Quasi-circular Vegetation Patches Research Articles

Size distribution of the quasi-circular vegetation patches in the Yellow River Delta, China

Recent empirical studies have suggested that the patch-size distribution of vegetation can be fitted by a power law, truncated power law, or lognormal model to provide explanatory mechanisms for vegetation pattern formation in arid and semiarid regions. However, contradictory results have been reported. Therefore, additional empirical studies are necessary to test the patch-size distribution of vegetation over several regions before it can be considered as an indicator for assessing the discontinuous transition of ecosystems and understanding the mechanisms of vegetation pattern formation. Analogous to arid and semiarid regions of the world, vegetation patterns are characterized by a two-phase mosaic composed of dense vegetation patches interspersed with areas of bare soil, referred to as quasi-circular vegetation patches (QVPs), in the Yellow River Delta (YRD), China. However, research on the patch-size distribution of the QVPs reflecting vegetation patterns and ecosystem functioning is lacking. To fill this gap, for the first time, we examined the patch-size distribution of the QVPs using the fused IKONOS high-spatial-resolution image and evaluated the statistical distributions that better fit the patch size data of the QVPs in the YRD. We found that a power law, truncated power law, or lognormal distribution was not supported in the study area, whereas gamma distribution reasonably fits the size data of QVPs, implying that micro-depressions, combined with the water-limited and salinization environments had considerable effects on vegetation pattern formation. Our results provide helpful insights and suggest that further studies are needed to classify different types of QVPs. Additionally, more efficient approaches need to be used to fit the statistical distributions for elucidating the spatial vegetation patterns in the YRD.

Mapping quasi-circular vegetation patch dynamics in the Yellow River Delta, China, between 1994 and 2016

Because of a decrease in water provision from the Yellow River and the influence of various human actions, the Shandong Yellow River Delta Wetland, selected a Ramsar Wetland of International Importance in 2013, has undergone substantial degradation. Although some restoration work has been undertaken, more adaptive active restoration strategies should be developed based on researches on spontaneous revegetation in this region. In this regard, the quasi-circular vegetation patches (QVPs), characterized by a rapid rate of succession, are thus considered as ideal research targets. The spatial and temporal quantification of QVPs is not only fundamental to determining QVP formation mechanism but also contributes to the development of adaptive restoration strategies for the degraded ecosystems. Therefore, we examined QVP dynamics over seven time periods spanning 22 years using one aerial photograph and seven high-spatial resolution satellite images. We found that although the total number of QVPs remained almost unchanged, the total area had an increasing trend, and that the shape index (SIi=Peremeteri2πAreai) of QVPs tended to initially increase and subsequently decrease, with QVPs finally attaining an almost circular shape. Observations for the period between 2000 and 2016 indicated that the area and shape of QVPs could reach maximum extents within about three years. This knowledge could provide a valuable basis for studying the interactions of QVPs with environment, and the implications/consequences to ecosystems, e.g., mechanisms of the evolution of ecosystems, ecological services etc. We accordingly suggest that developing a long-term remote sensing monitoring and ground-based survey program would make a significant contribution to developing a nature-based solution for the degraded ecosystems in the Yellow River Delta, China.

Mapping plant communities within quasi‐circular vegetation patches using tasseled cap brightness, greenness, and topsoil grain size index derived from GF-1 imagery

Quantifying the structure and composition of quasi-circular vegetation patches (QVPs) is key in identifying ecosystem function, which will help create a cost-effective nature-based solution for restoring the degraded wetland ecosystem in the Yellow River Delta (YRD), China. However, research on mapping plant communities of QVPs using remotely sensed data has not been conducted. In this study, we found that the pan-sharpened GF-1 imagery acquired in May was suitable for mapping plant communities of QVPs. Guided by field survey data and finer spatial resolution remotely sensed data, we constructed a simple decision tree classifier using the tasseled cap brightness (TCB), greenness (TCG), and topsoil grain size index (TGSI) of the pan-sharpened GF-1 image acquired in May. The classification results showed that the combination of the TCB and TCG components could efficiently distinguish the vegetation from non-vegetation, and the use of the TGSI was able to capture the variations in plant communities within QVPs in the YRD, China. However, the influence of the acquisition season and mixed pixels of GF-1 imagery (especially small canopy T. chinensis in small QVPs) on classification accuracy still needs further investigation.

