Agricultural land abandonment is an important environmental issue in Europe. The proper management of agricultural areas has important implications for ecosystem services (food production, biodiversity, climate regulation and the landscape). In the coming years, an increase of abandoned areas is expected due to socio-economic changes. The identification and quantification of abandoned agricultural plots is key for monitoring this process and for applying management measures. The Valencian Region (Spain) is an important fruit and vegetable producing area in Europe, and it has the most important citrus industry. However, this agricultural sector is highly threatened by diverse factors, which have accelerated land abandonment. Landsat and MODIS satellite images have been used to map land abandonment. However, these images do not give good results in areas with high spatial fragmentation and small-sized agricultural plots. Sentinel-2 and airborne imagery shows unexplored potential to overcome this thanks to higher spatial resolutions. In this work, three models were compared for mapping abandoned plots using Sentinel-2 with 10 m bands, Sentinel-2 with 10 m and 20 m bands, and airborne imagery with 1 m visible and near-infrared bands. A pixel-based classification approach was used, applying the Random Forests algorithm. The algorithm was trained with 144 plots and 100 decision trees. The results were validated using the hold-out method with 96 independent plots. The most accurate map was obtained using airborne images, the Enhanced Vegetation Index (EVI) and Thiam’s Transformed Vegetation Index (TTVI), with an overall accuracy of 88.5%. The map generated from Sentinel-2 images (10 m bands and the EVI and TTVI spectral indices) had an overall accuracy of 77.1%. Adding 20 m Sentinel-2 bands and the Normalized Difference Moisture Index (NDMI) did not improve the classification accuracy. According to the most accurate map, 4310 abandoned plots were detected in our study area, representing 32.5% of its agricultural surface. The proposed methodology proved to be useful for mapping citrus in highly fragmented areas, and it can be adapted to other crops.

Agricultural land abandonment is an increasing problem in Europe. The Comunitat Valenciana Region (Spain) is one of the most important citrus producers in Europe suffering this problem. This region characterizes by small sized citrus plots and high spatial fragmentation which makes necessary to use Very High-Resolution images to detect abandoned plots. In this paper spectral and Gray Level Co-Occurrence Matrix (GLCM)-based textural information derived from the Normalized Difference Vegetation Index (NDVI) are used to map abandoned citrus plots in Oliva municipality (eastern Spain). The proposed methodology is based on three general steps: (a) extraction of spectral and textural features from the image, (b) pixel-based classification of the image using the Random Forest algorithm, and (c) assignment of a single value per plot by majority voting. The best results were obtained when extracting the texture features with a 9 × 9 window size and the Random Forest model showed convergence around 100 decision trees. Cross-validation of the model showed an overall accuracy of the pixel-based classification of 87% and an overall accuracy of the plot-based classification of 95%. All the variables used are statistically significant for the classification, however the most important were contrast, dissimilarity, NIR band (720 nm), and blue band (620 nm). According to our results, 31% of the plots classified as citrus in Oliva by current methodology are abandoned. This is very important to avoid overestimating crop yield calculations by public administrations. The model was applied successfully outside the main study area (Oliva municipality); with a slightly lower accuracy (92%). This research provides a new approach to map small agricultural plots, especially to detect land abandonment in woody evergreen crops that have been little studied until now.

Since remote sensing of ocean colour began in 1978, several ocean-colour sensors have been launched to measure ocean properties. These measures have been applied to study water quality, and they specifically can be used to study algal blooms. Blooms are a natural phenomenon that, due to anthropogenic activities, appear to have increased in frequency, intensity, and geographic distribution. This paper aims to provide a systematic analysis of research on remote sensing of algal blooms during 1999–2019 via bibliometric technique. This study aims to reveal the limitations of current studies to analyse climatic variability effect. A total of 1292 peer-reviewed articles published between January 1999 and December 2019 were collected. We read all the literature individually to build a database. The number of publications increased since 2004 and reached the maximum value of 128 in 2014. The publications originated from 47 countries, but the number of papers published from the top 10 countries accounted for 77% of the total publications. To be able to distinguish between climate variability and changes of anthropogenic origin for a specific variable is necessary to define the baseline. However, long-term monitoring programs of phytoplankton are very scarce; only 1% of the articles included in this study analysed at least three decades and most of the existing algal blooms studies are based on sporadic sampling and short-term research programs.

