@article{AtaeeMaghsoudiLatifietal.2019, author = {Ataee, Mohammad Sadegh and Maghsoudi, Yasser and Latifi, Hooman and Fadaie, Farhad}, title = {Improving estimation accuracy of growing stock by multi-frequency SAR and multi-spectral data over Iran's heterogeneously-structured broadleaf Hyrcanian forests}, series = {Forests}, volume = {10}, journal = {Forests}, number = {8}, issn = {1999-4907}, doi = {10.3390/f10080641}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197212}, year = {2019}, abstract = {Via providing various ecosystem services, the old-growth Hyrcanian forests play a crucial role in the environment and anthropogenic aspects of Iran and beyond. The amount of growing stock volume (GSV) is a forest biophysical parameter with great importance in issues like economy, environmental protection, and adaptation to climate change. Thus, accurate and unbiased estimation of GSV is also crucial to be pursued across the Hyrcanian. Our goal was to investigate the potential of ALOS-2 and Sentinel-1's polarimetric features in combination with Sentinel-2 multi-spectral features for the GSV estimation in a portion of heterogeneously-structured and mountainous Hyrcanian forests. We used five different kernels by the support vector regression (nu-SVR) for the GSV estimation. Because each kernel differently models the parameters, we separately selected features for each kernel by a binary genetic algorithm (GA). We simultaneously optimized R\(^2\) and RMSE in a suggested GA fitness function. We calculated R\(^2\), RMSE to evaluate the models. We additionally calculated the standard deviation of validation metrics to estimate the model's stability. Also for models over-fitting or under-fitting analysis, we used mean difference (MD) index. The results suggested the use of polynomial kernel as the final model. Despite multiple methodical challenges raised from the composition and structure of the study site, we conclude that the combined use of polarimetric features (both dual and full) with spectral bands and indices can improve the GSV estimation over mixed broadleaf forests. This was partially supported by the use of proposed evaluation criterion within the GA, which helped to avoid the curse of dimensionality for the applied SVR and lowest over estimation or under estimation.}, language = {en} } @article{KhareDeslauriersMorinetal.2021, author = {Khare, Siddhartha and Deslauriers, Annie and Morin, Hubert and Latifi, Hooman and Rossi, Sergio}, title = {Comparing time-lapse PhenoCams with satellite observations across the boreal forest of Quebec, Canada}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {1}, issn = {2072-4292}, doi = {10.3390/rs14010100}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-252213}, year = {2021}, abstract = {Intercomparison of satellite-derived vegetation phenology is scarce in remote locations because of the limited coverage area and low temporal resolution of field observations. By their reliable near-ground observations and high-frequency data collection, PhenoCams can be a robust tool for intercomparison of land surface phenology derived from satellites. This study aims to investigate the transition dates of black spruce (Picea mariana (Mill.) B.S.P.) phenology by comparing fortnightly the MODIS normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) extracted using the Google Earth Engine (GEE) platform with the daily PhenoCam-based green chromatic coordinate (GCC) index. Data were collected from 2016 to 2019 by PhenoCams installed in six mature stands along a latitudinal gradient of the boreal forests of Quebec, Canada. All time series were fitted by double-logistic functions, and the estimated parameters were compared between NDVI, EVI, and GCC. The onset of GCC occurred in the second week of May, whereas the ending of GCC occurred in the last week of September. We demonstrated that GCC was more correlated with EVI (R\(^2\) from 0.66 to 0.85) than NDVI (R\(^2\) from 0.52 to 0.68). In addition, the onset and ending of phenology were shown to differ by 3.5 and 5.4 days between EVI and GCC, respectively. Larger differences were detected between NDVI and GCC, 17.05 and 26.89 days for the onset and ending, respectively. EVI showed better estimations of the phenological dates than NDVI. This better performance is explained by the higher spectral sensitivity of EVI for multiple canopy leaf layers due to the presence of an additional blue band and an optimized soil factor value. Our study demonstrates that the phenological observations derived from PhenoCam are comparable with the EVI index. We conclude that EVI is more suitable than NDVI to assess phenology in evergreen species of the northern boreal region, where PhenoCam data are not available. The EVI index could be used as a reliable proxy of GCC for monitoring evergreen species phenology in areas with reduced access, or where repeated data collection from remote areas are logistically difficult due to the extreme weather.