Mapping agricultural crops is an important application of remote sensing. However, in many cases it is based either on hyperspectral imagery or on multitemporal coverage, both of which are difficult to scale up to large-scale deployment at high spatial resolution. In the present paper, we evaluate the possibility of crop classification based on single images from very high-resolution (VHR) satellite sensors. The main objective of this work is to expose performance difference between state-of-the-art parcel-based smoothing and purely data-driven conditional random field (CRF) smoothing, which is yet unknown. To fulfill this objective, we perform extensive tests with four different classification methods (Support Vector Machines, Random Forest, Gaussian Mixtures, and Maximum Likelihood) to compute the pixel-wise data term; and we also test two different definitions of the pairwise smoothness term. We have performed a detailed evaluation on different multispectral VHR images (Ikonos, QuickBird, Kompsat-2). The main finding of this study is that pairwise CRF smoothing comes close to the state-of-the-art parcel-based method that requires parcel boundaries (average difference ≈ 2.5%). Our results indicate that a single multispectral (R, G, B, NIR) image is enough to reach satisfactory classification accuracy for six crop classes (corn, pasture, rice, sugar beet, wheat, and tomato) in Mediterranean climate. Overall, it appears that crop mapping using only one-shot VHR imagery taken at the right time may be a viable alternative, especially since high-resolution multitemporal or hyperspectral coverage as well as parcel boundaries are in practice often not available.

One of the challenges of remote sensing and computer vision lies in the three-dimensional (3-D) reconstruction of individual trees by using automated methods through very high-resolution (VHR) data sets. However, a successful and complete 3-D reconstruction relies on precise delineation of the trees in two dimensions. In this paper, we present an original approach to detect and delineate citrus trees using unmanned aerial vehicles based on photogrammetric digital surface models (DSMs). The symmetry of the citrus trees in a DSM is handled by an orientation-based radial symmetry transform which is computed in a unique way. Next, we propose an efficient strategy to accurately build influence regions of each tree, and then we delineate individual citrus trees through active contours by taking into account the influence region of each canopy. We also present two efficient strategies to filter out erroneously detected canopy regions without having any height thresholds. Experiments are carried out on eight test DSMs composed of different types of citrus orchards with varying densities and canopy sizes. Extensive comparisons to the state-of-the-art approaches reveal that our proposed approach provides superior detection and delineation performances through supporting a nice balance between precision and recall measures.

This research study aims to classify crop diversity in agricultural land with a segment-based approach using multi-temporal Kompsat-2 and Environmental Satellite (Envisat) advanced synthetic aperture radar (ASAR) data acquired in June, July and August on Karacabey Plain, Turkey. Analyses start with the image segmentation process applied to the fused optical images to search homogenous objects. The segmentation outputs are evaluated using multiple goodness measures, which take into consideration area and location similarities. Image classifications are performed on each multispectral (MS) single date image. In order to combine the most probable classes of the thematic maps, distance maps are generated. Evaluations of the thematic maps are performed through confusion matrices based on pixel-based and segment-based approaches. The results indicate that the highest overall accuracy of 88.71% and a kappa result of 0.86 are provided for the segment-based approach of the combined thematic map along with the microwave data, which is around 10% higher than the related pixel-based results.

Stone Pine (Pinus pinea L.) is currently the pine species with the highest commercial value with edible seeds. In this respect, this study introduces a new methodology for extracting Stone Pine trees from Digital Surface Models (DSMs) generated through an Unmanned Aerial Vehicle (UAV) mission. We developed a novel enhanced probability map of local maxima that facilitates the computation of the orientation symmetry by means of new probabilistic local minima information. Four test sites are used to evaluate our automated framework within one of the most important Stone Pine forest areas in Antalya, Turkey. A Hand-held Mobile Laser Scanner (HMLS) was utilized to collect the reference point cloud dataset. Our findings confirm that the proposed methodology, which uses a single DSM as an input, secures overall pixel-based and object-based F₁-scores of 88.3% and 97.7%, respectively. The overall median Euclidean distance revealed between the automatically extracted stem locations and the manually extracted ones is computed to be 36 cm (less than 4 pixels), demonstrating the effectiveness and robustness of the proposed methodology. Finally, the comparison with the state-of-the-art reveals that the outcomes of the proposed methodology outperform the results of six previous studies in this context.

This letter proposes a new approach for the automated detection of citrus trees from a single digital surface model (DSM) input. The new approach combines orientation symmetry information and local maxima cues in a probabilistic manner. Experiments are performed on eight test DSMs generated from unmanned aerial vehicle (UAV) images, and the results reveal that the new approach is capable of detecting citrus trees with a high success (overall F₁-score of 92.5%). The performance of our approach is also compared with leading approaches from the literature and from our own previous work and provided superior or comparable results.

Amaç: Bu araştırmada, son yıllarda kuraklık başta olmak üzere bazı aşırı hava olaylarının olumsuz etkilerinin yaşandığı İzmir ili Menemen ilçesinde bitkisel üretimde bulunan çiftçilerin yetiştiricilik faaliyetleri ile iklim değişikliğiyle ilgili risklerden kaçınmak ve söz konusu risklere maruz kalmayı azaltmak amacıyla uyguladıkları uyum eylemleri ve bu konudaki stratejilerinin ortaya konulması amaçlanmıştır. Tasarım/Metodoloji/Yaklaşım: Araştırmanın ana materyalini, İzmir ili Menemen ilçesinde 2022-2023 üretim sezonunda buğday yetiştiriciliği yapan 71 çiftçi ile yapılan karşılıklı görüşmelerden elde edilen veriler oluşturmaktadır. Verilerin analizinde basit istatiksel hesaplamalar ve Khi-kare analizinden yararlanılmıştır. Bulgular: Elde edilen bulgular, çiftçilerin son yıllarda kuraklık ve aşırı yağış gibi hava olaylarından olumsuz etkilendiklerini, riskleri ve risklere maruz kalmayı azaltmak amacıyla ürün değişikliğine gittiklerini, yetiştiricilik faaliyetlerinde risklere göre uygulama yaptıklarını ve dolayısıyla üretim maliyetlerinin arttığını, endüstriyel tarım yöntemlerinin akıllı sistemler ve teknolojik uygulamalarla devam ettirilmeye çalışıldığını ortaya koymaktadır. Özgünlük/Değer: Son yıllarda yaşanan iklim değişiklikleri ve bu değişikliklerin olası etkileri, iklim değişikliğine uyumun ve etkilerinin azaltılması konusunun daha dikkatle izlenmesi gerektiğini göstermektedir. Uyum ve etkilerin hafifletilmesi için yaşanan sorunlara çeşitli ve kalıcı çözümler geliştirmek önemlidir. İklim değişikliğinden olumsuz etkilenen tarımsal üretimin ve çiftçilerin değişen koşullara uyumu zorunludur. Bu nedenle bu çalışma, gerek Türkiye gerekse İzmir ili için önemli ve stratejik bitkisel ürünlerin yetiştiriciliğinin yapıldığı, Menemen ilçesi çiftçilerinin yaşadığı iklimsel sorunları ve üreticilerin uyum süreçlerinin anlaşılmasına katkıda bulunmasına olanak tanıyacak bireysel algılar ve hedefler de dâhil olmak üzere uyum eylemleri hakkındaki bilgileri geliştirecek olması bakımından önemlidir ve özgün değer taşımaktadır.