The identification and remote detection of alien invasive plants in commercial forests: An Overview

Invasive alien plants are responsible for extensive economic and ecological damage in forest plantations. They have the ability to aggressively manipulate essential ecosystem structural and functional processes. Alterations in these processes can have detrimental effects on the growth and productivity of forest species and ultimately impact on the quality and quantity of forest wood material. Using direct sampling field-based methods or visual estimations have generally expressed moderate success owing to the logistical and timely impracticalities. Alternatively, remote sensing techniques offer a synoptic rapid approach for detecting and mapping weeds affecting plantation forest environments. This paper reviews remote sensing techniques that have been used in detecting the occurrence of weeds and the implications for detecting S. mauritianum (bugweed); one of the most notorious alien plant invaders to affect southern Africa. Gaining early control of these alien plant invasions would reduce the impacts that may permanently alter our forested ecosystems, contributing to its successful eradication and promoting sustainable forest management practices. Furthermore, the review highlights the difficulties and opportunities that are associated with weed identification using remote sensing and future directions of research are also proposed

Remote sensing bio-control damage on aquatic invasive alien plant species

Aquatic Invasive Alien Plant (AIAP) species are a major threat to freshwater ecosystems, placing great strain on South Africa’s limited water resources. Bio-control programmes have been initiated in an effort to mitigate the negative environmental impacts associated with their presence in non-native areas. Remote sensing can be used as an effective tool to detect, map and monitor bio-control damage on AIAP species. This paper  reconciles previous and current research concerning the application of remote sensing to detect and map bio-control damage on AIAP species. Initially, the spectral characteristics of bio-control damage are  described. Thereafter, the potential of remote sensing chlorophyll content and chlorophyll fluorescence as  pre-visual indicators of bio-control damage are reviewed and synthesised. The utility of multispectral and  hyperspectral sensors for mapping different severities of bio-control damage are also discussed. Popular  machine learning algorithms that offer operational potential to classify bio-control damage are proposed. This paper concludes with the challenges of remote sensing bio-control damage as well as proposes  recommendations to guide future research to successfully detect and map bio-control damage on AIAP  species

Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

The Google Earth Engine (GEE) portal provides enhanced opportunities for undertaking earth observation studies. Established towards the end of 2010, it provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. However, the uptake and usage of the opportunity remains varied and unclear. This study was undertaken to investigate the usage patterns of the Google Earth Engine platform and whether researchers in developing countries were making use of the opportunity. Analysis of published literature showed that a total of 300 journal papers were published between 2011 and June 2017 that used GEE in their research, spread across 158 journals. The highest number of papers were in the journal Remote Sensing, followed by Remote Sensing of Environment. There were also a number of papers in premium journals such as Nature and Science. The application areas were quite varied, ranging from forest and vegetation studies to medical fields such as malaria. Landsat was the most widely used dataset; it is the biggest component of the GEE data portal, with data from the first to the current Landsat series available for use and download. Examination of data also showed that the usage was dominated by institutions based in developed nations, with study sites mainly in developed nations. There were very few studies originating from institutions based in less developed nations and those that targeted less developed nations, particularly in the African continent

Google Earth Engine Applications

The Google Earth Engine (GEE) is a cloud computing platform designed to store and process huge data sets (at petabyte-scale) for analysis and ultimate decision making [1]. Following the free availability of Landsat series in 2008, Google archived all the data sets and linked them to the cloud computing engine for open source use. The current archive of data includes those from other satellites, as well as Geographic Information Systems (GIS) based vector data sets, social, demographic, weather, digital elevation models, and climate data layers

Determining optimal new generation satellite derived metrics for accurate C3 and C4 grass species aboveground biomass estimation in South Africa

While satellite data has proved to be a powerful tool in estimating C3 and C4 grass species Aboveground Biomass (AGB), finding an appropriate sensor that can accurately characterize the inherent variations remains a challenge. This limitation has hampered the remote sensing community from continuously and precisely monitoring their productivity. This study assessed the potential of a Sentinel 2 MultiSpectral Instrument, Landsat 8 Operational Land Imager, and WorldView-2 sensors, with improved earth imaging characteristics, in estimating C3 and C4 grasses AGB in the Cathedral Peak, South Africa. Overall, all sensors have shown considerable potential in estimating species AGB; with the use of different combinations of the derived spectral bands and vegetation indices producing better accuracies. However,WorldView-2 derived variables yielded better predictive accuracies (R2 ranging between 0.71 and 0.83; RMSEs between 6.92% and 9.84%), followed by Sentinel 2, with R2 between 0.60 and 0.79; and an RMSE 7.66% and 14.66%. Comparatively, Landsat 8 yielded weaker estimates, with R2 ranging between 0.52 and 0.71 and high RMSEs ranging between 9.07% and 19.88%. In addition, spectral bands located within the red edge (e.g., centered at 0.705 and 0.745 m for Sentinel 2), SWIR, and NIR, as well as the derived indices, were found to be very important in predicting C3 and C4 AGB from the three sensors. The competence of these bands, especially of the free-available Landsat 8 and Sentinel 2 dataset, was also confirmed from the fusion of the datasets. Most importantly, the three sensors managed to capture and show the spatial variations in AGB for the target C3 and C4 grassland area. This work therefore provides a new horizon and a fundamental step towards C3 and C4 grass productivity monitoring for carbon accounting, forage mapping, and modelling the influence of environmental changes on their productivity