}, language = {en} } @article{KhareLatifiKhare2021, author = {Khare, Suyash and Latifi, Hooman and Khare, Siddhartha}, title = {Vegetation growth analysis of UNESCO World Heritage Hyrcanian forests using multi-sensor optical remote sensing data}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {19}, issn = {2072-4292}, doi = {10.3390/rs13193965}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-248398}, year = {2021}, abstract = {Freely available satellite data at Google Earth Engine (GEE) cloud platform enables vegetation phenology analysis across different scales very efficiently. We evaluated seasonal and annual phenology of the old-growth Hyrcanian forests (HF) of northern Iran covering an area of ca. 1.9 million ha, and also focused on 15 UNESCO World Heritage Sites. We extracted bi-weekly MODIS-NDVI between 2017 and 2020 in GEE, which was used to identify the range of NDVI between two temporal stages. Then, changes in phenology and growth were analyzed by Sentinel 2-derived Temporal Normalized Phenology Index. We modelled between seasonal phenology and growth by additionally considering elevation, surface temperature, and monthly precipitation. Results indicated considerable difference in onset of forests along the longitudinal gradient of the HF. Faster growth was observed in low- and uplands of the western zone, whereas it was lower in both the mid-elevations and the western outskirts. Longitudinal range was a major driver of vegetation growth, to which environmental factors also differently but significantly contributed (p < 0.0001) along the west-east gradient. Our study developed at GEE provides a benchmark to examine the effects of environmental parameters on the vegetation growth of HF, which cover mountainous areas with partly no or limited accessibility.}, language = {en} } @article{KhareLatifiRossietal.2019, author = {Khare, Siddhartha and Latifi, Hooman and Rossi, Sergio and Ghosh, Sanjay Kumar}, title = {Fractional cover mapping of invasive plant species by combining very high-resolution stereo and multi-sensor multispectral imageries}, series = {Forests}, volume = {10}, journal = {Forests}, number = {7}, issn = {1999-4907}, doi = {10.3390/f10070540}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197250}, year = {2019}, abstract = {Invasive plant species are major threats to biodiversity. They can be identified and monitored by means of high spatial resolution remote sensing imagery. This study aimed to test the potential of multiple very high-resolution (VHR) optical multispectral and stereo imageries (VHRSI) at spatial resolutions of 1.5 and 5 m to quantify the presence of the invasive lantana (Lantana camara L.) and predict its distribution at large spatial scale using medium-resolution fractional cover analysis. We created initial training data for fractional cover analysis by classifying smaller extent VHR data (SPOT-6 and RapidEye) along with three dimensional (3D) VHRSI derived digital surface model (DSM) datasets. We modelled the statistical relationship between fractional cover and spectral reflectance for a VHR subset of the study area located in the Himalayan region of India, and finally predicted the fractional cover of lantana based on the spectral reflectance of Landsat-8 imagery of a larger spatial extent. We classified SPOT-6 and RapidEye data and used the outputs as training data to create continuous field layers of Landsat-8 imagery. The area outside the overlapping region was predicted by fractional cover analysis due to the larger extent of Landsat-8 imagery compared with VHR datasets. Results showed clear discrimination of understory lantana from upperstory vegetation with 87.38\% (for SPOT-6), and 85.27\% (for RapidEye) overall accuracy due to the presence of additional VHRSI derived DSM information. Independent validation for lantana fractional cover estimated root-mean-square errors (RMSE) of 11.8\% (for RapidEye) and 7.22\% (for SPOT-6), and R\(^2\) values of 0.85 and 0.92 for RapidEye (5 m) and SPOT-6 (1.5 m), respectively. Results suggested an increase in predictive accuracy of lantana within forest areas along with increase in the spatial resolution for the same Landsat-8 imagery. The variance explained at 1.5 m spatial resolution to predict lantana was 64.37\%, whereas it decreased by up to 37.96\% in the case of 5 m spatial resolution data. This study revealed the high potential of combining small extent VHR and VHRSI- derived 3D optical data with larger extent, freely available satellite data for identification and mapping of invasive species in mountainous forests and remote regions.}, language = {en} } @article{LatifiValbuena2019, author = {Latifi, Hooman and Valbuena, Ruben}, title = {Current trends in forest ecological applications of three-dimensional remote sensing: Transition from experimental to operational solutions?