Continuous Detection of Forest Loss in Vietnam, Laos, and Cambodia Using Sentinel-1 Data

In this study, we demonstrate the ability of a new operational system to detect forest loss at a large scale accurately and in a timely manner. We produced forest loss maps every week over Vietnam, Cambodia, and Laos (>750,000 km2 in total) using Sentinel-1 data. To do so, we used the forest loss detection method based on shadow detection. The main advantage of this method is the ability to avoid false alarms, which is relevant in Southeast Asia where the areas of forest disturbance may be very small and scattered and detection is used for alert purposes. The estimated user accuracy of the forest loss map was 0.95 for forest disturbances and 0.99 for intact forest, and the estimated producer’s accuracy was 0.90 for forest disturbances and 0.99 for intact forest, with a minimum mapping unit of 0.1 ha. This represents an important step forward compared to the values achieved by previous studies. We also found that approximately half of forest disturbances in Cambodia from 2018 to 2020 occurred in protected areas, which emphasizes the lack of efficiency in the protection and conservation of natural resources in protected areas. On an annual basis, the forest loss areas detected using our method are found to be similar to the estimations from Global Forest Watch. These results highlight the fact that this method provides not only quick alerts but also reliable detections that can be used to calculate weekly, monthly, or annual forest loss statistics at a national scale.

Study of land suitability for rubber and cashew in the derived savanna zone of Edo State, Nigeria

This study attempted to assess the soil suitability in the derived savanna zone of Edo State, Nigeria for the cultivation of some tree crops. The research specifically aimed to evaluate soil suitability for cashew and rubber supported by suitability maps for both crops. In terms of land suitability evaluation, mapping units 1 and 2, with an area coverage of 27.4 ha of the entire research area (100 ha) were found to be marginally suitable (S3) for rubber cultivation but moderately suitable (S2) for cashew. Mapping unit 3, with area coverage of 38 ha was found to be currently not suitable (N1) for rubber but marginally suitable (S3) for cashew cultivation. Mapping unit 4 representing area coverage of 34.7 ha was found to be permanently not suitable (N2) for rubber cultivation but marginally suitable (S3) for cashew. Thus, technically, rubber can only be cultivated in that land at marginal level with an expected productivity of 27.4 ha (27.4 %). Cashew can be cultivated at moderate and marginal levels with an expected yield of 27.4 ha (27.4 %) and 72.6 ha (72.6 %), respectively, of the total land area. Thus, the preferred crop for the studied is cashew. It is recommended that for any significant investment in cultivation of this crop, the priority is the moderate levels with an expected productivity of 27.4 ha or 27.4 % of total land area.

Suitability assessment for oil palm (Elaeis guineensis) and coconut (Cocus nucifera) cultivation in coastal plain sands of South Southern Nigeria

Some soils of coastal plain sands in South Southern Nigeria were assessed for oil palm and coconut cultivation. This research was carried out to evaluate the soils of the study area using rigid grid soil survey methodology at detailed scale. The study area (128.11 ha) was delineated into four soil mapping units based on soil type. A pedon was sunk in each mapping unit and described according to FAO. Three soil orders, including Entisols (Ahiara), Inceptisols (Kulfo) and Ultisols (Orlu), were identified. Parametric and limitation methods of land suitability evaluation were used. Major limitations to assessment were climate (mean annual temperatures) and soil physical properties (texture/structure). Aggregate suitability rating (both actual and potential) showed that Ultisols (pedons 3 and 4, covering 56.73 ha) was moderately suitable (S2) for coconut and marginally suitable (S3) for oil palm, Inceptisols (pedon 2, covering 54.25 ha) was marginally suitable (S3) for coconut but not suitable (NS) for oil palm, and Entisols (Pedon 1covering 17.13 ha) was not suitable (NS) for any of the crops. Thus, area with Entisols should not be used for cultivation of any of the crops due to major limitation in texture. Moreover, both assessment approaches captured the major limitations. Therefore, the use of any of the approaches employed in this study and for these crops becomes a matter of choice as both of them showed no major differences in the application of their procedures.

Pedogenic characteristics of soil in Melur block, Madurai district, Tamil Nadu in India: A case study

Soil is an important source of human life and agricultural production. Studying on the pedon and its site characteristics pave the way for understanding the nature of soils and its utility. A study on pedological characterization of soils in Melur block, Madurai District (Tamil Nadu), was carried out during 2019-2020 using grid sampling with village map/cadastral maps. Soil mapping unit-based soil samples were collected in Chunampoor, Thuvarangulam, Poonjuthi and Veppapadupu and pedons were characterized as per the standard procedure. The results showed that soils were moderately deep to very deep in nature, ranging from 2.5 YR 3/6 to 10YR 4/6. The soil texture varied from sandy clay loam to sandy clay with weak to moderate sub-angular blocky structure. The consistency of soil varied from slightly hard to very hard when dry, very friable to firm when moist, slightly sticky to very sticky and slightly plastic to very plastic in wet condition. The crops viz., paddy, sugarcane, banana, groundnut and vegetables were very suitable for such type of soil of the Madurai district.

