Surface albedo as a proxy for land-cover clearing in seasonally dry forests: evidence from the Brazilian Caatinga

Abstract

Ongoing increase in human and climate pressures, in addition to the lack of monitoring initiatives, makes the Caatinga one of the most vulnerable forests in the world. The Caatinga is located in the semi-arid region of Brazil and its vegetation phenology is highly dependent on precipitation, which has a high spatial and temporal variability. Under these circumstances, satellite image-based methods are valued due to their ability to uncover human-induced changes from climate effects on land cover. In this study, a time series stack of 670 Landsat images over a period of 31 years (1985–2015) was used to investigate spatial and temporal patterns of land-cover clearing (LCC) due to vegetation removal in an area of the Caatinga. We compared the LCC detection accuracy of three spectral indices, i.e., the surface albedo (SA), the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI). We applied a residual trend analysis (TSS-RESTREND) to attenuate seasonal climate effects on the vegetation time series signal and to detect only significant structural changes (breakpoints) from monthly Landsat time series. Our results show that SA was able to identify the general occurrence of LCC and the year that it occurred with a higher accuracy (89 and 62%, respectively) compared to EVI (44 and 22%) and NDVI (46 and 22%). The overall outcome of the study shows the benefits of using Landsat time series and a spectral index that incorporates the short-wave infrared range, such as the SA, compared to visible and near-infrared vegetation indices for monitoring LCC in seasonally dry forests such as the Caatinga.

Monitoring Effects of Land Cover Change on Biophysical Drivers in Rangelands Using Albedo

“…Additionally, the link between modelled landscape change, surface albedo and changes in catchment water and carbon fluxes have not been investigated. Recently, surface albedo was extracted from satellite data per land cover class for calibration of land surface models (LSM) in climate modelling [34,35], while other authors have investigated the potential of albedo in land cover [36] and land cover change analyses [17]. The aim of this paper is to quantify trends and relationships between land cover change, surface albedo, NPP and ET to characterise catchment water and carbon fluxes and postulate consequences on ecosystem services provided by grasslands.…”

“…Betts [17] found surface albedo to be an accurate proxy for land cover change in a semi-arid region in Brazil, due to its sensitivity to seasonal phenological variation [17,18] and landscapes affected by land management practices [19]. Land cover change projections in the Eastern Cape of South Africa have highlighted the importance of focusing land and water resources management interventions on rehabilitation in catchments under dualistic 1 farming systems [20].…”

Evaluating single and multi-date Landsat classifications of land-cover in a seasonally dry tropical forest

“…1), with a territorial area of 1,682.87 km², located between the geographical coordinates 7º28 '30'' and 7º49'30'' South and 36º34'00''and 37º12'00'' West. In the study area, vegetation degradation has occurred mainly by human activities, such as agriculture and livestock exploitation and wood extraction (Moreira and Targino, 1997;Alves et al, 2017). The climate is hot semi-arid (BSh, Köppen classification), with two distinct seasons: the hot dry season (From June to January) and the very hot rainy season (from February to May), with an average annual rainfall of approximately 520 mm (Cunha et al, 2020). The soils are shallow and stony, which makes it difficult to retain water after the precipitation events (Moro et al, 2015).…”

“…In these different land-cover patterns, the strategies for adapting to the climate are distinct, resulting in different spatial responses and in the variation of their physical properties over time (Meiado et al, 2012;Vico et al, 2015). The particularities of the climate-vegetation interaction in this forest make it a challenge to distinguish the different landcover patterns through remote sensing (Cunha et al, 2020).…”

Uso do LiDAR na Estimativa de Atributos Florestais: Uma Revisão

“…Este tipo de estudo tem ganhado outra dimensão. Para monitorar processos ecológicos e obter dados biogeofísicos em diversas escalas espaciais e temporais tem se utilizado o sensoriamento remoto como uma importante ferramenta, o que tem contribuído para a criação de modelos de ecossistemas (Pettorelli et al, 2014;Sun et al, 2019) e outros diversos que visam melhor caracterizar as florestas (Teixeira et al, 2016;Cunha et al, 2020;Ferreira et al, 2020;Miranda et al, 2020;Tan et al, 2021;Medeiros et al, 2022).…”