}, series = {Forests}, volume = {10}, journal = {Forests}, number = {10}, issn = {1999-4907}, doi = {10.3390/f10100891}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193282}, year = {2019}, abstract = {The alarming increase in the magnitude and spatiotemporal patterns of changes in composition, structure and function of forest ecosystems during recent years calls for enhanced cross-border mitigation and adaption measures, which strongly entail intensified research to understand the underlying processes in the ecosystems as well as their dynamics. Remote sensing data and methods are nowadays the main complementary sources of synoptic, up-to-date and objective information to support field observations in forest ecology. In particular, analysis of three-dimensional (3D) remote sensing data is regarded as an appropriate complement, since they are hypothesized to resemble the 3D character of most forest attributes. Following their use in various small-scale forest structural analyses over the past two decades, these sources of data are now on their way to be integrated in novel applications in fields like citizen science, environmental impact assessment, forest fire analysis, and biodiversity assessment in remote areas. These and a number of other novel applications provide valuable material for the Forests special issue "3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function", which shows the promising future of these technologies and improves our understanding of the potentials and challenges of 3D remote sensing in practical forest ecology worldwide.}, language = {en} } @article{FekriLatifiAmanietal.2021, author = {Fekri, Erfan and Latifi, Hooman and Amani, Meisam and Zobeidinezhad, Abdolkarim}, title = {A training sample migration method for wetland mapping and monitoring using Sentinel data in Google Earth Engine}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {20}, issn = {2072-4292}, doi = {10.3390/rs13204169}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-248542}, year = {2021}, abstract = {Wetlands are one of the most important ecosystems due to their critical services to both humans and the environment. Therefore, wetland mapping and monitoring are essential for their conservation. In this regard, remote sensing offers efficient solutions due to the availability of cost-efficient archived images over different spatial scales. However, a lack of sufficient consistent training samples at different times is a significant limitation of multi-temporal wetland monitoring. In this study, a new training sample migration method was developed to identify unchanged training samples to be used in wetland classification and change analyses over the International Shadegan Wetland (ISW) areas of southwestern Iran. To this end, we first produced the wetland map of a reference year (2020), for which we had training samples, by combining Sentinel-1 and Sentinel-2 images and the Random Forest (RF) classifier in Google Earth Engine (GEE). The Overall Accuracy (OA) and Kappa coefficient (KC) of this reference map were 97.93\% and 0.97, respectively. Then, an automatic change detection method was developed to migrate unchanged training samples from the reference year to the target years of 2018, 2019, and 2021. Within the proposed method, three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the mean Standard Deviation (SD) of the spectral bands, along with two similarity measures of the Euclidean Distance (ED) and Spectral Angle Distance (SAD), were computed for each pair of reference-target years. The optimum threshold for unchanged samples was also derived using a histogram thresholding approach, which led to selecting the samples that were most likely unchanged based on the highest OA and KC for classifying the test dataset. The proposed migration sample method resulted in high OAs of 95.89\%, 96.83\%, and 97.06\% and KCs of 0.95, 0.96, and 0.96 for the target years of 2018, 2019, and 2021, respectively. Finally, the migrated samples were used to generate the wetland map for the target years. Overall, our proposed method showed high potential for wetland mapping and monitoring when no training samples existed for a target year.}, language = {en} } @article{StereńczakLaurinChiricietal.2020, author = {Stereńczak, Krzysztof and Laurin, Gaia Vaglio and Chirici, Gherardo and Coomes, David A. and Dalponte, Michele and Latifi, Hooman and Puletti, Nicola}, title = {Global Airborne Laser Scanning Data Providers Database (GlobALS) — a new tool for monitoring ecosystems and biodiversity}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {11}, issn = {2072-4292}, doi = {10.3390/rs12111877}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207819}, year = {2020}, abstract = {Protection and recovery of natural resource and biodiversity requires accurate monitoring at multiple scales. Airborne Laser Scanning (ALS) provides high-resolution imagery that is valuable for monitoring structural changes to vegetation, providing a reliable reference for ecological analyses and comparison purposes, especially if used in conjunction with other remote-sensing and field products. However, the potential of ALS data has not been fully exploited, due to limits in data availability and validation. To bridge this gap, the global network for airborne laser scanner data (GlobALS) has been established as a worldwide network of ALS data providers that aims at linking those interested in research and applications related to natural resources and biodiversity monitoring. The network does not collect data itself but collects metadata and facilitates networking and collaborative research amongst the end-users and data providers. This letter describes this facility, with the aim of broadening participation in GlobALS.