Biomass Index Research Articles

Spatial and temporal patterns in the Mediterranean populations of <em>Aristaeomorpha foliacea</em> and <em>Aristeus antennatus</em> (Crustacea: Decapoda: Aristeidae) based on the MEDITS surveys

The aristeids Aristaeomorpha foliacea and Aristeus antennatus are two species of great economic importance in the Mediterranean Sea, where they are exploited almost exclusively by bottom trawlers. We analysed their geographical and inter-annual patterns using data collected, following a standardized protocol, during the MEDITS international experimental bottom trawl surveys. The data used consisted of standardized abundance and biomass indices, size structure and individual biological information collected in the northern Mediterranean for more than two decades. Several univariate and multivariate methodologies were used to analyse the data. The current study confirms previous findings, such as the longitudinal gradient in the relative presence of the aristeids, but also offers additional information for a better understanding of the intrinsic variability of the data over the years and between areas. Moreover, geographical variability in the somatic condition index is discussed, taking into account the role of environmental conditions and fishing pressure. The large geographical variability found for both species in this regional approach reflects the importance of additional studies at a small-scale geographical level. Such studies could be particularly important for managing and ensuring the stability of these species, for which large regional approaches may be inadequate.

Spatial distribution pattern of European hake, <em>Merluccius merluccius</em> (Pisces: Merlucciidae), in the Mediterranean Sea

The present study provides updated information on the occurrence, abundance and biomass distribution patterns and length frequencies of Merluccius merluccius in the Mediterranean Sea, by analysing a time series of data from the Mediterranean International Trawl Surveys (MEDITS) from 1994 to 2015. The highest values of abundance and biomass were observed in the Sardinian Seas. The use of a generalized additive model, in which standardized biomass indices (kg km–2) were analysed as a function of environmental variables, explained how ecological factors could affect the spatio-temporal distribution of European hake biomass in the basin. High biomass levels predicted by the model were observed especially at 200 m depth and between 14°C and 18°C, highlighting the preference of the species for colder waters. A strong reduction of biomass was observed since the year 2009, probably due to the strengthening of the seasonal thermocline that had greatly reduced the availability of food. The general decrease in biomass of several stocks of anchovy and sardine, preys of European hake, might be indirectly connected to the decreasing biomass detected in the present study. The length analysis shows median values lower than 200 mm total length of most of the investigated areas.

Growth and Decay of High-Biomass Algal Bloom in the Northern Arabian Sea

The annual episodes of high-biomass algal bloom are a known phenomenon in the northern Arabian Sea. In the present study, we demonstrate growth and decay of bloom using long-term satellite-retrieved parameters such as chlorophyll-a (chl-a), sea surface temperature (SST), phytoplankton absorption (aph), detritus–coloured dissolved organic matter absorption (adg) and model-derived mixed layer depth (MLD). Chl-a, the index of phytoplankton biomass, showed an inverse relationship with SST with a lag of 2–8 days between lowest SST and highest chl-a. The deepening of MLD associated with high chl-a and low SST indicated a role of convective mixing in the formation of bloom. The average SST of ~ 25 °C was found to be a favourable condition for such open ocean bloom. The lag was more towards eastern boundary of the basin, indicating variable intensities of convective mixing influencing spatial extent of bloom. The positive correlation between aph and adg indicated simultaneous production and degradation showing delay of 3–10 days from peak production to degradation.

Estimation of Crop Biomass and Leaf Area Index from Multitemporal and Multispectral Imagery Using Machine Learning Approaches

Accurate estimation of biomass and Leaf Area Index (LAI) requires appropriate models and predictor variables. These biophysical parameters are indicative of crop productivity, and thus, are of interest in applications such as crop yield forecasting and precision farming. This study evaluated the potential of leveraging vegetation indices derived from multi-temporal RapidEye data using a machine learning approach to estimate crop biomass and LAI. Both near-infrared and red-edge based indices were considered in this study. In-situ measurements of these two parameters for three main cash crops, including canola, corn, and soybeans, were collected during a field campaign and used for model calibration and validation. Crops models were developed using the artificial neural network (ANN) and support vectors regression (SVR). Results showed that, for each crop, the SVR modeled LAI and biomass more accurately than ANN. For biomass, the SVR’s Root Mean Square Errors (RMSEs) were reported as 25.22 g/m2 for canola, 88.13 g/m2 for corn, 5.91 g/m2 for soybean, and 56.14 g/m2 for all crops pooled. Similarly, for the LAI, SVR provided the best model with RMSE = 0.59 m2/m2 for canola, RMSE = 0.27 m2/m2 for corn, RMSE = 0.21 m2/m2 for soybean, and RMSE = 0.51 m2/m2 for all crops together.

