Fractional Vegetation Research Articles

Distinguishing between early- and late-covering crops in the land surface model Noah-MP: impact on simulated surface energy fluxes and temperature

Abstract. Land surface models are essential parts of climate and weather models. The widely used Noah-MP land surface model requires information on the leaf area index (LAI) and green vegetation fraction (GVF) as key inputs of its evapotranspiration scheme. The model aggregates all agricultural areas into a land use class termed “cropland and pasture”. In a previous study we showed that, on a regional scale, the GVF has a bimodal distribution formed by two crop groups differing in phenology and growth dynamics: early-covering crops (ECC; e.g., winter wheat, winter rapeseed, winter barley) and late-covering crops (LCC; e.g., corn, silage maize, sugar beet). That result can be generalized for central Europe. The present study quantifies the effect of splitting the land use class cropland and pasture of Noah-MP into ECC and LCC on surface energy fluxes and temperature. We further studied the influence of increasing the LCC share, which in the study area (the Kraichgau region, southwest Germany) is mainly the result of heavily subsidized biomass production, on energy partitioning at the land surface. We used the GVF dynamics derived from high-resolution (5 m × 5 m) RapidEye satellite data and measured LAI data for the simulations. Our results confirm that the GVF and LAI strongly influence the partitioning of surface energy fluxes, resulting in pronounced differences between simulations of ECC and LCC. Splitting up the generic crop into ECC and LCC had the strongest effect on land surface exchange processes in July–August. During this period, ECC are at the senescence growth stage or already harvested, while LCC have a well-developed ground-covering canopy. The generic crop resulted in humid bias, i.e., an increase in evapotranspiration by +0.5 mm d−1 (latent heat flux is 1.3 MJ m−2 d−1), decrease in sensible heat flux (H) by 1.2 MJ m−2 d−1 and decrease in surface temperature by −1 ∘C. The bias increased as the shares of ECC and LCC became similar. The observed differences will impact the simulations of processes in the planetary boundary layer. Increasing the LCC share from 28 % to 38 % in the Kraichgau region led to a decrease in latent heat flux (LE) and a heating up of the land surface in the early growing season. Over the second part of the season, LE increased and the land surface cooled down by up to 1 ∘C.

Disentangling fractional vegetation cover: Regression-based unmixing of simulated spaceborne imaging spectroscopy data

The next generation of spaceborne imaging spectrometers will enable hyperspectral analysis of vegetation cover across large spatial extents. Spectral unmixing provides a means to assess subpixel vegetation composition in such imagery. Here we implement a regression-based unmixing approach to generate fractional vegetation cover on a regional scale from a simulated Environmental Mapping and Analysis Program (EnMAP) satellite scene derived from Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) imagery acquired over the San Francisco Bay Area, California, USA, an area with a mixture of temperate and Mediterranean climate forests, woodlands and shrublands. A hierarchical classification scheme was implemented that considered fractional cover of vegetation as a whole (vegetation vs non-vegetation), vegetation life forms (woody vs non-woody vegetation; tree vs shrub vs grass), and tree leaf type (needleleaf vs broadleaf). A Gaussian Process Regression (GPR) model was trained using synthetically-mixed training data generated from an endmember library, and mapping accuracy was assessed using an independent validation dataset across four ecoregions. Our approach was able to effectively model landscape patterns at all levels of the class hierarchy. Site-wide map accuracy was highest when mapping generic vegetation fractions (MAE = 3.8%) and expectedly decreased at more complex hierarchy levels, with highest errors observed when separating tree and shrub fractions. Still, fraction estimates of needleleaf trees (MAE = 10.6%), broadleaf trees (MAE = 13.1%) and shrubs (MAE = 15.3%) were mapped with low overall error. Using Landsat imagery led to an average decrease in map accuracy of 1.9% when compared to hyperspectral image analysis and a maximum decrease of 3.5% when separating broadleaf and needleleaf trees across all sites. Further, a single regional model was shown to yield comparable results to multiple local ecoregion-based models, facilitating the analysis of large regions without creating a separate model for each region. Our results highlight the utility of regression-based approaches for quantitative vegetation mapping, which is of particular interest for future spaceborne imaging spectroscopy missions operating across large areas at moderate spatial resolution.

