Estimates Of Vegetation Cover Research Articles

You eat what you find - Local patterns in vegetation structure control diets of African fungus-growing termites.

Fungus‐growing termites and their symbiotic Termitomyces fungi are critically important carbon and nutrient recyclers in arid and semiarid environments of sub‐Saharan Africa. A major proportion of plant litter produced in these ecosystems is decomposed within nest chambers of termite mounds, where temperature and humidity are kept optimal for the fungal symbionts. While fungus‐growing termites are generally believed to exploit a wide range of different plant substrates, the actual diets of most species remain elusive. We studied dietary niches of two Macrotermes species across the semiarid savanna landscape in the Tsavo Ecosystem, southern Kenya, based on carbon (C) and nitrogen (N) stable isotopes in Termitomyces fungus combs. We applied Bayesian mixing models to determine the proportion of grass and woody plant matter in the combs, these being the two major food sources available for Macrotermes species in the region. Our results showed that both termite species, and colonies cultivating different Termitomyces fungi, occupied broad and largely overlapping isotopic niches, indicating no dietary specialization. Including laser scanning derived vegetation cover estimates to the dietary mixing model revealed that the proportion of woody plant matter in fungus combs increased with increasing woody plant cover in the nest surroundings. Nitrogen content of fungus combs was positively correlated with woody plant cover around the mounds and negatively correlated with the proportion of grass matter in the comb. Considering the high N demand of large Macrotermes colonies, woody plant matter seems to thus represent a more profitable food source than grass. As grass is also utilized by grazing mammals, and the availability of grass matter typically fluctuates over the year, mixed woodland‐grasslands and bushlands seem to represent more favorable habitats for large Macrotermes colonies than open grasslands.

A Multiview Semantic Vegetation Index for Robust Estimation of Urban Vegetation Cover

Urban vegetation growth is vital for developing sustainable and liveable cities in the contemporary era since it directly helps people’s health and well-being. Estimating vegetation cover and biomass is commonly done by calculating various vegetation indices for automated urban vegetation management and monitoring. However, most of these indices fail to capture robust estimation of vegetation cover due to their inherent focus on colour attributes with limited viewpoint and ignore seasonal changes. To solve this limitation, this article proposed a novel vegetation index called the Multiview Semantic Vegetation Index (MSVI), which is robust to color, viewpoint, and seasonal variations. Moreover, it can be applied directly to RGB images. This Multiview Semantic Vegetation Index (MSVI) is based on deep semantic segmentation and multiview field coverage and can be integrated into any vegetation management platform. This index has been tested on Google Street View (GSV) imagery of Wyndham City Council, Melbourne, Australia. The experiments and training achieved an overall pixel accuracy of 89.4% and 92.4% for FCN and U-Net, respectively. Thus, the MSVI can be a helpful instrument for analysing urban forestry and vegetation biomass since it provides an accurate and reliable objective method for assessing the plant cover at street level.

Woody cover change in relation to fire history and land-use in the savanna-woodlands of north-east Namibia (1996–2019)

Vegetation cover estimates for trees, shrub-grass mosaics, and grassland and bare ground, were quantified in the savanna-woodland of Bwabwata National Park, north-east Namibia. Changes in woody cover were analysed using repeat photographs in combination with aerial photographs and recent satellite imagery taken between 1996 and 2019. Cover estimates for each vegetation type were obtained using object-based classification techniques and a non-parametric random forest classifier algorithm in eCognition Trimble software. Results show that over the two decades under investigation (1996–2019), trees declined (−10.6%), and the shrub-grass mosaic vegetation type increased (8.1%) across the park. The largest decline in trees occurred in the western land use areas (−36%), which also experienced the greatest increase in the shrub-grass mosaic (17%), when compared with areas in the east (11%). Variation of woody cover estimates is attributed to different seasonal fire management practices in the east versus the west of the park. The fire history (2000–2018) revealed that late dry season fires were frequent in the west, whereas in the east, early dry season fires were frequent. The stages of encroachment recorded in this study have consequences for biodiversity, people’s livelihoods, and tourism.