Ground-Based LiDAR Improves Phenotypic Repeatability of Above-Ground Biomass and Crop Growth Rate in Wheat.

Highly repeatable, nondestructive, and high-throughput measures of above-ground biomass (AGB) and crop growth rate (CGR) are important for wheat improvement programs. This study evaluates the repeatability of destructive AGB and CGR measurements in comparison to two previously described methods for the estimation of AGB from LiDAR: 3D voxel index (3DVI) and 3D profile index (3DPI). Across three field experiments, contrasting in available water supply and comprising up to 98 wheat genotypes varying for canopy architecture, several concurrent measurements of LiDAR and AGB were made from jointing to anthesis. Phenotypic correlations at discrete events between AGB and the LiDAR-derived biomass indices were significant, ranging from 0.31 (P < 0.05) to 0.86 (P < 0.0001), providing confidence in the LiDAR indices as effective surrogates for AGB. The repeatability of the LiDAR biomass indices at discrete events was at least similar to and often higher than AGB, particularly under water limitation. The correlations between calculated CGR for AGB and the LiDAR indices were moderate to high and varied between experiments. However, across all experiments, the repeatabilities of the CGR derived from the LiDAR indices were appreciably greater than those for AGB, except for the 3DPI in the water-limited environment. In our experiments, the repeatability of either LiDAR index was consistently higher than that of AGB, both at discrete time points and when CGR was calculated. These findings provide promising support for the reliable use of ground-based LiDAR, as a surrogate measure of AGB and CGR, for screening germplasm in research and wheat breeding.

Comparison of spectro-biophysical and yield parameters of cotton in irrigated lowlands of Amudaria River

This study aims defining the best predictors of biophysical parameters and yield with vegetation indices derived from Landsat 8 OLI surface reflectance data. The study was conducted in 2015 at five crop fields in Kulavat canal irrigation system in Khorezm province, Uzbekistan. The Environment for Visualizing Images (ENVI) ver. 4.5 and R programming software ver. 1.0.143 were used to process, calculate seven vegetation indices (VIs) and predict biophysical parameters and yield of cotton. The trend analysis show that in-situ measured biophysical parameters for the whole growth stage of cotton follows the 3rd order polinomial curve (R2 = 0.96-0.99). The NDVI, SAVI, TVI and RVI had linear interrelationship between each other with strong positive correlation of R2&gt;0.9 (highly significant with p-value=0). The VIs showed a logarithmic function relationship with crop height (crht), power function relationship with green biomass (gbm) and leaf area index (LAI), and linear function relationship with the fraction of photosyntetically active radiation below the plant canopy (FPAR) during the entire growing period of cotton. Among seven VIs tested in this study, the NDVI/SAVI and GCI explained 88 and 91 % of variation in crht, respectively. These three indices also well explained gbm variation (R2=0.86). The TVI was slightly better explained FPAR than NDVI and SAVI (all R2&gt;0.87). The NDVI, SAVI and TCG explained 82 % of variation in LAI. Among all VIs, GCI, NDGI and RVI were found to be the best predictor of cotton yield during August, explaining 76-79 % variability (p&lt;0.001). Based on spectro-biophysical analysis, VIs derived from RS data on July and August (anthesis and peak growth stages of cotton) is more reliable to use for modeling cotton yield (seed and lint yields together). Therefore, field data collection is recommended to perform during these months taking into account in-field crop condition and remotely sensed data acquisition date. In addition, September 5-20 is the second important period (i.e., cotton pick-up) to conduct yield data collection for establishment of relationships between cotton yields with VIs (July-August).