Object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution Imagery

The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of Object-Based Image Analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m2. This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85–93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales.

Soil mapping, classification and morphological characteristics of the University of Benin land

This study was conducted to map, and classify the soils of University of Benin Teaching, Research and Integrated Farm site. Rigid grid soil survey method at an intensive scale was done on a 62-hectare land that produced seven mapping units. In each mapping unit, a representative pedon was sunk, described and sampled. Soil samples were analyzed using standard methods. Data generated were analyzed using descriptive statistics to determine their coefficient of variation. The r esult indicated that the soils were reddish when moist at different contrasting levels. Textural classes ranged from Loamy sand to Sandy Clay Loam while structure ranged from Single grain crumb to Sub-angular blocky. The pedons were well drained except for pedon 5, which had mottles at subsurface horizon. Means of Sand fraction ranged from ≤649 to ≥ 931 gkg-1 ; Silt ranged from ≤13.2 to ≥ 47.7gkg-1 while Clay ranged from≤ 50 to ≥ 303 gkg-1 in all the pedons; clay fraction increased with increase in depth, forming argillic horizon in pedons 1,2 and 7. pH had means ranging from ≤4.23 to ≥5.28 and recorded low variation (≤ 3.6 to ≥ 13.0 %) in all the pedons. organic carbon had means ranging from ≤ 3.3 to ≥ 36.4 gkg-1; CEC ranged from ≤ 4.85 to ≥ 16.4 cmolkg-1 while Base saturation ranged from ≤16.6 to ≥ 51%. Hence pedons 1, 2 and 7 were placed in the order Ultisols ( Acrisols); pedons 3and 4 in Entisols ( Arenosols) Pedon 4; Pedons 5and 6 in Inceptisols (Cambisols) according to USDA Soil Taxonomy and correlated with WRB.

Pemetaan Status pH Tanah dan C-Organik Tanah Sawah Tadah Hujan di Kecamatan Panai Tengah Kabupaten Labuhanbatu

<p>The production center for lowland rice in Labuhanbatu Regency in the last three years, precisely in Panai Tengah District, North Sumatra Province, has decreased production since 2018. One of the causes is a decrease in production because the nutrient content in the soil has decreased and added by fertilizer application by farmers. Lowland rice is still common due to limited data on land resources. This study aims to evaluate the land through the identification of classes, distribution and status of soil pH and C-Organic nutrients as well as organic matter in agricultural land in Central Panai District, and to determine the level of soil damage in each Land Mapping Unit. This research was carried out in Central Panai District with elevation. place 11 meters above sea level. Soil analysis was carried out in the integrated science laboratory of the Faculty of Science and Technology, Labuhanbatu University from January 2021 to March 2021, while the method in this study was a semi-detailed grid survey with the density of observation of 1 sample per 250 meters. Soil pH measurements used the pH-H₂O and C-organic methods of soil using the Walkley and Black method. Furthermore, the results of the analysis of nutrient rainfed lowland soils are interpreted into a nutrient status map. The results showed that the pH content of rainfed lowland soil in Panai Tengah District, Labuhanbatu Regency was classified as very acidic and slightly acidic, while based on the soil nutrient status, namely C-organic and organic matter in the low category, so as to increase the productivity of rainfed lowland soil with soil organic matter content.up to 3% required organic fertilizer in the Central Panai District, Labuhanbatu Regency.</p>

Susceptibility Assessment of Small, Shallow and Clustered Landslide in Malipo, southwest China

