Assessment of forest cover dynamics in Paraguay A case study of the Upper Parana Atlantic Forest

The Upper Parana Atlantic Forest (BAAPA) in Paraguay is one of the most threatened tropical forests in the world. The rapid growth of deforestation has resulted in the loss of 91% of its original cover. Numerous efforts have been made to halt deforestation activities, however farmers’ perception towards the forest and its benefits has not been considered either in studies conducted so far or by policy makers. This research provides the first multi-temporal analysis of the dynamics of the forest within the BAAPA region on the one hand, and assesses the way farmers perceive the forest and how this influences forest conservation at the farm level on the other. Remote sensing data acquired from Landsat images from 1999 to 2016 were used to measure the extent of the forest cover and deforestation rates over 17 years. Farmers’ influence on the dynamics of the forest was evaluated by combining earth observation data and household survey results conducted in the BAAPA region in 2016. Outcomes obtained in this study demonstrate a total loss in forest cover of 7500 km2. Deforestation rates in protected areas were determined by management regimes. The combination of household level and remote sensing data demonstrated that forest dynamics at the farm level is influenced by farm type, the level of dependency/use of forest benefits and the level of education of forest owners. An understanding of the social value awarded to the forest is a relevant contribution towards preserving natural resources

Forest-Structure Analysis in the Paraguayan Chaco, combining Sentinel and GEDI data

Tropical forest ecosystems have been identified as one of the most diverse regions in the world. Offering extensive ecosystem services such as climate regulation and rich biodiversity has raised increasing concerns about their future and protection. Latest studies conducted at a global scale have defined Argentina, Brazil, and Paraguay as the countries with the highest rates of deforestation in South America. With an area of about 250 000 km², the Paraguayan Chaco covers about one fourth of the Great American Chaco which spreads out over Argentina, Bolivia and Paraguay. The Paraguayan Chaco comprises not only a great variety of ecosystems such as savannahs, shrublands, grasslands and wetlands but also holds the largest dry forest area on earth. Furthermore, the ecoregion has been acknowledged as an important carbon sink on a global scale. Nevertheless, uninterrupted deforestation activities between 1987 and 2012 resulted in the loss of 27 % of its original cover. The constant expansion of agricultural crops, cattle ranching, and illegal logging have severely fragmented the Paraguayan Chaco transforming the last forest remnants into isolated patches, jeopardizing not only their continuity but also the biodiversity comprised within. In this context, this study focuses on the assessment of the annual forest cover between 2016 and 2020 using Sentinel-1 and -2 on the one hand and estimating forest structure parameters with data from the Global Ecosystem Dynamics Investigation (GEDI) on the other. Annual forest / non-forest masks generated through machine learning algorithms show a continuous annual decrease of the forest cover. Between the years 2016 and 2020 9 % of the natural forest cover was lost, resulting in 12 500 km² (Figure 1a)). Regarding forest structure, more than 7.2 million valid lidar shots have been analysed to determine forest height, vertical Plant-Area-Volume�Density, Foliage-Height-Diversity-Index (FHDI) and Plant-Area-Index (PAI). Preliminary results exhibit that more than 55 % of the forest height is between 3 to 9 meters (Figure 1b)) and heights greater than 15 meters are mainly located in the North-East of the Paraguayan Chaco. Additionally, vegetation density appears to be rather sparse which is described by mean values of total canopy cover (17 %) and PAI (0.44 m²/m²). On the other hand, highest vegetation densities and closed forest canopies are observed in the North and North-East (Chaco Biosphere Reserve) where most of the protected areas and indigenous reserves are located. Overall continuous expansion of the agricultural frontier, illegal logging activities, and the constant demand for natural goods threaten the continuity of the tropical forest. To meet these challenges, there is an urgent need to develop methods and approaches in the field of remote sensing observations. This would simplify the implementation of environmental laws and conservation programs orientated toward protecting the last remnants of natural forest on the continent. Countries with large, forested areas such as Paraguay should be taking advantage of the latest natural resources available to halt and monitor deforestation activities in the country. With upcoming data from GEDI and fusion products of optical and lidar sensors, global forest structure data will improve estimates of above-ground biomass models to better quantify global carbon fluxes, further highlight drastic losses of forests and promote environmental-sound land use

Vegetation Structure Modelling and Explorative Statistics based on Sentinel-1, Sentinel-2 and GEDI in the Paraguayan Chaco