Comparison of pixel unmixing models in the evaluation of post-fire forest resilience based on temporal series of satellite imagery at moderate and very high spatial resolution

In Mediterranean fire-prone ecosystems, shifts in fire regime as a consequence of global change could modify the resilience of vegetation communities. In this paper, we aim to compare the efficiency of high and moderate spatial resolution satellite imagery in the evaluation of resilience in a fire-prone landscape under different fire regime categories using two pixel unmixing techniques. A time series of Landsat (ETM+ and OLI; spatial resolution of 30 m) and WorldView-2 (spatial resolution of 2 m) imagery collected between 2011 (pre-fire conditions) and 2016 were used to estimate the temporal variation of fractional vegetation cover (FVC) as a quantitative measure of forest resilience. For this time series, FVC was computed under four fire-regime categories of recurrence and severity using two approaches: dimidiate pixel model and multiple endmember spectral mixture analysis (MESMA). The dimidiate pixel model was computed using NDVI as spectral response for the case of Landsat imagery and NDVI and red-edge NDVI (RENDVI) for WorldView-2. MESMA was applied to unmix WorldView-2 and Landsat imagery into four fraction images: photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), soil and shade. The PV shade normalized fraction corresponds to the FVC. In summer of 2016 we established 85 30 × 30 m field plots and 360 2 × 2 m field plots to measure the percentage of total vegetation cover in order to validate the FVC estimates made from remote sensing data. The FVC time series showed the same general pattern with both spatial scales and modeling approaches, high fire recurrence categories registering the highest resilience. The accuracy of the dimidiate pixel model was significantly higher for WorldView-2 based estimates (RMSE: 5–10%) than for Landsat (RMSE: 10–15%). The dimidiate pixel model computed from NDVI for both Landsat and WorldView-2 underestimated FVC at high field-sampled vegetation cover, while MESMA estimations were accurate for the entire range of vegetation cover for both satellites. The fraction of photosynthetic vegetation calculated using WorldView-2 had a higher performance (RMSE: 4–6%) than that quantified from Landsat (RMSE: 6–8%). The linear relationships assumed for validation purposes were statistically significant for both sensors and modeling approaches. Our study demonstrates the highest performance of very high spatial resolution satellite imagery and MESMA models in the quantitative estimation of FVC as a measure of post-fire resilience.

Vineyard yield estimation by combining remote sensing, computer vision and artificial neural network techniques

In viticulture, it is critical to predict productivity levels of the different vineyard zones to undertake appropriate cropping practices. To overcome this challenge, the final yield was predicted by combining vegetation indices (VIs) to sense the health status of the crop and by computer vision to obtain the vegetated fraction cover (Fc) as a measure of plant vigour. Multispectral imagery obtained from an unmanned aerial vehicle (UAV) is used to obtain VIs and Fc, which are used together with artificial neural networks (ANN) to model the relationship between VIs, Fc and yield. The proposed methodology was applied in a vineyard, where different irrigation and fertilisation doses were applied. The results showed that using computer vision techniques to differentiate between canopy and soil is necessary in precision viticulture to obtain accurate results. In addition, the combination of VIs (reflectance approach) and Fc (geometric approach) to predict vineyard yield results in higher accuracy (root mean square error (RMSE) = 0.9 kg vine−1 and relative error (RE) = 21.8% for the image when close to harvest) compared to the simple use of VIs (RMSE = 1.2 kg vine−1 and RE = 28.7%). The implementation of machine learning techniques resulted in much more accurate results than linear models (RMSE = 0.5 kg vine−1 and RE = 12.1%). More precise yield predictions were obtained when images were taken close to the harvest date, although promising results were obtained at earlier stages. Given the perennial nature of grapevines and the multiple environmental and endogenous factors determining yield, seasonal calibration for yield prediction is required.