Using GF-2 Images to Detect Tamarix Chinensis Community within a Vegetation Patch

The quasi-circular vegetation patches (QVPs) are mainly composed of suaeda salsa, Tamarix chinensis, and Phragmites australis in the Yellow River Delta, China. The previous studies indicate that the shrubs within a vegetation patch play an important role in the establishment and disappearance of vegetation patch. Therefore, in this work, the method based on the tasselled cap brightness and greenness components derived from the April image and the August GF-2 image acquired after water replenishment with the decision tree classifier was developed for quickly detecting the Tamarix chinensis community. The detection rate was 73.5%, and the misclassification rate was 12.1%. In the future, more samples of plant community and multi-seasonal images such as the late fall-early winter leaf-off data needs to be applied to further improve the detection accuracy.

An Assessment of GF-1 Fused Multispectral Images in Different Months of Spring for Mapping Quasi-Circular Vegetation Patch

The vegetation phenological information derived from multi-seasonal imagery is helpful for mapping vegetation dynamics. The previous studies indicate that the spring imagery is considered as an optimal data to map the quasi-circular vegetation patches (QVPs) in the Yellow River Delta, China. In this work, the GF-1 images acquired in different months of spring was compared for mapping the QVPs using the decision tree classifier and watershed image segmentation technique based on the brightness and greenness components of tasselled cap transformation. The result of this study recommended that the April GF-1 image with only circle-like detection rules could result in higher detection accuracy compared with that of the March and May images. In the future, more effective image segmentation algorithms and quantitative influence of image quality on detection accuracy should be considered.

Detection of quasi-circular vegetation patches using GF-2 image with tasseled cap and watershed transformations

It is a key to detect the quasi-circular vegetation patches (QVPs) for studying the establishment and encroachment mechanisms of the QVPs in the Yellow River Delta, China. A variety of spatial resolution remote sensing data have been used to map the QVPs. However, the adhesion between the QVPs with the QVPs or the vegetations of other shape makes the detection accuracy of the QVPs unsatisfactory. This study applied the decision tree classifier to map the QVPs using the brightness and greenness components of the modified intensity-hue-saturation pansharpened Gaofen 2 imagery. Then, the watershed transformation was used to segment the classification result. The final result was obtained using the thresholds of statistical features of the QVPs. It indicated that the method of this work could well detect the QVPs. In the future, more effective image segmentation algorithms should be used to deal with the over-segmentation in order to further improve the detection accuracy.

Random forest and watershed transformations for mapping quasi-circular vegetation patch using fused multispectral CBERS-04 images

To identify the quasi-circular vegetation patches (QVPs) and monitor their pattern dynamic is the most essential procedure for studying the establishment, encroachment, and maintenance of the QVPs. High resolution satellite remotely sensed technique is a cost-effective approach to distinguish the QVPs. However, the adhesion between the QVPs or between the QVPs and the vegetations of other shapes makes the detection accuracy of the QVPs low. This study used two-seasonal CBERS-04 pansharpened multispectral images to detect the QVPs by integrating random forest classification and watershed transformation image segmentation technique. The results showed that the method proposed by this study could raise the detection accuracy of QVPs, and 5 m spatial resolution CBERS-04 pansharpened imagery may be enough to obtain the number of the QVPs, but not sufficient to calculate the area and shape of QVPs. In the future, more advanced image segmentation algorithms and finer resolution images should be applied to detect the QVPs.

An Evaluation of Several Pansharpening Methods for Mapping Quasi-circular Vegetation Patches Using GF-2 Imagery

It is important to identify the quasi-circular vegetation patches (QVPs), which will help understanding the local ecosystem structure, function, evolution, and maintenance, and is beneficial to make a vegetation restoration in the Yellow River Delta, China. The fused imagery with high spectral and spatial resolution are most appropriate data for mapping the QVPs. This study compared the widely used pansharpening approaches such as the modified intensity-hue-saturation, Gram-Schmidt, colour spectral sharpening, and principal component analysis (PC) approach for mapping the QVPs using the tasselled cap brightness and greenness components of one scene of the spring GF-2 imagery with the decision tree classifier. Overall, the PC method produced a slightly good detection result of the QVPs over the other three pansharpening methods. However, the detection accuracy was still low (F measure = 56.8%). It could be improved using multitemporal images and patch splitting techniques in the future.