Identifying crops at the plot level is essential for developing effective agricultural management policies across diverse scales. The agricultural landscape of the Comunitat Valenciana (CV) region in Spain is characterized by a high density of small plots and a wide variety of crops, ranging from rice fields to vine and tree orchards, the latter being the predominant type. This fragmentation poses challenges for current crop monitoring using satellite imagery provided by the Sentinel-2 (S2) mission, largely because its relatively low spatial resolution results in pixels overlapping field boundaries. However, this study proposes a methodological approach that exploits the high temporal resolution of S2 to help overcome these limitations and automatically classify the six most representative crop types in this fragmented landscape. The study analyzed temporal variations in the correlation structure of common spectral indices over the year, leading to the selection of the Normalized Difference Moisture Index (NDMI), Normalized difference Red Edge Index (NDRE), and Plant Senescence Reflectance Index (PSRI) for complementary information. Fourier coefficients of a year time series of these indices served as inputs for a random forest classifier. Relative importance of indices for the classification was also assessed. Additionally, a new metric for classification confidence at plot level is introduced. This metric enables strategies to balance between classification precision and the proportion of classified plots. The model achieved an overall accuracy of 86.85% and a kappa index of 0.82 without considering classification confidence levels. Applying a 70% confidence threshold increased overall accuracy to 93.44% and the kappa index to 0.91 at a cost of 16.19% of plots unclassified.

This study investigates biometric relationships in Oreochromis niloticus (gray tilapia) reared in controlled pond environments at the Centro de Investigación Piscícola (CINPIS), Universidad Nacional Agraria La Molina (Perú), over a 3-year period (2021–2023). Focusing on total length ( $${L}_{t}$$ L t ), standard length ( $${L}_{s}$$ L s ), height ( $$H$$ H ), and width ( $$A$$ A ), we developed models to estimate weight ( $$W$$ W ) based on these parameters, achieving strong model performances with R² values between 0.899 and 0.994. The model using $${L}_{t}$$ L t as a predictor of $$W$$ W proved most accurate with a mean relative error (MRE) of 11.2%, while models incorporating additional dimensions ( $$H$$ H and $$A$$ A ) did not enhance predictive accuracy. Comparative analyses show our model aligns with some studies on tilapia, though variations in L-W relationships due to environmental and breeding conditions are evident. Our results affirm the utility of $${L}_{t}$$ L t in non-invasive biomass estimation for aquaculture, while highlighting the limitations of applying these models universally across different conditions and fish populations. Condition factor ( $$K$$ K ) and relative condition factor ( $${K}_{r}$$ K r ) analyses further demonstrated stable and optimal growth conditions (mean $$K$$ K ≈ 1.76; $${K}_{r}$$ K r ≈ 1.01) for tilapia under local culture practices. Accordingly, we propose the L-W relationship $$W= {0.0265L}^{2.8469}$$ W = 0.0265 L 2.8469 for estimating the weight of gray tilapia grown in ponds with groundwater. This study provides a basis for the development of biomass estimation methods based on active acoustics or stereo video.

The Comunitat Valenciana region (Spain) is the largest citrus producer in Europe. However, it has suffered an accelerated land abandonment in recent decades. Agricultural land abandonment is a global phenomenon with environmental and socio-economic implications. The small size of the agricultural parcels, the highly fragmented landscape and the low spectral separability between productive and abandoned parcels make it difficult to detect abandoned crops using moderate resolution images. In this work, an approach is applied to monitor citrus crops using altimetric data. The study uses two sources of altimetry data: LiDAR from the National Plan for Aerial Orthophotography (PNOA) and altimetric data obtained through an unmanned aerial system applying photogrammetric processes (Structure from Motion). The results showed an overall accuracy of 67,9% for the LiDAR data and 83,6% for the photogrammetric data. The high density of points in the photogrammetric data allowed to extract texture features from the Gray Level Co-Occurrence Matrix derived from the Canopy Height Model. The results indicate the potential of altimetry information for monitoring abandoned citrus fields, especially high-density point clouds. Future research should explore the fusion of spectral, textural and altimetric data for the study of abandoned citrus crops.