Comparative Study of Fractional Vegetation Cover Estimation Methods Based on Fine Spatial Resolution Images for Three Vegetation Types

Comparative Study of Fractional Vegetation Cover Estimation Methods Based on Fine Spatial Resolution Images for Three Vegetation Types

Rapid estimation of fractional vegetation cover in grasslands using smartphones

Measurements of fractional vegetation cover (FVC) are important for monitoring grassland growth and predicting aboveground biomass. Thus, a method to rapidly estimate grassland FVC is highly desired. Although smartphones provide a faster and less expensive method for estimating grassland FVC than satellites and unmanned aerial vehicles, their accuracy is not well understood. Here, we evaluated the use of smartphones to accurately estimate grassland FVC by taking photos in direct and indirect sunlight at three time points (8:00 a.m., 12:00 p.m. and 4:00 p.m.). We found that smartphone photography could be applied to grassland FVC estimation by extracting a specified percentile of color vegetation index (VIC) to reduce the effect of bright lighting. The 60th percentile value was more suitable for estimating grassland FVC than the 90th percentile, which was proposed in a previous study. We also found that extracting the 60th percentile VIC value from smartphone photos taken at noon under direct sunlight efficiently minimized the influence of lighting on estimated grassland FVC. We propose a model based on the VIC percentile method that can quickly estimate grassland FVC using smartphone photography. This model is very versatile for estimating FVC in semi-arid grasslands. Our findings show that using smartphone photos to quickly estimate grassland FVC is feasible and could provide a practical solution to quickly and accurately estimate grassland FVC.

Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image

With high spatial resolution remote sensing images being increasingly used in precision agriculture, more details of the row structure of row crops are captured in the corresponding images. This phenomenon is a challenge for the estimation of the fractional vegetation cover (FVC) of row crops. Previous studies have found that there is an overestimation of FVC for the early growth stage of vegetation in the current algorithms. When the row crops are a form in the early stage of vegetation, their FVC may also have overestimation. Therefore, developing an algorithm to address this problem is necessary. This study used World-View 3 images as data sources and attempted to use the canopy reflectance model of row crops, coupling backward propagation neural networks (BPNNs) to estimate the FVC of row crops. Compared to the prevailing algorithms, i.e., empirical method, spectral mixture analysis, and continuous crop model coupling BPNNs, the results showed that the calculated accuracy of the canopy reflectance model of row crops coupling with BPNNs is the highest performing (RMSE = 0.0305). Moreover, when the structure is obvious, we found that the FVC of row crops was about 0.5–0.6, and the relationship between estimated FVC of row crops and NDVI presented a strong exponential relationship. The results reinforced the conclusion that the canopy reflectance model of row crops coupled with BPNNs is more suitable for estimating the FVC of row crops in high-resolution images.

Improving the estimation of alpine grassland fractional vegetation cover using optimized algorithms and multi-dimensional features

BackgroundFractional vegetation cover (FVC) is an important basic parameter for the quantitative monitoring of the alpine grassland ecosystem on the Qinghai-Tibetan Plateau. Based on unmanned aerial vehicle (UAV) acquisition of measured data and matching it with satellite remote sensing images at the pixel scale, the proper selection of driving data and inversion algorithms can be determined and is crucial for generating high-precision alpine grassland FVC products.MethodsThis study presents estimations of alpine grassland FVC using optimized algorithms and multi-dimensional features. The multi-dimensional feature set (using original spectral bands, 22 vegetation indices, and topographical factors) was constructed from many sources of information, then the optimal feature subset was determined based on different feature selection algorithms as the driving data for optimized machine learning algorithms. Finally, the inversion accuracy, sensitivity to sample size, and computational efficiency of the four machine learning algorithms were evaluated.Results(1) The random forest (RF) algorithm (R2: 0.861, RMSE: 9.5%) performed the best for FVC inversion among the four machine learning algorithms driven by the four typical vegetation indices. (2) Compared with the four typical vegetation indices, using multi-dimensional feature sets as driving data obviously improved the FVC inversion accuracy of the four machine learning algorithms (R2 of the RF algorithm increased to 0.890). (3) Among the three variable selection algorithms (Boruta, sequential forward selection [SFS], and permutation importance-recursive feature elimination [PI-RFE]), the constructed PI-RFE feature selection algorithm had the best dimensionality reduction effect on the multi-dimensional feature set. (4) The hyper-parameter optimization of the machine learning algorithms and feature selection of the multi-dimensional feature set further improved FVC inversion accuracy (R2: 0.917 and RMSE: 7.9% in the optimized RF algorithm).ConclusionThis study provides a highly precise, optimized algorithm with an optimal multi-dimensional feature set for FVC inversion, which is vital for the quantitative monitoring of the ecological environment of alpine grassland.

