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The Global Desertification(3/8)
By Khalil Ali Ganem Khalil Ali Ganem Scilit Preprints.org Google Scholar View Publications 1, 2, * , Yongkang Xue Yongkang Xue Scilit Preprints.org Google Scholar View Publications 1 , Ariane de Almeida Rodrigues Ariane de Almeida Rodrigues Scilit Preprints.org Google Scholar View Publications 3 , Washington Franca-Rocha Washington Franca-Rocha Scilit Preprints.org Google Scholar View Publications 4 , Marceli Terra de Oliveira Marceli Terra de Oliveira Scilit Preprints.org Google Scholar View Publications 2 , Nathália Silva de Carvalho Nathália Silva de Carvalho Scilit Preprints.org Google Scholar View Publications 2 , Efrain Yury Turpo Cayo Efrain Yury Turpo Cayo Scilit Preprints.org Google Scholar View Publications 5 , Marcos Reis Rosa Marcos Reis Rosa Scilit Preprints.org Google Scholar View Publications 4 , Andeise Cerqueira Dutra Andeise Cerqueira Dutra Scilit Preprints.org Google Scholar View Publications 2 and Yosio Edemir Shimabukuro Yosio Edemir Shimabukuro Scilit Preprints.org Google Scholar View Publications 2
The scientific grasp of the distribution and dynamics of land use and land cover (LULC) changes in South America is still limited. This is especially true for the continent’s hyperarid, arid, semiarid, and dry subhumid zones, collectively known as drylands, which are under-represented ecosystems that are highly threatened by climate change and human activity. Maps of LULC in drylands are, thus, essential in order to investigate their vulnerability to both natural and anthropogenic impacts. This paper comprehensively reviewed existing mapping initiatives of South America’s drylands to discuss the main knowledge gaps, as well as central methodological trends and challenges, for advancing our understanding of LULC dynamics in these fragile ecosystems. Our review centered on five essential aspects of remote-sensing-based LULC mapping: scale, datasets, classification techniques, number of classes (legends), and validation protocols. The results indicated that the Landsat sensor dataset was the most frequently used, followed by AVHRR and MODIS, and no studies used recently available high-resolution satellite sensors. Machine learning algorithms emerged as a broadly employed methodology for land cover classification in South America. Still, such advancement in classification methods did not yet reflect in the upsurge of detailed mapping of dryland vegetation types and functional groups. Among the 23 mapping initiatives, the number of LULC classes in their respective legends varied from 6 to 39, with 1 to 14 classes representing drylands. Validation protocols included fieldwork and automatic processes with sampling strategies ranging from solely random to stratified approaches. Finally, we discussed the opportunities and challenges for advancing research on desertification, climate change, fire mapping, and the resilience of dryland populations. By and large, multi-level studies for dryland vegetation mapping are still lacking.
Drylands refer to areas characterized by water deficit, high spatially and temporally variable precipitation, and seasonal climatic extremes [1, 2, 3, 4]. Globally, drylands consist of forests (18%), barren land (28%), grasslands (25%), croplands (14%), and other wooded lands (10%) [5]. They cover about 41% of the Earth’s surface and harbor more than a third of the world’s human population [6]. Drylands also have high ecological importance globally, as they contribute to about 40% of global net primary productivity (NPP) [7], host 35% of the biodiversity hotspots worldwide [2], and occupy 1.1 billion hectares (27%) of the forest area [5]. These environments are critically important to society yet exceptionally vulnerable to climate change and desertification [8, 9].
Latin America Must Play An Important Role In Combating Soil And Land Degradation”
Drylands’ vulnerability to predicted increases in global temperatures, as well as the severity of drought events and reduced rainfall in many regions [6], can lead to a substantial decline in land productivity and ecosystem functions and services, accelerating the desertification process [10]. The combined effects of climate change and desertification threaten plant species’ richness, which sustains dryland ecosystems’ multifunctionality, such as carbon storage and nutrient cycling [11]. Recent estimates suggest that 6% of the world’s drylands have undergone desertification, with a further 20% at high desertification risk [12], aggravating the threat of malnutrition, economic hardship, migratory movements, and poverty [13, 14]. As a result, desertification has become a globally defined environmental issue [15] and one of the most significant environmental challenges nowadays [16].
The preservation of drylands’ woody vegetation increases the protection of these ecosystems against desertification [17] and enhances their resilience to climate change [18]. Conservative action is critical to avoid overgrazing and woodcutting, two major desertification vectors [19]. On top of that, dryland woodlands are key ecosystems to regulate the global carbon cycle [18, 20] as their high variability contributes to short-term alterations in carbon stock [21]. In addition, drylands play a determining role in various essential ecosystem processes and related abiotic patterns [22], besides providing significantly relevant resources for local livelihoods and their food security. Hence, accurate and up-to-date information on the status of dryland vegetation and subsidizing resources from technical–scientific innovation in the Remote Sensing (RS) framework are necessary for efficient policymaking.