Understanding vegetation structures in forests is crucial to estimate species composition, assess habitat quality, and preserve forest resources to ensure carbon storage and climate buffering. Tropical forests are the most endangered forest areas on Earth due to massive deforestation and degradation processes, although holding the richest biodiversity due to its complex vegetation structures. The Paraguayan Chaco is a subtropical dry forest that has experienced a total loss in forest area of about 30 % since the 1980s, resulting in a heavily fragmented landscape. To better understand and characterize the forest areas being lost and to preserve remaining forested areas, the present study implemented a workflow to model vegetation structure characteristics based on complementary satellite remote sensing data sets of Sentinel-1 (synthetic aperture radar), Sentinel-2 (multispectral), and the Global Ecosystem Dynamics Investigation (GEDI, Light Detection and Ranging, LiDAR). Since the Paraguayan Chaco comprises a study area of about 240 000 km², the processing of multi-temporal metrics of Sentinel was conducted in the cloud-computing platform Google Earth Engine (GEE). GEDI attributes of vegetation structure, such as canopy height, canopy cover density, and vertical foliage complexity, served as modelling responses in a Random Forest Regression model trained with comprehensive spatio-temporal metrics derived from Sentinel of 2019. The integration of novel GEDI samples (GEDI sensor is operating since April 2019) allows for large-scale extrapolation of vegetation structure characteristics. Therefore, the first high-resolution maps (10 m) of canopy height, total canopy cover, Plant-Area-Index, and Foliage-Height-Diversity-Index for 2019 have been generated for the Paraguayan Chaco. In addition, comprehensive statistics have been carried out, to better understand spatial patterns of vegetation structure and correlations of various environmental variables with modelled vegetation structure attributes. Distinct differences in seasonality and the expansion of agricultural fields are key influences that shape the vegetation in the Paraguayan Chaco that ranges from dense and high dry forest (maximum canopy height and total canopy cover: 17.6 m, 78.1 %) to grasslands and savannahs (maximum canopy height and total canopy cover: 1.8 m, 10 %). The canopy height model reached highest accuracy (R²: 64.0 %), followed by total canopy cover (R²: 61.4 %), Plant-Area-Index (R²: 50.6 %), and Foliage-Height-Diversity-Index (R²: 48.0 %). Explorative statistics show longitudinal gradients of vegetation structure with elevated values in the eastern part that is characterized by higher precipitation rates. In addition, the modelled characteristics of vegetation structure reflect the characteristics of an Ecoregion classification: Ecoregions with more fertile soils, higher precipitation rates, and weaker seasonality (Humid Chaco, Pantanal; eastern part of the Paraguayan Chaco) present higher, denser, and more complex forest structures than the Dry Chaco and Médanos (western and central part of the Paraguayan Chaco) which are Ecoregions that are strongly influenced by changes in temperature and precipitation due to changes in seasonality. The provision of continuous information of vegetation structure and a deeper understanding of environmental drivers will allow for a more comprehensive assessment of forest resources to support strategies for environmental-sound land use and prioritization of conservation areas in order to halt continuing deforestation activities in the Paraguayan Chaco

Fusing Sentinel-1 and -2 to Model GEDI-Derived Vegetation Structure Characteristics in GEE for the Paraguayan Chaco

Abstract: Vegetation structure is a key component in assessing habitat quality for wildlife and carbon storage capacity of forests. Studies conducted at global scale demonstrate the increasing pressure of the agricultural frontier on tropical forest, endangering their continuity and biodiversity within. The Paraguayan Chaco has been identified as one of the regions with the highest rate of deforestation in South America. Uninterrupted deforestation activities over the last 30 years have resulted in the loss of 27% of its original cover. The present study focuses on the assessment of vegetation structure characteristics for the complete Paraguayan Chaco by fusing Sentinel-1, -2 and novel spaceborne Light Detection and Ranging (LiDAR) samples from the Global Ecosystem Dynamics Investigation (GEDI). The large study area (240,000 km²) calls for a workflow in the cloud computing environment of Google Earth Engine (GEE) which efficiently processes the multi-temporal and multi-sensor data sets for extrapolation in a tile-based random forest (RF) regression model. GEDI-derived attributes of vegetation structure are available since December 2019, opening novel research perspectives to assess vegetation structure composition in remote areas and at large-scale. Therefore, the combination of global mapping missions, such as Landsat and Sentinel, are predestined to be combined with GEDI data, in order to identify priority areas for nature conservation. Nevertheless, a comprehensive assessment of the vegetation structure of the Paraguayan Chaco has not been conducted yet. For that reason, the present methodology was developed to generate the first high-resolution maps (10 m) of canopy height, total canopy cover, Plant-Area-Index and Foliage-Height-Diversity-Index. The complex ecosystems of the Paraguayan Chaco ranging from arid to humid climates can be described by canopy height values from 1.8 to 17.6 m and canopy covers from sparse to dense (total canopy cover: 0 to 78.1%). Model accuracy according to median R² amounts to 64.0% for canopy height, 61.4% for total canopy cover, 50.6% for Plant-Area-Index and 48.0% for Foliage-Height-Diversity-Index. The generated maps of vegetation structure should promote environmental-sound land use and conservation strategies in the Paraguayan Chaco, to meet the challenges of expanding agricultural fields and increasing demand of cattle ranching products, which are dominant drivers of tropical forest los

