Effect of slope aspect on vegetation characteristics in mountain rangelands of Tajikistan: considerations for future ecological management and restoration

Topography-induced microclimate differences determine the local spatial variation of soil characteristics as topographic factors may play the most essential role in changing the climatic pattern. The aim of this study was to investigate the spatial distribution of soil organic carbon (SOC) with respect to the slope gradient and aspect, and to quantify their influence on SOC within different land use/cover classes. The study area is the Region of Niš in Serbia, which is characterized by complex topography with large variability in the spatial distribution of SOC. Soil samples at 0–30 cm and 30–60 cm were collected from different slope gradients and aspects in each of the three land use/cover classes. The results showed that the slope aspect significantly influenced the spatial distribution of SOC in the forest and vineyard soils, where N- and NW-facing soils had the highest level of organic carbon in the topsoil. There were no similar patterns in the uncultivated land. No significant differences were found in the subsoil. Organic carbon content was higher in the topsoil, regardless of the slope of the terrain. The mean SOC content in forest land decreased with increasing slope, but the difference was not statistically significant. In vineyards and uncultivated land, the SOC content was not predominantly determined by the slope gradient. No significant variations across slope gradients were found for all observed soil properties, except for available phosphorus and potassium. A positive correlation was observed between SOC and total nitrogen, clay, silt, and available phosphorus and potassium, while a negative correlation with coarse sand was detected. The slope aspect in relation to different land use/cover classes could provide an important reference for land management strategies in light of sustainable development.

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Quantifying the Variability of Forest Ecosystem Vulnerability in the Largest Water Tower Region Globally

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18147529 ◽

2021 ◽

Vol 18 (14) ◽

pp. 7529

Author(s):

Siqi Sun ◽

Yihe Lü ◽

Da Lü ◽

Cong Wang

Keyword(s):

Forest Ecosystems ◽

Human Impacts ◽

Regional Scale ◽

Principal Component ◽

Tibet Plateau ◽

Slope Aspect ◽

Environmental Disturbances ◽

Restoration Planning ◽

Development Goals ◽

Qinghai Tibet Plateau

Forests are critical ecosystems for environmental regulation and ecological security maintenance, especially at high altitudes that exhibit sensitivity to climate change and human activities. The Qinghai-Tibet Plateau—the world’s largest water tower region—has been breeding many large rivers in Asia where forests play important roles in water regulation and water quality improvement. However, the vulnerability of these forest ecosystems at the regional scale is still largely unknown. Therefore, the aim of this research is to quantitatively assess the temporal–spatial variability of forest vulnerability on the Qinghai-Tibet Plateau to illustrate the capacity of forests to withstand disturbances. Geographic information system (GIS) and the spatial principal component analysis (SPCA) were used to develop a forest vulnerable index (FVI) to assess the vulnerability of forest ecosystems. This research incorporates 15 factors covering the natural context, environmental disturbances, and socioeconomic impact. Results indicate that the measure of vulnerability was unevenly distributed spatially across the study area, and the whole trend has intensified since 2000. The three factors that contribute the most to the vulnerability of natural contexts, environmental disturbances, and human impacts are slope aspect, landslides, and the distance to the farmland, respectively. The vulnerability is higher in forest areas with lower altitudes, steeper slopes, and southerly directions. These evaluation results can be helpful for forest management in high altitude water tower regions in the forms of forest conservation or restoration planning and implementation towards sustainable development goals.

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Generating a Baseline Map of Surface Fuel Loading Using Stratified Random Sampling Inventory Data through Cokriging and Multiple Linear Regression Methods

Remote Sensing ◽

10.3390/rs13081561 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1561

Author(s):

Chinsu Lin ◽

Siao-En Ma ◽

Li-Ping Huang ◽

Chung-I Chen ◽

Pei-Ting Lin ◽

...