Drylands in South America represent approximately 31% of the continent’s total land area and 8.7% of the global drylands [5]. Despite its large distribution and ecological importance, few studies have directed attention to mapping land cover in drylands [23, 24, 25]. Current RS-based studies have been mainly developed for humid tropical forests (with closed canopy and high biomass, e.g., the Amazon) and are unsuitable for detecting, mapping, and monitoring drylands [26]. The predominance of sparse vegetation and the heterogeneity of vegetation composition, marked by the co-existence of trees, shrubs, and grasses, is the main challenge to remote sensing studies focused on drylands [27]. Moreover, the limited number of South American drylands’ land use and land cover (LULC) mapping poses limitations for studying the Earth’s environmental systems [28, 29], managing water resources and ecosystems, as well as understanding and modeling associated ecological and climate impacts [30]. In addition, integrating information from different remotely collected data sources remains overlooked, especially for studying the continent’s drylands. Therefore, accurate and timely LULC classification is needed to overcome the challenge of differentiating heterogeneous dryland areas [31] and to monitor the loss of these fragile ecosystems in South America [32].
Fossil Pollen Records Indicate That Patagonian Desertification Was Not Solely A Consequence Of Andean Uplift
The information gap is also explained by the use of coarse spatial resolution satellite data to produce the previous LULC maps for global assessments [33]. As a result, global or continental maps do not provide sufficient details to represent regional dryland ecosystems of South America spatially. Even at regional scales, the heterogeneity, higher spectral variability, and relatively low radiative signals of drylands make mapping their vegetation and structure challenging [34]. Therefore, mapping South American drylands is essential to monitor the status of specific land cover types of these highly threatened ecosystems regionally and globally. Consequently, updating the state-of-the-art of South American dryland mapping contributes to understanding the impact of LULC changes on each type of vegetation formation and how they influence the global energy balance, CO
The objective of this study was to identify existing mapping initiatives of South America’s drylands and discuss how they advance our understanding of LULC dynamics in these ecosystems, what the main knowledge gaps are, as well as the major methodological trends and challenges. For that, we conducted a literature review focused on five essential components of RS-based land cover mapping: scale, satellite datasets, classification techniques, number of classes (legends), and validation protocols. By analyzing current mapping efforts, we discussed the opportunities and challenges related to adequately mapping LULC in drylands to advance research on desertification, climate change, fire mapping, and the resilience of dryland populations.
Aridity is a long-term hydrologic and climatic condition of water scarcity. Numerous aridity indices have been proposed and widely applied in the scientific literature to quantify the degree of dryness at a given location and, thus, spatially delimit arid climatic zones [35, 36, 37]. The aridity index (see Equation (1)) represents the counterbalancing between natural moisture inputs and losses [38]:
South America Vegetation
Where AI corresponds to the Aridity Index, P to precipitation (mm), and PET to potential evapotranspiration (mm), calculated based on the Penman–Monteith method (see [39] for further details).
This index is considered biologically accurate in climates highly influenced by seasonality [40], and it is widely used to define the location of drylands. From a general perspective, the overlapping of the
The information gap is also explained by the use of coarse spatial resolution satellite data to produce the previous LULC maps for global assessments [33]. As a result, global or continental maps do not provide sufficient details to represent regional dryland ecosystems of South America spatially. Even at regional scales, the heterogeneity, higher spectral variability, and relatively low radiative signals of drylands make mapping their vegetation and structure challenging [34]. Therefore, mapping South American drylands is essential to monitor the status of specific land cover types of these highly threatened ecosystems regionally and globally. Consequently, updating the state-of-the-art of South American dryland mapping contributes to understanding the impact of LULC changes on each type of vegetation formation and how they influence the global energy balance, CO
The objective of this study was to identify existing mapping initiatives of South America’s drylands and discuss how they advance our understanding of LULC dynamics in these ecosystems, what the main knowledge gaps are, as well as the major methodological trends and challenges. For that, we conducted a literature review focused on five essential components of RS-based land cover mapping: scale, satellite datasets, classification techniques, number of classes (legends), and validation protocols. By analyzing current mapping efforts, we discussed the opportunities and challenges related to adequately mapping LULC in drylands to advance research on desertification, climate change, fire mapping, and the resilience of dryland populations.
Aridity is a long-term hydrologic and climatic condition of water scarcity. Numerous aridity indices have been proposed and widely applied in the scientific literature to quantify the degree of dryness at a given location and, thus, spatially delimit arid climatic zones [35, 36, 37]. The aridity index (see Equation (1)) represents the counterbalancing between natural moisture inputs and losses [38]:
South America Vegetation
Where AI corresponds to the Aridity Index, P to precipitation (mm), and PET to potential evapotranspiration (mm), calculated based on the Penman–Monteith method (see [39] for further details).
This index is considered biologically accurate in climates highly influenced by seasonality [40], and it is widely used to define the location of drylands. From a general perspective, the overlapping of the
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