Forestry

Zagros Forest provides various ecosystem services such as food, timber, water, carbon storage, air purification, wildlife habitat as well as social and cultural benefits for both humans and animals. Due to the economic expansion beginning in the early 1990s in Iran and its resulting desertification, Zagros has lost numerous forests over the last 30 years. To overcome this issue, Zagros Forest Preservation Plan (ZFPP) has been under implementation in Northwestern Iran since 2003 to reduce forest destruction and attract the people's participation. However, it faced shortcomings caused by a variety of factors including insufficient funds, incomplete preservation, and unsuitable organization during implementation. This research aims to study the effect of ZFPP on forest loss in Sardasht County as a representative of Iranian Northern Zagros Forests. A series of Landsat images were used to analyze the forest loss before ZFPP implementation (1993–2002), after 10 years of implementation (2002–2012), and finally after its revision (2012–2016). Land Change Modeler (LCM) was employed to detect land cover changes for land cover prediction in 2024. We used land cover maps between 2002 and 2012 for calibration. We then compared the predicted land cover map from LCM with actual land cover map in 2016 for validation. The results indicated that 3330, 4562, and 1234 ha of forestlands converted to agricultural lands, rangelands, and built-up areas during 1993–2002, 2002–2012, and 2012–2016, with annual deforestation rates of -0.40%, -0.52% and -0.36%, respectively. The highest deforestation rate was observed between 2002 and 2012 because of the destructive effects of population growth. Land cover prediction indicated that per capita forest area would continue to decline while the other land cover uses would continue to grow more severely around the existing agricultural lands and built-up areas in Sardasht until 2024.

The overall objective of this study was to investigate urban infill development policies as a good solution to counteract urban sprawl and protect the peri-urban area of Ardabil in Northwestern Iran. In this context, we used a mixed methodology (two quantitative methods). Landsat imagery, including a patchy Landsat ETM+ for the year 2000 and a Landsat 8 for the year 2020, was used to map and assess land use to investigate sprawl and land-use change, and ArcGIS was used to investigate the potential for infill development in this city. The results show that between 2000 and 2020, 967 hectares of peri-urban land was lost to urban expansion. CA-Markov projections also showed that 452 hectares will be lost by 2030. The assessment of the city’s internal capacity for infill development showed that more than 999 hectares of land within the city are suitable to support this strategy and provide the land needed for urban expansion over the next decade. Finally, the study of the city’s master ...

Forest nationalization policies in developing countries have often led to a reduction in local forest ownership rights and short- or long-term exploitative behaviors of stakeholders. The purpose of this research is to quantify the effect of Iran’s Forest Nationalization Law (FNL) in a part of Zagros Forest over a 68-year time period (1955–2022) using 1955 historical aerial photos, 1968 Corona spy satellite photography, and classification of multi-temporal Landsat satellite images. A past classification change detection technique was used to identify the extent and the pattern of land use changes in time. For this purpose, six periods were defined, to cover the time before and after the implementation of FNL. A 0.27% deforestation trend was identified over the period after the FNL. Dense and open forested area has decreased from 7175.62 ha and 68,927.46 ha in 1955 to 5664.26 ha and 59,223.38 ha in 2022. The FNL brought decisive changes in the legal and forest management systems at th...

Implementing a scheduled and reliable estimation of forest characteristics is important for the sustainable management of forests. This study aimed at evaluating the capability of Sentinel-2 satellite data to estimate above-ground biomass (AGB) in coppice forests of Persian oak (Quercus brantii var. persica) located in Western Iran. To estimate the AGB, field data collection was implemented in 80 square plots (40×40 m, area of 1600 m2). Two diameters of the crown were measured and used to calculate the AGB of each tree based on allometric equations. Then, the performance of satellite data in estimating the AGB was evaluated for the area of study using the field-based AGB (dependent variable) as well as the spectral band values, spectrally-derived vegetation indices (independent variables) and four machine learning (ML) algorithms: Multi-Layer Perceptron Artificial Neural Network (MLPNN), k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Regression (SVR). A five-fold c...