Variation in soil bulk density and hydraulic conductivity within a quasi-circular vegetation patch and bare soil area

Soil bulk density and hydraulic conductivity are two of many key factors for vegetation establishment and the other way around. Variations in them may cause patchy vegetation distribution in arid and semiarid areas. Characterizing their spatial variations in relation to quasi-circular vegetation patches (QVPs) is important for the effective revegetation of wetlands in the Yellow River Delta (YRD), China. This study investigated the variations of soil bulk density and hydraulic conductivity based on 86 soil bulk density and 63 hydraulic conductivity samples within a quasi-circular vegetation patch area in the YRD. The nonparametric Mann-Whitney U and Kruskal-Wallis H tests, respectively, were used to evaluate the differences in soil bulk density and hydraulic conductivity between the QVPs and bare soil areas. There were significant differences in soil bulk density and hydraulic conductivity between QVPs and bare soil areas. Conversely, there were no significant differences in the two parameters among different QVPs. The average QVP bulk density and hydraulic conductivity (2.0 cm suction) were 12.4% lower and 191.8% higher, respectively, than those of bare soil areas. Plant species communities within QVPs did not significantly differ in terms of their influences on hydraulic conductivity but did differ in terms of their impacts on soil bulk density. Soil bulk density increased from the QVP centers to their edges whereas the opposite was observed for hydraulic conductivity. This finding indicated that QVPs drastically increase soil hydraulic conductivity, and soil bulk density influenced the spatial distribution of plant species communities within QVPs. This study expanded our understanding of occurrence and encroachment of QVPs in the YRD, may help understand spontaneous plant colonization and succession mechanism and lay the foundation for developing adaptive wetland restoration strategies in the YRD.

Comparing Pixel-Based Random Forest and the Object-Based Support Vector Machine Approaches to Map the Quasi-Circular Vegetation Patches Using Individual Seasonal Fused GF-1 Imagery

The seasonal effect on land cover classification has been widely recognized. It is important to use the imagery acquired at key points of vegetation phenological development to obtain a higher classification accuracy for land cover. This study compared the effect of seasons on landscape classification and the quasi-circular vegetation patches (QVPs) detection from four fused Gaofen 1 images acquired in the different seasons by using the pixel-based random forest (RF) and object-based support vector machine (SVM) methods over the Yellow River Delta, China. The results from this study demonstrated that the seasonal effect on classifying landscapes and detecting the QVPs is significant, especially for the pixel-based RF method. The object-based SVM method was more appropriate for classifying landscape from the non-growing season images, while the pixel-based RF approach was more suitable for classifying the growing-season images. The spring data (April imagery; overall accuracy = 99.8%) and the winter data (February imagery; F measure = 65.9%) yielded the best results for landscape classification and QVP detection, respectively, by using the object-based SVM approach. Therefore, in practice, we recommend the use of February to April imagery with the object-based SVM approach to map the QVPs in the future.

Evaluating the Potential of Multi-Seasonal CBERS-04 Imagery for Mapping the Quasi-Circular Vegetation Patches in the Yellow River Delta Using Random Forest

High-resolution satellite imagery enables decametric-scale quasi-circular vegetation patch (QVP) mapping, which greatly aids the monitoring of vegetation restoration projects and the development of theories in pattern evolution and maintenance research. This study analyzed the potential of employing five seasonal fused 5 m spatial resolution CBERS-04 satellite images to map QVPs in the Yellow River Delta, China, using the Random Forest (RF) classifier. The classification accuracies corresponding to individual and multi-season combined images were compared to understand the seasonal effect and the importance of optimal image timing and acquisition frequency for QVP mapping. For classification based on single season imagery, the early spring March imagery, with an overall accuracy (OA) of 98.1%, was proven to be more adequate than the other four individual seasonal images. The early spring (March) and winter (December) combined dataset produced the most accurate QVP detection results, with a precision rate of 66.3%, a recall rate of 43.9%, and an F measure of 0.528. For larger study areas, the gain in accuracy should be balanced against the increase in processing time and space when including the derived spectral indices in the RF classification model. Future research should focus on applying higher resolution imagery to QVP mapping.