Monitoring the quality of life in urban area using TDVI- Case study of Kalaburagi city

Many vegetation indices have been proposed over last decades made specialists search for the most suitable vegetation index for a given remote sensing application. Measuring the Quality of Place (QOP) is a hard task since it involves both physical and socio-economic dimensions. Being one of the major land use categories, urban vegetation plays a significant role in one‟s judgment for QOP in a neighborhood. Both quantity and quality of the community parks and recreation areas are major determinants of neighborhood attraction. For these reasons, detection of urban vegetation cover has been one of the important implication areas of urban image classification techniques. “Transformed Difference Vegetation Index (TDVI) developed by Bannari et al. (2002), is tested in a previous work where the index has performed better than NDVI and SAVI. In that work, a comparative study between TDVI, SAVI and NDVI for estimating vegetation cover in urban environment from the Indian Remote Sensing Satellite (IRS-1D) imagery has been conducted. The validation of the obtained results according to the ground truth showed that the TDVI is an excellent tool for vegetation cover monitoring in urban environment. It does not saturate like NDVI or SAVI, it shows an excellent linearity as a function of the rate of vegetation cover. This paper adds on the previous work by analyzing the performance of TDVI in urban image classification. Results indicate that, the performance of TDVI in urban image classification is better than NDVI and SAVI. The new index not only differentiates the urban vegetation cover better but also helps to minimize the error in classifying other unclassified pixels of urban categories.

Analysis of Forest Stand Resistance to Insect Attack According to Remote Sensing Data

Methods for analyzing the resistance of large woodlands (such as Siberian taiga forests) to insect attacks based on remote sensing data are proposed. As an indicator of woodland’s resistance, we suggest a function of normalized difference vegetative index (NDVI) susceptibility to changes in the land surface temperature (LST). Both NDVI and LST are obtained via the TERRA/AQUA satellite system. This indicator function was calculated as the spectral transfer function of the response in the integral equation connecting the changes in NDVI and LST. The analysis was carried out for two test sites, both of which are fir stands of the Krasnoyarsk region taiga zone. In the first case, the fir stands have suffered damage inflicted by Siberian silk moth caterpillars, Dendrolimus sibiricus Tschetv. since 2015. Adjacent intact fir forest areas were also analyzed. In the second case, the object of the study was a fir tree site damaged by Black Fir Sawyer Monochamus urussovii Fischer in 2013. It is demonstrated that the above-mentioned indicator function changed significantly 2–3 years prior to the pest population outbreaks, and therefore this indicator function can be used to assess the risk of pest population outbreak. Thereby, the proposed indicator compares favorably with vegetation cover estimates using NDVI, which register significant defoliation of tree stands and cannot be used for forecasting.

Contributions of green spaces and isolated trees to landscape connectivity in an urban landscape

Vegetation plays a fundamental role in conserving the biological diversity of urban areas. From isolated trees to multi-hectare patches, green spaces are critical to species conservation in urban landscapes. The abundance of green spaces can regulate the availability of habitat and resources for species’ survival. In many urban areas, the lack of vegetation inventories and ecological information at fine spatial resolutions limits conservation efforts. To evaluate the potential of very high-resolution imagery (WorldView-2, 30-cm resolution) to fill this gap, we sought to a) map contrasting urban vegetation cover types (grass, shrubs and woody vegetation) at the meter scale and with high accuracy, b) quantify the structural and functional connectivity of urban vegetation, and c) estimate the status of productive vegetation. We focused our analysis on land use and cover in the city of Xalapa in Veracruz, Mexico, and employed multiscale satellite imagery to estimate vegetation cover, connectivity, and health. We identified 38,579 patches of woody vegetation, of which 93 % corresponded to isolated trees contributing to 27 % of vegetation connectivity. Without woody vegetation, the connectivity index decrease up to 97 %; removing grass or shrubs would reduce connectivity by 38 % or 32 %, respectively. We identified 10 patches disproportionately important to connectivity, of which seven had some type of legal protection. Across vegetation types, 38 % of the vegetated area was unhealthy. Wooded element connectivity assessment at a fine spatial resolution is crucial to conserve and restore these key resources for biodiversity and human well-being in urban environments.

