30+ YEARS OF LAND COVER AND LAND USE CHANGE IN SOUTH AMERICA

The modification of the Earth’s surface constitutes the most impactful way in which humans affect their surrounding environment, with broad and lasting consequences. Changes in land cover accelerate biodiversity loss, contribute to climate change, and affect the provisioning of ecosystem services. Such negative environmental impacts can have important effects on human health and livelihoods. The South American continent, in particular, has undergone significant transformations over the past decade, due in large part to the conversion of natural land to more economically productive land uses, such as crops, pastures, and tree plantations. The agricultural commodities produced in South America are traded and consumed globally, and land will likely continue to be converted if demand for these commodities continues to rise. Despite the environmental and commercial importance of land cover and land use change dynamics in South America, the extent and rates of land change have not yet been thoroughly characterized and quantified. This dissertation aims to advance scientific knowledge on the extent and rates of change of important land covers and land uses, especially as they relate to the production of agricultural commodities, by leveraging the 34-year Landsat archive of Earth observation data. The general approach employed throughout follows a two-step process of mapping and sampling, in order to provide spatially explicit information on the patterns of land cover/land use change, as well as associated unbiased area estimates. This approach is first employed for the use-case of Brazilian cropland expansion from 2000 to 2014, and results show a near doubling of cropland area, the majority of which (80%) came about through the conversion of existing pastures. The methodology is then repeated at broader thematic, temporal, and geographic scales, resulting in area estimates of changes in cropland, pasture, plantation, natural tree regrowth, semi-natural land, tree cover and degraded tree cover from 1985 to 2018. Altogether, these changes amount to a 60% increase in human impact on natural land over the study period. Finally, an analysis and evaluation of the methodology employed for mapping and sampling when there is a multitude of target classes instead of a single one is provided as an assessment of methodological approaches

Harmonisation, Mosaicing and Production of the Global Land Cover 2000 Database.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Temporal comparison of multiple sensors for monitoring paddock management in an integrated crop-livestock system.

The objective of this study was to assess and compare the temporal profile of the Normalised Difference Vegetation Index (NDVI) time series from different sensors for paddock monitoring in intensively managed pasture fields.Editores: Douglas Francisco Marcolino Gherardi, Ieda Del´Arco Sanches, Luiz Eduardo Oliveira e Cruz de Aragão

Reading Greenness in Urban Areas: Possible Roles of Phenological Metrics from the Copernicus HR-VPP Dataset

Vegetation phenology is that branch of science that describes periodic plant life cycle events across the growing seasons. Remote sensing typically monitors these significant events by means of time series of vegetation indices, permitting to characterize vegetation dynamics. It is well known that vegetation in urban areas, i.e., green spaces in general, may benefit human health mainly by mitigating noise and air pollution, promoting physical or social activities, and improving mental health. Based on the influence that green space exposure seems to exert on Public Health and using a multidisciplinary approach, we mapped phenological behavior of urban green areas to explore yearly persistence of their potential favorable effect, such as heat reduction, air purification, noise mitigation, and promotion of physical/social activities and improvement of mental health. The study area corresponds to the municipality of Torino (about 800,000 inhabitants, NW, Italy). Renouncing to a rigorous at-species level phenological description, this work investigated macro-phenology of vegetated areas for the 2018, 2019 and 2020 years with reference to the new free and open Copernicus HR-VPP dataset. Vegetation type, deduced with reference to the 2019 BDTRE official technical map of the Piemonte Region, was considered and related to the correspondent macro-phenology using a limited number of metrics from the HR-VPP dataset. Investigation was aimed at exploring their capability of providing synthetic and easy-to-use information for urban planners. No validation was achieved about phenological metrics values (assuming their accuracy correspondent to the nominal one reported in the associated manuals). Nevertheless, a spatial validation was operated to investigate the capability of the dataset to properly recognize vegetated areas, thus providing correspondent metrics. Preliminary results showed a spatial inconsistency related to the HR-VPP dataset, that greatly overestimates (about 50%) vegetated areas in the city, assigning metric values to pixels that, if compared with technical maps, do not fall within vegetated areas. The work found out that, among HR-VPP metrics, LOS (Length Of Season) and SPROD (Seasonal Productivity) well characterized vegetation patches, making it possible to clearly read vegetation behavior, which can be effectively exploited to zone the city and make management of green areas and real estate considerations more effective

Micro-topography associated to forest edges

Forest edges are often defined as the discontinuity between the forest habitat and an adjacent open habitat, thus they are based on a clear difference in the structure of the dominant vegetation. However, beside this very general definition, in the field we can observe a large diversity of edges, with often different kinds of micro-topography features: bank, ditch, stone wall, path, etc. As these elements are rather common in many temperate forest edges, it seems important to start to characterize them more clearly and with consistency. From a set of observations in south-western France, we build a first typology of the micro-topographic elements associated to forest edges. For each of them we describe the process, natural or human induced, at their origin, and according to the literature available, we identify some of their key ecological roles. Banks, generated by the differential erosion between forest and crops along slopes, are especially analyzed since they are the most common micro-topographic element in our region. It offers many micro-habitat conditions in the soil used by a wide range of species, notably by several bee species. More research is required to study in details the importance of such micro-topographic elements

Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices

There are growing opportunities to leverage new technologies and data sources to address global problems related to sustainability, climate change, and biodiversity loss. The emerging discipline of GeoAI resulting from the convergence of AI and Geospatial science (Geo-AI) is enabling the possibility to harness the increasingly available open Earth Observation data collected from different constellations of satellites and sensors with high spatial, spectral and temporal resolutions. However, transforming these raw data into high-quality datasets that could be used for training AI and specifically deep learning models are technically challenging. This paper describes the process and results of synthesizing labelled-datasets that could be used for training AI (specifically Convolutional Neural Networks) models for determining agricultural land use pattern to support decisions for sustainable farming. In our opinion, this work is a significant step forward in addressing the paucity of usable datasets for developing scalable GeoAI models for sustainable agriculture

Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin

Front-line remote sensing tools, coupled with machine learning (ML), have a significant role in crop monitoring and disease surveillance. Crop type classification and a disease early warning system are some of these remote sensing applications that provide precise, timely, and cost-effective information at different spatial, temporal, and spectral resolutions. To our knowledge, most disease surveillance systems focus on a single-sensor based solutions and lagging the integration of multiple information sources. Moreover, monitoring larger landscapes using unmanned aerial vehicles (UAV) are challenging, and, therefore combining high resolution satellite imagery data with advanced machine learning (ML) models through the use of mobile apps could help detect and classify banana plants and provide more information on its overall health status. In this study, we classified banana under mixed-complex African landscapes through pixel-based classifications and ML models derived from multi-level satellite images (Sentinel 2, PlanetScope and WorldView-2) and UAV (MicaSense RedEdge) platforms. Our pixel-based classification from random forest (RF) model using combined features of vegetation indices (VIs) and principal component analysis (PCA) showed up to 97% overall accuracy (OA) with less than 10% omission and commission errors (OE and CE) and Kappa coefficient of 0.96 in high resolution multispectral images. We used UAV-RGB aerial images from DR Congo and Republic of Benin fields to develop a mixed-model system combining object detection model (RetinaNet) and a custom classifier for simultaneous banana localization and disease classification. Their accuracies were tested using different performance metrics. Our UAV-RGB mixed-model revealed that the developed object detection and classification model successfully classified healthy and diseased plants with 99.4%, 92.8%, 93.3% and 90.8% accuracy for the four classes: banana bunchy top disease (BBTD), Xanthomonas Wilt of Banana (BXW), healthy banana cluster and individual banana plants, respectively. These approaches of aerial image-based ML models have high potential to provide a decision support system for major banana diseases in Afric

Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion

The Changing Matrix: Reforestation and Connectivity in a Tropical Habitat Corridor

In the last two decades, export-oriented crops and timber and fruit plantations have joined small-scale cultivation and pasture as important causes of tropical deforestation. Widespread conversion of tropical forest to agriculture threatens to isolate protected areas, which has led to efforts to maintain functional connectivity in landscapes between protected areas. Relatively few "landscape conservation" efforts have been assessed for their effect on deforestation, but advances in remote sensing now permit detailed monitoring of tropical land uses over time, including mapping of tree crops and plantations. This dissertation evaluates the long-term impact of forest conservation and reforestation policies on tropical forests in a habitat corridor. The following chapters test the capability of remote sensing to monitor tropical conservation efforts and assess whether landscape conservation policies can maintain forest cover and connectivity in the face of rapid agricultural expansion. Costa Rica has one of the most comprehensive landscape conservation policies in the tropics: a 1996 Forest Law banned deforestation and expanded payments for environmental services (PES) to protect forests and plant trees, prioritizing designated habitat corridors between protected areas. The long-term effect of the program on land-use transitions is not well known. To take advantage of this regional policy experiment, I used a time-series of five moderate-resolution Landsat images to track land-use change from 1986 to 2011in the oldest habitat corridor, the San Juan-La Selva Biological Corridor (SJLSBC). Forest conservation policies were associated with a 40% decline in deforestation after 1996 despite a doubling in the area of cropland in the last decade. The proportion of cropland derived from mature forest dropped from 16.4% to 1.9% after 1996, while one fifth of pasture expansion continued to be derived from mature forest. These results suggest that forest conservation policies can successfully lower deforestation, and that they can be more effective with large export producers than small-scale cattle producers. Tree plantations are an important component of Costa Rican PES, but knowledge of their distribution and contribution to connectivity in the corridor region is poor. After reviewing the remote sensing literature, I employed a novel integration of hyperspectral images and a Landsat time-series to create the first regional map of tropical tree plantation species. Including multitemporal data significantly improved overall hyperspectral map accuracy to 91%; the six tree plantation species were classified with 83% mean producer's accuracy. Non-native species made up 89% of tree plantations, and they were cleared more rapidly than native tree plantations and secondary forests. I combined existing land cover maps, field behavioral experiments, and a graph connectivity model to estimate whether landscape conservation policies increased connectivity for understory insectivorous birds, a representative forest-dependent group. The field playback experiments indicated both native and exotic tree plantations with a dense shrubby understory were acceptable dispersal habitat for all species, and that birds traveled readily near secondary forest edges but rarely into forested pasture. Graph model parameters were informed by these results. For all of these bird species, functional connectivity declined by 14-21% with only a 4.9% decline in forest area over time, implying that conservation policies have not caused a net increase in functional connectivity in the SJLSBC region. Despite making up 2% of the region, tree plantations had little effect on regional connectivity because of their placement in the landscape; we demonstrate that spatially-targeted reforestation of 0.1% of the region could increase connectivity by 1.8%. Collectively, the results presented in these chapters underline the potential and limitations of landscape conservation policies and corridor plans in the tropics; combining regulations and PES can lower deforestation over the medium-term, but increased enforcement, improved monitoring with remote sensing, and targeted conservation effort is needed to combat illegal deforestation and restore functional connectivity. Given numerous new tropical corridor and PES programs and the qualified successes of landscape conservation policies in Costa Rica and other tropical countries, our approach to the analysis can be applied to monitor and evaluate connectivity across the tropics