Optimal window period for mapping Parthenium weed in South Africa,using high temporal resolution imagery and the ExtraTrees classifier

Parthenium weed (Parthenium hysterophorus) is one of the most noxious herbaceous weeds in the world with adverse impacts on among others animal and human health, crop production, the environment, local as well as national economies. To optimize Parthenium mitigation, it is necessary to accurately monitor its spread using earth observation data. However, one of the challenges of mapping Parthenium weed is that its spectral response is similar to that of surrounding herbaceous plant species, resulting in low classification accuracies. Due to variability in its phenological characteristics and associated species, determining differences within the growing season may optimize the discrimination and subsequent mapping of the Parthenium weed. However, determination of the window(s) with the most prominent variability has been overlooked in past studies. Furthermore, no specific algorithm has been determined to be efficient in finding such window(s). ExtraTrees (EXT), an underused classifier in earth observation studies, possess interesting properties for satellite image processing such as high speed and performance. In this regard, this study attempted to (1) determine the optimal window period for discriminating Parthenium weed from coexisting plant species and (2) to compare the performance of EXT and the random forest (RF) algorithms. Results showed that the beginning of February was the optimal period for mapping Parthenium weed, with overall accuracy of 88.1%. EXT outperformed RF for most of the dates. This study lays the foundation for optimizing earth observation data derived models for characterizing invasive species, leveraging on the high temporal resolution of the new generation sensors.

     

Explaining grass-nutrient patterns in a savanna rangeland of southern Africa

Aim The search for possible factors influencing the spatial variation of grass quality is an important step towards understanding the distribution of herbivores, as well as a step towards identifying crucial areas for conservation and restoration. A number of studies have shown that grass quality at a regional scale is influenced by climatic variables. At a local scale, site factors and their interaction are considered important. In this study, we aimed at examining environmental correlates of grass quality at a local scale. The study also sought to establish if biotic factors interact significantly with abiotic factors in influencing a variation in grass quality. Location The study area is located in the Kruger National Park of South Africa. The study area stretches from west (22°49′ S and 31°01′ E) to east, (22°44′ S and 31°22′ E) covering an area of about 25 × 6 km in the far northern region of the Kruger National Park. Methods We collected environmental data such as soil texture, percentage grass cover and biomass as well as grass samples for chemical analysis from specific locations in the study area. In addition, a digital elevation model (DEM) with a resolution of 5 m was used to derive elevation, slope and aspect using a geographic information system (GIS), which were related to grass quality. We used correlation analysis and anova to relate environmental variables to grass quality. Multivariate analysis techniques were used to simultaneously analyse and explore the complex interactions between variables. Results and conclusions Our results indicate that there is a significant relationship between grass quality parameters and site‐specific factors such as slope, altitude, percentage grass cover, aspect and soil texture. Relatively, percentage grass cover and soil texture were more critical in explaining a variation in grass quality. Plant characteristics such as species type interact significantly with slope, altitude and geology in influencing nutrient distribution. The results of this study may give a better insight on foliar nutrient distribution patterns at a landscape scale in savanna rangelands. Furthermore, the results of this study may help in the selection of ancillary information, which could be used in conjunction with other data such as remotely sensed data to map grass quality – an important step towards understanding the distribution and feeding patterns of wildlife. However, we acknowledge that this study is based on one seasonal snapshot, therefore some slightly different findings may be obtained during other times of the year. Nevertheless, the study has revealed that under the conditions experienced during the study period, nutrient distribution varies with varying biotic and abiotic factors.     

Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review

Wetland vegetation plays a key role in the ecological functions of wetland environments. Remote sensing techniques offer timely, up-to-date, and relatively accurate information for sustainable and effective management of wetland vegetation. This article provides an overview on the status of remote sensing applications in discriminating and mapping wetland vegetation, and estimating some of the biochemical and biophysical parameters of wetland vegetation. Research needs for successful applications of remote sensing in wetland vegetation mapping and the major challenges are also discussed. The review focuses on providing fundamental information relating to the spectral characteristics of wetland vegetation, discriminating wetland vegetation using broad- and narrow-bands, as well as estimating water content, biomass, and leaf area index. It can be concluded that the remote sensing of wetland vegetation has some particular challenges that require careful consideration in order to obtain successful results. These include an in-depth understanding of the factors affecting the interaction between electromagnetic radiation and wetland vegetation in a particular environment, selecting appropriate spatial and spectral resolution as well as suitable processing techniques for extracting spectral information of wetland vegetation.     

Remotely sensed C3 and C4 grass species aboveground biomass variability in response to seasonal climate and topography

Seasonal climate and topography influence C3 and C4 grass species aboveground biomass (AGB). Climate change further threatens these grasses AGB, thereby compromising their ability to provide ecosystem goods and services. This emphasises the need to monitor their AGB for well‐informed management. New‐generation sensors, with improved resolution capabilities present an opportunity to explore C3 and C4 AGB. This study therefore investigated the response of remotely sensed C3 and C4 grasses AGB to seasonal climate and topography. Overall, the spatial and temporal responses of AGB due to seasonal climate and topography were observed across the study area. For example, in March, a marked increase in C4 AGB was associated with an increase in rainfall, with the highest significant positive relationship (R² = 0.82, p < 0.005). Elevation had very significant positive relationship (R² = 0.84; p < 0.005) with C3 and highest negative (R² = ?0.77; p < 0.005) with C4 AGB. During the winter fall, AGB significantly decreased from averages of 2.592 and 1.101 kg/m² in winter (May), to 0.718 and 0.469 kg/m² in August, for C3 and C4 grasses, respectively. These findings provide a key step in monitoring rangelands and assessing management practices to boost productivity.     

Assessing edge effect on the spatial distribution of selected forest biochemical properties derived using the Worldview data in Dukuduku forests,South Africa

This work explores the potential of the high‐resolution WorldView‐2 sensor in quantifying edge effects on the spatial distribution of selected forest biochemical properties in fragmented Dukuduku forest in KwaZulu‐Natal, South Africa. Specifically, we sought to map fragmented patches within forested areas in Dukuduku area, using very high spatial resolution WorldView‐2 remotely sensed data and to statistically determine the effect of these fragmented patches on the spatial distribution of selected forest biochemical properties. Edge effects on carbon, LAI and foliar nitrogen were quantified based on the models derived by Omer et al. (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8, 4825). Edge effect statistical results on the spatial distribution of carbon, LAI and nitrogen showed significant (α = 0.05) variations with change in distance from fragmented patches (>150 m²). Forest foliar carbon concentrations significantly (p‐value = 0.016) increased from 44.8% to 45.3% with increasing distance (25–375 m) from fragmented patches. A similar trend was observed for LAI. Nevertheless, for nitrogen the results show that its concentration significantly (p = 0.016) decreased with increase in distance from the fragmented patches. Overall, the findings of this work underscore the invaluable potential and strength of WorldView‐2 data set in assessing edge effect on the spatial distribution of selected forest biochemical properties.