Ecological correlates of fecal corticosterone metabolites in female Greater Sage-Grouse (Centrocercus urophasianus)

Measurement of physiological responses can reveal effects of ecological conditions on an animal and correlate with demographic parameters. Ecological conditions for many animal species have deteriorated as a function of invasive plants and habitat fragmentation. Expansion of juniper (genus Juniperus L.) trees and invasion of annual grasses into sagebrush (genus Artemisia L.) ecosystems have contributed to habitat degradation for Greater Sage-Grouse (Centrocercus urophasianus (Bonaparte, 1827); hereinafter Sage-Grouse), a species of conservation concern throughout its range. We evaluated relationships between habitat use in a landscape modified by juniper expansion and annual grasses and corticosterone metabolite levels (stress responses) in feces (FCORTm) of female Sage-Grouse. We used remotely sensed data to estimate vegetation cover within the home ranges of hens and accounted for factors that influence FCORTm in other vertebrates, such as age and weather. We collected 35 fecal samples from 22 radio-collared hens during the 2017–2018 brood-rearing season (24 May–26 July) in southwestern Idaho (USA). Concentrations of corticosterone increased with home range size but decreased with reproductive effort and temperature. The importance of home range size suggests that maintaining or improving habitats that promote smaller home ranges would likely facilitate a lower stress response by hens, which should benefit Sage-Grouse survival and reproduction.

Estimation of dry vegetation cover and mass from MODIS data: Verification by roughness length and sand saltation threshold

Monitoring of dry vegetation in arid and semi-arid regions is crucial in environmental investigations such as assessments of wind erosion risk, livestock forage, and fire risk. However, there has been little research into estimating the coverage and mass of dry (non-photosynthetic) vegetation. In our study area at Tsogt-Ovoo (TsO) in the northern Gobi Desert, Mongolia, we used MODIS data to compare observed dry vegetation cover (VCd_obs) and mass (Md_obs) with values estimated from the Normalized Difference Vegetation Index (NDVI) and six other potential Short Wavelength InfraRed (SWIR) dry vegetation indices. Of the seven vegetation indices, the Soil Tillage Index (STI) and Normalized Difference Tillage Index (NDTI), both of which are calculated from SWIR bands 6 and 7, provided good estimates of VCd_obs and Md_obs. Our comparisons of observed roughness length (z0) and threshold friction velocity (u*t) for sand saltation at stony and non-stony sites with dry vegetation cover determined from STI verified its suitability for estimating VCd(STI). At both sites, the observed z0and u*tincreased with increasing estimated VCd(STI). Based on their relationships with STI, we predicted values of z0and u*tin the absence of dry vegetation that were in accordance with published data. The threshold wind speeds at 10 m height derived from u*tat both the stony and non-stony sites were reasonably close to the value that is commonly used in dust models. Thus, we verified that STI is a suitable index for estimating the amount of dry vegetation in the Gobi Desert and, potentially, in other arid and semi-arid regions. Although the relationships of z0and u*twith STI require further validation, it appears that the use of STI can improve future wind erosion risk assessments in arid and semi-arid environments.

