High-resolution maps show that rubber causes substantial deforestation

Understanding the effects of cash crop expansion on natural forest is of fundamental importance. However, for most crops there are no remotely sensed global maps1, and global deforestation impacts are estimated using models and extrapolations. Natural rubber is an example of a principal commodity for which deforestation impacts have been highly uncertain, with estimates differing more than fivefold1,2,3,4. Here we harnessed Earth observation satellite data and cloud computing5 to produce high-resolution maps of rubber (10 m pixel size) and associated deforestation (30 m pixel size) for Southeast Asia. Our maps indicate that rubber-related forest loss has been substantially underestimated in policy, by the public and in recent reports6,7,8. Our direct remotely sensed observations show that deforestation for rubber is at least twofold to threefold higher than suggested by figures now widely used for setting policy4. With more than 4 million hectares of forest loss for rubber since 1993 (at least 2 million hectares since 2000) and more than 1 million hectares of rubber plantations established in Key Biodiversity Areas, the effects of rubber on biodiversity and ecosystem services in Southeast Asia could be extensive. Thus, rubber deserves more attention in domestic policy, within trade agreements and in incoming due-diligence legislation

Detection And Characterization of Forest Harvesting In Piedmont Through Sentinel-2 Imagery: A Methodological Proposal

This study evaluated the effectiveness of Sentinel-2 (S2) as a tool for early detection and estimation of forest harvesting in the Piemonte Region, which can be used by the regional forest administration. The priority was the detection, at the regional scale, of annual forest cover changes with the following goals: i) mapping of irregular (in respect of the regional Forestry Regulation) forest cuts; ii) quantification of the intensity of the silvicultural interventions. Results are expected to support forest police controls.The proposed procedure is based on a supervised classification approach based on Random Forest algorithm. Accuracy of harvested areas detection proved to be high (overall accuracy 98%). Characterization of the occurred forest cuts was obtained computingthe local coefficient of variationof the normalized difference vegetation index (NDVI) after harvesting, that showed to be a good predictor of forest harvesting intensity

Monitoring forest gain and loss based on LandTrendr algorithm and Landsat images in KTH Pati social forestry area, Indonesia

Social forestry schemes are now being implemented in numerous state forest areas in Indonesia, aiming to reduce deforestation and improve the community’s livelihood. However, spatial monitoring in the social forestry area is still limited to see how the implementation progresses. The present study aimed to identify the change of forest taking a case in Pati Forest Farmer Communities (KTH Pati) social forestry area from 1996 to 2022 using the LandTrendr algorithm based on Normalized Burn Ratio (NBR) value of Landsat image series. The results detected forest loss and gain covering an area of 453.97 ha and 494.18 ha, respectively. Two main reasons causing the forest loss are the country’s financial and political situation from 1997 to 2003 and the harvest of forest plantations in 2017–2018. However, it was found that the study area had a positive forest gain with the current continuous growth of 292.32 ha (20.16% of the total area). Even though the social forestry policy has not significantly shown a positive impact on forest growth, spatial monitoring through remote sensing can be a great tool for observing the progress

Landsat archive for detection of change in Mediterranean ecosystems: The case of Northern Morocco

The study of changes in land cover provides a better understanding of the interactions between humans and natural ecosystems. In this context, the present study focused on the dynamics of natural ecosystems in the Rif region of Northern Morocco. The methodology was based on the inspection and visual interpretation of Landsat and Google Earth image captures, the time series of five Landsat 4-8 image bands, and the Tasseled Cap indices for a random sample of 500 points from 1984 to 2022. The study found that changes affected practically the whole study region over the study period, with around a third of them being ignored due to their very tiny magnitudes or being false positives. The findings demonstrated a general declining trend in the measured changes, indicating a reduction in pressure on different ecosystems. Furthermore, this tendency may be due in part to the availability of Google Earth images during the 2000s, which has significantly reduced the number of false positives. In terms of the year of first change, only 5.7% of pixels experienced their first events after the year 2000, implying that these pixels underwent no change for at least the first 16 years of the study period. On the other hand, 2.5% of the pixels had their last events during the first ten years and have thus remained unmodified for at least 27 years. For the year 2020, the confidence rating of the visual land cover categorization is medium to high for 88.9% of pixels using high-resolution Google Earth photos, whereas the classification quality was inadequate for 64% of pixels in 1984. Despite the stresses on the ecosystems structured by shrubs/shrubs, forests, and herbaceous/shrubs caused by the different disturbances identified, the majority of these ecosystems have not been converted to new land cover classes. According to the study, agriculture is the primary driving force underlying the conversion of forests, herbaceous/shrublands, and even shrublands/shrublands. The area increases for the latter three ecosystems represent, on the one hand, their ability to regenerate themselves and, on the other, Morocco's restoration efforts