A Spectral Mixture Analysis and Landscape Metrics Based Framework for Monitoring Spatiotemporal Forest Cover Changes: A Case Study in Mato Grosso, Brazil

An increasing amount of Brazilian rainforest is being lost or degraded for various reasons, both anthropogenic and natural, leading to a loss of biodiversity and further global consequences. Especially in the Brazilian state of Mato Grosso, soy production and large-scale cattle farms led to extensive losses of rainforest in recent years. We used a spectral mixture approach followed by a decision tree classification based on more than 30 years of Landsat data to quantify these losses. Research has shown that current methods for assessing forest degradation are lacking accuracy. Therefore, we generated classifications to determine land cover changes for each year, focusing on both cleared and degraded forest land. The analyses showed a decrease in forest area in Mato Grosso by 28.8% between 1986 and 2020. In order to measure changed forest structures for the selected period, fragmentation analyses based on diverse landscape metrics were carried out for the municipality of Colniza in Mato Grosso. It was found that forest areas experienced also a high degree of fragmentation over the study period, with an increase of 83.3% of the number of patches and a decrease of the mean patch area of 86.1% for the selected time period, resulting in altered habitats for flora and fauna

Estimating grassland biomass and livestock carrying capacity using Sentinel data to strengthen grazing management on local to national scales in Armenia

Livestock farming is an important part of the Armenian agricultural development strategy. The agricultural sector employs more than one third of Armenia’s labor force and accounts for 13% of GDP, hence threats to livestock and pastures can significantly impact livelihoods. For sustaining and developing that sector, fodder provision from grasslands is a key factor. Grasslands constitute 39% of the total territory of Armenia and 57% of the agricultural lands. Apart from resources for livestock, they provide important areas for biodiversity and ecosystem services. The condition of natural pastures and grasslands, however, is being deteriorated due to anthropogenic pressure and unsustainable management practices, leading to overgrazing and erosion. These risks are potentially further aggravated through climatic changes such as more frequent droughts, heat waves, and lack of snow cover. Hence, the setup of an integrated management approach for local decision-making becomes important, emphasizing the need of robust and up-to-date spatial data. In the context of the “GrassAM” project conducted by DLR and GIZ, we aimed at mapping grassland extent, grasslands types, grassland above ground biomass (AGB) and livestock carrying capacities at 10 m spatial resolution in the entire country of Armenia in the year 2020. In order to create a grassland mask for Armenia, a land use and land cover (LULC) classification was carried out using in situ data together with Sentinel-1, Sentinel-2, and digital elevation (DEM) data in a random forest classification approach implemented on Google Earth Engine. 400 sample points of 7 classes (“pasture”, “meadow”, “other grasslands”, “annual arable land”, “perennial arable land”, “bushland”, “bare soil”) were collected by the partner organization ICARE during summer 2020, distributed over all districts and ecological zones. To complement the classification, additional points were sampled on screen for the class “water”. Urban and forest areas were masked using DLR’s World Settlement Footprint 2015 [1] at 10 m resolution as well as the Hansen Global Forest Change maps [2] at 30 m resolution, respectively. The resulting classification achieved an overall accuracy of 80%, while the grassland area was slightly overestimated with 79% user’s accuracy and 92% producer’s accuracy. Of the 400 in situ sites, 147 pasture and meadow points also included wet and dry AGB samples. These measurements have been collected in one 30 x 30 cm plot per field (mowing at 2 cm height), which was assumed to be representative for the surrounding 30 x 30 m. The fresh plant mass was placed in a paper container, labeled and weighed with precision of 0.1 grams. Samples were then dried at room temperature for 48 - 72 hours and weighed again. The measured green AGB ranges from 1.733 – 27.800 kg/ha, with a mean of 12.367 kg/ha, and dry AGB ranges from 1.011 – 14.300 kg/ha, with a mean of 5.416 kg/ha. The AGB measurements were split 60/40 in training and validation data. To create a spatially balanced training data, the selection of training samples was based on spatial allocation of points in hexagon tessellation (1 point per grid cell; 2 points if there are more than four samples are available per cell). Biomass was modeled in a next step using a random forest regression model. The training samples have been used to test a set of 730 different geospatial features (monthly statistics and bi-weekly interpolated features of B2 - B12 Sentinel-2 bands and of eight vegetation indices, elevation, slope, monthly mean temperature at 2 m, monthly precipitation sums) as predictors using a Sequential Forward Feature Selection. Six features (Sentienl-2 mid-June and mid-July NDVI, Band 12 median, May precipitation, June temperature, elevation) were selected and achieved a R-square of 0.66 with an RMSE of 4.013 kg/ha for green AGB. The country-wide biomass maps are the basis to model grassland carrying capacity, i.e. the maximum number of cattle equivalent animals that can be sustained in a given grassland area in a season. AGB was multiplied with a proper use factor of 0.65 as it was suggested by [3] to estimate the available fodder. This amount is divided by the daily requirement of fodder per animal unit (equivalent of 400 kg live weight of cows) multiplied by pasture season length. For both quantities, landscape-zone specific assumptions have been made, resulting in an optimal stocking density of 1- 3 animals per hectare. Test for improving biomass and carrying capacity models as well as the input data sets are still ongoing. The resulting maps, that characterize the allowable grazing pressure on a country-wide scale, could be used to improve grassland management and to increase the resilience of grassland ecosystems to future climate conditions. [1] Marconcini, M., Metz-Marconcini, A., Üreyen, S., Palacios-Lopez, D., Hanke, W., Bachofer, F., Zeidler, J., Esch, T., Gorelick, N., Kakarla, A., & Strano, E. (2020). Outlining Where Humans Live –The World Settlements Footprint 2015. Scientific Data7(242). doi.org/10.1038/s41597-020-00580-5. [2] Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. [3] de Leeuw, J. Rizayeva, A., Namazov, E., Bayramov, E., Marshall, M. T., Etzold, J., Neudert,R. (2019): Application of the MODIS MOD 17 Net Primary Production product in grassland carrying capacity assessment, International Journal of Applied Earth Observation and Geoinformation 78, 66-76, https://doi.org/10.1016/j.jag.2018.09.014