Assessing the effect of spatial resolution on the delineation of management zones for smallholder farming in southern Brazil

Remote sensing estimates of cover-crop aboveground biomass (AGB) have been used to delineate management zones for smallholder farming in southern Brazil. In this study, we investigated the spatial resolution influence on the AGB estimates of rye, calculated from regression relationships with the Normalized Difference Vegetation Index (NDVI), and on the subsequent delineation of management zones using the Management Zone Analyst (MZA) software. Data acquired by an Unmanned Aerial Vehicle (UAV) Parrot Sequoia camera (0.20 m spatial resolution) in Brazil were compared with observations from the PlanetScope (PS) satellite constellation (3 m) and the Operational Land Imager (OLI)/Landsat-8 (30 m). A three-endmember mixture model (green vegetation, soil, and shadow) was applied to surface reflectance data of these instruments for evaluating the cover-crop development at two dates in August 2017. Because of the differences in the technical specifications of the sensors, we resampled the UAV dataset into four levels of spatial resolution (1, 3, 10, and 30 m). Using the UAV map (0.20 m) as a reference, we obtained confusion matrices for the original and resampled data. The results showed that the increasing amounts of rye AGB from the beginning to the end of August promoted significant changes in surface reflectance and in soil-green vegetation fractions calculated at variable spatial resolution. The performance of the regression models to estimate cover-crop AGB was approximately similar in the transition from the sub-metric (0.20 m) to the metric (3 m) spatial resolutions, or from the UAV camera to the PS data. For all datasets, the MZA detected two management zones with zone 2 having higher cover-crop AGB than zone 1. When compared to the UAV management zone map (reference), the PS map had a moderate-to-substantial agreement, while the OLI/Landsat-8 map had a fair-to-moderate concordance. Substantial agreements with the reference map were observed at simulated 1 m and 3 m data, as indicated by Kappa coefficients of 0.73 and 0.63 and overall accuracies of 86.40% and 81.40%, respectively. We conclude that the 3 m spatial resolution data of the PS comprise an alternative to delineate management zones for smallholder farming in southern Brazil when compared to the very-high spatial resolution observations of the UAV cameras.

Development of integrated wetland change detection approach: In case of Erdos Larus Relictus National Nature Reserve, China

Wetlands are threatened by the global warming and the human exploitation pressure, and have been shrinking quickly in recent years. Timely and accurate wetland area change detection is the primary task for wetland conservation and restoration. The objective of this study is to develop an integrated change detection approach which integrates the advantages of spectral mixture analysis (SMA) and change vector analysis (CVA) for the change identification of wetland dynamics. In the proposed approach, water, vegetation and soil fractions of wetlands were derived by SMA; then, the detailed change information (including change magnitude and 12 change direction categories) were calculated through CVA. The proposed approach was applied for the wetlands change in Erdos Larus Relictus National Nature Reserve (ELRNNR), China, using time-series Landsat images during 1977–2017. We found that the wetland faced serious degradation, with water fraction changed to soil (5.79 km2), to vegetation (1.35 km2) and to both soil and vegetation (3.53 km2). From 1977 to 2000, a slight degradation occurred in the northeast edge of Bojiang Lake and a marginal degradation in Bojiang and Houjia Lakes inside the ELRNNR, with water fraction changed to soil and vegetation. During 2000–2010, severe degradation occurred in ELRNNR, and from 2010 to 2017, the wetland was more susceptible to the precipitation change and human activities. Analysis of the result indicated that the long-term drought and effects of mismanagement as well as misuse by human beings were the driving factors of wetland degradation. The proposed approach in this study achieves a higher accuracy than the classification approach to detect wetland change, with the ability to obtain more detailed change information.