Towards quantifying changes in forest cover in the Araucaria forest-grassland mosaic in southern Brazil

Quantitative estimates of past vegetation cover are needed both regionally so human-landscape interactions can be better understood, and globally to evaluate the effects of changing vegetation cover on the climate system. Models reducing the bias in the pollen representation of vegetation cover have been developed in the temperate regions of the northern hemisphere, while experience applying them in other parts of the world is limited. The Araucaria forest-grassland mosaic of southern Brazil is an area where open and forested plant communities exist naturally and have changed their cover over time due to changing climate and human activities. Therefore, this area is of particular interest for such studies. Modern pollen and vegetation comparisons were carried out in two protected areas: Vila Velha State Park in Paraná state and Aparados da Serra National Park in Rio Grande do Sul. Vegetation mapping and analysis focused on Araucaria angustifolia as the characteristic tree of this vegetation type. In the Araucaria forest-grassland mosaic open vegetation and woodland change at a scale of hundreds of metres. These changes are difficult to capture based on pollen proportions in surface samples. While the abundance of Poaceae pollen is not a good indicator of locally open conditions, several pollen taxa can be used as indicators of the local vegetation cover. Pollen vegetation ratios (R-values) compare well between the two study regions, indicating that pollen production of individual species within the large plant families of Poaceae and Asteraceae are similar within the overall region. Araucaria angustifolia pollen is underrepresented with regards to its vegetation cover, while Poaceae are among the highest pollen producers in the region. Diverse woodland species other than A. angustifolia were grouped as one forest taxon and as the species composition of woodlands differed between the two study areas, so did the estimated pollen productivity of this group. It would be rewarding in future investigations to estimate pollen productivity for groups of trees with the same pollen dispersal syndrome. The application of pollen dispersal models designed for closed canopy in the protected areas was challenging. Further model development is required to deal with pollen released at different levels in semi-open vegetation types.

Method for accurate multi-growth-stage estimation of fractional vegetation cover using unmanned aerial vehicle remote sensing

BackgroundFractional vegetation cover (FVC) is an important parameter for evaluating crop-growth status. Optical remote-sensing techniques combined with the pixel dichotomy model (PDM) are widely used to estimate cropland FVC with medium to high spatial resolution on the ground. However, PDM-based FVC estimation is limited by effects stemming from the variation of crop canopy chlorophyll content (CCC). To overcome this difficulty, we propose herein a “fan-shaped method” (FSM) that uses a CCC spectral index (SI) and a vegetation SI to create a two-dimensional scatter map in which the three vertices represent high-CCC vegetation, low-CCC vegetation, and bare soil. The FVC at each pixel is determined based on the spatial location of the pixel in the two-dimensional scatter map, which mitigates the effects of CCC on the PDM. To evaluate the accuracy of FSM estimates of the FVC, we analyze the spectra obtained from (a) the PROSAIL model and (b) a spectrometer mounted on an unmanned aerial vehicle platform. Specifically, we use both the proposed FSM and traditional remote-sensing FVC-estimation methods (both linear and nonlinear regression and PDM) to estimate soybean FVC.ResultsField soybean CCC measurements indicate that (a) the soybean CCC increases continuously from the flowering growth stage to the later-podding growth stage, and then decreases with increasing crop growth stages, (b) the coefficient of variation of soybean CCC is very large in later growth stages (31.58–35.77%) and over all growth stages (26.14%). FVC samples with low CCC are underestimated by the PDM. Linear and nonlinear regression underestimates (overestimates) FVC samples with low (high) CCC. The proposed FSM depends less on CCC and is thus a robust method that can be used for multi-stage FVC estimation of crops with strongly varying CCC.ConclusionsEstimates and maps of FVC based on the later growth stages and on multiple growth stages should consider the variation of crop CCC. FSM can mitigates the effect of CCC by conducting a PDM at each CCC level. The FSM is a robust method that can be used to estimate FVC based on multiple growth stages where crop CCC varies greatly.

Vegetation cover analysis and ecotourism business model for sustainable forest management in Gunung Leuser National Park, Indonesia

