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  - 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  -