Investigating microclimate effects in an oasis-desert interaction zone

To investigate oasis-desert microclimate effects, we performed a series of numerical simulations in an idealized oasis-desert system based on an improved computational fluid dynamics (CFD) model for simulating atmospheric boundary layer flows, air temperature and humidity. Numerical simulations were designed based on the hydrometeorological observations obtained during the HiWATER-MUSOEXE (Heihe Watershed Allied Telemetry Experimental Research, Multi-Scale Observation Experiment on Evapotranspiration over heterogeneous land surfaces) campaign. The results are summarized as follows: (1) Oasis-desert interactions are significantly affected by background wind conditions. We observed the oasis-desert local circulation under calm background wind conditions and the oasis thermal internal boundary layer under low wind speed conditions induced by hydrothermal contracts. These interactions will disappear when the background wind speed is sufficiently high, and there is only an oasis dynamic internal boundary layer caused by the aerodynamic roughness length contrast. (2) Oasis-desert interactions lead to a series of microclimate effects, including the oasis cold-wet island effect, air humidity inversion effect within the surrounding desert and oasis wind shield effect, which are important for the stability and sustainability of the oases-desert ecosystem. (3) The hydrothermal conditions due to the difference between the oasis and desert, the vegetation fraction and distribution patterns impact the oasis-desert microclimate effects. The intensity of oasis-desert interactions increases with the land surface temperature (LST) difference in the oasis-desert. The oasis-desert interactions are gradually strengthened with the increase of the vegetation fraction within the oasis. Integrated ecological and economic benefits of the oasis, the oasis vegetation pattern, which includes the croplands and shelterbelts staggered within the oasis and the shelterbelts surrounding the outside, is beneficial to limiting the loss of water vapor and preventing sandstorms from the oasis. The findings of the current study improve the fundamental understanding of the microclimate and provide implications for maintaining the sustainability of oasis-desert ecosystems.

An improved inversion algorithm for spatio-temporal retrieval of soil moisture through modified water cloud model using C- band Sentinel-1A SAR data

An approach to incorporate vegetation fraction into Modified Water Cloud Model (MWCM) and the evaluation of potential of multi-target random forest regression (MTFER) were done for the retrieval of spatio-temporal variability of soil moisture (SM) in Varanasi district of Uttar Pradesh, India. The Sentinel-1A SAR images were acquired for three different dates (19/12/2016, 05/02/2017 and 25/03/2017) for two types of spatial regions covered with vegetated and sparse vegetated soil field for SM retrieval. The vegetation fraction (fveg), computed from Landsat – 8 satellite data, was inserted into the modified water cloud model (MWCM) as a modification factor. Leaf area index (LAI) was used as a vegetation descriptor parameter (V) in the MWCM. Subsequently, a machine learning based MTRFR algorithm with a regularization routine was used for stable and optimum solution for complex problems related to the inversion of the MWCM for the accurate estimation of SM. The coefficient of determination (R2), root mean square error (RMSE) and nash sutcliffe efficiency (NSE) indicated significantly better results in the region-2 for all the temporal changes occurred than those of region-1. The results showed that incorporation of fveg to the MWCM provided high potential to retrieve spatio-temporal SM in the region-2 where soil fields were mostly covered with wheat and barley crops rather than in region-1 having sparse vegetated soil field. The overall accuracy of spatio-temporal retrieval of SM after incorporating vegetation factor to MWCM showed significantly better R2 = 0.82, RMSE = 3.18 (%) and NSE = 0.85. The inversion results proximate that the MTRFR techniques applied to the MWCM, including vegetation factor, has great a capability for an accurate SM retrieval in the vegetated soil field.

Spatial study of particulate matter distribution, based on climatic indicators during major dust storms in the State of Arizona

Arizona residents have been dealing with the suspended particulate matter caused health issues for a long time due to Arizona’s arid climate. The state of Arizona is vulnerable to dust storms, especially in the monsoon season because of the anomalies in wind direction and magnitude. In this study, a high-resolution Weather Research and Forecasting (WRF) model coupled with a chemistry module (WRF-Chem) was simulated to compute the particulate matter spatiotemporal distribution as well as the climatic parameters for the state of Arizona. Subsequently, Ordinary Least Square (OLS), spatial lag, spatial error, and Geographically Weighted Regression (GWR) techniques were utilized to develop predictive models based on the climatic indicators that impacted the formation and dispersion of the particulate matter during dust storms. Census tracts were adopted to create local spatial averages for the chosen variables. Terrain height, temperature, wind speed, and vegetation fraction were designated as the most significant variables, whereas base state and perturbation pressures, planetary boundary layer height and soil moisture were adopted as supplementary variables. The determination coefficient for OLS, spatial lag, spatial error, and GWR models peaked at 0.92, 0.93, 0.96, and 0.97, respectively. These models provide a better understanding of the current distribution of the particulate matter and can be used to forecast future trends.