Assessing the Wildfire Activity in the Protected Areas of the Amazon Basin, General Assembly EGU 2021

Despite growing concerns regarding the Amazonian wildfires, the magnitude of the problem is poorly understood. In this study, we assessed the wildfire activity in the protected natural sites (n= 428) of Bolivia, Brazil, Colombia, Ecuador, French Guyana, Guyana, Peru, Suriname, and Venezuela, encompassing an area of 1.4 million km2 of the Amazon basin. A 250 m resolution spectroradiometer sensor imaging (MODIS) was used to obtain land-use/land-cover (MODIS land use land cover product) changes and derive the wildfire activity data (ignition locations and burned areas (MODIS active fire products)) from 2001 to 2018. First, we characterized the mean fire return interval, wildfire occurrence, and empiric burn probability. Then, we implemented a transmission analysis to assess the burned area from incoming fires. We used transmission analysis to characterize the land use and anthropic activities associated to fire ignition locations across the different countries. On average, 867 km 2 of natural forests were burned in protected natural sites annually, and about 85 incoming fires per year from neighboring areas accounted for 10.5% (9,128 ha) of the burned area. The most affected countries were Brazil (53%), Bolivia (24%), and Venezuela (16%).Considerable amount of fire ignition points were detected in open savannas (29%) and grasslands (41%) , where the fire is periodically used to clear extensive grazing properties. The incoming fires from savannas were responsible for burning the largest forest areas within protected sites, affecting as much as 9,800 ha in a single fire event. In conclusion, we discuss the potential implications of the main socioeconomic factors and environmental policies that could explain increasing trends of burned areas. Wildfire risk mitigation strategies include the fire ignition prevention in developed areas, fire use regulation in rural communities, increased fuels management efforts in the buffer areas surrounding natural sites, and the early detection system that may facilitate a rapid and effective fire control response. Our analysis and quantitative outcomes describing the fire activity represent a sound science-based approach for an well defined wildfire management within the protected areas of the Amazonian basin

Assessing Forest Cover Dynamics and Forest Perception in the Atlantic Forest of Paraguay, Combining Remote Sensing and Household Level Data

