Assessing UN indicators of land degradation neutrality and proportion of degraded land for Botswana using remote sensing based national level metrics

Abstract

Achieving land degradation neutrality (LDN) has been proposed as a way to stem the loss of land resources globally. To date, LDN operationalization at the country level has remained a challenge both from a policy and science perspective. Using an approach incorporating cloud-based geospatial computing with machine learning, national level datasets of land cover, land productivity dynamics, and soil organic carbon stocks were developed. Using the example of Botswana, LDN and proportion of degraded land were assessed. Between 2000 and 2015, grassland lost approximately 17% of its original extent, the highest level of loss for any land category; land productivity decline was highest in artificial surface areas (11%), whereas 36% of croplands show early signs of decline. With the use of national metrics (NM), degraded areas were found to be 32.6% compared to 51.4% of the total land area when global default datasets (DD) were used.Estimates of degraded land computed with NM and DD were validated in Palapye, an agro-pastoral region in eastern Botswana, where Composite Land Degradation Index (CLDI) field-based data exists. Comparing land degradation (LD) in the three datasets (NM, DD, and CLDI), NM estimates were closest to the field data. The extra efforts put into developing national level data for LD assessment in this study is, thus, well-justified.Beyond demonstrating remote sensing viability for LD assessment, the study developed procedures for generating and validating national level datasets. Using these procedures, LD monitoring will be enhanced in Botswana and elsewhere since these remote sensing datasets can be updated using freely available satellite datasets.

Assessment of Land Degradation in Semiarid Tanzania—Using Multiscale Remote Sensing Datasets to Support Sustainable Development Goal 15.3

“…This finding is confirmed by several studies highlighting the importance of using high-resolution imagery to detect LD, especially on heterogeneous landscapes, such as KK districts, dominated by heterogenous small-scale farms [50,55,56]. Recent studies that used ground-truth data for validation showed that using Landsat data for the LC sub-indicator captured LD better than using ESA-based 300 m datasets [50]. Nevertheless, certain factors could have impacted the AM, such as the scan-line failure in Landsat ETM+ data.…”

“…The resulting differences between LD estimates based on DM and AM were striking and could be primarily attributed to the difference in the pixel size of 6.25 ha (MODIS) versus 0.09 ha (Landsat), which could be critical in specific areas where fine LD patterns prevailed. This finding is confirmed by several studies highlighting the importance of using high-resolution imagery to detect LD, especially on heterogeneous landscapes, such as KK districts, dominated by heterogenous small-scale farms [50,55,56]. Recent studies that used ground-truth data for validation showed that using Landsat data for the LC sub-indicator captured LD better than using ESA-based 300 m datasets [50].…”

“…The presented study is the first in Africa to support the monitoring of the SDG 15.3.1 indicator using fine-spatial-resolution (30 m) satellite time series data for LD assessment. This is a key contribution considering that previous studies used 250 m to 8 km resolution data [24,26,50] for LP sub-indicator monitoring, unlike our study that utilized long-term Landsat time series for SDG 15.3.1 monitoring. Furthermore, it is the first sub-national study that assesses the SDG 15.3.1 indicator in Tanzania for the BP and includes the MP until 2019.…”

Divergent processes and trends of desertification in Inner Mongolia and Mongolia

“…We conducted the widely used Mann-Kendall (MK) test method (Akinyemi, Ghazaryan, & Dubovyk, 2020;Gichenje & Godinho, 2018;Touré et al, 2020) to test the significance of the long-term trends in time series. In the MK trend test, the original hypothesis H o is that there are no trends; the alternative hypothesis is that there is a trend that could either be negative, positive, or nonnull (Mann, 1945).…”

Vegetation Trends, Drought Severity and Land Use-Land Cover Change during the Growing Season in Semi-Arid Contexts

“…These datasets are a consistent series of multi-sensor annual maps from 1992 to 2018 [34]. The land categories in the ESA-LC were aggregated into six categories for compatibility with previous studies in Botswana [35]-tree-covered areas, grassland, cropland, water bodies, artificial surfaces (settlement including infrastructure) and otherland (Table S2). Tree-covered areas comprise forests and woodlands.…”