Limitations in Validating Derived Soil Water Content from Thermal/Optical Measurements Using the Simplified Triangle Method

We assess the validity of the surface moisture availability parameter (Mo) derived from satellite-based optical/thermal measurements using the simplified triangle method. First, we show that Mo values obtained from the simplified triangle method agree closely with those generated from a soil/vegetation/atmosphere/transfer (SVAT) model for scenes over a field site at the Allahabad district, India. Next, we compared Mo values from the simplified triangle method for these same overpass scenes with surface soil water content measured at depths of 5 and 15 cm at this field site. Although a very weak correlation exists between remotely sensed values of Mo for the full scenes and measured soil water content measured at both depths, correlations increasingly improve for the 5 cm samples (but not for the 15 cm samples) as pixels were limited to increasingly smaller vegetation fractions. We conclude that the simplified triangle method would yield reasonable values of Mo and demonstrate good agreement with ground measurements, provided that validation is limited to pixels with little or no vegetation and to soil depths of 5 cm or less.

Vegetation Fraction Images Derived from PROBA-V Data for Rapid Assessment of Annual Croplands in Brazil

This paper presents a new method for rapid assessment of the extent of annual croplands in Brazil. The proposed method applies a linear spectral mixing model (LSMM) to PROBA-V time series images to derive vegetation, soil, and shade fraction images for regional analysis. We used S10-TOC (10 days synthesis, 1 km spatial resolution, and top-of-canopy) products for Brazil and S5-TOC (five days synthesis, 100 m spatial resolution, and top-of-canopy) products for Mato Grosso State (Brazilian Legal Amazon). Using the time series of the vegetation fraction images of the whole year (2015 in this case), only one mosaic composed with maximum values of vegetation fraction was generated, allowing detecting and mapping semi-automatically the areas occupied by annual crops during the year. The results (100 m spatial resolution map) for the Mato Grosso State were compared with existing global datasets (Finer Resolution Observation and Monitoring—Global Land Cover (FROM-GLC) and Global Food Security—Support Analyses Data (GFSAD30)). Visually those maps present a good agreement, but the area estimated are not comparable since the agricultural class definition are different for those maps. In addition, we found 11.8 million ha of agricultural areas in the entire Brazilian territory. The area estimation for the Mato Grosso State was 3.4 million ha for 1 km dataset and 5.3 million ha for 100 m dataset. This difference is due to the spatial resolution of the PROBA-V datasets used. A coefficient of determination of 0.82 was found between PROBA-V 100 m and Landsat-8 OLI area estimations for the Mato Grosso State. Therefore, the proposed method is suitable for detecting and mapping annual croplands distribution operationally using PROBA-V datasets for regional analysis.

Zoning of the republic of Kazakhstan as to the risk of natural focal diseases in animals: the case of rabies and anthrax

Rabies and anthrax, being natural focal diseases, are characterized by the ability to persist in areas with a certain combination of environmental factors without human intervention. These infections annually cause sporadic outbreaks in domestic, livestock and wild animals in the Republic of Kazakhstan (RK) receiving close attention of the veterinary service. In particular, targeted mass vaccination and surveillance are conducted, which requires zoning of the country according to the exposure to the diseases.This paper presents a zoning approach based on the estimation of suitability to the study diseases using the Environmental Niche Modelling method. Retrospective data on animal rabies outbreaks in the RK for 2003-2014, as well as data on anthrax burial sites for 1933-2014 were used. The following environmental factors were treated as potential explanatory variables: 1) a set of climate data derived variables BIOCLIM; 2) altitude above the sea level; 3) land cover type; 4) the maximum green vegetation fraction and 5) soil type.The modelling outcomes for both diseases indicate elevated risks along the northern and southeastern borders of the RK that not only follows the distribution of historic disease cases, but also accounts for potentially suitable environmental conditions. To comply with the requirements of the veterinary service, gridded risk maps were converted into categorical maps by averaging risk values within municipal districts and ranking according to four categories: low, medium, high, and very high.The maps obtained may be used as recommendations to the veterinary service as a basis for developing regionspecific anti-epizootic measures.

