Effectiveness of Natural Language Processing Techniques in Automated Data Extraction and Standardization from Large-Scale Forestry Land Inventory Reports

Large-scale land inventory reports in the forestry sector are critically important for ecosystem monitoring and the development of management plans. However, these reports are typically prepared in semi-structured text formats and contain heterogeneous data structures with varying regional terminologies and formatting standards. This situation necessitates the manual digitization and standardization of data, a process that is both time-consuming and error-prone. This study systematically examines the effectiveness and error rates of Natural Language Processing (NLP) techniques in automated data extraction and standardization from forestry land inventory reports. Various deep learning-based models (BERT, SciBERT, LayoutLM) and rule-based approaches were compared across NLP subtasks including Named Entity Recognition (NER), text classification, relation extraction, and table parsing. Experimental evaluations were conducted on a 2,400-page corpus compiled from Turkey's General Directorate of Forestry inventory reports. Findings demonstrate that transformer-based models achieved the highest performance in entity extraction with an F1 score of 89.3%, while rule-based systems attained 94.1% accuracy in specific structured fields. The overall error rate was reduced to 6.2% through a hybrid approach. Results indicate that NLP-based automated data extraction can provide significant efficiency gains in forestry inventory management, though domain-specific pre-training and terminology dictionaries play a decisive role in model performance.