}, language = {en} } @article{LatifiHeurich2019, author = {Latifi, Hooman and Heurich, Marco}, title = {Multi-scale remote sensing-assisted forest inventory: a glimpse of the state-of-the-art and future prospects}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, number = {11}, issn = {2072-4292}, doi = {10.3390/rs11111260}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197358}, year = {2019}, abstract = {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.}, language = {en} } @article{RokhafrouzLatifiAbkaretal.2021, author = {Rokhafrouz, Mohammad and Latifi, Hooman and Abkar, Ali A. and Wojciechowski, Tomasz and Czechlowski, Mirosław and Naieni, Ali Sadeghi and Maghsoudi, Yasser and Niedbała, Gniewko}, title = {Simplified and hybrid remote sensing-based delineation of management zones for nitrogen variable rate application in wheat}, series = {Agriculture}, volume = {11}, journal = {Agriculture}, number = {11}, issn = {2077-0472}, doi = {10.3390/agriculture11111104}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-250033}, year = {2021}, abstract = {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.}, language = {en} } @article{FakhriLatifi2021, author = {Fakhri, Seyed Arvin and Latifi, Hooman}, title = {A consumer grade UAV-based framework to estimate structural attributes of coppice and high oak forest stands in semi-arid regions}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {21}, issn = {2072-4292}, doi = {10.3390/rs13214367}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-248469}, year = {2021}, abstract = {Semi-arid tree covers, in both high and coppice growth forms, play an essential role in protecting water and soil resources and provides multiple ecosystem services across fragile ecosystems. Thus, they require continuous inventories. Quantification of forest structure in these tree covers provides important measures for their management and biodiversity conservation. We present a framework, based on consumer-grade UAV photogrammetry, to separately estimate primary variables of tree height (H) and crown area (A) across diverse coppice and high stands dominated by Quercus brantii Lindl. along the latitudinal gradient of Zagros mountains of western Iran. Then, multivariate linear regressions were parametrized with H and A to estimate the diameter at breast height (DBH) of high trees because of its importance to accelerate the existing practical DBH inventories across Zagros Forests. The estimated variables were finally applied to a model tree aboveground biomass (AGB) for both vegetative growth forms by local allometric equations and Random Forest models. In each step, the estimated variables were evaluated against the field reference values, indicating practically high accuracies reaching root mean square error (RMSE) of 0.68 m and 4.74 cm for H and DBH, as well as relative RMSE < 10\% for AGB estimates. The results generally suggest an effective framework for single tree-based attribute estimation over mountainous, semi-arid coppice, and high stands.}, language = {en} } @article{YangYaoLietal.2022, author = {Yang, Xuting and Yao, Wanqiang and Li, Pengfei and Hu, Jinfei and Latifi, Hooman and Kang, Li and Wang, Ningjing and Zhang, Dingming}, title = {Changes of SOC content in China's Shendong coal mining area during 1990-2020 investigated using remote sensing techniques}, series = {Sustainability}, volume = {14}, journal = {Sustainability}, number = {12}, issn = {2071-1050}, doi = {10.3390/su14127374}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-278939}, year = {2022}, abstract = {Coal mining, an important human activity, disturbs soil organic carbon (SOC) accumulation and decomposition, eventually affecting terrestrial carbon cycling and the sustainability of human society. However, changes of SOC content and their relation with influential factors in coal mining areas remained unclear. In the study, predictive models of SOC content were developed based on field sampling and Landsat images for different land-use types (grassland, forest, farmland, and bare land) of the largest coal mining area in China (i.e., Shendong). The established models were employed to estimate SOC content across the Shendong mining area during 1990-2020, followed by an investigation into the impacts of climate change and human disturbance on SOC content by a Geo-detector. Results showed that the models produced satisfactory results (R\(^2\) > 0.69, p < 0.05), demonstrating that SOC content over a large coal mining area can be effectively assessed using remote sensing techniques. Results revealed that average SOC content in the study area rose from 5.67 gC·kg\(^{-1}\) in 1990 to 9.23 gC·kg\(^{-1}\) in 2010 and then declined to 5.31 gC·Kg\(^{-1}\) in 2020. This could be attributed to the interaction between the disturbance of soil caused by coal mining and the improvement of eco-environment by land reclamation. Spatially, the SOC content of farmland was the highest, followed by grassland, and that of bare land was the lowest. SOC accumulation was inhibited by coal mining activities, with the effect of high-intensity mining being lower than that of moderate- and low-intensity mining activities. Land use was found to be the strongest individual influencing factor for SOC content changes, while the interaction between vegetation coverage and precipitation exerted the most significant influence on the variability of SOC content. Furthermore, the influence of mining intensity combined with precipitation was 10 times higher than that of mining intensity alone.