Numerical assessment of the urban green space scenarios on urban heat island and thermal comfort level in Tehran Metropolis

The growing rate of urbanization and alterations in natural land-use/land-cover properties cause many worldwide environmental issues such as Urban Heat Island (UHI), low Thermal Comfort Level (TCL) and air pollution in megacities. Development of urban green spaces is one of the most proposed measures for mitigation of UHI and improvement of thermal comfort satisfaction which causes lower cooling energy consumption and indirectly improves the urban air quality. Therefore, the efficiency of realistic and executable green scenarios in both surface and roof levels (surface vegetation, green roof, surface vegetation + green roof) on early summertime UHI and TCL are evaluated and compared in Tehran Metropolis, using coupled Weather Research and Forecasting model with Single Layer Urban Canopy Model (WRF/SLUCM). Model predefined urban parameters (the urban density and vegetation fraction) are modified before the main simulations by data of remote-sensing methods which let us define surface green spaces developmental scenarios without any change in the basic urban morphology. Results are presented for three areas with different urban morphologies (Low and High-Intensity Residential (LR and HR) and Commercial and Industrial (C/I)). Three indexes (THI, ETI and RSI) are calculated for assessing TCL variations. Generally, results show that environmental operations of selected scenarios are different over selected areas. In LR area, diurnal cooling (up to −0.86 °C in green roof approach) and improvement in thermal comfort condition are observed in all scenarios. On the other hand, in HR and C/I areas, the daytime cooling (nighttime warming) effect up to −0.85 °C (up to +0.63 °C) in the third scenario are simulated. TCL alterations show similar deteriorating nightly effects in all scenarios in selected regions. Averaged diurnal decrease in wind speed is another undesirable impact of green development scenarios due to the enhanced surface roughness that can reduce urban natural heat and pollution ventilations. Current results clarify that the adoption of efficient urban green spaces programs needs to consider different environmental concerns. By comparison between roof level and surface greenery, it can be concluded that the green roof approach with the least undesirable nightly effects is a more efficient approach than surface vegetation development in high-density Tehran megacity.

Urban land cover type determines the sensitivity of carbon dioxide fluxes to precipitation in Phoenix, Arizona.

Urbanization modifies land surface characteristics with consequent impacts on local energy, water, and carbon dioxide (CO2) fluxes. Despite the disproportionate impact of cities on CO2 emissions, few studies have directly quantified CO2 conditions for different urban land cover patches, in particular for arid and semiarid regions. Here, we present a comparison of eddy covariance measurements of CO2 fluxes (FC) and CO2 concentrations ([CO2]) in four distinct urban patches in Phoenix, Arizona: a xeric landscaping, a parking lot, a mesic landscaping, and a suburban neighborhood. Analyses of diurnal, daily, and seasonal variations of FC and [CO2] were related to vegetation activity, vehicular traffic counts, and precipitation events to quantify differences among sites in relation to their urban land cover characteristics. We found that the mesic landscaping with irrigated turf grass was primarily controlled by plant photosynthetic activity, while the parking lot in close proximity to roads mainly exhibited the signature of vehicular emissions. The other two sites that had mixtures of irrigated vegetation and urban surfaces displayed an intermediate behavior in terms of CO2 fluxes. Precipitation events only impacted FC in urban patches without outdoor water use, indicating that urban irrigation decouples CO2 fluxes from the effects of infrequent storms in an arid climate. These findings suggest that the proportion of irrigated vegetation and urban surfaces fractions within urban patches could be used to scale up CO2 fluxes to a broader city footprint.

ANALYSIS OF TEMPORAL AND SPATIAL CHARACTERISTICS OF MANGROVE PHYSIOLOGICAL STRUCTURE PARAMETERS QUANTITATIVE INVERSION BASED ON TIME SERIES SENTINEL-2 – TAKING SHANKOU MANGROVE NATURE RESERVE AS AN EXAMPLE