Soil physicochemical properties associated with quasi-circular vegetation patches in the Yellow River Delta, China

In recent years, decreased runoff and sediment, intensified storm surge and seawater intrusion, urbanization, petroleum exploration, and production, and other factors have gradually deteriorated the unique wetland ecosystem in the Yellow River Delta (YRD). Although previous studies have analyzed the relationships between vegetation and soil properties at different scales, few studies have documented quasi-circular vegetation patches (QVPs) in the YRD, which appear to exhibit faster succession rates that make them ideal for studies on the relationships between vegetation and soil properties. Therefore, this study investigated differences in soil physicochemical properties at 0–20 cm depths between QVPs and inter-QVP bare soil areas, among QVPs, and among plant species communities. Additionally, spatial variations in soil physicochemical properties were explored in south-north and west-east transects across and within QVPs. The results showed that soils beneath QVPs had significant fertility island effects associated with soil organic matter (SOM) and total nitrogen (TN). Furthermore, these areas were richer in clay content, showed slightly higher pH values, and exhibited less available phosphorus (APH), available potassium (APO), total salt content, and silt content than those of bare soil areas. A multiple independent samples nonparametric Kruskal-Wallis H test detected significant differences in SOM, TN, APH, and pH among 18 QVPs, but no significant differences were found in APO, total salt content, sand, silt, and clay contents. Regardless of plant species, different plant species communities had significant effects on soil physicochemical properties. Particularly, soils underneath plant species communities with Tamarix chinensis were richer in SOM and TN, and they contained less APH, APO, and total salt content than those without T. chinensis. In each QVP, values of SOM and TN in soil samples along the patch perimeter were less than those of samples located midway toward the perimeter, and these values were less than those of samples centered in the patch. However, APO and total salt content values exhibited opposite changes from the perimeter to the center of the patch. The results also indicated significant differences in SOM, TN, APH, APO, and pH between south-north transect A and west-east transect B across QVPs, but no significant differences were found in total salt content, sand, silt, and clay contents. Therefore, the results of this study indicate that QVPs have significant effects on topsoil nutrients and salinity but little effects on soil texture. Furthermore, the occurrence and development of QVPs are associated with suitable soil conditions, including higher clay fractions and lower total salt content values.

Comparison of CBERS-04, GF-1, and GF-2 Satellite Panchromatic Images for Mapping Quasi-Circular Vegetation Patches in the Yellow River Delta, China.

Vegetation in arid and semi-arid regions frequently exists in patches, which can be effectively mapped by remote sensing. However, not all satellite images are suitable to detect the decametric-scale vegetation patches because of low spatial resolution. This study compared the capability of the first Gaofen Satellite (GF-1), the second Gaofen Satellite (GF-2), and China-Brazil Earth Resource Satellite 4 (CBERS-04) panchromatic images for mapping quasi-circular vegetation patches (QVPs) with K-Means (KM) and object-based example-based feature extraction with support vector machine classification (OEFE) in the Yellow River Delta, China. Both approaches provide relatively high classification accuracy with GF-2. For all five images, the root mean square errors (RMSEs) for area, perimeter, and perimeter/area ratio were smaller using the KM than the OEFE, indicating that the results from the KM are more similar to ground truth. Although the mapped results of the QVPs from finer-spatial resolution images appeared more accurate, accuracy improvement in terms of QVP area, perimeter, and perimeter/area ratio was limited, and most of the QVPs detected only by finer-spatial resolution imagery had a more than 40% difference with the actual QVPs in these three parameters. Compared with the KM approach, the OEFE approach performed better for vegetation patch shape description. Coupling the CBERS-04 with the OEFE approach could suitably map the QVPs (overall accuracy 75.3%). This is important for ecological protection managers concerned about cost-effectiveness between image spatial resolution and mapping the QVPs.