Gunung Leuser National Park (GLNP) is part of the Tropical Rainforest Heritage program of Sumatra that was designated by UNESCO. GLNP is very vulnerable to vegetation land cover decrease due to illegal logging, encroachment, oil palm expansion, and inappropriate management of ecotourism. GLNP authority has the responsibility to handle forest threats by estimating vegetation cover and manage the ecotourism sector. This study aims to estimate vegetation cover and investigate strategy in developing ecotourism business model for sustainable forest management in GLNP. The methods used Normalized Vegetation Index (NDVI) analysis in estimating vegetation cover, and Social Enterprise - Business Model Canvas (SE-BMC) in developing a strategic business ecotourism model. NDVI was classified into 5 classes from class 1 to 5 (non-vegetation or very low dense vegetation to very high dense vegetation) based on unsupervised classification (natural breaks in ArcGIS). The results showed the largest vegetation area in GLNP belongs to class 4 (445876 ha or 32.68%) and class 5 (355018 ha or 26.02%). This indicates that the majority of GLNP by area is still densely vegetated. Based on SE-BMC analysis, GLNP management needs to collaborate with the local community and other stakeholders. We recommend improving ecotourism management and enhancing community development.

Synergistic Effects of Rooted Aquatic Vegetation and Drift Wrack on Ecosystem Multifunctionality

Ecosystem multifunctionality is an increasingly popular concept used to approximate multifaceted ecosystem functioning, which in turn may help advance ecosystem-based management. However, while experimental studies have shown a positive effect of diversity on multifunctionality, observational studies from natural systems—particularly aquatic—are scarce. Here, we tested the relative importance of species richness and cover of rooted aquatic vegetation, as well as cover of the loose-lying form of the macroalgae bladderwrack (Fucus vesiculosus), for ecosystem multifunctionality in shallow bays along the western Baltic Sea coast. We estimated multifunctionality based on four indicators of functions that support ecosystem services: recruitment of large predatory fish, grazer biomass, inverted ‘nuisance’ algal biomass, and water clarity. Piecewise path analysis showed that multifunctionality was driven by high cover of rooted aquatic vegetation and bladderwrack, particularly when the two co-occurred. This synergistic effect was nearly three times as strong as a negative effect of land-derived nitrogen loading. Species richness of aquatic vegetation indirectly benefitted multifunctionality by increasing vegetation cover. Meanwhile, high bladderwrack cover tended to decrease vegetation species richness, indicating that bladderwrack has both positive and negative effects on multifunctionality. We conclude that managing for dense and diverse vegetation assemblages may mitigate effects of anthropogenic pressures (for example, eutrophication) and support healthy coastal ecosystems that provide a range of benefits. To balance the exploitation of coastal ecosystems and maintain their multiple processes and services, management therefore needs to go beyond estimation of vegetation cover and consider the diversity and functional types of aquatic vegetation.

A Fixed-Threshold Method for Estimating Fractional Vegetation Cover of Maize under Different Levels of Water Stress

Accurate estimation of fractional vegetation cover (FVC) from digital images taken by commercially available cameras is of great significance in order to monitor the vegetation growth status, especially when plants are under water stress. Two classic threshold-based methods, namely, the intersection method (T1 method) and the equal misclassification probability method (T2 method), have been widely applied to Red-Green-Blue (RGB) images. However, the high coverage and severe water stress of crops in the field make it difficult to extract FVC stably and accurately. To solve this problem, this paper proposes a fixed-threshold method based on the statistical analysis of thresholds obtained from the two classic threshold approaches. Firstly, a Gaussian mixture model (GMM), including the distributions of green vegetation and backgrounds, was fitted on four color features: excessive green index, H channel of the Hue-Saturation-Value (HSV) color space, a* channel of the CIE L*a*b* color space, and the brightness-enhanced a* channel (denoted as a*_I). Secondly, thresholds were calculated by applying the T1 and T2 methods to the GMM of each color feature. Thirdly, based on the statistical analysis of the thresholds with better performance between T1 and T2, the fixed-threshold method was proposed. Finally, the fixed-threshold method was applied to the optimal color feature a*_I to estimate FVC, and was compared with the two classic approaches. Results showed that, for some images with high reference FVC, FVC was seriously underestimated by 0.128 and 0.141 when using the T1 and T2 methods, respectively, but this problem was eliminated by the proposed fixed-threshold method. Compared with the T1 and T2 methods, for images taken in plots under severe water stress, the mean absolute error of FVC obtained by the fixed-threshold method was decreased by 0.043 and 0.193, respectively. Overall, the FVC estimation using the proposed fixed-threshold method has the advantages of robustness, accuracy, and high efficiency, with a coefficient of determination (R2) of 0.99 and root mean squared error (RMSE) of 0.02.