Abstract. The physiological structure parameter of vegetation is an important index to measure the ecological health status of mangroves, and it is of great value to measure the ecological health status. Taking Shankou Mangrove Nature Reserve of Guangxi as the study area, The Chlorophyll-A/B(CAB),Leaf Area Index (LAI),Fractional Vegetation Cover (FVC) and Fraction of absorbed photo synthetically active radiation (FAPAR) were calculated by using Sentinel-2 image data to calculate the chlorophyll content of mangrove vegetation in the study area. The BP neural network algorithm is used to verify the accuracy difference between the inversion results and the corresponding products of MODIS, and the dynamic changes of physiological structure parameters of vegetation in mangroves are further studied. Results showed that: (1)The correlation coefficients between LAI, CAB, FAPAR, FVC and MODIS products were higher than 0.71 in 95% confidence interval in mangrove years, it is proved that Sentinel-2A/B multispectral image inversion of mangrove physiological structure parameters has high accuracy and quality. (2)The physiological structure parameters of mangroves fluctuated during the year. In February, the lowest values of LAI, CAB, FAPAR and FVC were 0.30, 0.08, 0.08 and 0.13, respectively. The highest values were 0.69 in October, 0.29 in December, 0.27 in August, 0.40 in April, 0.24 and 0.24 in September, and 0.27 and 0.33 in LAI in November, respectively, with the highest LAI in October, 0.29 in December, 0.27 in August, 0.40 in April, 0.24 and 0.24 in September, and 0.27 and 0.33 in November. The results provide the basis for the monitoring of mangrove vegetation change and provide a reference for ecological assessment and protection.

Vegetation cover dependence on accumulated antecedent precipitation in Australia: Relationships with photosynthetic and non-photosynthetic vegetation fractions

The development of fractional vegetation cover products for Australia that resolve cover into photosynthetic vegetation (FPV), non-photosynthetic vegetation (FNPV) and bare soil/rock (FBS) provides a new basis for examination of responses of vegetation cover to long term precipitation cycles, and to explore the interaction between these responses and land cover type, land use and other land surface properties. In this study, the relationship between accumulated antecedent precipitation (AAP) from 1 to 60 months and average monthly FPV and FNPV from the MODIS Fractional Cover Product is examined over a 17 year period from 2001 to 2018. The maximum R2 value, regression coefficients for the maximum R2, and number of accumulated months to the maximum R2 were mapped for each month of the year for FPV and FNPV. Behaviour of responses in relation to land use, land cover, and soil water holding capacity was analysed based on pixel frequencies of classes at sub-region scale in the Interim Biogeographic Regionalisation of Australia. The analysis showed that FPV is largely dependent upon AAP in the preceding 12 months, however responses to longer periods of AAP also occur in specific land use-vegetation type combinations. The study also showed that positive responses in FNPV could be driven by AAP from as much as 60 months, but that FNPV is reduced in many areas in response to increased AAP. The presence or absence of domesticated livestock grazing as defined by Australian land use mapping was a major influence on response to AAP of both FPV and FNPV with statistical analysis indicating interactions between major natural vegetation type and grazing. In highly responsive areas (R2 > 0.6) monitoring of land condition could be enhanced by testing of regional cover levels for major deviations from the long terms responses that might suggest land management concerns.

The MODIS Global Vegetation Fractional Cover Product 2001–2018: Characteristics of Vegetation Fractional Cover in Grasslands and Savanna Woodlands

Vegetation Fractional Cover (VFC) is an important global indicator of land cover change, land use practice and landscape, and ecosystem function. In this study, we present the Global Vegetation Fractional Cover Product (GVFCP) and explore the levels and trends in VFC across World Grassland Type (WGT) Ecoregions considering variation associated with Global Livestock Production Systems (GLPS). Long-term average levels and trends in fractional cover of photosynthetic vegetation (FPV), non-photosynthetic vegetation (FNPV), and bare soil (FBS) are mapped, and variation among GLPS types within WGT Divisions and Ecoregions is explored. Analysis also focused on the savanna-woodland WGT Formations. Many WGT Divisions showed wide variation in long-term average VFC and trends in VFC across GLPS types. Results showed large areas of many ecoregions experiencing significant positive and negative trends in VFC. East Africa, Patagonia, and the Mitchell Grasslands of Australia exhibited large areas of negative trends in FNPV and positive trends FBS. These trends may reflect interactions between extended drought, heavy livestock utilization, expanded agriculture, and other land use changes. Compared to previous studies, explicit measurement of FNPV revealed interesting additional information about vegetation cover and trends in many ecoregions. The Australian and Global products are available via the GEOGLAM RAPP (Group on Earth Observations Global Agricultural Monitoring Rangeland and Pasture Productivity) website, and the scientific community is encouraged to utilize the data and contribute to improved validation.