The Upper Parana Atlantic Forest (BAAPA) in Paraguay is one of the most threatened tropical forests in the world. The rapid growth of deforestation has resulted in the loss of 91% of its original cover. Numerous efforts have been made to halt deforestation activities, however farmers’ perception towards the forest and its benefits has not been considered either in studies conducted so far or by policy makers. This research provides the first multi-temporal analysis of the dynamics of the forest within the BAAPA region on the one hand, and assesses the way farmers perceive the forest and how this influences forest conservation at the farm level on the other. Remote sensing data acquired from Landsat images from 1999 to 2016 were used to measure the extent of the forest cover and deforestation rates over 17 years. Farmers’ influence on the dynamics of the forest was evaluated by combining earth observation data and household survey results conducted in the BAAPA region in 2016. Outcomes obtained in this study demonstrate a total loss in forest cover of 7500 km 2 . Deforestation rates in protected areas were determined by management regimes. The combination of household level and remote sensing data demonstrated that forest dynamics at the farm level is influenced by farm type, the level of dependency/use of forest benefits and the level of education of forest owners. An understanding of the social value awarded to the forest is a relevant contribution towards preserving natural resources

Understanding the relationship between environmental policies and deforestation activities in the Paraguayan Chaco

Over the past 40 years, Paraguay has lost the majority of its natural forest cover, thus becoming one of the countries with the highest deforestation rates in the world. Uninterrupted deforestation practices in the Paraguayan Chaco between 1987 and 2012 resulted in the loss of 27% of its original cover, accounting for almost 44,000 km2 of forested areas depleted. The rapid expansion of the agricultural frontier, cattle ranching, and illegal logging has converted the last forest remnants into isolated patches, thus endangering their continuity and biodiversity within them. In response to these events, the Paraguayan government has implemented numerous environmental programs and regulations to amend the damage that had happened in the past. Although governmental agencies claim a reduction in deforestation activities in the region, proper scientific environmental data that analyze the long-term effect of such regulations/programs remain scarce. Within this context, the present research analyzes the impact of local governments on changes occurred in Paraguayan Chaco forest between the years 1986 and 2020. Remote sensing data acquired from Landsat 4, 5, 7 and 8 images were used to derive the extent of the forest cover and deforestation rates over 34 years. Dynamics of the forest cover was correlated with each of the 10 government terms within the timeframe of the study. By analyzing the forest cover data during and after each term (around five years per government term), we sought to understand the influence of local policies on deforestation activities in the eco-region, aiming to identify social, political, and institutional drivers of change. A comprehensive assessment of creation and effectivities of protected areas, land concession to indigenous communities, and development/implementation of local policies and environmental laws are part of this study. Preliminary results show a significative difference on forest cover loss among governments terms ranging between 1% (2,385 km2) and 9% (14,422 km2). The lack of clear regulations, sound law enforcement, financial support, and inappropriate governance were initially identified as key drivers of change. The use of multi-temporal information was demonstrated to be a key component for designing, supporting, and monitoring conservation strategies and policies. It is crucial to consider not only the outlook of laws and policies aiming to halt deforestation activities but their actual influence on the behavior of natural resources over tim

Deep Learning on Synthetic Data Enables the Automatic Identification of Deficient Forested Windbreaks in the Paraguayan Chaco

Abstract: The Paraguayan Chaco is one of the most rapidly deforested areas in Latin America, mainly due to cattle ranching. Continuously forested windbreaks between agricultural areas and forest patches within these areas are mandatory to minimise the impact that the legally permitted logging has on the ecosystem. Due to the large area of the Paraguayan Chaco, comprehensive in situ monitoring of the integrity of these landscape elements is almost impossible. Satellite-based remote sensing offers excellent prerequisites for large-scale land cover analyses. However, traditional methods mostly focus on spectral and texture information while dismissing the geometric context of landscape features. Since the contextual information is very important for the identification of windbreak gaps and central forests, a deep learning-based detection of relevant landscape features in satellite imagery could solve the problem. However, deep learning methods require a large amount of labelled training data, which cannot be collected in sufficient quantity in the investigated area. This study presents a methodology to automatically classify gaps in windbreaks and central forest patches using a convolutional neural network (CNN) entirely trained on synthetic imagery. In a two-step approach, we first used a random forest (RF) classifier to derive a binary forest mask from Sentinel-1 and -2 images for the Paraguayan Chaco in 2020 with a spatial resolution of 10 m. We then trained a CNN on a synthetic data set consisting of purely artificial binary images to classify central forest patches and gaps in windbreaks in the forest mask. For both classes, the CNN achieved an F1 value of over 70%. The presented method is among the first to use synthetically generated training images and class labels to classify natural landscape elements in remote sensing imagery and therewith particularly contributes to the research on the detection of natural objects such as windbreaks