High Spatial Resolution and High Temporal Frequency (30-m/15-day) Fractional Vegetation Cover Estimation over China Using Multiple Remote Sensing Datasets: Method Development and Validation

High spatial resolution and high temporal frequency fractional vegetation cover (FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite (HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index (NDVI) was acquired by using the continuous correction (CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product (GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors (MEs) of forest, cropland, and grassland were −0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809, root-mean-square deviation (RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial-temporal consistency and similar magnitude (RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.

Impacts of soil erosion and forest quality on water quality in Samanalawewa watershed, Sri Lanka

Soil erosion is one of the serious environmental concerns in Sri Lanka which affects on water quality of watersheds. Forest quality is an important indicator of the healthiness of forests which often contribute to mitigate the adverse impacts resulting from watershed disturbances and to improve water quality in river watersheds. Thus, the prime aim of this study is to evaluate and map the rate of soil erosion and forest quality of the Samanalawewa watershed and to establish their relationship with water quality of the watershed. Soil erosion and forest quality of 12 sub-watersheds of Samanalawewa watershed were evaluated using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), InVEST Sediment Delivery Ratio (SDR) and Carbon models developed by the NatCap-Project, Sanford University-USA and also using the Normalized Difference Vegetation Index (NDVI), respectively. Water quality parameters viz. temperature, pH, electrical conductivity, total dissolved solids, dissolved oxygen, biochemical oxygen demand, alkalinity, hardness, nitrate–N, phosphate-P were analyzed in monthly basis for water samples collected from 12 sampling locations (n = 12) in the sub-watersheds within month of May–July 2019. Pearson correlation was conducted to establish the relationships between water quality parameters and soil erosion and forest quality/NDVI of each sub-watershed. The results of the study indicated that the soil erosion of the watersheds ranges from 0 to 3555.7 t/ha/yr with an annual average of 139.9 t/ha/yr, which is ~ 28 times greater than the soil loss tolerance (T) in Sri Lanka. The average NDVI values (forest quality) of the sub-watersheds varied between − 0.2907 and 0.4628. All the water quality parameters in the study area were within the standards limits of SLS (614:2013). Estimation of vegetation cover of the study area using the InVEST Carbon model revealed that the above ground biomass is the dominant carbon storage among the other carbon pools. No significant correlation (p < 0.05) was found between the water quality parameters and the soil erosion rates in the watershed possibly attributed to the mitigation effects of the healthy forest cover within the study catchment. This was further confirmed by the significant negative correlation (r2 = 0.521, p = 0.008) established between the average NDVI values and nitrate–N levels of the river water of the catchment. The findings of the study provided the empirical evidence of the contribution of the healthy forest cover on the improvement of watershed water quality. The outcome of the research is useful for formulating watershed management policies and planning for watershed management

Fractional vegetation cover estimation in southern African rangelands using spectral mixture analysis and Google Earth Engine

Grasslands are under continuous threat of conversion and subsequent degradation, which has a detrimental impact on grassland productivity and grazing capacity, affecting the livestock industry. Fractional vegetation cover as indicator of grassland condition and productivity has been extensively researched, however, existing approaches and products are limited with respect to accessibility, affordability, applicability, and transferability. This study evaluated the use of publicly available satellite imagery, spectral mixture analysis and cloud geoprocessing technologies for dynamic, continuous, and accurate estimation of FVC for sustainable management. A linear spectral mixture model was developed, calibrated, and implemented in Google Earth Engine using Sentinel-2 and Landsat 8 imagery. Model accuracy and spatial and temporal transferability were evaluated using existing benchmark products and field data. It was found that Sentinel-2 performed the best using a feature combination of the SWIR2 band and the NDVI, EVI, MSAVI2 and DBSI indices. Accuracies were further improved by dividing the woody and bare endmembers into subclasses. The approach proved both spatially and temporally transferable, thus this research provides a robust approach to FVC estimation using limited field data and open source remote sensing imagery. The combination of this research with further grassland productivity modelling could prove valuable for sustainable environmental and economical rangeland planning and management.