}, language = {en} } @article{LatifiHolzwarthSkidmoreetal.2021, author = {Latifi, Hooman and Holzwarth, Stefanie and Skidmore, Andrew and Brůna, Josef and Červenka, Jaroslav and Darvishzadeh, Roshanak and Hais, Martin and Heiden, Uta and Homolov{\´a}, Lucie and Krzystek, Peter and Schneider, Thomas and Star{\´y}, Martin and Wang, Tiejun and M{\"u}ller, J{\"o}rg and Heurich, Marco}, title = {A laboratory for conceiving Essential Biodiversity Variables (EBVs)—The 'Data pool initiative for the Bohemian Forest Ecosystem'}, series = {Methods in Ecology and Evolution}, volume = {12}, journal = {Methods in Ecology and Evolution}, number = {11}, doi = {10.1111/2041-210X.13695}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-262743}, pages = {2073-2083}, year = {2021}, abstract = {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.}, language = {en} } @article{GhasemiLatifiPourhashemi2022, author = {Ghasemi, Marziye and Latifi, Hooman and Pourhashemi, Mehdi}, title = {A novel method for detecting and delineating coppice trees in UAV images to monitor tree decline}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {23}, issn = {2072-4292}, doi = {10.3390/rs14235910}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-297258}, year = {2022}, abstract = {Monitoring tree decline in arid and semi-arid zones requires methods that can provide up-to-date and accurate information on the health status of the trees at single-tree and sample plot levels. Unmanned Aerial Vehicles (UAVs) are considered as cost-effective and efficient tools to study tree structure and health at small scale, on which detecting and delineating tree crowns is the first step to extracting varied subsequent information. However, one of the major challenges in broadleaved tree cover is still detecting and delineating tree crowns in images. The frequent dominance of coppice structure in degraded semi-arid vegetation exacerbates this problem. Here, we present a new method based on edge detection for delineating tree crowns based on the features of oak trees in semi-arid coppice structures. The decline severity in individual stands can be analyzed by extracting relevant information such as texture from the crown area. Although the method presented in this study is not fully automated, it returned high performances including an F-score = 0.91. Associating the texture indices calculated in the canopy area with the phenotypic decline index suggested higher correlations of the GLCM texture indices with tree decline at the tree level and hence a high potential to be used for subsequent remote-sensing-assisted tree decline studies.}, language = {en} } @article{KanmegneTamgaLatifiUllmannetal.2023, author = {Kanmegne Tamga, Dan and Latifi, Hooman and Ullmann, Tobias and Baumhauer, Roland and Thiel, Michael and Bayala, Jules}, title = {Modelling the spatial distribution of the classification error of remote sensing data in cocoa agroforestry systems}, series = {Agroforestry Systems}, volume = {97}, journal = {Agroforestry Systems}, number = {1}, issn = {0167-4366}, doi = {10.1007/s10457-022-00791-2}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324139}, pages = {109-119}, year = {2023}, abstract = {Cocoa growing is one of the main activities in humid West Africa, which is mainly grown in pure stands. It is the main driver of deforestation and encroachment in protected areas. Cocoa agroforestry systems which have been promoted to mitigate deforestation, needs to be accurately delineated to support a valid monitoring system. Therefore, the aim of this research is to model the spatial distribution of uncertainties in the classification cocoa agroforestry. The study was carried out in C{\^o}te d'Ivoire, close to the Ta{\"i} National Park. The analysis followed three steps (i) image classification based on texture parameters and vegetation indices from Sentinel-1 and -2 data respectively, to train a random forest algorithm. A classified map with the associated probability maps was generated. (ii) Shannon entropy was calculated from the probability maps, to get the error maps at different thresholds (0.2, 0.3, 0.4 and 0.5). Then, (iii) the generated error maps were analysed using a Geographically Weighted Regression model to check for spatial autocorrelation. From the results, a producer accuracy (0.88) and a user's accuracy (0.91) were obtained. A small threshold value overestimates the classification error, while a larger threshold will underestimate it. The optimal value was found to be between 0.3 and 0.4. There was no evidence of spatial autocorrelation except for a smaller threshold (0.2). The approach differentiated cocoa from other landcover and detected encroachment in forest. Even though some information was lost in the process, the method is effective for mapping cocoa plantations in C{\^o}te d'Ivoire.}, language = {en} }