Weed Management in Wheat by Cuscuta Alone and in Combination with Commercial Weedicides Allymax and Axial

ABSTRACT: The present study was to assess the additive effects of cuscuta (akasbel) aqueous extract alone and in combination with full and reduced doses of commercially available weedicides against weeds in wheat crop during the year 2016-2017. The study was conducted in a randomized complete block design (RCBD) with 3 replications. The results showed that hand weeding and use of weedicides Allymax and Axial (full and half recommended doses) significantly controlled all prevailing weeds and increased yield and yield contributing parameters over the weedy check/control and sole application of cuscuta aqueous extract. Hand weeding and Allymax (full dose) showed minimum weed population, relative density, fresh and dry weed biomass and higher leaf area index (LAI), leaf area duration (LAD), crop growth rate (CGR), plant height, 1000 grain weight and grain yield while net assimilation rate (NAR), chlorophyll content, number of tillers, spike length, grains spike-1 and biological yield were maximum only in hand weeding. These findings suggest that in order to attain highest wheat yield, the field should be kept free from weeds by hand weeding (if labour is not a limiting factor) or use recommended dose of weedicide Allymax for efficient weed management in wheat.

Digital monitoring of agro-ecosystems indicators on the basis of space and unmanned technologies

The aim of the research is to develop a digital monitoring methodology for agro-ecosystem indicators based on space and unmanned technologies with the conversion of digital aerial photography results into real parameters of agrophytocenosis indicators as physical units. The paper uses the results of research on digital monitoring of agrophytocenosis of single- and poly-species crops of grain and leguminous crops on the experimental field of the Samara State Agrarian University in 2018 (southern forest-steppe of Trans-Volga region). The soil is a typical medium heavy black loamy chernozem (humus content is 5.9 %, easily hydrolysed nitrogen is 80–120 mg/kg, labile phosphorus is 135–145 mg/kg, exchangeable potassium is 150–195 mg/kg, salt extract pH is 6.9). The research conducted in 2018 revealed that the stated aim is achievable in principle. According to the results of 2018, the greatest correlation between the vegetative biomass index NDVI and grain yield for winter and spring cereal crops was detected at the milky stage of the grains (up to r = 0.82).

Digitalization in Realization of Ecological and Economic Principles of Managing Sustainable Development of Agrarian Enterprises

The article describes the digitalization of ecological and economic principles of agricultural enterprise development management. It presents effective techniques for adapting enterprises to the challenges of the smart economy, which changes human needs, products, services, value chains, markets, processes, business models, forms of competition and business partnerships. The article proves the efficiency of precision farming, which includes parallel driving, yield mapping, differentiated application of seeds and fertilizers. It is determined that the lack of systemic demand for digital solutions from domestic agricultural enterprises and rural communities and unpreparedness (strategic, financial, technological, personnel, organizational, etc.) of most enterprises and communities to cardinal innovations significantly deter the formation of smart agribusiness in Ukraine. The article describes the system of measured indicators and capabilities of the Hummingbird Technologies platform and consequences of its implementation for land management, in particular normalized relative biomass index, leaf surface index, crop heterogeneity map, plant lodging risk map, weed map, seedling density map, electronic maps creation tasks, task map for differentiated desiccation, task map for differentiated application of herbicides, task map for differentiated application of growth regulators in plants. It justifies the need to intensify the work of rural a community, which try to communicate with agricultural producers on mutually beneficial terms and with other stakeholders for the sustainable development of rural areas. The issues of participation of rural communities in projects to increase soil fertility, organization of organic farms, service cooperatives, which are directly or indirectly related to the effective management of environmental and economic development of agricultural enterprises. The article shows the necessity of professional development of business engineers, personnel and strengthening of role of analytics for agricultural enterprises to digitalize the economic activity.

Improving data-limited stock assessment with sporadic stock index information in stock reduction analysis

As most exploited fisheries lack a coherent time series of biomass index, development of data-limited stock assessment methods such as stock reduction analysis (SRA), is critical for fishery stock assessment due to their modest data requirements for estimating stock status and overfishing catch limits. In this study, we propose that sporadic time series of biomass indices, if available, may be fully utilized to inform priors of recent relative biomass (BT/B1) for data-limited stocks. We evaluated the performance of SRA incorporating this index-based prior by comparing two other common SRA priors (a deterministic prior set at 40% of the unfished biomass and a catch-based prior) with estimates from the likelihood-based assessments of 91 fish stocks from the RAM Legacy database. We extended our analysis by evaluating performance based on life history attributes and two depletion levels with BT/BMSY equaling 1 as the breakpoint. Results suggest index-based priors enhance accuracy for fish stocks at both depletion levels. We demonstrate that performance of SRA can be affected by three factors: the reliability of priors for BT/B1, recent depletion level, and life history.