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Random Sampling ◽

Fuel Load ◽

Estimation Accuracy ◽

Slope Aspect ◽

Stratified Random Sampling ◽

Inventory Data ◽

Fuel Loading ◽

Regression Methods

Surface fuel loading is a key factor in controlling wildfires and planning sustainable forest management. Spatially explicit maps of surface fuel loading can highlight the risks of a forest fire. Geospatial information is critical in enabling careful use of deliberate fire setting and also helps to minimize the possibility of heat conduction over forest lands. In contrast to lidar sensing and/or optical sensing based methods, an approach of integrating in-situ fuel inventory data, geospatial interpolation techniques, and multiple linear regression methods provides an alternative approach to surface fuel load estimation and mapping over mountainous forests. Using a stratified random sampling based inventory and cokriging analysis, surface fuel loading data of 120 plots distributed over four kinds of fuel types were collected in order to develop a total surface fuel loading model (lntSFL-BioTopo model) and a fine surface fuel model (lnfSFL-BioTopo model) for generating tSFL and fSFL maps. Results showed that the combination of topographic parameters such as slope, aspect, and their cross products and the fuel types such as pine stand, non-pine conifer stand, broadleaf stand, and conifer–broadleaf mixed stand was able to appropriately describe the changes in surface fuel loads over a forest with diverse terrain morphology. Based on a cross-validation method, the estimation of tSFL and fSFL of the study site had an RMSE of 3.476 tons/ha and 3.384 tons/ha, respectively. In contrast to the average loading of all inventory plots, the estimation for tSFL and fSFL had a relative error of 38% (PRMSE). The reciprocal of estimation bias of both SFL-BioTopo models tended to be an exponential growth function of the amount of surface fuel load, indicating that the estimation accuracy of the proposed method is likely to be improved with further study. In the regression modeling, a natural logarithm transformation of the surface fuel loading prevented the outcome of negative estimates and thus improved the estimation. Based on the results, this paper defined a minimum sampling unit (MSU) as the area for collecting surface fuels for interpolation using a cokriging model. Allocating the MSUs at the boundary and center of a plot improved surface fuel load prediction and mapping.

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Landslide Susceptibility Mapping in the Commune of Oudka, Taounate Province, North Morocco: A Comparative Analysis of Logistic Regression, Multivariate Adaptive Regression Spline, and Artificial Neural Network Models

Environmental and Engineering Geoscience ◽

10.2113/eeg-2243 ◽

2020 ◽

Vol 26 (2) ◽

pp. 185-200

Author(s):

Said Benchelha ◽

Hasnaa Chennaoui Aoudjehane ◽

Mustapha Hakdaoui ◽

Rachid El Hamdouni ◽

Hamou Mansouri ◽

...

Keyword(s):

Logistic Regression ◽

Landslide Susceptibility ◽

Vegetation Index ◽

Multivariate Adaptive Regression Spline ◽

Landslide Susceptibility Mapping ◽

Slope Aspect ◽

Neural Network Models ◽

Regression Spline ◽

Adaptive Regression ◽

Artificial Neural

ABSTRACT Landslide susceptibility indices were calculated and landslide susceptibility maps were generated for the Oudka, Morocco, study area using a geographic information system. The spatial database included current landslide location, topography, soil, hydrology, and lithology, and the eight factors related to landslides (elevation, slope, aspect, distance to streams, distance to roads, distance to faults, lithology, and Normalized Difference Vegetation Index [NDVI]) were calculated or extracted. Logistic regression (LR), multivariate adaptive regression spline (MARSpline), and Artificial Neural Networks (ANN) were the methods used in this study to generate landslide susceptibility indices. Before the calculation, the study area was randomly divided into two parts, the first for the establishment of the model and the second for its validation. The results of the landslide susceptibility analysis were verified using success and prediction rates. The MARSpline model gave a higher success rate (AUC (Area Under The Curve) = 0.963) and prediction rate (AUC = 0.951) than the LR model (AUC = 0.918 and AUC = 0.901) and the ANN model (AUC = 0.886 and AUC = 0.877). These results indicate that the MARSpline model is the best model for determining landslide susceptibility in the study area.