High-resolution maps show that rubber causes substantial deforestation

Understanding the effects of cash crop expansion on natural forest is of fundamental importance. However, for most crops there are no remotely sensed global maps1, and global deforestation impacts are estimated using models and extrapolations. Natural rubber is an example of a principal commodity for which deforestation impacts have been highly uncertain, with estimates differing more than fivefold1-4. Here we harnessed Earth observation satellite data and cloud computing5 to produce high-resolution maps of rubber (10 m pixel size) and associated deforestation (30 m pixel size) for Southeast Asia. Our maps indicate that rubber-related forest loss has been substantially underestimated in policy, by the public and in recent reports6-8. Our direct remotely sensed observations show that deforestation for rubber is at least twofold to threefold higher than suggested by figures now widely used for setting policy4. With more than 4 million hectares of forest loss for rubber since 1993 (at least 2 million hectares since 2000) and more than 1 million hectares of rubber plantations established in Key Biodiversity Areas, the effects of rubber on biodiversity and ecosystem services in Southeast Asia could be extensive. Thus, rubber deserves more attention in domestic policy, within trade agreements and in incoming due-diligence legislation

Landsat time series reveal simultaneous expansion and intensification of irrigated dry season cropping in Southeastern Turkey

Long-term monitoring of the extent and intensity of irrigation systems is needed to track crop water consumption and to adapt land use to a changing climate. We mapped the expansion and changes in the intensity of irrigated dry season cropping in Turkey´s Southeastern Anatolia Project annually from 1990 to 2018 using Landsat time series. Irrigated dry season cropping covered 5,779 km² (± 479 km²) in 2018, which represents an increase of 617% over the study period. Dry season cropping was practiced on average every second year, but spatial variability was pronounced. Increases in dry season cropping frequency were observed on 40% of the studied croplands. The presented maps enable the identification of land use intensity hotspots at 30 m spatial resolution, and can thus aid in assessments of water consumption and environmental degradation. All maps are openly available for further use at https://doi.org/10.5281/zenodo.4287661.Peer Reviewe

Analysis of Site-dependent Pinus halepensis Mill. Defoliation Caused by ‘Candidatus Phytoplasma pini’ through Shape Selection in Landsat Time Series

High levels of ‘Candidatus Phytoplasma pini’ have produced extensive forest mortality on Pinus halepensis Mill forests in eastern Spain. This has led to the widespread levels of forest mortality. We used archival Landsat imagery and shapes algorithm implemented in the Google Earth Engine to explore the potential of the LandTrendr algorithm and its outputs, together with field observations, to analyze and predict the health status in P. halepensis stands affected by ‘Candidatus Phytoplasma pini’ in Andalusia (south-eastern Spain). We found that the Landsat time series algorithm (LandTrendr) has captured both long- and short-duration trends and changes in spectral reflectance related to phytoplasma disturbance in the Aleppo pine forest stands investigated. The normalized burn ratio (NBR) trends were positively associated with environmental variables: Annual precipitation, mean temperature, soil depth, percent base saturation and aspect. Environmental variables were tested for their contributions to the mapping of changes in Aleppo pine cover in the study area, as an empirical modeling approach to disturbance mapping in forests of south-eastern Spain. The methodology outlined in this paper has produced valuable results that indicate new possibilities for the use in forest management of remote-sensing technologies based on spectral trajectories associated with pest-diseases defoliation. Given the likely increase in pest risks in the forests of southern Europe, accurate assessment and map of pest outbreaks on forests will become increasingly important, both for research and for practical applications in forest management