Populus nigra as a phytoremediator for Cd, Cu, and Pb in contaminated soil

The contamination of agricultural soil with heavy metals is a complex phenomenon that causes negative consequences for various organisms. Poplars may have considerable phytoremediation potential, and this plant species can tolerate Cd, Cu, and Pb up to 15.6, 63.6, and 173.3 mg kg-1 soil, respectively, with 100% survival. The analyzed data revealed significant reduction in vegetative growth traits and leaf N, P, K, and carbohydrate (%) and leaf green color degree. However, a simultaneously significant increase in enzymatic activities and electrolyte leakage were recorded in comparison to control plants. A bioconcentration factor of plant organs was ˂ 1, and the translocation factors (TF) of Cd and Cu were ˂ 1 ( ˂100%) under various concentrations of each heavy metal, while TF of Pb was ˃ 1(&gt;100%), except for the first level. More Cd, Cu, and Pb contents were localized in roots compared to leaves or stems. Thus, the risk of contamination through leaf can be minimized. Populus nigra has defense mechanisms against Cd, Cu, and Pb up to 7.8, 29.8, and 91.1 mg/kg soil, respectively because the tolerance index (TI) of either biomass or root was &gt;0.8. Finally, it is a good candidate for research of phytoremediation and phytoextraction.

Seasonal Dynamics of Vegetation on Fallow Lands in Krasnoyarsk Forest Steppe According to Terrain and Satellite Data

This article presents investigation data on the seasonal dynamics of productivity, status, and species composition of vegetation on fallow lands in the Krasnoyarsk forest steppe (Middle Siberia) obtained from terrain and satellite materials from 2017. The results of the study of grass plant communities on the basis of geobotanical descriptions and terrain spectrometry were have been used for a more accurate interpretation of cosmic photographs of moderate and low resolution. For studying vegetation on fallow lands, we analyze the seasonal dynamics of various vegetative indices (NDVI, EVI, LSWI, and LAI) and parameters (NPP, FPAR, and LST (land surface temperature)) obtained from MODIS satellite images. Our analysis of satellite data shows the absence of evidences of plowing and mowing in the studied area. A positive correlation is revealed between vegetation indices of biomass (NDVI, EVI, LAI, and NPP) and parameters of hydrothermal conditions (LSWI, FPAR, and LST).

Influence of torrefaction pretreatment on corncobs: A study on fundamental characteristics, thermal behavior, and kinetic

The effects of torrefaction pretreatment on corncobs properties and its pyrolysis kinetic parameters were investigated in this study. Proximate and ultimate analyses indicated that torrefaction increased the H/Ceff ratio and higher heating value of corncobs, and reduced its oxygen content. Although the mass yield was also reduced, the corresponding energy yield was relatively higher. The crystallinity index of biomass showed a first upward and then downward trend with the torrefaction temperature. Kinetic parameters obtained from three models indicated that both the activation energy and the pre-exponential factor increased with the elevated torrefaction temperature and it's better to calculate the activation energy by the OFW method and to use the KAS and DAEM methods to calculate the pre-exponential factor. In addition, it was found that the optimum pretreatment temperature of corncobs was 240 °C.

Drought Avoidance and Phenotypic Flexibility of Sweet Potato (Ipomoea batatas (L.) Lam.) Under Water Scarcity Conditions

Sweet potato (Ipomoea batatas (L.) Lam.) is an important staple food in several regions of the world. Water scarcity is the most devastating abiotic stress, with a great impact on crop productivity, food security, and subsistence. Drought restricts the nutrient intake and transport into the plant. Tolerant crops have morphological mechanisms of drought avoidance and/or phenotypic flexibility, showing also good water and nutrient efficiency. However, that information is scarce for sweet potato, which is usually based on physiological traits of plant productivity. Here, we show the physiological responses of eight sweet potato accessions subjected to a 3 months’ drought period, by recording their differences for nutrient and leaf chlorophyll content, biomass and stress level. Our results showed that the differences in water use efficiency (WUE, +68.1%), chlorophyll content index (CCI, -5.3%), total plant biomass (TPB, -55.4%), nutrient efficiency (NER, +38.1%) and nutrient harvest index (NHI, +2.9%) where significantly correlated with the water regime. The water shortage led to a drought avoidance response, with TPB loss in all accessions. Distinct phenotypic flexibility responses were also recorded and explained by the root:shoot ratio (R:S) and stress index (SI) variation of the storage root and shoot growth. This information could be relevant for the development of sweet potato breeding programs, adapting this crop to climate change.&#x0D; &#x0D; *********&#x0D; In press - Online First. Article has been peer reviewed, accepted for publication and published online without pagination. It will receive pagination when the issue will be ready for publishing as a complete number (Volume 47, Issue 4, 2019). The article is searchable and citable by Digital Object Identifier (DOI). DOI link will become active after the article will be included in the complete issue.&#x0D; *********