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Variation in Vegetation and Its Driving Force in the Middle Reaches of the Yangtze River in China

Water ◽

10.3390/w13152036 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2036

Author(s):

Yang Yi ◽

Bin Wang ◽

Mingchang Shi ◽

Zekun Meng ◽

Chen Zhang

Keyword(s):

Yangtze River ◽

Remote Sensing Data ◽

Slope Aspect ◽

The Yangtze River ◽

Vegetation Growth ◽

Modis Ndvi ◽

Sunshine Hours ◽

Ndvi Time Series ◽

Temporal And Spatial Characteristics ◽

Afforestation Policy

The temporal and spatial characteristics of vegetation in the middle reaches of the Yangtze River (MRYR) were analyzed from 1999 to 2015 by trend analysis, co-integration analysis, partial correlation analysis, and spatial analysis using MODIS-NDVI time series remote sensing data. The average NDVI of the MRYR increased from 0.72 to 0.80, and nearly two-thirds of the vegetation showed a significant trend of improvement. At the inter-annual scale, the relationship between NDVI and meteorological factors was not significant in most areas. At the inter-monthly scale, NDVI was almost significantly correlated with precipitation, relative humidity, and sunshine hours, and the effect of precipitation and sunshine hours on NDVI showed a pronounced lag. When the altitude was less than 2500 m, NDVI increased with elevation. NDVI increased gradually as the slope increased and decreased gradually as the slope aspect changed from north to south. NDVI decreased as the population density and per capita GDP increased and was significantly positively correlated with afforestation policy. These findings provide new insights into the effects of climate change and human activities on vegetation growth.

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Sex dimorphism in dioecious Palmer amaranth (Amaranthus palmeri) in response to water stress

Planta ◽

10.1007/s00425-021-03664-7 ◽

2021 ◽

Vol 254 (1) ◽

Author(s):

Mohsen B. Mesgaran ◽

Maor Matzrafi ◽

Sara Ohadi

Keyword(s):

Water Stress ◽

Weed Management ◽

Amaranthus Palmeri ◽

Ecological Management ◽

State University ◽

Dioecious Species ◽

Seed Output ◽

Flowering Asynchrony ◽

Secondary Sex Characters ◽

Response To Water

Abstract Main conclusion Phenological isolation can potentially reduce seed output and may be exploited as a novel tool for ecological management of dioecious weeds. Abstract Dioecious plants may benefit from a maximized outcrossing and optimal sex-specific resource allocation; however, this breeding system may also be exploited for weed management. Seed production in dioecious species is contingent upon the co-occurrence and co-flowering of the two genders and can be further disturbed by flowering asynchrony. We explored dimorphism in secondary sex characters in Amaranthus palmeri, and tested if reproductive synchrony can be affected by water stress. We have used seeds of A. palmeri from California, Kansas and Texas, and studied secondary sex characters under natural conditions and in response to water stress. Seeds of A. palmeri from California (CA) and Kansas (KS) were cordially provided by Dr. Anil Shrestha (California State University, Fresno, California) and Dr. Dallas E. Peterson (Kansas State University, Manhattan, Kansas), respectively. Seeds of a third population were collected from mature plants (about 30 plants) from a set-aside field in College Station, Texas. A. palmeri showed no sexual dimorphism with regard to the timing of emergence, plant height, and relative growth rate. While the initiation of flowering occurred earlier in males than females, females preceded males in timing of anthesis. Water stress delayed anthesis in males to a greater extent than females increasing the anthesis mismatch between the two sexes by seven days. Our data provide the first evidence of environment-controlled flowering asynchrony in A. palmeri. From a practical point of view, phenological isolation can potentially reduce seed output and may be exploited as a novel tool for ecological management of dioecious weeds.

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A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia

Sensors ◽

10.3390/s19224893 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4893 ◽

Cited By ~ 22

Author(s):

Hejar Shahabi ◽

Ben Jarihani ◽

Sepideh Tavakkoli Piralilou ◽

David Chittleborough ◽

Mohammadtaghi Avand ◽

...

Keyword(s):

Machine Learning ◽

Image Segmentation ◽

Vegetation Index ◽

Scale Parameter ◽

Slope Aspect ◽

Support Vector ◽

Topographic Wetness Index ◽

Slope Length ◽

Optimal Scale ◽

Object Based

Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.

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