 1989–1990 participants of the joint Soviet-Mongolian Complex Biological Expedition conducted the works on the estimation of the present-day state of nature ecosystems in Mongolia. The anthropogenic dynamics (transformation) of steppe ecosystems was studied in the East Steppe Stationary in the territory of state-farm Tumen-Tsogt in the Sukhebator District. During these works the series of maps was compiled for the territory of state-farm and for separate key plots (S. 1 : 1 000 000; 1 : 200 000; 1 : 100 000) showing the anthropogenic change of vegetation of animal populations, of soils and of ecosystems as a whole. The article represents some results of the investigations on transformation of vegetation cover under grazing the leading anthropogenic factor in Mongolia. The basic map is the vegetation map in scale of 1 : 100 000 (fig. 1, fragment). The legend of this map reflects the relations between vegetation and relief and soils. The highest divisions of the legend represent the vegetation of major forms of relief: «Vegetation of low mountains», «Vegetation of flat alluvial-deluvial plains», etc. These divisions subordinate the subzonal and altitudinal-subbelt types: «Rich in forbs grass meadow steppes on the mountain chernoziom soils», «Forb and forb-bunch grass steppes on the dark-chestnut soils», etc. The communities and their combinations, belonging to the definite edaphic variants of zonal vegetation, are taken as mapping units. 7 edaphic variants are distinguished in the whole. In the legend the concrete series of transformation of vegetation cover are given. Numbers 1–17 represent the undisturbed communities. The figure indices at the numbers (for instance: 10'–104) differentiate communities of the same digression serie - fr om less to most degradated ones. In the case of fallow lands such indices indicate the stage of reconstruction – from less to the most reconstructed vegetation (18–185). It has been paid attention to the heterogeneity of vegetation that is reflected in the map legend in characterizing the map divisions. The attention has been attracted also to the pattern of the horizontal structure of these complexes. The classic complexes of plant communities are not characteristic of the steppe zone of Mongolia, especially of its central and eastern regions as they are characteristic, for instance of the Kazakhstan steppes wh ere their distribution and development are caused by the processes of salt accumulation and salt removing from soils. In Mongolia the heterogeneity of vegetation and soil cover is conditioned by the burrow activity of rodents. The patterns of horizontal structure of phytocoenotic complexes appeared to be diverse and peculiar of definite landscapes depending on animal population and environmental conditions. It was ascertained that at grazing not only the phytocoenotic parameters (species composition, coverage, abundance, etc.) are transformed but also community dimensions, their proportion in complexes and the pattern itself of these complexes. It was interesting to reveal the transformation series of these patterns in complex biogeocoenoses. Fig. 2 shows the fragment of map representing the horizontal structure of biogeocoenoses, s. 1 : 1 000 000. The legend of the map is given in the table form (see table). The map shows both the reconstructed and the actual structure of vegetation cover. The undisturbed and slightly disturbed structural types are given by the main numbers (1–12) with figure index 1 (V–12'). The structures disturbed in various extents are shown by figure indices at the main number (for instance, 22–24). The indices correspond the degree of disturbance: 2 – middle disturbed, 3 – strongly disturbed, 4 – the most disturbed. The schematic drawings of horizontal structure of the natural and transformed complexes of plant communities are given in figure 3. Figure 4 proposes the fragment of analytic map of horizontal structure of biogeocoenoses. This map shows the actual pattern of plant community complexes. The last map (fig. 5) represents the percentage contribution of zoophytochoras in background undisturbed vegetation in various landscape elements.

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.

Large-scale vegetation mapping of the Pinega river valley (the surroundings of Golubino village, Arkhangelsk oblast)

2018 ◽  
pp. 19-39
Author(s):  
M. A. Makarova
Keyword(s):  
Plant Communities ◽  
Indicator Species ◽  
Dominant Species ◽  
Large Scale ◽  
Vegetation Mapping ◽  
River Valley ◽  
Floodplain Forests ◽  
Floodplain Vegetation ◽  
Wet Meadows ◽  
Arkhangelsk Oblast

Geobotanical survey of floodplain natural complexes near gypsum outcrops in the Pinega river valley was done in 2015. Large-scale geobotanical map of the key polygon (scale 1 : 30 000) was composed. Typological units of vegetation were selected on the basis of the composition of dominant species and groups of indicator species. Homogeneous and heterogeneous territorial units of vegetation (serial series, combinations, environmental series) were used. 53 mapped unit types (25 homogeneous types and 28 heterogeneous types) were recognized. The floodplain vegetation consists of 17 homogeneous types of plant communities, 3 series, 14 combinations and 6 ecological series. The sites of old floodplain forests, such as willow forests with Urtica sondenii rare in the Arkhangelsk region and oxbow wet meadows with Scolochloa festucacea were identified.

Classification of woody-shrub vegetation of the forest-steppe complex of Privolzhskaya Upland

2009 ◽  
pp. 27-53
Author(s):  
A. Yu. Kudryavtsev
Keyword(s):  
Plant Community ◽  
Plant Communities ◽  
Dominant Species ◽  
Large Scale ◽  
Nature Reserve ◽  
Vegetation Mapping ◽  
Vegetation Classification ◽  
Forest Steppe ◽  
Mapping Data

Diversity of plant communities in the nature reserve “Privolzhskaya Forest-Steppe”, Ostrovtsovsky area, is analyzed on the basis of the large-scale vegetation mapping data from 2000. The plant community classi­fication based on the Russian ecologic-phytocoenotic approach is carried out. 12 plant formations and 21 associations are distinguished according to dominant species and a combination of ecologic-phytocoenotic groups of species. A list of vegetation classification units as well as the characteristics of theshrub and woody communities are given in this paper.

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.

Sign in / Sign up

Export Citation Format

Share Document