Development of Nutrient-Rich Media Through Cyanobacterial Amendment and Their Characterization

Exogenous addition of growth stimulants or other chemicals to soil-less medium is a common practice for enhancing biomass and productivity, and a promising amendment option can be cyanobacteria, a ubiquitous group of photosynthetic, plant growth promoting and agriculturally beneficial microorganisms, used as biofertilizers in agriculture. The performance of three nitrogen-fixing cyanobacterial cultures- Anabaena torulosa (BF1), Anabaena doliolum (BF4) and Anabaena laxa (RPAN8), through amendment to two media - namely rooting mix (cocopeat, vermiculite and perlite in 3:1:1) and sand contained in Pro-Trays was evaluated at fortnightly intervals up to 30 days. An enhancement, up to 2- to 3-fold in chlorophyll (as an index of photosynthetic biomass) and 30–45% IAA was observed in the extracts of cyanobacterium–amended media, over control; rooting mix was more promising as compared to sand. The addition of cyanobacteria also brought a significant enhancement of 30–40% in the availability of nitrogen and micronutrients–Mn, Cu, Zn and Fe in both media. Phytotoxicity studies with seeds of mustard, radish, maize and wheat, revealed no inhibition, highlighting the promise of these fortified rooting mixes as organic inputs in nursery propagation.

Assessment modelling approaches for stocks with spawning components, seasonal and spatial dynamics, and limited resources for data collection.

The true spatiotemporal structure of a fish population is often more complex than represented in assessments because movement between spawning components is disregarded and data at the necessary scale are unavailable. This can generate poor advice. We explore the impacts of modelling choices and their associated risks given limited data and lack of biological knowledge on spawning component structure and connectivity. Pseudo-data for an age structured fish population were simulated with two spawning components that experience various levels of connectivity and that might overlap during a certain period but segregate during reproduction. A variety of implicit spatiotemporal and simpler models were fitted to the pseudo-datasets, mimicking different situations of data availability. To reproduce the true stock characteristics, the spatiotemporal models required total catch data disaggregated by spawning component; however, catch-at-age was not as important nor were disaggregated biomass indices to reproduce true dynamics. Even with just 5% connectivity between spawning components, both the spatiotemporal models and simpler alternatives generally overestimated stock biomass. Although bias was smallest when considering one unit population, spawning components might still need to be considered for management and conservation. In such case, the spatiotemporal model was less influenced by ignored connectivity patterns compared to a model focussing on one spawning component only.

Time-variant productivity in biomass dynamic models on seasonal and long-term scales

Abstract The productivity of fish populations varies naturally over time, dependent on integrated effects of abundance, ecological factors, and environmental conditions. These changes can be expressed as gradual or abrupt shifts in productivity as well as fluctuations on any time scale from seasonal oscillations to long-term changes. This study considers three extensions to biomass dynamic models that accommodate time-variant productivity in fish populations. Simulation results reveal that neglecting seasonal changes in productivity can bias derived stock sustainability reference levels and, thus, fisheries management advice. Results highlight the importance of biannual biomass indices and their timing relative to the peaks of the seasonal processes (i.e. recruitment, growth, mortality) for the estimation of seasonally time-variant productivity. The application to real-world data of the eastern Baltic cod (Gadus morhua) stock shows that the model is able to disentangle differences in seasonal fishing mortality as well as seasonal and long-term changes in productivity. The combined model with long-term and seasonally varying productivity performs significantly better than models that neglect time-variant productivity. The model extensions proposed here allow to account for time-variant productivity of fish populations leading to increased reliability of derived reference levels.