Effect of landscape pattern and spatial configuration of vegetation patches on urban warming and cooling in Harare metropolitan city, Zimbabwe

The spatial configuration of vegetation patches in the landscape has implications for the provision of ecosystem services, human adaptation to climate change, enhancement, or mitigation of urban heat island. Until recently, the effect of spatial configuration of vegetation to enhance or mitigate urban heat island has received little consideration in urban thermal assessments. This study examines the impact of spatial configuration of vegetation patches on urban thermal warming and cooling in Harare metropolitan city, Zimbabwe. The study used Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat and Sentinel 2 data acquired between 1994 and 2017 to derive detailed information on vegetation patches, landscape metrics, and land surface temperature LST(°C). The spatial configuration of urban vegetation patterns was analyzed using landscape metrics in Fragstats program. Getis Ord Gi* as a Local Indicator of Spatial Association (LISA) was used to characterize the spatial clustering and dispersion of urban vegetation patches. Results of the Getis Ord Gi* showed that clustered vegetation lowers surface temperatures more effectively than dispersed and fragmented patterns of vegetation. The size, density, shape complexity, and cohesion of vegetation patches conferred different levels of cooling but Patch Cohesion Index had the strongest negative relationship with LST(°C) at three spatial resolutions of 10 m (Sentinel 2), 15 m (ASTER) and 30 m (Landsat 8). The Spatial Lag Regression model performed better than the Ordinary Least Squares regression analysis in exploring the relationship between LST(°C) and landscape metrics. Specifically, the Spatial Lag Regression model showed higher R2 values and log likelihood, lower Schwarz criteria, and Akaike information criterion, and reduced spatial autocorrelations. The overall information provides important insights into the provision of larger, connected, and less fragmented urban vegetation patches to derive maximum and higher cooling effects which is critical for urban planning and design approaches for mitigating increasing surface temperatures in cities

Advancements in satellite remote sensing for mapping and monitoring ofalien invasive plant species (AIPs)

Detecting and mapping the occurrence, spatial distribution and abundance of Alien Invasive Plants (AIPs) have recently gained substantial attention, globally. This work, therefore, provides an overview of advancements in satellite remote sensing for mapping and monitoring of AIPs and associated challenges and opportunities. Satellite remote sensing techniques have been successful in detecting and mapping the spatial and temporal distribution of AIPs in rangeland ecosystems. Also, the launch of high spatial resolution and hyperspectral remote sensing sensors marked a major breakthrough to precise characterization of earth surface feature as well as optimal resource monitoring. Although essential, the improvements in spatial and spectral properties of remote sensing sensors presented a number of challenges including the excessive acquisition and limited temporal resolution. Therefore, the use of high spatial and hyperspectral datasets is not a plausible alternative to continued and operational scale earth observation, especially in financially constrained countries. On the other hand, literature shows that image classification algorithms have been instrumental in compensating the poor spatial and spectral resolution of remote sensing sensors. Furthermore, the emergence of robust and advanced non-parametric image classification algorithms have been a major development in image classification algorithms

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 quan‐tifying edge effects on the spatial distribution of selected forest biochemical proper‐ties 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 se‐lected forest biochemical properties

Progress in remote sensing of grass senescence: A review on the challenges and opportunities

Grass senescence estimation in rangeland is particularly important for monitoring the conditions of forage quality and quantity. During senescence, grasses lose their nutrients from the leaves to the root and thereby affecting forage productivity. Studies on the remote sensing of grasslands have been conducted during the senescent phenological stage. However, despite the efforts made in previous remote sensing studies on grass senescence, its role in estimating grass senescence is rudimentary. More so, the strengths and limitations presented by the newly developed remote sensing instruments in grass senescence estimation are not well documented. This work, therefore, provides a detailed overview on the progress of remote sensing applications in characterizing grass senescence

Exploring the utility of the additional WorldView-2 bands and support vector machines in mapping land use/land cover in a fragmented ecosystem, South Africa

Land use/land cover (LULC) classification is a key research field in environmental applications of remote  sensing on the earthfs surface. The advent of new high resolution multispectral sensors with unique bands has  provided an opportunity to map the spatial distribution of detailed LULC classes over a large fragmented area. The objectives of the present study were: (1) to map LULC classes using multispectral WorldView-2 (WV-2) data and SVM in a fragmented ecosystem; and (2) to compare the accuracy of three WV-2 spectral data sets in distinguishing amongst various LULC classes in a fragmented ecosystem. WV-2 image was spectrally  resized to its four standard bands (SB: blue, green, red and near infrared-1) and four strategically located  bands (AB: coastal blue, yellow, red edge and near infrared-2). WV-2 image (8bands: 8B) together with SB and AB subsets were used to classify LULC using support vector machines. Overall classification accuracies of 78.0% (total disagreement = 22.0%) for 8B, 51.0% (total disagreement = 49.0%) for SB, and 64.0% (total disagreement = 36.0%) for AB were achieved. There were significant differences between the performance of all WV-2 subset pair comparisons (8B versus SB, 8B versus AB and SB versus AB) as demonstrated by the results of McNemarfs test (Z score .1.96). This study concludes that WV-2 multispectral data and the SVM classifier have the potential to map LULC classes in a fragmented ecosystem. The study also offers relatively accurate information that is important for the indigenous forest managers in KwaZulu-Natal, South Africa for making informed decisions regarding conservation and management of LULC patterns.Keywords: land use/cover classification, fragmented ecosystem, WorldView-2, support vector  machines