TY - JOUR A1 - Rokhafrouz, Mohammad A1 - Latifi, Hooman A1 - Abkar, Ali A. A1 - Wojciechowski, Tomasz A1 - Czechlowski, Mirosław A1 - Naieni, Ali Sadeghi A1 - Maghsoudi, Yasser A1 - Niedbała, Gniewko T1 - Simplified and hybrid remote sensing-based delineation of management zones for nitrogen variable rate application in wheat JF - Agriculture N2 - Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate application of mineral nitrogen in wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield and crop data availability. Three models were applied, including (1) a modified “RS- and threshold-based clustering”, (2) a “hybrid-based, unsupervised clustering”, in which data from different sources were combined for MZ delineation, and (3) a “RS-based, unsupervised clustering”. Various data processing methods including machine learning were used in the model development. Statistical tests such as the Paired Sample T-test, Kruskal–Wallis H-test and Wilcoxon signed-rank test were applied to evaluate the final delineated MZ maps. Additionally, a procedure for improving models based on information about phenological phases and the occurrence of agricultural drought was implemented. The results showed that information on agronomy and climate enables improving and optimizing MZ delineation. The integration of prior knowledge on new climate conditions (drought) in image selection was tested for effective use of the models. Lack of this information led to the infeasibility of obtaining optimal results. Models that solely rely on remote sensing information are comparatively less expensive than hybrid models. Additionally, remote sensing-based models enable delineating MZ for fertilizer recommendations that are temporally closer to fertilization times. KW - precision agriculture KW - management zones KW - remote sensing KW - Sentinel-2 KW - clustering KW - winter wheat KW - drought KW - digital agriculture Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-250033 SN - 2077-0472 VL - 11 IS - 11 ER - TY - JOUR A1 - Latifi, Hooman A1 - Heurich, Marco T1 - Multi-scale remote sensing-assisted forest inventory: a glimpse of the state-of-the-art and future prospects JF - Remote Sensing N2 - Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use of multiple-band new active and passive remote sensing data for forest inventory, but also in the methodic and algorithmic developments and/or adoptions that aim at maximizing the predictive or calibration performances, thereby minimizing both random and systematic errors, in particular for multi-scale spatial domains. With this in mind, this editorial note wraps up the recently-published Remote Sensing special issue “Remote Sensing-Based Forest Inventories from Landscape to Global Scale”, which hosted a set of state-of-the-art experiments on remotely sensed inventory of forest resources conducted by a number of prominent researchers worldwide. KW - remote sensing KW - forest resources inventory KW - spatial scale Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197358 SN - 2072-4292 VL - 11 IS - 11 ER - TY - JOUR A1 - Latifi, Hooman A1 - Holzwarth, Stefanie A1 - Skidmore, Andrew A1 - Brůna, Josef A1 - Červenka, Jaroslav A1 - Darvishzadeh, Roshanak A1 - Hais, Martin A1 - Heiden, Uta A1 - Homolová, Lucie A1 - Krzystek, Peter A1 - Schneider, Thomas A1 - Starý, Martin A1 - Wang, Tiejun A1 - Müller, Jörg A1 - Heurich, Marco T1 - A laboratory for conceiving Essential Biodiversity Variables (EBVs)—The ‘Data pool initiative for the Bohemian Forest Ecosystem’ JF - Methods in Ecology and Evolution N2 - Effects of climate change‐induced events on forest ecosystem dynamics of composition, function and structure call for increased long‐term, interdisciplinary and integrated research on biodiversity indicators, in particular within strictly protected areas with extensive non‐intervention zones. The long‐established concept of forest supersites generally relies on long‐term funds from national agencies and goes beyond the logistic and financial capabilities of state‐ or region‐wide protected area administrations, universities and research institutes. We introduce the concept of data pools as a smaller‐scale, user‐driven and reasonable alternative to co‐develop remote sensing and forest ecosystem science to validated products, biodiversity indicators and management plans. We demonstrate this concept with the Bohemian Forest Ecosystem Data Pool, which has been established as an interdisciplinary, international data pool within the strictly protected Bavarian Forest and Šumava National Parks and currently comprises 10 active partners. We demonstrate how the structure and impact of the data pool differs from comparable cases. We assessed the international influence and visibility of the data pool with the help of a systematic literature search and a brief analysis of the results. Results primarily suggest an increase in the impact and visibility of published material during the life span of the data pool, with highest visibilities achieved by research conducted on leaf traits, vegetation phenology and 3D‐based forest inventory. We conclude that the data pool results in an efficient contribution to the concept of global biodiversity observatory by evolving towards a training platform, functioning as a pool of data and algorithms, directly communicating with management for implementation and providing test fields for feasibility studies on earth observation missions. KW - bohemian forest ecosystem KW - data pool KW - forest ecosystem science KW - remote sensing KW - remote sensing‐enabled essential biodiversity variables Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-262743 VL - 12 IS - 11 ER -