Effects of Mixing Feldspathic Sandstone and Sand on Soil Microbial Biomass and Extracellular Enzyme Activities—A Case Study in Mu Us Sandy Land in China

Microbial biomass, extracellular enzyme activity, and their stoichiometry in soil play an important role in ecosystem dynamics and functioning. To better understand the improvement of sand soil quality and the limitation of soil nutrients after adding feldspathic sandstone, we investigated changes in soil microbial activity after 10 months of mixing feldspathic sandstone and sand, and compared the dynamics with soil properties. We used fumigation extraction to determine soil microbial biomass carbon (MBC), nitrogen (MBN), phosphorus (MBP), and microplate fluorometric techniques to measure soil β-1,4-glucosidase (BG), β-1,4-xylosidase (BX), β-D-cellobiohydrolase (CBH), N-acetyl-β-glucosaminidase (NAG), and Alkaline phosphatase (AKP). We also measured soil organic carbon (SOC), pH, electrical conductivity (EC), soil inorganic carbon (SIC), and soil water content (SWC). Our results showed that the soil microbial biomass C, N, P, and individual extracellular enzyme activities significantly increased in mixed soil. Similarly, the soil microbial biomass C:N, C:P, N:P, MBC:SOC, and BG:NAG significantly increased by 54.3%, 106.3%, 33.1%, 23.0%, and 65.4%, respectively. However, BG:AKP and NAG:AKP decreased by 19.0% and 50.3%, respectively. Additionally, redundancy analysis (RDA) and Pearson’s correlation analysis showed that SWC, SOC, porosity and field capacity were significantly associated with soil microbial biomass indices (i.e., C, N, P, C:N, C:P, N:P in microbial biomass, and MBC:SOC) and extracellular enzyme activity metrics (i.e., individual enzyme activity, ecoenzymatic stoichiometry, and vector characteristics of enzyme activity), while pH, EC, and SIC had no correlation with these indices and metrics. These results indicated that mixing feldspathic sandstone and sand is highly susceptible to changes in soil microbial activity, and the soil N limitation decreased while P became more limited. In summary, our research showed that adding feldspathic sandstone into sand can significantly improve soil quality and provide a theoretical basis for the development of desertified land resources.

The effect of bottom trawl fishery on biomass variations of demersal chondrichthyes in the eastern Mediterranean

Chondrichthyan species are considered highly susceptible to fishing activities and as “indicator species” for overfishing and ecosystem stability. In European waters, chondrichthyes have become a focus of conservation actions due to the abundance declines of several species.In the present study, fishery independent data obtained from the Mediterranean International Trawl Surveys (MEDITS) in the Aegean and E. Ionian Seas (eastern Mediterranean) were coupled with bottom trawl fishing effort data obtained from vessel monitoring systems (VMS) in order to evaluate the effect of fishing on biomass variations of demersal chondrichthyan species. The study focused on commonly captured species such as Raja clavata, Galeus melastomus, Scyliorhinus canicula and Squalus spp, as well as to the total chondrichthyan biomass. The effect of fishing effort on biomass indices, expressed in terms of kg per square km of swept area, was examined by means of Generalized Additive Model techniques, using also the sampling position, year and depth as co-variates.Overall findings suggested a biomass decline of chondrichthyan species with increasing fishing effort. Nevertheless, it was also found that certain species (e.g G. melastomus in both areas and S. canicula in the Aegean Sea) can withstand fishing pressure and it is likely that discard survival rates, depth preferences and fisheries exploitation patterns, are the main drivers explaining variations regarding the impact of fishing on the different stocks.

A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images

This study proposes a workflow for land use and land cover (LULC) classification of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) images of the Brazilian tropical savanna (Cerrado) biome. The following LULC classes were considered: forestlands; shrublands; grasslands; reforestations; croplands; pasturelands; bare soils/straws; urban areas; and water reservoirs. The proposed approach combines polarimetric attributes, image segmentation, and machine-learning procedures. A set of 125 attributes was generated using polarimetric ALOS-2/PALSAR-2 images, including the van Zyl, Freeman–Durden, Yamaguchi, and Cloude–Pottier target decomposition components, incoherent polarimetric parameters (biomass indices and polarization ratios), and HH-, HV-, VH-, and VV-polarized amplitude images. These attributes were classified using the Naive Bayes (NB), DT J48 (DT = decision tree), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) algorithms. The RF, MLP, and SVM classifiers presented the most accurate performances. NB and DT J48 classifiers showed a lower performance in relation to the RF, MLP, and SVM. The DT J48 classifier was the most suitable algorithm for discriminating urban areas and natural vegetation cover. The proposed workflow can be replicated for other SAR images with different acquisition modes or for other types of vegetation domains.