Assessment of forest cover and carbon stock changes in sub-tropical pine forest of Azad Jammu & Kashmir (AJK), Pakistan using multi-temporal Landsat satellite data and field inventory.

This study aimed at estimating temporal (1989–2018) change in forest cover, carbon stock and trend in corresponding CO2 emissions/sequestration of a sub-tropical pine forest (STPF) in AJK, Pakistan. Our field inventory estimation shows an average above ground biomass (AAGB) accumulation of 0.145 Kt/ha with average carbon stock (ACS) value of 0.072 Kt/ha. Landsat TM, ETM+ and OLI images of 1989, 1993, 1999, 2005, 2010, 2015 and 2018 were used to extract vegetation fractions through Linear Spectral Mixture Analysis (LSMA) and forest area was calculated for respective years. Based on the forest area and estimated ACS value, the biomass carbon stock with corresponding CO2 emissions/sequestration was worked out for each time and change in forest carbon stock was determined for different time periods from 1989 to 2018. Our analysis shows net increase of 561 ha in forest cover and 40.39 Kt of ACS along with increase in corresponding CO2 sequestrations of 147.83 Kt over the study period. The results based on combination of remote sensing and field inventory provide valuable information and scientific basis to plan and ensure sustainable forest management (SFM) through reforestation, protection and conservation to enhance and maintain adequate forest cover and reduce CO2 emissions.

Earth observation based indication for avian species distribution models using the spectral trait concept and machine learning in an urban setting

Abstract Birds respond strongly to vegetation structure and composition, yet typical species distribution models (SDMs) that incorporate Earth observation (EO) data use discrete land-use/cover data to model habitat suitability. Since this neglects factors of internal spatial composition and heterogeneity of EO data, we suggest a novel scheme deriving continuous indicators of vegetation heterogeneity from high-resolution EO data. The deployed concepts encompass vegetation fractions for determining vegetation density and spectral traits for the quantification of vegetation heterogeneity. Both indicators are derived from RapidEye data, thus featuring a continuous spatial resolution of 6.5 m. Using these indicators as predictors, we model breeding bird habitats using a random forest (RF) classifier for the city of Leipzig, Germany using a single EO image. SDMs are trained for the breeding sites of 44 urban bird species, featuring medium to very high accuracies (59–90%). Analysing similarities between the models regarding variable importance of single predictors allows species groups to be determined based on their preferences and dependencies regarding the amount of vegetation and its spatial and structural heterogeneity. When combining the SDMs, models of urban bird species richness can be derived. The combination of high-resolution EO data paired with the RF machine learning technique creates very detailed insights into the ecology of the urban avifauna, opening up opportunities of optimising greenspace management schemes or urban development in densifying cities concerning overall bird species richness or single species under threat of local extinction.

Discriminating Land Use and Land Cover Classes in Brazil Based on the Annual PROBA-V 100 m Time Series

Brazil, with more than 8 million km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , presents six different biomes, ranging from natural grasslands (Pampa biome) to tropical rainfall forests (Amazônia biome), with different land-use types (mostly pasturelands and croplands) and pressures (mainly in the Cerrado biome). The objective of this article is to present a new method to discriminate the most representative land use and land cover (LULC) classes of Brazil, based on the PROBA-V images. The images were converted into vegetation, soil, and shade fraction images by applying the linear spectral mixing model. Then, the pixel-based, highest proportion, annual mosaics of the fraction images, and their corresponding standard deviation images were derived and classified using the random forest algorithm. The following LULC classes were considered: forestlands, shrublands, grasslands, croplands, pasturelands, water bodies, and others. An agreement analysis was conducted with two available LULC maps derived from the Landsat satellite, the MapBiomas, and the Finer Resolution Observation and Monitoring-Global Land Cover (FROM-GLC) projects. Forestlands (412 million ha) and pasturelands (242 million ha) were the two most representative LULC classes; and croplands accounted for 30 million ha. The results presented an overall agreement of 69% and 58% with the MapBiomas and FROM-GLC projects, respectively. The proposed method is a good alternative to support operational projects of LULC map production that are important for planning biodiversity conservation or environmentally sustainable land occupation.