&lt;p&gt;&lt;strong&gt;Abstract: &lt;/strong&gt;Susceptibility assessment of landslides over a large area depends on the basic spatial unit of mapping, each unit is assumed to have unique assessment value, so the division of mapping unit is directly related to the evaluation rate, grid cell or slope unit are usually be used in many researches. Grid cell divide the study region into regular squares of predefined size, each cell is assigned a value of influence factor. Slope unit based on hydrology divides the region by ridge and valley lines, which is more related to geological environment and it is hard to identify the subbasin boundary. Both units are used in this study for the assessment of small shallow and clustered landslides in vegetated slopes in Malipo, southwest China. Google earth map on February 7, 2019 was used to interpret the landslides. ArcGIS 10.2 software was used to produce landslide inventory map and obtained 1435 landslides in the study area; most frequent landslide areas are in the range of 62m&lt;sup&gt;2&lt;/sup&gt; to 900m&lt;sup&gt;2&lt;/sup&gt;. Field survey was carried out to verify uncertain factors and measure moisture soil content. Soil moisture content (SMC) map was obtained by Kriging Interpolation methods based on the field measured soil moisture content of 48 sample points. Information value (IV) model was used to generate landslide susceptibility assessment map and improved information value (IIV) model was used to determine whether the mapping unit with or without landslide. Seven factors, including slope angle, slope aspect, elevation, normalized difference vegetation Index (NDVI), Soil Moisture Content (SMC), distance to river and road were used as landslide influence factors. The Area under curve (AUC) values of the slope unit IIV, IV and grid cell were 0.814, 0.802 and 0.702 respectively for success rate. For prediction rate, the AUC values of the slope unit and grid cell were 0.803(IIV), 0.790(IV) and 0.699 respectively. Slope unit is more suitable than grid cell for assessing susceptibility of Small, Shallow and Cluster Landslide (Fig.1). Improved information value model can increase the accuracy of susceptibility assessment model for this characteristic landslide.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: &lt;/strong&gt;Landslide susceptibility assessment; Slope unit; Grid cell; Information value&lt;/p&gt;&lt;p&gt;&amp;#160; &amp;#160;&amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160;&amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &lt;strong&gt;&amp;#160;(a)&lt;/strong&gt; &amp;#160;&lt;img src=&quot;https://contentmanager.copernicus.org/fileStorageProxy.php?f=gepj.612a5aa6550062062690161/sdaolpUECMynit/12UGE&amp;app=m&amp;a=0&amp;c=e934e3e9858f863f856c55ba7f923603&amp;ct=x&amp;pn=gepj.elif&amp;d=1&quot; alt=&quot;&quot; width=&quot;289&quot; height=&quot;206&quot;&gt;&amp;#160; &lt;strong&gt;&amp;#160;(b)&lt;/strong&gt; &amp;#160; &lt;img src=&quot;https://contentmanager.copernicus.org/fileStorageProxy.php?f=gepj.c0a33eb6550062262690161/sdaolpUECMynit/12UGE&amp;app=m&amp;a=0&amp;c=f9c48114412d0742a895968d55be3fbd&amp;ct=x&amp;pn=gepj.elif&amp;d=1&quot; alt=&quot;&quot; width=&quot;293&quot; height=&quot;212&quot;&gt;&amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160;&lt;strong&gt;&amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160;&amp;#160;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; Figure 1&lt;/strong&gt; Landslide susceptibility maps (a)Slope unit-based and (b)Grid cell-based&lt;/p&gt;

National scale soil sealing monitoring data as a new explanatory variable for landslide susceptibility models

&lt;div&gt; &lt;p&gt;It is widely known that human activities can negatively affect the equilibrium of slope systems, triggering or predisposing to landslides. In Italy, ISPRA (Italian Institute for Environmental Protection Research) uses remote sensing techniques to monitor the expansion of artificialization of the territory and releases every year an updated map of soil sealing, which is defined as the destruction or covering of natural soils by totally or partially impermeable artificial material. The soil sealing map covers the entire national territory and has a fine spatial resolution (10 m).&lt;/p&gt; &lt;p&gt;In this work, for the first time, soil sealing indicators are used as explanatory variables in a landslide susceptibility assessment. Three new parameters were derived from the raw soil sealing map: &amp;#8220;soil sealing aggregation&amp;#8221; (continuous variable expressing the percentage of sealed soil within each mapping unit), &amp;#8220;soil sealing&amp;#8221; (categorical variable expressing if a mapping unit is mainly natural or sealed), &amp;#8220;urbanization&amp;#8221; (categorical variable subdividing each unit into natural, semi-urbanized, or urbanized).&lt;/p&gt; &lt;p&gt;These parameters were added to a set of state-of-the-art explanatory variables in a random forest landslide susceptibility model. In particular, the parameters derived from soil sealing were compared with two state-of-the-art parameters widely used to account for human disturbance: land cover/land use (as derived from a CORINE land cover map) and road network. &amp;#160;&lt;/p&gt; &lt;p&gt;Results were compared in terms of AUC (area under receiver operating characteristics curve, expressing the overall effectiveness of the configurations tested) and out-of-bag-error (used to quantify the relative importance of each variable). We found that the parameter &amp;#8220;soil sealing aggregation&amp;#8221; significantly enhanced the model performances. The results open new perspectives for the use of data derived from soil sealing monitoring programs to improve landslide hazard studies.&amp;#160;&amp;#160;&lt;/p&gt; &lt;/div&gt;